United States
Environmental Protection
Agency
Policy, 1
And Evaluation
(PM-221)
EPA230-B-92-002
November 1992
x>EPA State Workbook
Methodologies For Estimating
Greenhouse Gas Emissions
Printed on Recycled Paper
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STATES WORKBOOK
METHODOLOGIES FOR ESTIMATING
GREENHOUSE GAS EMISSIONS
U.S. Environmental Protection Agency
Office of Policy, Planning and Evaluation
Washington, DC 20460
November 1992
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PREFACE
Global climate change has the potential for affecting all citizens in their work and
personal lives. The Framework Convention on Climate Change, signed by President Bush in
Rio de Janeiro in June of 1992, creates a basis for action by both developed and developing
countries. Specifically, it obligates signatories to inventory national sources and sinks of
greenhouse gases and to develop national policies and measures to address climate change.
The States will play a critical part in this process given their jurisdiction over key policy
areas such as land use, utility, agricultural, and tranportation planning; building codes; etc.
States need to understand their contributions to greenhouse gas emissions, to assess the impacts
of climate change on their state economies and environment, and to examine the merits of
various policy options.
This workbook is a tool that has been developed by the U.S. EPA to assist States in the
initial stages of climate change policy development and implementation by assembling an
inventory of greenhouse gas emissions. It is based on the draft methods document (Estimation
of Greenhouse Gas Emissions and Sinks. August, 1991) developed for the Intergovernmental
Panel on Climate Change (IPCC) as part of the global cooperation leading up to the climate
convention. The methods and formulas are the best available in the scientific community. It
is recognized, however, that the science is evolving quickly and that changes are anticipated in
the future. Nonetheless, the EPA's objectives are to develop a workbook to (1) assist State
personnel without a meteorological or statistical background to assemble an inventory of
greenhouse gas emissions, (2) provide a primer on greenhouse gases, (3) provide a mechanism
for summarizing greenhouse gas emissions for use in further policy work, and (4) make available
additional information on alternative methods. I hope that this workbook meets those objectives
and assists the States to develop institutional capabilities to address climate change issues.
The Climate Change Division of the Environmental Protection Agency provides both
technical and financial assistance to states interested in addressing climate change. For
additional information, please contact Katherine Sibold, Director of State Outreach Programs,
at 202/260-4314.
Dennis Tirpak
Director
Climate Change Division
November 30, 1992
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TABLE OF CONTENTS
Page
Executive Summary ES-1
Introduction - >
Directions for Completing Workbook
Directions xiii
Emissions Summary Table xv
WORKBOOK
1. Carbon Dioxide Emissions from Combustion of Fossil and
Biomass Fuels 1-1
2. Greenhouse Gas Emissions from Production Processes 2-1
3. Methane and Carbon Dioxide Emissions from Natural Gas
and Oil Systems 3-1
4. Methane Emissions from Coal Mining 4-1
5. Methane and Carbon Dioxide Emissions from Landfills 5-1
6. Methane Emissions from Domesticated Animals ..,.._,,..,,.,,«,.-..--.-...-, 6-1
7. Methane Emissions from Animal Manure 7-1
8. Methane Emissions from Flooded Rice Fields 8-1
9. Nitrous Oxide Emissions from Fertilizer Use 9-1
10. Greenhouse Gas Emissions from Land-Use Change 10-1
11. Greenhouse Gas Emissions from Burning of Agricultural
Crop Waste 11-1
DISCUSSION
1. Carbon Dioxide Emissions from Combustion of Fossil and
Biomass Fuels Dl-1
Z Greenhouse Gas Emissions from Production Processes D2-1
STATES WORKBOOK November 1992
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3. Methane and Carbon Dioxide Emissions from Natural Gas
and Oil Systems D3-1
4. Methane Emissions from Coal Mining D4-1
5. Methane and Carbon Dioxide Emissions from Landfills D5-1
6. Methane Emissions from Domesticated Animals D6-1
7. Methane Emissions from Animal Manure D7-1
8. Methane Emissions from Flooded Rice Fields D8-1
9. Nitrous Oxide Emissions from Fertilizer Use D9-1
10. Greenhouse Gas Emissions from Land-Use Change D10-1
11. Greenhouse Gas Emissions from Burning of Agricultural
Crop Waste Dll-1
12. Other Greenhouse Gas Emissions from Stationary Combustion D12-1
13. Other Greenhouse Gas Emissions from Mobile Combustion D13-1
APPENDICES
Glossary, Chemical Symbols, and Conversion Factors Al-1
State Agency Contacts for Climate Change A2-1
Bibliography of .Key Reports A3-1
STATES WORKBOOK November 1992
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INTRODUCTION
A. THE PURPOSE OF THE WORKBOOK
The purpose of this workbook is to provide states with a set of methodologies to inventory
their emissions of greenhouse gases as a first step toward developing ]X>licies and strategies to reduce
greenhouse gas emissions and to assess the various options available for responding to the effects of
global wanning. The workbook offers both simple approaches to conducting an emissions inventory
and more sophisticated approaches depending on the amount of data available and the level of effort
a state can undertake.
There is no question that anthropogenic (human-related) emissions of greenhouse gases are
changing the composition of the Earth's atmosphere. The concentration of carbon dioxide (CO^
in the atmosphere has risen 25 percent since pre-industrial times (1750-1800). Over this same time
period, methane (CH^ concentrations nearly doubled, while nitrous oxide (N2O) concentrations
increased at about 8 percent. Moreover, the anthropogenically produced chloroQuorocarbons (CFOs)
have increased at a faster rate than the other greenhouse gases - at a minimum of 4 percent per year
over the past few decades.1 Present emissions trends will lead to a continuing buildup of these gases
in the atmosphere (IPCC, 1990).
Estimating the impact of increasing greenhouse gas concentrations on global climate has been
a focus of research within the atmospheric science community for more than a decade (Lashof and
Tirpak, 1990). On the basis of current evidence from climate model studies it appears that the
change in globally averaged surface temperature due to doubling CO2 probably lies in the range of
2.7 to 8.1 degrees F (IPCC, 1990). Global warming of just .a few degrees would represent an
enormous change in climate. For example, the difference in mean annual temperature between
Boston and Washington is about 6 degrees F and the difference between Chicago and Atlanta is
about 12 degrees F (Lashof and Tirpak, 1990).
The effects in the U.S. of such an increase could include a northward shift of southern forest
species', forest die-back from heat and dry soils along southern portions of tree species ranges, and
changes in forest productivity. Climate changes would also affect crop yields and result in northward
shifts in cultivated lands, stress livestock production, increase crop irrigation requirements and
increase the incidence of agricultural pests and diseases. Certain dry regions could become more
vulnerable to drought as a result of higher temperatures, earlier snowmelt, and/or shifts in
precipitation. Air quality would likely deteriorate as a result of tropospheric (lower atmosphere)
ozone build-up. Global wanning could raise seas level approximately 1 meter by the year 2100 by
expanding ocean water, melting mountain glaciers, and causing ice sheets in Greenland to melt or
slide into the ocean. Such a rise would inundate coastal wetlands and lowlands, erode beaches,
1 Chlorofluorocarbons (CFCs) are man-made gases that only occur in the atmosphere as a result of human activities.
They are important due to the role they play in depleting the Earth's stratospheric: ozone layer, in addition to contributing
to the greenhouse effect
STATES WORKBOOK i November 1992
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increase the risk of flooding, and increase the salinity of estuaries, aquifers and wetlands (Smith and
Tirpak, 1989).
Drastic cuts in emissions would be required to stabilize atmospheric composition. Because
greenhouse gases, once emitted, remain in the atmosphere for decades to centuries, stabilizing
emissions at current levels would allow the greenhouse effect to intensify for more than a century
(Lasbof and Tirpak, 1990).
Scientific consensus that the threat of global warming is real has triggered a wave of response
actions by governments at the international, national, and state levels. Under the auspices of the
United Nations Environment Program (UNEP) and the World Meteorological Organization (WMO),
an Intergovernmental Panel on Climate Change (IPCC) was formed to conduct studies on emission
sources, possible consequences, and mitigation strategies concerning global warming. In addition, an
International Negotiating Committee of the United Nations has begun the process of developing a
global convention (agreement) on climate change. International agreement has already been reached,
through the Montreal Protocol and the London Amendments to the Protocol, to seek the complete
elimination of CFCs by the year 2000 or earlier. At the state level, the National Governors'
Association Task Force on Global Warming has proposed more than 20 strategies, consistent with
international goals, for responding to the threat of global wanning (NGA, 1991).
B. THE ROLE OF THE STATES
States will need to consider a diversity of issues, ranging from mass transit to forestry, and
from the recycling of wastes to the reduction of CFC use, in order to develop climate change policies.
Many states have already begun to address these issues. Examples include: a California law calling
for the California Energy Commission to study the potential impact of climate change on the state's
energy supply/demand, economy, environment, agriculture, and water resources; a Connecticut law
establishing a broad range of energy conservation measures; and, an Oregon law requiring the Oregon
Department of Energy to develop strategies for reducing greenhouse gas emissions (Silbiger and
Gongring, 1992).
There are several reasons why states can significantly affect their emissions of greenhouse
gases. First, state governments hold direct regulatory authority over the sources of more than half
of CO2 emissions: gas and electric utilities. Second, states also determine the acceptability of building
specifications and land-use planning, thereby affecting emissions from residential, commercial, and
transportation sectors. States have the jurisdiction over determining regulations concerning the use
and recycling of CFCs, the management of municipal solid wastes (and consequently methane
emissions), and the promotion of energy savings from secondary manufacturing. In addition, many
states regulate forestry practices on non-federal land.
A wide variety of policy options are available that have the technical potential to reduce
greenhouse gas emissions. Many appear to be consistent with other economic, development,
environmental, and social goals. One such policy includes identifying and implementing opportunities
for cost-effective energy efficiency improvements. Efficiency investments that pay for themselves over
the life of the equipment through reduced energy costs suggest that the accompanying reduction in
STATES WORKBOOK ii November 1992
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carbon dioxide emissions may be essentially a cost-free by-product of a more efficient economy.2
Efficiency improvements can also reduce emissions of other pollutants, improve economic
competitiveness, and enhance U.S. environmental quality, energy independence, national security, and
public health (NGA, 1990). Expanding the use of non-fossil energy sources and increasing
afforestation are other possible policy options with multiple benefits.
Policymakers and planners will need to design policies and strategies to deal with both the
uncertainties of climate change and the potentially significant impacts climate change could have on
their region's natural resources3. This requires a two-step process: (1) an assessment of the
vulnerability of resources to climate change impacts; and (2) an evaluation of adaptation options.
Assessing the vulnerability of a state and region to climate change impacts involves estimating a range
of regional climate change scenarios on local resources.
After the vulnerability assessment has been completed, a state can weigh its vulnerabilities
against the economic, environmental, and social costs and benefits of various response options to the
possibility of such events as sea-level rise, changes in rainfall and temperature, water conservation,
forest health and production, and protection of biological diversity. The efficient implementation of
these policies can best be achieved through the establishment of priorities among suggested
anticipatory options (EPA, 1991).
Considerable uncertainty exists regarding the economic and social costs and benefits associated
with preventative measures to combat the potential effects of climate change. Some estimates show
that the costs associated with stabilizing greenhouse gas emissions will range anywhere from 0 to 6
percent of the U.S. GNP (Manne and Richels, 1989), while a National Academy of Sciences panel
has concluded that the potential exists to reduce greenhouse gas emissions in the United States by
10 to 40 percent of 1990 levels at a very low cost and possibly at 2 net savings (NAS, 1991).
Before a state can effectively develop pob'cies to reduce greenhouse gas emissions and
respond to climate change, it should identify its anthropogenic emissions sources and estimate the
contribution of these emissions to the greenhouse effect The methodologies presented in this
workbook have been adapted from work done by the U.S. EPA for international workshops on the
estimation of greenhouse gas emission sources and sinks, held in February 1991 in Paris and
December 1991 in Geneva sponsored by the Organization for Economic Cooperation and
Development (OECD) and the IPCC. In certain areas the basic information required to calculate
thorough emissions estimates is unavailable. Further research, data collection, and data analysis in
these areas are needed and are currently being conducted. The IPCC has adopted the initial
2 According to the National Academy of Science report "Policy Implications of Greenhouse Warming-Mitigation Panel",
NAS Press, 1991, as quoted by Richard A. Ken, the most cost-effective measures for reducing emissions are by increasing
the energy efficiency of residential and commercial buildings and activities, vehicles, and industrial process that use electricity.
(Science, Vol 252, 21 June 1991, pg 252.)
'Adaptation options will be necessary in the future if current and planned capabilities are found to be insufficient to
address the adverse impacts of climate change. Under these options falls the debate over anticipatory versus reactive
measures. Reactive measures are those which are made as climate change impacts occur, anticipatory measures are made
before climate change impacts are felt Crucial to this debate is the analysis of the economic, environmental, and social costs
and benefits of any suggested options (EPA, 1991).
STATES WORKBOOK iij November 1992
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methodologies developed at the Paris workshop (and presented herein) and intends to pursue a
program over the next several years to refine these methodologies.
Whatever methodologies a state may decide to follow, the key to a sound emissions inventory
is documentation of activity data and emission factors being used, their derivation, and definitions of
specified activities. Any emission inventory that is not accompanied by sound documentation is
unverifiable. Without clear documentation on the methods employed and data used, it will be
impossible to refine and improve the accuracy of greenhouse gas inventories. States may also at some
point want to compare their inventories with other states, or pool statistics in a regional inventory.
C A GLOBAL WARMING PRIMER
The Greenhouse Effect and Global Climate Change
The climate of the Earth is affected by changes in radiative forcing due to several sources
including the concentrations of radiatively active (greenhouse) gases, solar radiation, aerosols and
albedo. The major contributor to increases in radiative forcing due to increased concentrations of
greenhouse gases since pre-industrial times is carbon dioxide (CO^, with substantial contributions
from methane (CH4), nitrous oxide (N2O), chlorofluorocarbons (CFCs), and other radiatively
important gases (IPCC, 1990).
Greenhouse gases in the atmosphere are virtually transparent to sunlight (shortwave
radiation), allowing it to pass through the air and to heat the Earth's surface. The surface absorbs
the sunlight and emits thermal radiation (longwave radiation) back to the atmosphere. Because
several of the gases in the atmosphere, particularly CO2, are not transparent to the outgoing thermal
radiation, they absorb some of it and heat the atmosphere. The atmosphere emits thermal radiation,
both upward to outer space and downward to the Earth, further warming the surface. At natural
levels, these greenhouse gases enable the Earth to maintain enough warmth to support life (without
this natural "greenhouse effect", the Earth would be approximately 60° F colder than it is today).
However, the increasing concentrations of these gases have been implicated in warming the planet,
with the potential to raise temperatures to a level that would disrupt the activities of today's natural
systems and human societies.
The Greenhouse Gases
Carbon Dioxide
Atmospheric concentrations of C02, the most abundant greenhouse gas after water vapor,
have risen 25 percent since the beginning of the Industrial Revolution. Presently, 160 billion more
tons of carbon exist in the atmosphere than prior to industrialization (Trexler, 1991). Even if all
man-made emissions of CO2 could be halted today, the effects of past emissions would be felt for
4 "Radiative Forcing" refers to changes in the radiative balance of the Earth, Le., a-change in the existing balance between
incoming and outgoing radiation. This balance can be upset by natural causes, e.g., volcanic eruptions, as well as man-made
causes, e.g., greenhouse gas emissions.
STATES WORKBOOK jv November 1992
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more than a century. It has been estimated, therefore, that at least a SO to 80 percent reduction in
CO2 emissions from current levels is needed to prevent the further buildup of this gas in the
atmosphere (Lashof and Tirpak, 1990). The combustion of liquid, solid, and gaseous fossil fuels is
the major anthropogenic source of CO2 emissions. Deforestation and other non-energy production
processes (e.g. cement-production) also emit notable quantities of carbon dioxide into the
atmosphere. Atmospheric concentrations of carbon dioxide are increasing at a rate of approximately
0.4 to 0.7 percent per year (NAS, 1991) accounting for about 66 percent of total radiative forcing
(Figure 1-1).
The atmosphere exchanges CO2 with the terrestrial biosphere and with the oceans. It is
generally assumed that the major sink for carbon dioxide is the large expanse of southern oceans
where there are strong winds and cold waters.5 Forests, as well as vegetation and soils in temperate
latitudes of the northern hemisphere, also act as sinks for excess
Figure 1-1
Global Contributions To Integrated Radiative Forcing by Gas for 1990
Carbon Dioxide:
Nitrous
Ox i de : 556
CFCs:
Methane:
Estimated on a carbon dioxide equivalent basis using IFCC (1990) global warming potentials
for a 100-year time horizon. Anthropogenic emissions only.
Methane
Methane is produced through anaerobic decomposition in biological systems. Agricultural
processes such as wetland rice cultivation, enteric fermentation in animals, the decomposition of
J A "sink" is a mechanism that leads to the removal and/or destruction of greenhouse gases.
STATES WORKBOOK DRAFT v
November 1992
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animal wastes, and the decomposition of municipal solid wastes emit significant amounts of CH4.
Methane is also a major component of natural gas, and some CH4 is emitted due to the production
and distribution of this fuel CH4 is also released as a byproduct of coal production activities. The
major sink of CH4 is its chemical destruction in the troposphere. Methane is increasing at a rate of
about 0.6 percent per year (Steele et al. 1992) and accounts for approximately 18 percent of radiative
forcing (IPCC, 1990).
Chlorofluorocerbons (CFCs)
Chlorofluorocarbons (CFCs) are anthropogenic chemicals that are not only greenhouse gases,
but are also contributors to stratospheric ozone depletion. Because of this, nations have agreed to
limit production of these gases in an international agreement signed in Montreal hi 1987. In June
of 1990, the London Amendments to the Protocol called for a complete phase-out of CFCs and
related chemicals. Under the 1990 Clean Air Act Amendments, the U.S. will phase out the
production and use of CFCs by the year 2000. The most important of these gases, and largest
contributors to the greenhouse effect, are CFC-11 (CFC13) and CFC-12 (CFO^. Uses of CFCs, for
example, include refrigerants, aerosols, foam-blowing agents, and solvents. Substitutes for CFCs are
being developed whose environmental impact are less harmful and atmospheric lifetimes are shorter
than CFCs, and therefore, do not accumulate for sustained periods hi the atmosphere. CFCs account
for 11 percent of radiative forcing (IPCC, 1990).
Nitrous Oxide (N2O)
Anthropogenic sources of nitrous oxide emissions include increased emissions from soils due
to deforestation, combustion, biomass burning, the use of nitrate and ammonium fertilizers, and
leaching of nitrogen fertilizers from soils into groundwater. Natural sources of N2O include soils in
both tropical and temperate forests and oceans. Nitrous oxide contributes around 5 percent to
radiative forcing (IPCC, 1990). While much progress has been made during the last five years in
quantifying the sources and sinks of N2O in the atmosphere, a considerable amount of uncertainty
remains in the global budget and in the contributions of individual sources. The uncertainties arise
not only because of the scarcity of measurements of N2O fluxes, but also, as in the case for CH4,
because of the complexity of the biochemical interactions in which N2O is produced.
Other Radiatively Important Gases
Ozone
Ozone is a particularly effective greenhouse gas in the upper troposphere and lower
stratosphere, and also plays a key role in absorbing solar ultraviolet radiation. About 90 percent of
the total column of ozone resides in the stratosphere, with the remaining 10 percent in the
troposphere (IPCC, 1992). Though O3 is not emitted directly by human activity, anthropogenic
emissions of several gases influence its concentration in the stratosphere and troposphere. Chlorine
and bromine-containing chemicals reduce stratospheric ozone, while carbon monoxide, hydrocarbons,
and oxides of nitrogen contribute to the production of tropospheric ozone.
STATES WORKBOOK . vi November 1992
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Carbon Monoxide (CO)
Carbon monoxide is an odorless, invisible gas created when carbon-containing fuels are burned
incompletely. Participating in various chemical reactions in the atmosphere, CO contributes to smog
formation, acid rain, and the buildup of CH4. CO elevates concentrations of CH4 and tropospberic
O3 by chemical reactions with the atmospheric constituents (i.e., the hydroxyl radical) that would
otherwise assist in destroying CH4 and O3.6
Nitrogen Oxides (NOJ
One form of odd-nitrogen, denoted as NOr is defined as the sum of two species, NO and
NO^ NOX is created in lightning, in natural fires, in fossil-fuel combustion, and hi the stratosphere
from N2O. It plays an important role in the global wanning process due to its contribution to the
formation of O3. -
Non-methane Volatile Organic Compounds (VOCs)
Volatile organic compounds along with nitrogen oxides are participants in atmospheric
chemical and physical processes that result in the formation of ozone and other photochemical
oxidants. The largest sources of reactive VOC emissions are transportation sources and industrial
processes. Miscellaneous sources, primarily forest wildfires and non-industrial consumption of organic
solvents, also contribute significantly to total VOC emissions (USE?A, 1991).
D. GLOBAL WARMING POTENTIAL (GWP)
When discussing greenhouse gases in a policy context, it is useful to have some means of
estimating the relative effects of «*-*«* greenhouse gas on radiative forcing -of the atmosphere over
some future time horizon, without performing the complex and tune-consuming task of calculating
and integrating changes in atmospheric composition over the period. In short, the need is for an
index that translates the level of emissions of various gases into a common metric in order to compare
the climate forcing effects without directly calculating the changes in atmospheric concentrations
(Lashof and Tirpak, 1990). This information can then be used for calculations of the cost-
effectiveness of reductions, e.g., CO2 emissions compared to CH4 emissions.
A number of approaches, called Global Wanning Potential (GWP) indices, have been
developed in recent years. These indices account for direct effects due to growing concentrations of
carbon dioxide (COj), methane (CH4), chlorofluorocarbons (CFCs), and nitrous oxide (N2O). They
also estimate indirect effects on radiative forcing due to emissions which are not themselves
greenhouse gases, but lead to chemical reactions that create or alter greenhouse gases. These
emissions include carbon monoxide (CO), nitrogen oxides (NOX), and volatile organic compounds
* The hydrracyl radical (OH), which eventually removes CO from the atmosphere, is also the main component which
destroys CH4 and O3. When CO levels rise, OH is employed at a more rapid pace in order to remove the excess CO from
the atmosphere, thereby, decreasing the amount of OH radicals that may act as a sink for CH4 and O3. CO also aids in the
conversion of NO (nitric oxide) to
STATES WORKBOOK vii November 1992
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(VOC), all of which contribute to formation of tropospheric ozone, which is a greenhouse gas
(Lasbof and Tirpak, 1990).
This workbook follows the methodology used by the Intergovernmental Panel on Climate
Change (IPCC, 1992). In Figure M, the GWPs specified by the IPCC for a 100-yr time horizon are
used to illustrate the relative importance of each greenhouse gas based on ail global emissions.
However, there is no universally accepted methodology for combining all the relevant factors into a
single global warming potential for greenhouse gas emissions. In addition to the IPCC, there are
several other noteworthy attempts to define a concept of global warming potential, including Lasbof
and Ahuja (1990), Rodhe (1990), Derwent (1990), WRI (1990), and Nordhaus (unpublished).
The concept of global warming potential developed by the IPCC is based on a comparison
of the radiative forcing effect of the concurrent emission into the atmosphere of an equal quantity
of CO2 and another greenhouse gas. Each gas has a different instantaneous radiative forcing effect
In addition, the atmospheric concentration attributable to a specific quantity of each gas declines with
time. In general, other greenhouse gases have a much stronger instantaneous radiative effect than
does CO2; however, CO2 has a longer atmospheric lifetime and a slower decay rate than most other
greenhouse gases. Atmospheric concentrations of certain greenhouse gases may decline due to
atmospheric chemical processes, which in turn create other greenhouse gases or contribute to their
creation or longevity. There is a substantial amount of uncertainty in our understanding of many
atmospheric chemical processes, including latitudinal and temporal variations, that makes it impossible
to quantify how certain gases may indirectly affect climate. Due to these uncertainties over the
indirect effects, they have not been included in the GWP of each gas at this time (IPCC, 1992). Only
the ability of gases to directly affect radiative forcing is included here.
Following this convention, the GWP is defined as the time-integrated commitment to climate
forcing from the instantaneous release of 1 kilogram of a trace gas expressed relative to that from
1 kilogram of carbon dioxide. The magnitude of the GWP is, however, sensitive to the time horizon
over which the analysis is conducted (i.e., the time period over -which the integral is emulated). For
example, Table M summarizes the GWPs of key greenhouse gases assuming 20-year, 100-year, and
500-year time horizons. The assumed integration period defines the tune period over which the
radiative effects of the gas are measured. These GWPs indicate, for example, that 1 kilogram of
methane emissions is estimated to have approximately 11 times the direct impact on radiative forcing
as 1 kilogram of carbon dioxide for a 100-year time horizon. If a 500-year time horizon is assumed,
however, methane is estimated to have only 4 times the direct impact on radiative forcing compared
to an equivalent amount of carbon dioxide. The differences between the values for 100 years and
500 years incorporate the differences in atmospheric lifetime. Because methane is a much shorter-
lived gas than carbon dioxide - about 10 years versus 120 years - its relative contribution to global
climate change decreases (increases) as the time horizon increases (decreases).7
1 Due to methane's shorter atmospheric lifetime, the GWP of methane relative to CO2 decreases over time. However,
methane's overall contribution to global warming could increase relative to COZ as methane's concentration in the atmosphere
may be increasing at a faster rate than COt The annual growth rate of the atmospheric concentration of methane has been
estimated to be as high as 13 percent per year in 1988 to about 0.6 percent per year in 1992 (IPCC 1992). In comparison,
atmospheric concentrations of CO2 are estimated to be increasing at a rate of approximately 0.4 to 0.7 percent per year (NAS,
1991).
STATES WORKBOOK viii November 1992
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For this discussion, the GWFs presented in Table 1-1 for a mid-level time horizon, Le., 100
years, are used to convert all greenhouse gases to a CO2-equivalent basis so that the relative
magnitudes of different quantities of different greenhouse gases can be readily compared. There is
nothing particularly unique about this time horizon. Nevertheless, it is sufficiently long that many of
the atmospheric processes currently thought to affect concentrations can be considered without
excessively weighting longer-term impacts on atmospheric processes that are not well understood.
Using the GWPs presented in Table 1-1, the relative contribution of each greenhouse gas to
global warming for any greenhouse gas emission estimates can be estimated. For example, in Figure
1-2, U.S. contributions to global wanning by greenhouse gas are represented using U.S. emission
estimates for the year 1988 based on conversion to a CO2-equrvalent basis using 100-year GWPs.
''... : '."Table.!-!' .-: -.< ::''. " ' -J^-^:; :
Global Warming Potential
Trace .Gas
Lifetime
For Key Greenhouse Gases
Direct Effects
fvearet (integration time horizon, vears)
Carbon Dioxide
Methane
Nitrous Oxide
CFC-11
CFC-12
HCFC-22
CFC-113
CC14
CH3ca3
CF3Br
CO
NOX
NMHC
a
10.5
132
55
116
15.*
110
47
6.1
77
months
days
days to months
20
1
35
260
4500
7100
4200
4600
1800
360
5600
-
-
"
10Q
1
11
270
3400
7100
J600
4500
1300
100
4900
- .
-
~
3 The persistence of carbon dioxide has been estimated by
diffusion model
of Siegenthaler (1983)
5QQ
1
4
170
1400
4100
....540
2500
480
34
2300
-..
-
~
explicitly
; an approximate lifetime is 120
Sign of
Indirect Effects
none
positive
uncertain
negative
negative
negative
negative
negative
negative
negative
positive
uncertain
positive
integrating the box-
years.
Source: IPCC 1992.
STATES WORKBOOK
ix
November 1992
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Figure 1-2
U.S. Contributions to Integrated Radiative Forcing by Gas for 1988
Carbon Dioxide: BB%
Nitrous
Oxide: 3%
CFCs: 2496
Methane: 556
Source: Based on emission estimates found in U.S. Government, 1991. Estimates were convened to CO:-
equivalent basis using IPCC (1992) GWPs for a 100-year time horizon. Only direct radiative effects are included.
The GWP potential will be an important concept for states in determining the relative
importance of r.ar.h of the major emissions sources and in developing appropriate mitigation
strategies.
The remainder of this report is divided into two main sections: workbook calculations and
discussion. The workbook contains simplified instructions for completing a state inventory of
greenhouse gas emissions. The discussion section contains background information on each source
and more detailed information on the recommended methods for estimating emissions as well as a
description of alternate methods. The Directions, beginning on page xiii, provide specific instructions
for completing the workbook in the most efficient manner.
Benioff, R. 1990. Potential State Responses to Climate Change. Prepared for the Office of Policy,
Planning, and Evaluation, U.S. Environmental Protection Agency, Washington, D.C 1990
STATES WORKBOOK x November 1992
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Derwent, R.G. 1990. Trace Gases and Their Relative Contribution to the Greenhouse Effect (Report
AERE-R13716). Atomic Energy Establishment, Harwell, Oxon.
EPA (United States Environmental Protection Agency). 1991. Adapting to Climate Change; What
Governments Can Do. U.S. Environmental Protection Agency, Office of Policy, Planning and
Evaluation, Climate Change Adaptation Branch. 1991.
IPCC (Intergovernmental Panel on Climate Change). 1990. Scientific Assessment of Climate
Change. Report prepared for IPCC by Working Group 1. June 19SK).
IPCC (Intergovernmental Panel on Climate Change). 1992. Climate Change J992: The
Supplementary Report to the IPCC Scientific Assessment. Report Prepared for IPCC by Working
Group 1.
Lashof, D.A. and D.R. Ahuja. 1990. Relative contributions of greenhouse gas emissions to global
warming. Nature 344:529-531.
Lashof, D.A and E.L. Washburn. 1990. The Statehouse Effect: Slate Policies to Cool the Greenhouse
Effect. Natural Resources Defense Council, Washington, D.C 1990.
Lashof, D.A and D.A Tirpak. 1990. Policy Options for Stabilizing Global Climate Change. Office
of Policy, Planning, and Evaluation, U.S. Environmental Protection Agency, Washington, D.C. 1990.
Lyman, F., I. Mintzer, K. Courrier, and J. MacKenzie. 1990. The Greenhouse Trap. World
Resources Institute, Washington, D.C. 1990.
Manne, AS. and R.G. Richels. 1989. CO2 emissions limits - An economic cost analysis for the
U.S.A The Energy Journal. November, 1989.
National Academy of Sciences. 1991. Policy Implications of Greenhouse Warming. National
Academy Press, Washington, D.C. 127 pp.
National Governors' Association (NGA). 1991. A World of Difference: Report of the Task Force on
Global Climate Change, NGA Washington, D.C. 36 pp.
Nordhaus, W.D. 1990. Economic Policy in the Face of Global Warming. Unpublished paper.
Oregon Department of Energy (ODOE). 1991. Fourth Biennial Energy Plan. Oregon Department
of Energy, Salem, Oregon.
Rodhe, H. 1990. A comparison of the contribution of various |;ases to the greenhouse effect.
Science 248:1217-1219.
Siegenthaler, V. 1983. Uptake of excess CO2 by an outcrop-diffusion model of the ocean. Journal
of Geophysical Research 88:3599-3608.
STATES WORKBOOK xi November 1992
-------
Silbiger, A. and N. Gonring. 1992. Selected Summary of Current State Responses to Climate Change.
Prepared for Office of Policy, Planning, and Evaluation, United States Environmental Protection
Agency, Washington, D.C. July 1992.
Smith, J. and D. Tirpak. 1989. The Potential Effects of Global Climate Change on the United States.
Office of Policy, Planning and Evaluation, U.S. Environmental Protection Agency, Washington, D.C
1990.
Steele, L.P., EJ. Dlugokencky, P.M Lang, PJP Tans, R.C. Margin, and KA. Masarie. 1992. "Slowing
down of the global accumulation of atmospheric methane during the 1980s." Nature. Volume 358.
July 23,1992.
Trexler, M. 1991. Minding the Carbon Store. World Resources Institute, Washington, D.C 1991.
USEPA (U.S. Environmental Protection Agency). 1991. National Air Pollutant Emissions Estimates
(1940 - 1989). National Air Data Branch, Research Triangle Park, NC (March, 1991).
U.S. Government 1991. Preliminary Estimate of Greenhouse Cos Emissions and Sinks for the United
States, 1988. Prepared for the IPCC October 1991.
World Resources Institute (WRI). 1990. World Resources Report. Oxford University Press, New
York. 1990.
STATES WORKBOOK xii November 1992
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DIRECTIONS
This report has two main objectives: 1) to provide states with methodologies for estimating
greenhouse gas emissions from the major anthropogenic sources; and, 2) to. provide important
background information on each of these sources, in order to best achieve these two objectives, the
report is divided into two sections:
1) Workbook, and
2) Discussion.
The workbook chapters present step-by-step instructions on how to estimate emissions from a
particular source. For each workbook chapter, there is a companion discussion chapter that contains
background information on emissions sources and more detailed information on the methodology for
calculating emissions shown in the workbook. For example, chapter 1 of the Workbook presents the
workbook calculations for CO2 emissions from energy combustion, wliile chapter 1 of the Discussion
offers the more detailed information on CO2 emissions from energy combustion.
Workbook preparers should first read through the background information in the discussion
chapter. The background section should provide a sufficient informational foundation to allow
preparers to begin working on the calculations shown in the workbook. Each workbook chapter
contains simplified instructions for estimating emissions from a particular source. The workbook
instructions are intended to be straightforward and to require a limited amount of time to complete.
Once the calculations have been completed for each chapter, emissions estimates should be
recorded in the Summary Table, (shown on page xv).
The discussion chapters provide more detailed information on the methodology used to
develop the instructions shown in the workbook. Therefore, readers seeking a more thorough
understanding of the recommended workbook methodology should consult the discussion chapter.
Additionally, the discussion chapter provides alternate methods for estimating emissions. These
alternate methods typically are more time consuming to complete and often require more detailed
emissions data than does the recommended workbook methodology. However, in some instances they
may also result in more precise estimates. States that have access to detailed data are encouraged
to estimate emissions following the alternate methodologies and to compare the results with the
estimates calculated using the recommended workbook method.
WORKBOOK CHAPTERS
The workbook contains eleven chapters, each of which pertains to a particular activity that
results in the emissions of a greenhouse gas. Chapter 1 covers carbon dioxide emissions from the
combustion of fossil and biomass fuels. Chapters 2 covers CFC and other emissions from production
processes. Chapters 3 through 8 address methane emissions from natural gas and oil systems, coal
mining, landfills, domesticated animals, animal wastes, and flooded rice fields, respectively. Also
included in Chapter 3 are instructions for estimating carbon dioxide emissions from venting and
flaring from oil and gas production activities. Similarly, chapter 5 includes calculations for estimating
STATES WORKBOOK xiii November 1992
-------
carbon dioxide emissions from flaring of landfill gas. Nitrous oxide emissions from fertilizer use are
addressed in chapter 9. The final two chapters present methods for estimating all greenhouse gas
emissions that result from land use changes (chapter 10) and burning of agricultural crop wastes
(chapter 11).
It is recommended that states complete all eleven chapters. While all chapters are important,
states should spend the greatest amount of time on chapter 1, since CO2 emissions from energy
combustion are likely- to be the single greatest source of greenhouse gas emissions. After this
chapter, states should work on chapters 2 through 9, because these chapters address the next largest
sources of greenhouse gas emissions. However, it should be noted that some states will not need to
complete the calculations for all chapters. la particular, not all states produce natural gas (chapter
3), coal (chapter 4) or rice (chapter 8).
Each workbook chapter includes suggested sources for the data that are required to complete
the emissions calculations. In some cases, default values are provided in the event that state
information is not available. However, in all cases, state information should be used where possible.
DISCUSSION CHAPTERS
As mentioned previously, each of the workbook chapters described above has a corresponding
discussion chapter. The purpose of the discussion chapter is to present more complete background
information on the emissions and to describe in greater detail the methodology of the calculations
presented in the workbook. Additionally, the discussion chapters provide information on alternate
methods for calculating emissions, where appropriate. Finally, the discussion chapters indicate
potential limitations of the methodologies presented and provide additional reference information.
Discussion chapters 12 and 13 (Emissions from stationary and mobile sources) do not have
corresponding workbook chapters because the calculations required to estimate these emissions are
very tune-consuming, data intensive and complex Moreover, states may already be estimating these
emissions (at least CO, NOP and NMVOCs) as a result of ongoing efforts to monitor compliance
with the Clean Air Act. Accordingly, it is not recommended that states estimate emissions from these
sources. However, it is recommended that the workbook preparer read through the background
information in these discussions chapters.
APPENDICES
In addition to the Workbook and Discussion sections for individual chapters, there are three
general appendices. For the convenience of the reader, Appendix A includes a glossary of global
wanning terms and a list of chemical symbols and conversion factors. Appendix B comprises a list
of state environmental and energy offices that could aid states in their work on climate change
responses. Finally, Appendix C is a brief bibliography of key reports on climate change impacts,
adaptation measures, and emissions reduction actions that would be useful to a state developing
adaptation and mitigation strategies.
STATES WORKBOOK xiv November 1992
-------
Source
SUMMARY TABLE FOR REPORTING EMISSIONS ESTIMATES
Emissions
Gas (tons! GWP
Emissions
fCO,-Eouivalent)
Fossil Fuel Combustion
W1" «> *.«">_ I* *1 "'J1' *"
&fQfn3S& Fttftf Combustion ^^f %v "?
Production Processes
Natural Gas and OU Systems
Coal Mining
Landfills
Domesticated Animals
Animal Manure
Flooded Rice Fields
Fertilizer Use
Land-Use Change
Burning of Agricultural Crop
Wastes
Total Emissions - All Sources
(excluding biomass fuels)
C02
aa,'""*;
C02
C02
CH4
CH4
CH4
CH4
CH4
CH4
N20
CO2
CH4
N2O
CH4
N2O
NOX
CO
CO2
CH4
N2O
NO,
CO
?'? w ' * * *>?< ,
bf * < ^ *
1
- 1*,*
1
1
11
11
11
11
11
11
270
1
11
270
11
270
NA
NA
1
11
270
NA
NA
/'« ^ >.
STATES WORKBOOK DRAFT
xv
November 1992
-------
-------
WORKBOOK CHAPTERS
STATES WORKBOOK November 1992
-------
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WORKBOOK 1
CARBON DIOXIDE EMISSIONS FROM COMBUSTION OF FOSSIL AND BIOMASS FUELS
Carbon dioxide is emitted during the combustion of fossil and biomass fuels. Fossil fuels
include coal, oil, and natural gas. For this calculation, biomass fuels primarily include wood,
charcoal, bagasse, and-agricultural wastes.
To estimate state emissions of carbon dioxide from fossil and biomass fuels, four steps
should be performed: 1) obtain the required data; 2) estimate the total carbon content in fuels; 3)
estimate carbon oxidized from energy uses; and, 4) convert to total CO2 emissions from energy
consumption. These four steps are outlined in detail below. A worlcsheet has been provided in
Table 1-1 to assist in the calculations. A more detailed description of the method used to
calculate carbon dioxide emissions is provided in the discussion section on CO2 emissions from
fossil and biomass fuels (Discussion 1).
Step (1): Obtain Required Data {Column (1) Table 1-1]
Required Data. The information needed to perform this exercise is annual state energy
consumption data based on fuel type (e.g., gasoline, residual oil, bituminous coal, lignite,
natural gas, etc.) by end-use sector (i.e., residential, commercial, industrial, transportation,
and electric utility). A list of suggested sector/fuel categoric; are provided in Table 1-1.
Additionally, further disaggregation may be done (such as by individual industries within
the industrial sector) if the appropriate data are available.
Data Source. In-state sources, such as state energy commissions or public utility
commissions, should first be consulted. Alternatively, state energy data by fuel type and
end use sector for fossil fuels can be found in the U.S. DOEI/EIA State Energy Data
Report and Coal Production. For those wishing to disaggregate the data further (for
example, by individual industry), an appropriate source would need to be obtained showing
state-level data at this level of detail.
Units for Reporting Data. Biofuel data should be reported in pounds of biomass (including
the weight of water). Fossil fuel statistics should be provided on an energy basis (i.e. Btu).
If fuel data are reported in other units, the conversion factors listed in Table 1-2 may be
applied in order to convert to million Btu.
Example: According to the EIA State Energy Data Report, total U.S. energy
consumption of distillate fuel for the residential sector in 19B9
was 1,040.5 x 1012 Btu, or 1,040,500,000 million Btu.
STATES WORKBOOK i.j November 1992
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Table 1-1. Worksheet to Calculate CO2 Emissions from Fossil & Biomass Fuels
Input lupat (l)x (2)+2000 (3)xOJ9*
(4) s 44112
Sector/Foe)
(1) (2) P)
Emission Total
Consumption Coefficient Carbon
(10* Btii)* (Ibs C/l«* Bto)" (tons Q
(4)
Total C
Oxidized
"
Gasoline
41-8
Residual Oil
Other Liquids
44.2
Bituminous Coal
Other Solids
57.0
Natural Gas
Biomass
OJ7
Total,
COMMERCIAL
Gasoline
<", V
Distillate Fuel
44U
Residual Oil
46-6
LPG
38.0
Liipiidfi
Bituminous Coal
57.0
Other Solids
W jj. >. ^.^ V
Natural Gas
32.0
Total
INDUSTRIAL
Gasoline
Distillate Fuel
Residual OU
46.6
Other Liquids
Bitnininous :Coal
Sub-Bit. Coal
59.0
Other Solids
57.0
Natural Gas
STATES WORKBOOK
1-2
November 1992
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Table 1-1. Worksheet to Calculate CO2 Emissions from Fossil & Biomass Fuels
Input Input (l)x (2)+2000 (3)xOJ9* (4) * 44112
Sectoi/Fnel
(1) (2) (3)
Emission Total
Consumption Coefficient Carbon
(lO'Btn)* (Ibs CflO* Btn)* (tons C)
(4)
Total C
Oxidized
(tons C)
(5)
CO,
Emissions
(tons CO,)
Biomass
0.27
TRANSPORTATION
Distillate Fuel
44.2
LPC
d&O
Bituminous Coal
57.0
Natural Gas
32.0
Biomass r'
Total
Gasoline
41.8
Residual Oil
46*
LPG
?3&0:;
Other Liquids
Bituminous Coal
Sub-Bit Coal
59.0
Lignite
Other Solids
57.0
Natural Gas
Biomass
0.27
Total
TOTAL
* For Biomass Fuels, consumption data should be reported in pounds and the emissions coefficient as % carbon.
Also, to calculate Total Carbon Oxidized for biomass fuels, multiply Total Carbon (from
column 3) by 0.90.
STATES WORKBOOK
1-3
November 1992
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Table 1-2. Conversion Factors to Million Btn.
Fuel Type
Gasoline
Distillate Fuel
Residual Oil
LPG
Other Petroleum Products
Bituminous Coal
Sub-bituminous Coal
Lignite
Other Solid Fuels
Natural Gas
If data is in
barrels
short tons
metric tons
barrels
short tons
metric tons
barrels
short tons
metric tons
barrels
short tons
metric tons
barrels
short tons
metric tons
short tons
metric tons
short tons
metric tons
short tons
metric tons
short tons
metric tons
billion cubic feet
Teracalories
Multiply by
5.253
40.55
44.69
5.825
39.22
43.23
6287
36.38
40.10
4.011
42.82
47J20
5.800
38.65
42.61
21.69
23.91
17.00
18.74
13.00
1433
21.33
23.51
1.03 x 106
3968
STATES WORKBOOK
1-4
November 1992
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Step (2): Estimate Total Carbon Content in Fuels [Column (3) Table 1-1]
Carbon content varies according to fuel type. To estimate the total carbon that could be
released from the fuels, multiply energy consumption for each fuel type by the appropriate
carbon emissions coefficient This calculation should be done for the fuel types in each
end use sector. To estimate carbon content released from biofuels, multiply consumption
by the percentage of carbon.
Fuel Type
Carbon Emissions Coefficient
Gasoline Consumption (106 Btu) x
Distillate Fuel Cons. (106 Btu) x
Residual Oil Consumption (106 Btu) x
LPG Consumption (106 Btu) x
Other Liquid Fuels Cons. (106 Btu) x
Bituminous Coal Cons. (10° Btu) x
Sub-Bit. Coal Cons. (10° Btu) x
Lignite Coal Consumption (106 Btu) x
Other Solid Fuels Cons. (106 Btu) x
Natural Gas Consumption (106 Btu) x
Biofuel Consumption (Ibs) x
41.8 (Ibs C/106 Btu) = Total Carbon (Ibs C)
44.2 (Ibs C/106 Btu) = Total Carbon (Ibs C)
46.6 (Ibs C/106 Btii) = Total Carbon (Ibs C)
38.0 (Ibs C/106 Btu) = Total Carbon (Ibs C)
44.2 (Ibs C/106 Btu) = Total Carbon (Ibs C)
57.0 (Ibs C/106 Btu) = Total Carbon (Ibs C)
59.0 (Ibs C/106 Btu) = Total Carbon (Ibs C)
61.0 (Ibs C/106 Btu) = Total Carbon (Ibs C)
57.0 (Ibs C/106 Btu) = Total Carbon (Ibs C)
32.0 (Ibs C/106 Btu) = Total Carbon (Ibs C)
027 (% C Content) » Total Carbon (Ibs C)
For each fuel type, divide the results by 2000 Ibs/ton to obtain tons of carbon. For each
end use sector, sum the results of the fuel types to obtain the total carbon content in tons.
Example: To calculate Total Carbon Content for distill
-------
Fuel Type Percent Oxidized
Total Carbon Content of Solid Fuel (tons) x 0.99 = Total Carbon Oxidized for
Solids (tons C)
Total Carbon Content of Liquid Fuel (tons) x 0.99 = Total Carbon Oxidized for
Liquids (tons C)
Total Carbon Content of Natural Gas (tons) x 0.99 = Total Carbon Oxidized for
Gas (tons C)
Total Carbon Content of Biofuels (tons) x 0.90 = Total Carbon Oxidized for
Biofuels (tons Q
Sum the results to obtain the total amount of carbon oxidized from all fuel types.
Example: To calculate the total amount of Carbon Oxidized from the
combustion of distillate fuel in the U.S. residential sector,
22,995,050 tons C x 0.99 = 22,765,100 tons C
Step (4): Convert to Total CO2 Emissions from Energy Consumption [Column (5) Table 1-1]
Multiply Total Carbon Oxidized for each fuel and sector by the molecular weight ratio of
CO2 to C (44/12) to obtain Total CO2 Emissions.
Sum across each fuel and each sector to find total state emissions of CO2 from energy
consumption.
Example: To convert the amount of Carbon Oxidized {from step (3)] to
Total CO 2 Emissions from distillate fuel consumption in the
U.S. residential sector,
22,765,100 tons C X (44/12) - 83,472,033 tons CO2
STATES WORKBOOK L6 November 1992
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WORKBOOK 2
GREENHOUSE GAS EMISSIONS FROM PRODUCTION PROCESSES
Emissions are often produced as a by-product of various production processes. That is,
these emissions are produced directly from the process itself and are not a result of the energy
that may be consumed during the production process. Carbon dioxide emitted during the cement
production process represents the only major non-energy source of industrial carbon dioxide
emissions. However, numerous other production processes also contribute to emissions of
carbon dioxide, methane, nitrous oxide and other greenhouse gases. Perhaps the most potent by-
products of production processes in terms of global wanning and stratospheric ozone depletion
are Ozone Depleting Compounds (ODCs). The processes resulting in emissions of ODCs are
varied and include refrigeration, air conditioning, solvent cleaning, foam production and
sterilization.
This workbook chapter only includes a method for estimating carbon dioxide emissions
from cement production. However, a method for estimating emissions of ODCs is included in the
discussion chapter. This method has not been included in the workbook section for several
reasons, including the fact that emissions of ODCs are rapidly declining because the use and
emissions of ODCs are already being controlled in the U.S., and the calculations are time
consuming and some of the required data may be difficult to obtain at the state level. However,
those interested may want to through the suggested method in order to gain an understanding of
the states contribution to ODC emissions. The discussion chapter also identifies the greenhouse
gas emissions resulting from other production processes.
The basic methodology for estimating CO2 emissions from cement manufacturing is to
multiply total cement production by the appropriate emission factor. This methodology is
outlined below. A more detailed description of the method used to calculate CD2 emissions
appears in the discussion chapter.
Step (1): Obtain Required Data
Required Data. The only information needed to calculate CO2 emissions from cement
production is annual cement production in short tons in the state.
Data Source. In-state sources should be consulted first. Additionally, cement production
by state can be found in the Cement Mineral Yearbook, published by the U.S. Bureau of
Mines.
Example: According to the U.S. Bureau of Mines Cement Mineral
Yearbook, total U.S. cement production in 1988 was
73,272,000 short tons.
STATES WORKBOOK 2-1 November 1992
-------
Step (2): Estimate CO2 Emissions from Cement Production
Multiply cement production by an emissions factor of 0.4985 tons CCy ton of cement
produced to yield total CO2 emissions from cement production.
Total CO2 Emissions (tons)
Total Cement Production (tons) x 0.4985 (tons
of cement produced)
Examp/e; To calculate Total CO2 Emissions from U.S. cement
production in 1988,
73272,000 tons x 0.4985 tons CO^ton cement * 36,526,092 ton* C02
STATES WORKBOOK
2-2
November 1992
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WORKBOOK 3
METHANE AND CARBON DIOXIDE EMISSIONS FROM
NATURAL GAS AND OIL SYSTEMS
Emissions from natural gas and oil systems are primarily methane, although smaller
quantities of non-methane VOCs, carbon dioxide, and carbon monoxide can be emitted. Methane
emissions occur throughout the total fuel cycle - during field production, processing, storage and
injection, transmission, distribution, and from engine exhaust While emissions occur during all
these various stages, emissions estimates addressed in this workbook are limited to CO2 and CH4
emissions that result from natural gas venting and flaring only. Emissions from other sources
associated with natural gas and oil production are not estimated due to a lack of reliable data on
the frequency and rate at which emissions may occur.
*
To estimate state emissions of CO2 and CH4 from venting and flaring, the following steps
should be taken: 1) obtain the required data; 2) calculate CO2 emissions from flaring and
venting; and 3) calculate CH4 emissions from venting. A more detailed discussion of the
suggested method for estimating CO2 and CH4 emissions from venting and flaring is contained in
discussion chapter 3 along with a description of other methodologies for estimating emissions
from natural gas and oil systems.
Step (1): Obtain Required Data
Required Data. The required data is the quantity of natural gas "vented and flared" in
each state for the most recent year that information is available. Additionally, the portion
of "vented and flared" gas that is vented should be obtained.
Source, In-£tate source? should be n"*d to -determine natural gas production for
each state, including the amount that is "vented and flared." Alternatively, the Natural
Gas Annual produced by the Department of Energy and the Energy Information
Administration (DOE/EIA) provides summary statistics for natural gas production in each
state including the portion "vented and flared." If in-state data is not available showing
the portion of methane "vented and flared" that is vented, this percentage may be
obtained from Table C2.34 of the report An Evaluation of the Relationship Between the
Production and Use of Energy and Atmospheric Methane Emissions (U.S. Department of
Energy, April 1990). For convenience, this table is shown on the last page of this section
(Table 3-1). States that do not produce natural gas are not listed in this table.
Units for Reporting Data. Data should be reported in million (mint cubic feet.
Example: According to the Natural Gas Annual 1990 (DOE/EIA) the
amount of natural gas in the U.S. in 1990 that was vented and
flared was 150,460 million cubic feet.
STATES WORKBOOK 3-1 November 1992
-------
Step (2): Estimate CO2 Emissions from Venting and Flaring
In order to calculate CO2 emissions, multiply natural gas vented and flared by the assumed
carbon emissions coefficient of 0.0328 Ibs C/cubic foot
0.0328 Ibs C/cf x mmcf natural gas vented and flared = mm Ibs C emitted.
Next, since the emissions estimate is determined in units of carbon, it should be multiplied
by 44/12 to convert to CO^ Finally, convert to tons of CO2 by dividing by 2000 Ibs/ton.
Example: To calculate total U.S. emissions of CO2from venting and
flaring:
150,460 mm cubic feet x .0328 Ibs/cf = 4,935 mm Ibs C.
4,935 IDS C X 44/12 = 18,095 mm Ibs CO2
18,095mm Ibs « 9 mm tons CO 2
Step (3): Estimate Methane Emissions from Venting
A portion of the total carbon emissions calculated above is vented as CH4. In order to
estimate this subset of emissions, the "vented" portion of the total carbon emissions from
"venting and flaring" should be estimated. The percent that is vented should be multiplied
by the total carbon emissions from venting and flaring (calculated above).
Next, estimate the portion of vented gas that is methane. A value of 90 percent may be
assumed (90 percent is the U.S. average).
Multiply the units of carbon by 16/12 to convert to the molecular weight of CH4. Convert
to tons by dividing by 2000 Ibs/ton.
Example: As shown in the example above, total U.S. emissions of
carbon from venting are approximately 4,935 mm Ibs C.
Assuming that the portion of natural gas that is vented is 20%
of total venting and flaring, methane emissions from venting
are calculated as follows:
4,935 mm Ibs C x 20% vented = 987 mm Ibs C vented.
987 mm Ibs C x 90% methane x 16/12 = 1,184 mm IDS
methane vented. 1,184 mm Ibs = 0.59 mm tons.
STATES WORKBOOK
3-2
November 1992
-------
Table 3-1
Assessment of Venting and Flaring in 1985
For States that Produce Natural Gas
STATE
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Florida
Illinois
Indiana
Kansas
Kentucky
Louisiana
Maryland
Michigan
Mississippi
Missouri
PERCENT OF
VENTED&
FLARED GAS
THAT IS VENTED
5
5
90
20
5
5
5
5
5
80
5
5
Unknown
5
2
Unknown
STATE
Montana
Nebraska
New Mexico
New York
North Dakota
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
Utah
Virginia
West Virginia
Wyoming
Oregon
South Dakota
PERCENT OF
VENTED &
FLARED GAS
THAT IS VENTED
10
5
90
5
5
5
20
5
20
5
10
5
5
20
5
5
Source: An Evaluation of the Relationship Between the Production and Use of Energy and
Atmospheric Methane Emissions (U.S. Department of Energy, April 1990)
Note: It is recommended that a state should obtain up-dated information on the percentage of
natural gas that is vented. These data should only be used when current in-state information
is not available.
STATES WORKBOOK
3-3
November 1992
-------
-------
WORKBOOK 4
METHANE EMISSIONS FROM COAL MINING
Methane and coal are formed together during coatification, a process in which vegetation
is converted by geological and biological forces into coal. Methane is released when pressure
within a coalbed is reduced, either through mining or through natural erosion or faulting.
To estimate state emissions of methane from coal mining, the following steps should be
performed: 1) obtain the required data - annual coal production from surface and underground
mines; 2) calculate methane emissions from underground coal mining; 3) calculate methane
emissions from surface mining; 4) calculate post-mining emissions; and, 5) calculate total coal
mining emissions. These steps are outlined in detail below. A more thorough description of
methane emissions from coal mines and of alternate methods for estimating emissions is provided
in discussion chapter 4 on coal mining.
Step (1): Obtain Required Data
Required Data. The data required to estimate methane emissions from coal mining are
annual coal production from surface mines and from underground mines.1
Data Source. State energy offices should be able to provide annual coal production from
surface and underground mines. Alternatively, the annual Coal Production reports
produced by the Department of Energy/Energy Information Administration (DOE/EIA)
contain surface and underground coal production for each state.
Units for Reporting Data. Data should be reported in million fmm) short tons.
Step (2): Calculate Methane Emissions from Underground Mines
The first step in calculating methane emissions from underground mines is to find the
annual underground coal production for the state. State underground coal production
should be recorded in the first row of column 1 in table 4-1, which is located on the last
page of this section. Most underground mining occurs in the eastern United States in the
Appalachian Basins (including Pennsylvania, Virginia, West Virginia, and eastern
Kentucky), the Illinois Basin, and the Black Warrior Basin of Alabama. However, some
western states such as Utah and Colorado also produce coal from underground mines.
Next, record the appropriate methane emissions coefficient (methane emitted per ton of
coal mined) in column 2 of table 4-1. Emissions coefficients are shown below for four
different regions in the U.S.2 Both a low and high emissions coefficient are given so that
1 It is important to distinguish between underground production and surface production because
shallow, surface mined coals tend to hold less methane than deeper, underground mined coals.
This emissions coefficient accounts for emissions from both ventilation and degasification systems in
underground mines. Previously developed methods used emissions coefficients for ventilation system
emissions only (see Discussion chapter 4).
STATES WORKBOOK 4-1 November 1992
-------
the potential range of emissions may be calculated.
Methane Emissions Coefficient for
Coal Produced from Underground Mines
Basin
Central and Northern Appalachian Basins:
Eastern Kentucky,1 Maryland, Ohio,
Pennsylvania, Tennessee, Virginia, West
Virginia
Black Warrior Basin (Alabama Only)
Rockies and Southwest Basins:
Colorado, New Mexico, Utah, Wyoming
Illinois Basin and Other
Illinois, Indiana, western Kentucky, and all
other states.
Emissions Coefficient
(cubic feet methane/ton of coal mined)
Low: 220 High: 780
Low: 1120 High: 2500
Low 410 High: 570
Low: 160 High: 480
1 Coal production for Kentucky should be divided between eastern and western Kentucky.
The Illinois Basin emissions coefficients should be used for western Kentucky.
Example: According to DOE/EIA's Coat Production 1990, coal
production from underground mines in West Virginia was
approximately 113.006 tnfllion short tons in 1989. Using the
emissions coefficients in the table above, estimated methane
emissions from underground mines in West Virginia are:
Low: 113.006 mm tons x 220 cffton = 24,861 rnrncf.
High: 113.006 mm tons x 780 cfAon = 88,145 mmcf.
Step (3): Calculate Methane Emissions from Surface Mines
Coal production from surface mines should be recorded in the second row of column 1 in
Table 4-1. Surface coal mining primarily occurs in the western U.S. and the eastern
bituminous coal basin in Illinois, Indiana, and western Kentucky.
» For all surface mined coal, the low and high assumed methane emissions coefficients are
15 cf/ton and 150 cf/ton of coal mined, respectively. These coefficients have already been
recorded in row 2 of column 2 in table 4-1.
STATES WORKBOOK
4-2
November 1992
-------
Calculate methane emissions from surface mines by multiplying the low and high methane
emissions coefficients by surface coal production. Record the resulting estimated
emissions in row 2 of column 3 in table 4-1.
Example: According to DOE/EiA's Coal Production 1990, coal .
production from surface mines in West Virginia was
approximately 40.137 million short tons. Using the given low
and high emissions coefficients, estimated methane emissions
from surface mines in West Virginia are:
Low: 40.137 mm tons x 15 cfAon =602 mmcf.
High: 40.137mmtonsxiSOcfAon = 6,020mmcf.
Step (4): Calculate Post-Mining Methane Emissions
Some methane remains in the coal after it has been mined and can be emitted during
transportation and handling of the coal. Post-mining emissions should be calculated for
both surface and underground mined coals. Hist, record coal production in column 1 of
table 4-1 record underground coal production in row 3 and surface coal production in
row 4 (rows 3 and 4 will be identical to rows 1 and 2 for column 1).
Next, surface and underground coal production should be multiplied by the appropriate
emissions coefficient to estimate post-mining methane emissions. For all surface mined
coal, the low and high post-mining methane emissions coefficients are 3 cubic feet and 30
cubic feet per ton of coal mined, respectively. For all underground mined coal, the low
and high post-mining methane p-niiMinm coefficients are 30 n'Kfc feet ?n^ 100 cubic feel
per ton of coal mined. These emissions coefficients are already recorded in column 2 of
table 4-1.
Record post-mining emissions for surface and underground mined coal in column 3 of
table 4-1.
Example: Using the surface and underground coal production shown in
the examples above, post-mining methane emissions for West
Virginia are calculated as follows:
Surface: Low » 40.137 mm tons x 3 cf/ton = 120 mmcf
High = 40.137 mm tons x 30 cf/ton - 1,204 mmcf
Underground: Low
High
113.006 mm tons x 30 cfAon » 3,390 mmcf
113.006 mm tons x 100 cfAon = 11,301 mmcf
STATES WORKBOOK
4-3
November 1992
-------
Step (5): Calculate Total Methane Emissions from Coal Mining
To find the low and high total emissions from coal mining, add together emissions from
underground mines and surface mines and post-mining emissions. Record the low and
high total emissions from coal mining in the last row of column 3 in Table 4-1. The low
and high total emissions represent the potential range of state coal mine methane
emissions.
Next, calculate the midpoint of the low and high total emissions estimates. This value may
be used as a single approximation of state coal mining methane emissions. However, it is
important to note that there is a large degree of uncertainty associated with using a single
emissions estimate. The low and high ranges represent the best estimates of state
emissions.
A number of mines in Alabama and one mine in Utah recover methane for sale to natural
gas pipelines. Additionally, several Virginia mines have begun to develop methane
recovery and utilization projects. Methane recovered from coal mines that is utilized
rather than vented to the atmosphere should be subtracted from total coal mine methane
emissions.3
Finally, total methane emissions should be converted from million cubic feet to tons by
multiplying by 20.66 tons/mmcf.
Example:
Total coal mine methane emissions for West Virginia are
calculated as follows:
Low (mmd) High
Underground:
Surface:
Post-mining (underground):
Post-mining (surface):
Total:
88,145
6,020
11,301
1.204
106,670
Avg. - (28,973 + 106,670)/2 = 67,822 mmcf
67,822 x mmcf x 20.66 tons/mmcf = 1,401,192 tons
3 In 1988, Alabama coal mines sold approximately 12 billion cubic feet of recovered methane to
pipelines, and sales from the Utah coal mine were about 1 billion cubic feet If in-state data are not
available for Alabama and Utah, these values may be used as approximations for the amount of methane
currently recovered and sold.
STATES WORKBOOK
4-4
November 1992
-------
Table 4-1 - Calculations for Estimating Methane Emissions from Coal Mines
1 Underground Mines
2 Surface Mines
3 Post-mining
(Underground)
4 Post-mining
(Surface)
1
Coal Production
(million short tons)
2
Emissions
Coefficient
(cf/ton)
Low
15
30
3
High
150
100
30
3
Methane Emitted
column 1 x column 2
(mmef methane)
Low
Total
Low:
High
Total
High:
Average:
- CH4 Recovered
Total (mmcf):
Total (tons):
STATES WORKBOOK
4-5
November 1992
-------
-------
WORKBOOK 5
METHANE AND CARBON DIOXIDE EMISSIONS FROM LANDFILLS
Landfill gas, consisting primarily of methane and carbon dioxide (CO^, is produced as a
result of the decomposition of waste in an anaerobic (without oxygen) environment Most landfill
gas is emitted directly to the atmosphere. However, at some landfills, the gas is recovered and
either flared or used as an energy source. When landfill gas is flared, the methane portion of the
gas is converted to CO2. Estimating methane and CO2 emissions from landfills requires the
following steps: 1) obtain the required data - primarily the total municipal solid waste generated
per year, the portion of waste that is landfilled, and the amount of landfill gas recovered or flared;
2) calculate methane emissions; 3) calculate CO2 emissions; and 4) calculate additional CO2
emissions from flaring. These steps are outlined in detail below. A more detailed description of
the methodology is provided in discussion chapter 5.
Step (1): Obtain Required Data
Required Data. The information needed to estimate methane and CO2 emissions from
landfills is: 1) total municipal solid waste (MSW) generated per year, 2) the portion of
MSW that is disposed of in a landfill; and, 3) the amount of landfill gas that is flared and
the amount recovered to be used as an energy source rather than emitted.
Data Source. In-state sources should be able to provide the best estimate of the amount
of MSW generated, the portion of MSW that is landfilled, and the amount of landfill
methane recovered. Alternatively, the amount of MSW generated and the portion of
MSW landfilled may be obtained from the article The State of Garbage: 1992 Nationwide
Survey" (BioCyde magazine. April, 1992). If in-state information is not available, the
amount of methane recovered can be calculated from the Methane Recovery From Landfill
Yearbook (Governmental Advisory Associates, Inc.), which reports the amount of methane
recovered on a landfill by landfill basis in the U.S.
Units for Reporting Data. Total MSW Generated and the amount of landfill methane
recovered for energy use or flared should be reported in pounds per year.
Example: According to EPA's Solid Waste Disposal in the United Stares,
Volume II. total MSW generated in the U.S. in 1988 was
347.7 billion pounds. The portion of MSW that is disposed of
in landfills is estimated to be about 83 percent According to
Methane Emissions from Municipal Solid Waste Landfills in the
United States (Colt et al., 1990), the amount of methane
recovered from landfills was 1.5 billion pounds CH4.
1 The Methane Recovery From Landfill Yearbook is available, for a fee, from Governmental
Advisory Associates Inc. (New York, NY).
STATES WORKBOOK 5-1 November 1992
-------
Step (2): Estimate Methane Emissions
Enter the required data into the following equation:
Methane Emissions
Total MSW generated (Ibs/yr) x portion of MSW
landfflled x 022 x 0.77 x 0.67 Ibs CH4/lb biogas -
Recovered CH4 (Ibsftr)
where:
022 - Percent of degradable organic carbon (DOC) contained in
the MSW, and;
0.77 = Percent of DOC that is dissimilated.
The above percentages represent default values for waste characteristics and waste
management practices for the entire United States. However, these percentages vary
signiGcantly by state, and states should substitute specific values if such data are available.
Divide the result by 2000 Ibs/ton to obtain annual methane emissions from landfills in
tons.
Example: Annual methane emissions from landfills for the U.S. in 1986
are calculated as follows:
(a) CH4 Emissions
347.7 billion Ibs/yr x 0.83 x 0.22 x 0.77 x
0.67 Ibs CHyib biogas - 1.5 billion Ibs CH/yr
32.8 billion Ibs CH yyr - 1.5
31.3 billion Ibs
(b) 31.3 (billion Ibs CH^yr) + 2000 IbsAon « 15.7 million tons CH4/yr
Step (3): Estimate CO2 Emissions
LandGIl gas is approximately SO percent CO2 and SO percent methane by volume, although
the percentage of CO2 may be smaller because some CO2 dissolves in landfill water (see
reference to Bingemer and Crutzen (1987) in discussion chapter). Assuming that the
quantity of CO2 and methane in landfill gas are roughly equal, CO2 emissions can be
calculated by multiplying methane emissions from Step (2) above by 44/16 to convert to
tons of CO2-
STATES WORKBOOK
5-2
November 1992
-------
Example: Annual CO2 emissions from landfills for the U.S. in 1986 are
calculated as follows:
(a) 15.7 million tons CH4/yr x 44/16 = 43 million tons COf
Step (4): Estimate CO2 Emissions from Flaring of Landfill Gas
Landfill gas that is recovered - instead of released to the atmosphere - is either flared or
used as an energy source. When landfill gas is flared, the methane in the gas is convened
to CO2. In order to calculate CO2 emissions from this source, the total amount of landfill
gas that is flared must be estimated. The portion of this amount that was originally CO2
(assume 50%) should be added to total CO2 emissions, because CO2 is not converted
during flaring.
To calculate additional CO2 emissions resulting from the conversion of methane to CO2
multiply the quantity of methane flared by 0.98 (an estimated 98% of methane flared will
be converted to CO^ and then by 44/16 to convert to CO2.
Ibs methane flared x 0.98 x 44/16 = Ibs CO2.
STATES WORKBOOK 5-3 November 1992
-------
-------
WORKBOOK 6
METHANE EMISSIONS FROM DOMESTICATED ANIMALS
Methane is produced during the normal digestive process of animals. Ruminant animals
cattle, buffalo, sheep, and goats) are the major emitters of methane. Non-ruminant animals
(including swine, horses, and mules) also contribute to emissions, but their digestive physiology
precludes them from emitting large quantities of methane.
Estimating methane emissions from domesticated animals requires two steps: 1) obtain
data on animal populations; 2) multiply animal populations by a methane emissions factor. The
basic methodology is outlined below. A more detailed description of the method used to estimate
emissions is provided in discussion chapter 6.
Step (1): Obtain Required Data
Required Data. The information needed to estimate methane emissions from domesticated
animals is animal populations for the following animals: dairy cattle (include heifers), beef
cattle, range cattle, buffalo, sheep, goats, swine, horses, mules, and asses.
Data Source. Departments within each state responsible for conducting agricultural
research and overseeing the agricultural sector should be able to provide state animal
populations. Additionally, state animal populations can be found in the Census of
Agriculture, Volume 1: Geographic Area Series, published by the Bureau of the Census.
Also, if requested, the USD A can produce state by state inventories on domesticated
animal populations.
Units for Reporting Data. Animal population should be reported in number of head.
example: According to the 7987 Census of Agriculture, total U.S. beef
cattle numbered 31,652,593 head in 1987.
Step (2): Estimate Methane Emissions
Multiply each animal population by the appropriate emissions factor. The following
emissions factors may be used:
STATES WORKBOOK 6-1 November 1992
-------
Animal
Dairy Cattle
Beef Cattle
Range Cattle
Horses
Mules/Asses
Sheep
Goat
Swine
Emissions Factor (Ibs CHj/head/yr)
184
142
119
40
22
18
11
33
Animal Population (bead) x Emissions Factor (Ibs. CH4/head) = Methane Emissions
(Ibs.)
For each animal, divide the results by 2000 Ibs/ton to obtain tons of methane. Sum across
all animal types to obtain total methane emissions from domesticated animals.
Example: Methane emissions from U.S. beef cattle in 1987 are
calculated as follows:
(a) 31,652,593 head x 142 (Ibs CHyhead) = 4,494,668,206 Ibs CH4
(b) 4,494,668,206 IDS CH4' + 2000 ()bs/ton) = 2,247,334.1 tons CH4
STATES WORKBOOK
6-2
November 1992
-------
WORKBOOK 7
METHANE EMISSIONS FROM ANIMAL MANURE
Methane is produced during the anaerobic decomposition of the organic material in
animal manure. Only manure from animals managed by humans for production of animal
products is included in the workbook calculations (i.e., wild animals are excluded).
To estimate methane emissions from animal manure, the following steps should be
performed: 1) obtain the required data on animal populations and manure management practices;
2) calculate the amount of volatile solids (VS) produced by each animal; 3) estimate methane
emissions from each manure management system; 4) convert emissions to tons of methane; and 5)
sum estimates to obtain total annual methane emissions for the state. Each of these steps is
outlined in detail below. A worksheet is provided in Table 7-13 to assist in the calculations. A
more detailed description of the methodology is provided in discussion chapter 7.
Step (1): Obtain Required Data
Required Data. The information needed to estimate methane emissions from manure is
animal populations for the following animals types:
Feedlot Beef Cattle Swine
Steers Market
Heifers Breeding
Cows/Other Poultry
Other Beef Cattle Layers
Calves Broilers
Heifers Ducks
Steers Turkeys
Cows Other
Bulls Sheep
Dairy Cattle Goats
Heifers Donkeys
Cows Horses/Mules
In addition, data on the percentage of animal manure handled in each manure
management system are required. A list and description of the major livestock manure
systems in use in the U.S. appear in discussion chapter 7.
Data Source. Departments within each state responsible for conducting agricultural
research and monitoring agricultural waste practices should be consulted for animal
population data. Alternatively, animal population data are provided by the Agriculture
Statistics Board of the USDA. Also, these data can be found in the Census of Agriculture,
Volume 1: Geographic Area Series, published by the Bureau of the Census. Manure
management usage percentages for most states and management practices are provided in
Tables 7-1 to 7-9.
STATES WORKBOOK 7-1 November 1992
-------
Units for Reporting Data. Animal population should be reported in number of bead.
Manure management usage should be reported as percentages.
Example: According to the Agriculture Statistics Board's Cafl/e on Feed,
total U.S. feedtot beef steers numbered 7,367,000 in 1987.
: According to Global Methane Emissions from Livestock and
'£ ; Poultry Manure (Safley et aL, 1992), the percentage of this
manure handled in drylot manure management systems is
10%.
Step (2): Calculate the amount of volatile solids (VS) produced.
For each animal type i, multiply the animal population by the typical animal mass (TAMj)
and the average annual volatile solids production per unit of animal mass (vsj). Values for
the TAM and vs for each of the animal types are provided in Table 7-10.
Animal; Population (head) x TAMj (Ibs/head) x vsj (Ibs VS/lb animal mass)
= Total VSj produced (Ibs)
The total amount of volatile solids (VS) produced for U.S.
feedlot beef steers in 1987 is calculated as follows:
Example:
7,367,000 head x 915 Ibs/hd. x 2.6 Ibs VS/lb animal mass = 17.53 billion Ibs
Step (3): Estimate Methane Emissions for Each Manure Management System
« For each animal type i and manure system;, multiply the amount of volatile solids
produced (VSj) by the methane producing capacity of the manure (B0 j) times the
methane producing potential (MCF:) of the manure system times the percent of the
animals' manure that is managed in that manure system (WS%;:). Default values for B0
and MCF by state are presented in Tables 7-11 to 7-12. WS% values for most states and
management practices are provided in Tables 7-1 to 7-9.
j x Boi x MCFj x WS%jj
where:
VS,
B0i
MCF,
Methane Emissions for animal i in system j (ft3 CH4)
total volatile solids produced (Ibs/yr) for animal i;
maximum methane producing capacity per pound of
VS for animal i (f^/lb-VS);
methane conversion factor for each manure system./
STATES WORKBOOK
7-2
November 1992
-------
percent of animal f s manure managed in manure
system; (%).
Example: Total annual methane emissions from U.S. feedlot beef steer
on a drylot manure management system is calculated as
follows:
17.53 billfon |bs x 5.29 (ft3CH4«x.VS) x 1.3%*x 10% =120.5 million ft3 CH4
dfyiot MCFfor the U.S.
Step (4): Convert to Tons of Methane
For each animal f and manure management system; multiply methane emissions by the
density of methane (0.0413 Ibs/ft3) to convert from cubic feet to pounds.
Divide the results by 2000 to obtain methane emissions from each animal and manure
management system in tons.
Example:
(a)
(b)
Annual methane emissions from U.S. feedlot beef cattle in a
drylot manure management system [from Step(3)J are
converted from cubic feet to pounds as follows:
12D.5 million*3CH4 x 0.0413 Ibs/ft9 = 4.98 million tbs CH4
4.98 million Ibs CH4 * 2000 Ibs/ton - 2,490 tons CH4
Step (5): Estimate Total Annual Methane Emissions
Sum across all manure management systems; and all animal types i to obtain total
methane emissions from animal manure.
Total Annual Methane Emissions (tons CH4) = £ ETotal Methane Emissionsy (tons)
STATES WORKBOOK
7-3
November 1992
-------
TABLE 7-1: MANURE MANAGEMENT SYSTEMS FOR US, BEEF
STATE
AL
AK
AZ
AR
CA
CO
CT
DE
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
Nt>
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
U.S. Average
An-Lag
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
2%
1%
0%
2%
0%
0%
0%
0%
0%
2%
0%
0%
1%
0%
1%
0%
0%
0%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
<1%
Diytot
2%
0%
30%
1%
12%
25%
0%
0%
0%
1%
10%
13%
14%
17%
13%
23%
1%
1%
0%
4%
0%
22%
13%
1%
1%
3%
31%
5%
0%
6%
8%
2%
0%
2%
12%
5%
5%
6%
0%
3%
5%
1%
13%
5%
0%
2%
.15%
2%
5%
6%
10%
Liq/Slur
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1%
2%
1%
0%
0%
0%
0%
0%
1%
0%
2%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
<1%
Pasture
98%
100%
70%
99%
88%
72%
100%
100%
99%
99%
90%
86%
83%
81%
87%
76%
99%
99%
100%
95%
100%
75%
85%
99%
98%
97%
68%
95%
100%
94%
92%
97%
97%
98%
87%
95%
94%
94%
100%
97%
94%
99%
87%
95%
100%
98%
85%
98%
95%
94%
89%
Other
0%
0%
0%
0%
0%
3%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
STATES WORKBOOK
7-4
November 1992
-------
TABLE 7-2: MANURE MANAGEMENT SYSTEMS FOR U.S. DAIRY
STATE
AL
AK
AZ
AR
CA
CO
CT
DE
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
Rl
sc
SD
TN
TX
UT
VT
VA
V/A
WV
WI
WY
U.S. Average
An.
Lagooo
50%
10%
10%
25%
40%
5%
0%
5%
2%
35%
31%
10%
5%
10%
3%
0%
19%
6%
0%
2%
0%
5%
0%
10%
60%
12%
0%
1%
0%
0%
90%
0%
5%
0%
5%
15%
42%
0%
0%
80%
25%
5%
25%
1%
0%
0%
40%
2%
0%
12%
11%
Liq/
Slurry
0%
71%
0%
0%
0%
10%
53%
35%
0%
5%
57%
85%
15%
60%
20%
40%
8%
0%
29%
48%
29%
30%
30%
1%
0%
19%
5%
1%
40%
. 29%
0%
20%
35%
20%
30%
0%
35%
2%
29%
5%
25%
40%
60%
1%
29%
75%
50%
.40%
15%
19%
21%
Daily
Spread
50%
2%
0%
75%
0%
85%
47%
60%.
10%
5%
6%
2%
45%
20%
8%
60%
30%
4%
58%
45%
58%
45%
40%
2%
40%
39%
35%
8%
20%
58%
10%
70%
50%
10%
45%
5%
5%
95%
58%
10%
30%
20%
15%
8%
58%
25%
10%
30%
70%
39%
41%
Solid
Stor.
0%
2%
0%
0%
0%
0%
1%
0%
0%
0%
0%
0%
10%
10%
65%
0%
0%
0%
13%
5%
13%
12%
30%
2%
0% -
23%
0%
90%
40%
13%
0%
10%
10%
70%
12%
0%
1%
3%
13%
5%
20%
0%
0%
90%
13%
0%
0%
20%
15%
23%
18%
Other
0%
15%
90%
0%
60%
0%
0%
0%
88%
55%
6%
3%
25%
0%
4%
0%
43%
90%
0%
0%
0%
8%
0%
85%
0%
7%
60%
0%
0%
0%
0%
0%
0%
0%
8%
80%
17%
0%
0%
0%
0%
35%
0%
0%
0%
0%
0%
8%
0%
7%
8%
STATES WORKBOOK
7-5
November 1992
-------
TABLE 7.3: MANURE MANAGEMENT SYSTEMS FOR U.S. SWINE
STATE
AL
AK
AZ
AR
CA
CO
CT
DE
FL
GA
m
ID
IL
IN
1A
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
Rl
SC
SD
TN
TX
UT
vr
VA
WA
WV
WI
WY
U.S. Average
An.
Lagoon
90%
100%
100%
70%
90%
24%
15%
20%
35%
68%
32%
40%
25%
25%
3%
30%
80%
95%
3%
50%
3%
42%
0%
59%
80%
0%
35%
25%
5%
3%
10%
5%
70%
20%
37%
60%
25%
0%
3%
90%
20%
80%
35%
25%
3%
90%
30%
25%
0%
24%
29%
Drylot
0%
0%
0%
20%
0%
25%
0%
10%
64%
20%
7%
15%
15%
10%
30%
40%
12%
5%
53%
10%
53%
12%
20%
14%
20%
40%
5%
75%
90%
53%
70%
30%
15%
20%
8%
30%
6%
39%
53%
5%
30%
15%
20%
75%
53%
0%
0%
25%
10%
25%
20%
Pit SL
<1 ninth
0%
0%
0%
0%
0%
21%
0%
0%
1%
0%
17%
5%
10%
5%
11%
0%
7%
0%
2%
0%
2%
4%
20%
5%
0%
25%
55%
0%
0%
2%
10%
5%
0%
30%
1%
10%
35%
1%
2%
0%
25%
0%
15%
0%
2%
0%
10%
25%
20%
21%
12%
Pit St.
>1 math
10%
0%
0%
10%
0%
24%
0%
70%
0%
10%
36%
35%
45%
60%
39%
30%
1%
0%
42%
40%
42%
39%
40%
9%
0%
25%
5%
0%
5%
42%
10%
60%
15%
30%
46%
0%
12%
60%
42%
5%
25%
5%
10%
0%
42%
10%
60%
25%
70%
24%
32%
Other
0%
0%
0%
0%
10%
6%
85%
0%
0%
2%
8%
5%
5%
0%
13%
o%-
0%
0%
0%
0%
0%
3%
20%
13%
0%
10%
0%
0%
0%
0%
0%
0%
0%
0%
8%
0%
22%
0%
0%
0%
0%
0%
20%
0%
0%
0%
0%
0%
0%
6%
7%
STATES WORKBOOK 7-6 November 1992
-------
TABLE 7-4: MANURE MANAGEMENT SYSTEMS FOR US. CAGED LAYERS
STATE
AL
AK
AZ
AR
CA
CO
CT
DE
FL
GA
HI
ID
n.
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MI
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
U.S. Average
An.
Lagoon
80%
15%
0%
40%
7%
4%
0%
0%
12%
1%
80%
0%
10%
0%
2%
0%
61%
95%
0%
0%
0%
3%
0%
85%
0%
4%
0%
0%
0%
0%
20%
0%
30%
5%
0%
0%
11%
0%
0%
40%
20%
7%
40%
0%
0%
0%
0%
0%
0%
4%
14%
Deep
Fit
10%
63%
100%
0%
45%
88%
100%
100%
70%
30%
10%
40%
90%
95%
90%
100%
3%
0%
81%
100%
81%
85%
75%
0%
80%
88%
100%
75%
100%
81%
45%
60%
15%
90%
100%
80%
80%
65%
81%
50%
80%
3%
10%
50%
81%
30%
90%
0%
55%
88%
56%
Liq/
Slimy
10%
12%
0%
60%
3%
8%
0%
0%
6%
5%
0%
60%
0%
5%
4%
0%
33%
0%
9%
0%
9%
3%
25%
5%
20%
8%
0%
0%
0%
9%
10%
30%
5%
5%
0%
20%
9%
5%
9%
0%
0%
90%
0%
0%
9%
0%
10%
0%
5%
8%
10%
Other
0%
10%
0%
0%
45%
0%
0%
0%
12%
65%
10%
0%
0%
0%
4%
0%
3%
5%
10%
0%
10%
10%
0%
10%
0%
0%
0%
25%
0%
10%
25%
10%
50%
0%
0%
0%
0%
30%
10%
10%
0%
0%
50%
50%
10%
70%
0%
100%
40%
0%
20%
STATES WORKBOOK
7-7
November 1992
-------
TABLE 7-5: MANURE MANAGEMENT
SYSTEMS FOR U.S. BROILERS
TABLE 7-fc MANURE MANAGEMENT
SYSTEMS FOR U.S. TURKEYS
State
AL
AK
AZ
AR
CA
CO
CT
DE
FL
GA
m
ID
n.
IN
IA
KS
KY
LA
ME
MA
MD
MI
MN
MS
MO
MT
NC
ND
JJH
NJ
NM
NY
NE
NV
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
wv
WA
Wl
WY
Other
U.S. Average
Litter
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
100%
Otber
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
State
AR
AK
AZ
AR
CA
CO
CT
DE
FL
CA
HI
ID
IL
IN
1A
KS
KY
LA
ME
MA
MD .
MI
MN
MS
MO
MT
NC
ND
NH
NJ
NM
NY
NE
NV
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WV
WA
WI
WY
Other
U.S. Average
Utter
95%
93%
0%
50%
85%
95%
100%
100%
75%
90%
93%
100%
100%
90%
40%
100%
75%
100%
100%
100%
100%
90%
95%
100%
0%
94%
90%
88%
92%
Range
5%
7%
100%
50%
15%
5%
0%
0%
25%
10%
7%
0%
0%
10%
60%
0%
25%
0%
0%
0%
0%
10%
5%
0%
100%
6%
10%
12%
8%
Other
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
«%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
STATES WORKBOOK
7-8
November 1992
-------
TABLE 7-7: MANURE MANAGEMENT
SYSTEMS FOR US. SHEEP
TABLE 74k MANURE MANAGEMENT
SYSTEMS FOR VS. GOATS
SlATE
AL
AK
AZ
AR
CA
CO
CT
DE
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
Other
U.S. Average
Pasture
100%
100%
90%
95%
50%
95%
95%
90%
99%
100%
95%
100%
66%
66%
66%
94%
90%
90%
98%
90%
98%
100%
66%
100%
65%
98%
95%
95%
100%
91%
50%
100%
100%
80%
95%
66%
100%
100%
90%
97%
95%
100%
92%
Other
0%
0%
10%
5%
50%
5%
5%
10%
1%
0%
5%
0%
34%
34%
34%
6%
10%
10%
2%
10%
2%
0%
34%
0%
35%
2%
5%
5%
0%
9%
50%
0%
0%
20%
5%
34%
0%
0%
10%
3%
5%
8%
STAIB
AL
AK
AZ
AR
CA
CO
CT
DE
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
Other
U.S. Average
Pasture
100%
100%
95%
99%
0% '
100%
100%
100%
80%
100%
92%
92%
100%
100%
100%
100%
99%
100%
100%
100%
100%
99%
100%
95%
100%
99%
100%
98%
100%
100%
100%
100%
90%
100%
100%
100%
84%
100%
100%
100%
100%
100%
80%
100%
100%
99%
100%
80%
95%
100%
84%
Otber
0%
0%
5%
1%
100%
0%
0%
0%
20%
0%
8%
8%
0%
0%
0%
0%
1%
0%
0%
0%
0%
1%
0%
5%
0%
1%
0%
2%
0%
0%
0%
0%
10%
0%
0%
0%
16%
0%
0%
0%
0%
0%
20%
0%
0%
1%
0%
20%
5%
0%
16%
STATES WORKBOOK
7-9
November 1992
-------
TABLE 7-9: MANURE MANAGEMENT SYSTEMS FOR VS. HORSES
STATE
AL
AK
AZ
AR
CA
CO
CT
DE
FL
GA
HI
ID
IL
IN
IA
KS
KY
LA
ME
MD
MA
MI
MN
MS
MO
MT
NE
NV
NH
NJ
NM
NY
NC
ND
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VT
VA
WA
WV
WI
WY
VS. Average
Paddock
50%
10%
35%
10%
20%
17%
50%
50%
15%
33%
45%
35%
30%
50%
8%
10%
30%
25%
35%
35%
35%
36%
50%
40%
10%
1%
5%
20%
90%
35%
75%
50%
10%
30%
95%
20%
45%
50%
35%
50%
20%
25%
0%
20%
35%
1%
50%
75%
15%
17%
27%
Pasture
50%
90%
65%
90%
80%
83%
50%
50%
60%
60%
55%
60%
40%
50%
92%
90%
70%
75%
65%
65%
65%
64%
50%
60%
90%
99%
95%
80%
10%
65%
25%
25%
65%
70%
5%
80%
55%
50%
65%
50%
80%
75%
60%
80%
65%
99%
50%
25%
50%
83%
66%
Other
0%
0%
0%
0%
0%
0%
0%
0%
25%
7%
0%
5%
30%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
25%
25%
0%
0%
0%
0%
0%
0%
0%
0%
0%
40%
0%
0%
0%
0%
0%
35%
0%
7%
STATES WORKBOOK
7-10
November 1992
-------
Table 7-10. U.S. Average Animal Size and VS Production
Animal Type
Feedlot Beef Cattle
Other Beef Cattle
Daily Cattle
Swine
Poultry
Otber
Steers/Heifers
Calves
Heifers
Steers
Cows
Bulb
Heifers
Cows
Market
Breeding
Layers
Broilers
Ducks
Turkeys
Sheep
Goats
Donkeys
Horses and Mules
Typical
Animal
MassfTAM)
Ibs
915
397
794
794
1102
1587
903
1345
101
399
3.5
1.5
3.1
15
154
141
661
992
Volatile
Solids (vs)
IbsVS/
Ib animal mass
2.6
2.6
2.6
2.6
2.6
2.6
3.65
3.65
3.1
3.1
4.4
6.2
6.75
3.32
336
3.48
3.65
3.65
Table 7-11. Maximum Methane Producing Capacity for U.S. Estimates
Animal Type
Cattle
Swine
Poultry
Sbcep
Goats
Horses & Mules
Category
Beef in Feedlocs
Beef Not in Feedlots
Dairy
Breeder
Market
Layers
Broilers
Turkeys
Ducks
In Feedlots
Not in Feedlots
Maximum Potential
Emissions (BJ
(ftj CH«flb-VS)
5.29
2.72
3.84
5.77
7.53
5.45
4.81
4.81
5.13
5.77
3.04
2.72
5.29
STATES WORKBOOK
7-11
November 1992
-------
Table 7-12. Methane Conversion Factor* for US. Livestock Manure Systems
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut ~
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
MnvuirtmitftH
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Pasture,
Range*
Paddocks
1,4%
1.4%
13%
13.%
0.9%
0.9%
1.2%
15%
1.4%
0.8%
1.1%
1.0%
0.9%
1.1%
12%
1.4%
0.8%
1.1%
0.9%
0.8%
08%
1.4%
1.1%
0.7%
1.0%
1.2%
0.8%
1.0%
12%
0.9%
13%
0.7%
1.0%
1.4%
1.1%
0.9%
1.0%
1J%
0.8%
U%
1.4%
0.9%
0.8%
1.2%
1.0%
1.2%
0.8%
0.8%
Drytot
1.9%
1.9%
1.8%
1.4%
1.0%
1.0%
1.4%
2.4%
1.8%
0.8%
13%
12%
1.1%
15%
1.5%
2,1%
0.8%
12%
1.0%
05%
0.8%
1.9%
1.4%
0.8%
1.1%
1.4%
0.8%
1.1%
13%
0.9%
1.5%
0.7%
1.1%
1.9%
1.1%
1.0%
1.1%
1.7%
0.9%
1.6%
2.1%
1.0%
0.8%
1.4%
1.0%
13%
0.8%
0.8%
Solid
Storage
1.4%
1.4%
13%
12%
0.9%
0.9%
12%
1.5%
1.4%
0.8%
1.1%
1.0%
0.9%
1.1%
12%
1.4%
0.8%
1.1%
0.9%
0.8%
0.8%
1.4%
1.1%
0.7%
1.0%
12%
0.8%
1.0%
12%
0.9%
13%
.0.7%
1.0%
1.4%
1.1%
0.9%
1.0%
13%
0.8%
13%
1.4%
0.9%
0.8%
12%
1.0%
12%
0.8%
03%
Other Systems: Pit Storaee for less than 30 davx is assumed to have an
MCF for Liquid/Slurry.
liquid/slurry. Anaerobic
MCF of 10%.
Pit Storage
lagoons are
for more than 30
assumed to nave
Daily
Spread
0.4%
0.4%
0.4%
03%
02%
02%
03%
0.6%
0.4%
02%
03%
03%
02%
03%
03%
0.5%
02%
03%
02%
02%
02%
0.4%
03%
02%
02%
03%
02%
0.3%
03%
02%
03%
02%
02%
0.4%
02%
02%
02%
0.4%
02%
03%
0.5%
02%
02%
03%
02%
03%
02%
02%
MCF eaual to
Liquid/
Slurry
29.0%
28.9%
27.6%
21.9%
182%
18.5%
22.6%
38.6%
29.0%
15.5%
218%
21.5%
20.7%
24.7%
23.8%
323%
15.5%
21.0%
18.1%
17.0%
18.0%
293%
24.1%
15.8%
20.8%
22.1%
163%
20.6%
213%
18.1%
24.5%
16.8%
202%
28.7%
162%
18.7%
18.7%
273%
19.1%
24.8%
31.7%
17.4%
16.6%
22^%
15.5%
21.4%
17.0%
15.9%
50% of the
days is assumed to nave an MCF equal to
an MCF of 90%;
litter and deep
pit stacks an
STATES WORKBOOK
7-12
November 1992
-------
Table 7-13 Worksheet to Calculate Methane Emissions from Animal Manure
Input
Input
Input
Input
* (5)
Animal Type
(1)
Population
(bead)
(2)
Typical Animal
Ma»(TAM)
(3)
(4)
Total VS
Produced
(5)
CH4ProdBdnc
Capadljr (Bo)
(cable fl/lb-VSl
Mas. Potential
Emissions
(cable ft)
Input
Input
(9) x 0.0413
Manure System
(7)
Methane CODY.
Factor (MCF)
1*1
(8)
Waste System
Usage (WS%)
Methane
Emissions
(cubic rt)
(10)
Methane
Emissions
Pasture/Range
Daily Spread
Solid Storage
Drylot
Deep Pit Stacks
Litter
Paddock
Liquid/Slurry
Anaerobic Lagoon
Pit Storage <1 mo
Pit Storage >1 mo
Total Methane Emissions (tons/yr):
I Sum Column (10) and divide by 2000]
STATES WORKBOOK
7-13
November 1992
-------
-------
WORKBOOK 8
METHANE EMISSIONS FROM FLOODED RICE FIELDS
Methane (CH4) is produced through the anaerobic decomposition of organic material in
flooded rice fields. Non-flooded rice fields, such as dry upland rice fields, do not produce
significant quantities of CH4. Additionally, deepwatcr, floating rice fields (>1 meter floodwater
depth) are not believed to produce significant quantities of CH4. Accordingly, only flooded, non-
deepwater, rice fields are accounted for in these calculations.
Only six U.S. states produce significant quantities of rice: Arkansas, California, Louisiana,
Mississippi, Missouri, and Texas. Other states may skip this section of the workbook.
To estimate methane emissions from flooded rice fields, the following steps are required:
1) obtain the required data on area harvested; 2) calculate the average number of acre-days
harvested annually; and 3) apply an emissions rate range to the annual harvested non-deepwater,
wetland area. These steps are outlined in detail below. A more detailed description of the
methodology is provided in discussion chapter 8.
Step (1): Obtain Required Data
* Required Data. The information needed to calculate methane emissions from flooded rice
fields is the total area harvested (not including upland or deepwater rice fields) for three
consecutive years centered on the study year (e.g., to calculate 1990 emissions, data from
1989, 1990, and 1991 are needed) and the length of the growing season.
Data Source. State agencies responsible for overseeing the agricultural sector should be
consulted. Alternatively, rice area harvested for the major rice producing states can be
found in the U.S. Department of Agriculture's annual Crop Production report
Units for Reporting Data. Rice area harvested should be reported in acres, while the
length of the growing season should be in days.
Example: According to the USDA's Crop Production 1990 Summary, the
total amount of wetland rice area harvested in acres was:
2,333,000 in 1987; 2,900,000 In 1988, and 2,687,000 In
1989. According to Matthews et al. (1991), the length of the
U.S. rice growing season is 153 days.
STATES WORKBOOK
8-1
November 1992
-------
Step (2): Calculate the Average Number of Acre-Days Harvested Annually
Multiply the number of acres harvested each year by the length of the growing season to
obtain the total number of acre-days harvested in those years.
Area Harvested (acres) x Length of Growing Season (days) = Acre-days per year
Sum the total number of acre-days for each year and divide by three to calculate the
average number of acre-days harvested annually for the three-year period.
Example:
(a)
(b)
Trie average number of acre-days harvested in the U.S. from
1987-1989 is calculated as follows:
1987: 2,333,000 acres x 153 days
1988: 2,900,000 acres x 153 days
1989: £687,000 acres x 153 days
356,949,000 acre-days
443,700,000 acre-days
411,111,000 acre-days
(356,949,000 + 443,700,000 + 411,111,000) + 3 =
403,920,000 acre-day*
Step (3): Estimate Methane Emissions
Multiply the average number of acre-days harvested annually by the endpoints of the daily
emissions rate range (1.35 - 4.04 Ibs CH4/acre/day) to obtain the range of methane
emissions from flooded rice Gelds.
Average # of Acre-Days x 135 Ibs CH4/acre-day = CH4 Emissions-low (Ibs
Average # of Acre-Days x 4.04 Ibs CH4/acre-day = CH4 Emissions-high (Ibs CH4/yr)
Divide the results by 2000 to obtain methane emissions in tons CH4.
Example:
(a)
Annual methane emissions from flooded rice fields for the U.S.
is calculated as follows:
AVQ. Acre-Days
Emissions Coefficient CH. Emissions
low: 403.920,000 acre-days x 1.35 Ibs CH ^acre-day = 545,292.000 Ibs CH4
high: 403,920.000 acre-days x 4.04 Ibs CH^acre-day = 1.631,836,800 Ibs CH4
(b)
low: 545,292,000 Ibs CH4 + 2000 Ibs/ton = 272,646 tons CH4
high: 1,631,836,800 Ibs CH4 + 2000 Ibs/ton = 815,918 torts CH4
STATES WORKBOOK
8-2
November 1992
-------
WORKBOOK 9
NITROUS OXIDE EMISSIONS FROM FERTILIZER USE
Nitrous Oxide (N2O) is naturally produced in soils by microbial processes. Commercial
nitrogen fertilizers provide an additional nitrogen source and therefore increase the emissions of
N2O from the soil -
To estimate state emissions of N2O from fertilizer use, four steps should be performed: 1)
obtain the required data on fertilizer use; 2) calculate the three-year average for annual fertilizer
consumption; 3) estimate nitrous oxide emissions in units of nitrogen; 4) convert total emissions to
units of N2O. A worksheet is provided in Table 9-2 to assist in the calculations. A more detailed
description of the method used to calculate N2O emissions is provided in the fertilizer discussion
section.
Step (1): Obtain Required Data [Columns (1),(2),(3) Table 9-2]
Required Data. The information needed to estimate N2O emissions from fertilizer use is
annual fertilizer consumption, by fertilizer type, for three consecutive years centered on
the study year (e.g., to calculate 1988 N2O emissions, data for 1987, 1988, and 1989 are
needed). A list of various fertilizer types can be found in Table 9-1. Three years of data
are recommended to avoid unusual annual variations due to economic, climatic, or other
variables.
* Data Sources. Departments within each state responsible for conducting agricultural
research and overseeing the agricultural sector should be consulted first Additionally,
state fertilizer consumption data can be found in fertilizer Summary Data and-Commeraat
Fertilizers, both published by the Tennessee Valley Authority.
Units for Reporting Data. Fertilizer data should be reported in mass units of nitrogen (i.e.,
tons N). If fertilizer consumption is given in tons of material (as it does in the TVA
Fertilizer Summary Data) rather than in tons N, the total mass may be converted to
nitrogen content using the percentages in Table 9-1:
Table 9-1. Nitrogen Content of Principal Fertilizers
MATERIAL
Nitrogen
Ammonia, Anhydrous
Ammonia, Aqua
Ammonium nitrate
Ammonium nitrate-limestone mixtures
Ammonium sutfate
Ammonium sulfate-nitrate
Calcium cyanamide
Calcium nitrate
% NITROGEN
82
16-25
33.5
20.5
21
26
21
15
STATES WORKBOOK
9-1
November 1992
-------
Table 9-1. Nitrogen Content of Principal Fertilizers (cont'd.)
MATERIAL
Nitrogen (cont'd.)
Nitrogen solutions
Sodium nitrate
Urea
Urea-form
Phosphate
Basic slag, Open hearth
Bone meal
Phosphoric acid
Rock phosphate
Superphosphate, Normal
Superphosphate, Concentrated
Superphosphoric acid
Potash
Potassium chloride (muriate)
Potassium magnesium sulfaie
Potassium sulfate
Multiple Nutrient
Ammoniated superphosphate
Ammonium phosphate-nitrate
Ammonium phospbate-sulfate
Dunnrnoniutn phosphate
Monoammomum phosphate
Nitric phosphates
Nitrate of soda-potash
Potassium nitrate
Wood ashes
Blast furnace slag
Dolomite
Gypsum
Kieserite (emjeo)
Limestone
Lime-sulfur solution
Magnesium sulfate (Epsom salt)
Sulfur
* NITROGEN
21-49
16
46
38
»
2-4.5
-
-
-
-
-
-
-
3-6
27
13-16
16-21
11
14-22
15
13
-
'
.
-
-
-
-
-
Note: A dash (-) indicates that the fertilizer contains either no nitrogen or a
negligible amount of nitrogen.
STATES WORKBOOK
9-2
November 1992
-------
Table 9-2. Worksheet to Calculate N2O Emissions from Nitrogen Fertilizer Use
Input Input Input Artngt Input Input Input (4) x (5) (4) x (i) (4) x ( 7) (8) x 44128 (9) x 44128 (10) x 44128
(1) (2) (3) (4) (5) (6) (7) (8) (9) . (10) (11) (12) (13)
Fertilizer
Ammonium Sulphate
Ammonium Nitrate
Sodium Nitrate
Urea
Ammonium Phosphate
Anhydrous Ammonia
Aqua Ammonia
Calcium Nitrate
Potassium Nitrate
Other
Total
Fertilizer Consumption
(tons N)
1987
1988
1989
3-yr avg
Emission Factor
(% N20-N produced)
median
0.12
0.26
0.03
0.11
0.12
1.63
1.63
0.03
0.03
0.11
low
0.02
0.04
0.001
0.07
0.02
0.86
0.86
0.001
0.001
0.001
high
1.50
1.71
0.50
1.50
1.50
6.84
6.84
0.50
0.50
6.84
NIO-N Emissions
(tons N2O-N)
median
low
high
r
N2O Emissions
(tons N2O)
median
low
high
STATES WORKBOOK
9-3
November 1992
-------
Example: According to the 7VA Fertilizer Summary Data, total U.S.
consumption of ammonium nitrate in tons of material was:
1,643,904 in 1987; 1,768,719 in 1988; and 1.898,650 in 1989.
To convert this to tons of nitrogen, multiply by the percent N
; content of ammonium nitrate (33.5%),
"1,643.904 tons of material x 33.5%
1,768,719 tons of material x 33.5%
1.898.650 tons of material x 33.5%
550,708 tons N
592,521 tons N
636,048 tons N
Step (2): Calculate Average Annual Nitrogen Consumption By Fertilizer Type [Column (4)
Table 9-2]
For each fertilizer type, calculate the three-year average annual consumption of nitrogen
in the fertilizer.
The three-year average annual consumption for ammonium
nitrate in the U.S. from 1987-1989 is calculated as follows:
Example:
(550,708 + 592,521 -I- 636,048) + 3 = 593,092.3 tons N
Step (3): Estimate Nitrous Oxide Emissions [Columns (8),(9),(10) Table 9-2]
Multiply the three-year average for each fertilizer type by the appropriate emissions
coefficient. The emissions coefficients for each fertilizer type are shown in Table 9-3.
Low, median, and high values for the percentage of N2O-N produced are provided. All
three values should be used to calculate the full range of emissions from each fertilizer
type. The result will be N2O emissions in units of N.
Total N2O-N Emissions (tons N2O-N)
Total Nitrogen Content of Fertilizer (tons
N) x Emission Coefficient (tons N2O-N/ton
N applied)
Sum across all types of fertilizers to produce the total low, median, and high range
estimates of N2O-N emissions from fertilizer use.
STATES WORKBOOK
9-4
November 1992
-------
Example: To estimate total Np-N emissions for the U.S. from
ammonium nitrate, : ;
Nitrogen Content Emissions Coefficient Np Emissions funfrs of
low: 593,092.3 tons NX 0.0004
tned; 593,092.3 tonsM * ; I 0.0026
high: 593;09Z3tonsN x ,0.0171
237.24 tons
1,542.04 tons KL.O-N
10,141.88 tons N^O-N
Table 9-3. Fertilizer Derived N2O Emissions By Fertilizer Type
Fertilizer Type
ANHYDROUS AMMONIA
AQUA AMMONIA
AMMONIUM NITRATE
Ammonium Sulfate Nitrate
Calcium Ammonium Nitrate
AMMONIUM TYPE
Ammonium Sulfate
Ammonium Phosphate
UREA
NITRATE
Calcium Nitrate
Potassium Nitrate
Sodium Nitrate
OTHER NITROGEN
FERTILIZERS
OTHER COMPLEX
FERTILISERS
% N2O-N produced
(Median)
1.63
0.26
0.12
0.11
0.03
0.11
0.11
% N2O-N produced
(Range)
0.86-6.84
0,04-1.71
0.02-1.5
0.07-15
0.001-0.5
0.001-6.84
0.001-6.84
Step (4): Convert to Units of N2O [Columns (11),(I2),(13) Table 9-2]
Multiply the low, median, and high range emission estimates by 44/28 to convert them
from units of N to units of N2O.
STATES WORKBOOK
9-S
November 1992
-------
Sum across all fertilizer types to produce total N2O emissions from fertilizer use in units
of N2O.
Example: To convert Np-N emission estimates from ammonium nitrate
[from step (3)J into units of i
'V'"'-;^" low;-::;.;;:23724 tons Np-N ;& 44/28 ;W 372.81 ions NjO:.
med: 1,542.04 tons Np-N x 44/28 2,423^1 tons r
high: 10/141.88 tons Np-N x 44/28 = 15,937^4 tons N2O
STATES WORKBOOK
9-6
November 1992
-------
WORKBOOK 10
GREENHOUSE GAS EMISSIONS FROM LAND-USE CHANGE
Land-use changes that alter the amount of biomass on land produce a net exchange of
greenhouse gas emissions between the atmosphere and the land surface, Biomass includes
organic material both aboveground and belowground, both living and dead (e.g., trees, crops,
grasses, tree litter, roots, etc.).
Because there are a variety of land-use change activities which together require a large
data set and a number of calculations, this chapter is divided into eight subsections:
(a) Net Emissions Due to Conversion of Forests to Permanent Cropland, Pasture, and
Other Uses.
(b) Emissions Due to Logging
(c) Emissions Due to Forest Degradation and Death from Air Pollution.
(d) Uptake Due to Plantation Establishment and Other Tree Planting Activities.
(e) Emissions Due to Flooding of Lands.
(f) CH4 Emissions Reduction and CO2 Emissions Due to Wetland Drainage.
(g) CH4 Uptake Reduction and CO2 Emissions Due to Conversion of Grasslands to
Cultivated Land.
(h) Calculation of Net Emissions from Land-Use Change
In each subsection, emissions will be calculated in mass units of carbon (C) or nitrogen (N),
rather than full molecular weights, i.e., CO2, CH4, etc. At the end of the chapter, after emissions
and uptake are summed for each gas, emissions will be converted to full molecular weights.
A more detailed description of the methodology is provided in the discussion section on land-use
change. The user is .cautioned that estimating emissions from these activities can be very time-
consuming and, in many states, emissions from land-use change may be very small (or even
negative, since forests may be a net sink for carbon as overall forest area increases).
Data Availability
The data needed to calculate greenhouse gas emissions due to land-use change using the
methodology outlined below are forest and agriculture area statistics. There is no single source of
reliable data. Most states will have their own forest and agriculture statistics with which these
areas can be estimated. Satellite imagery, aerial photography, and land-based surveys are all
possible sources of this data.
Many states have colleges or universities engaged in research on forestry and other aspects
of land use. In addition, the U.S. Forest Service has a network of forest experiment stations
located throughout the country, some of which are engaged in studies relating to forest ecosystem
biomass, timber inventories, timber growth and yield, forest products, etc., which can provide
information relating to the amount, type, and volume of forest biomass and forest land use
changes at the state level.
STATES WORKBOOK 10-1 November 1992
-------
USDA Forest Service Experiment Stations
Intennountain: 324 25th St, Ogden, UT 84401
North Central: 1992 Folwell Ave., St. Paul, MN 55108
Northeastern: 100 Matsonford Rd, Radnor, PA 19087
Pacific Northwest: P.O. Box 3890, Portland, OR 97208
Pacific Southwest: 1960 Addison SL, Berkeley, CA 94704
Southeastern: 200 Weaver Blvd., Asheville, NC 2802
10A. Net Emissions Dae to Conversion of Forests to Permanent Cropland. Pasture, and Other
Uses
Step (1): Obtain Required Data
Required Data. The information needed to calculate emissions from permanent clearing is
the annual forest area cleared mechanically.
Data Source. See the Data Availability discussion at the beginning of this chapter.
Units for Reporting Data. Forest area cleared should be reported in acres.
Step (2): Calculate the Amount of Carbon Cleared Annually
Multiply the annual forest area cleared by the appropriate average carbon storage per acre
from Table 10-1 and by 0.41 to obtain the amount of carbon contained in aboveground
biomass.
Forest Area Cleared (ac) x Avg. Carbon Storage (Ibs/ac) x 0.41 = Carbon Cleared (Ibs C)
Divide the result by 2000 to obtain the gross amount of aboveground biomass carbon
released in tons.
Step (3): Calculate the Net Release of Aboveground Carbon
Multiply the annual forest area cleared by 2 tons C/acre to obtain the amount of biomass
that regrows on the land.
Forest Area Cleared (acres) x 2 tons C/acre = Carbon in Biomass Regrowth (tons C)
Subtract the amount of carbon contained in the biomass regrowth from the gross amount
of aboveground biomass carbon released to obtain the net release of aboveground carbon.
Aboveground Biomass Carbon Cleared (tons) - Carbon in Biomass Regrowth (tons)
= Net Release of Aboveground Carbon (tons Q
Step (4): Calculate Emissions of CO2 from Soil Disturbances
Multiply the annual forest area cleared by the carbon content of the soil of that land
(Table 10-2) and by the fraction of carbon released from the soil (50%). Divide by 25 to
account for a 25 year average release.
Forest Area Cleared (acres) x Carbon Content of the Soil (tons/acre) x 50% + 25
= CO2-C Released from the Soil (tons)
STATES WORKBOOK i0-2 November 1992
-------
Table 10-1
Average and Total Storage of Carbon in Live Trees in the United States by Region and State, 1987
Ml Ion
end All foreet Unrecorved
Stale land tlofeerlond
ooorvod Other foroot
t later lend lend
₯0
All foroot
lend
Southeast t
florid*
Qeorole
orth Caroline
South Corel In*
Virginia
TOTAL
South control t
Ai obom
Arkoneeo
louUlena
iMleelppI
OklohoM
Tonnooooo .
fOKOO
TOTAL
orthooet ond NI4
Connect (cut
Oolouoro
Kentucky
Nolno
Nor y 1 ond
Nooooehuootto
ON NeojxMro
OH Jorooy
MOM fork
Ohio
Pennsylvania
Rhode lot ond
Verownt
Weot Vlrfllnlo
TOTAL
32941
46837
56426
31105
38376
48950
42070
48020
SSS77
49229
24274
55368
41S2S
46366
Atlontlei
57119
61330
54990
42234
68662
54638
57799
38912
44103
47814
46416
46095
55378
54609
49232
34110
46858
56496
51105
58433
49377
42070
48216
55377
49250
26012
55393
43157
47279
57145
61330
55025
42290
68700
54902
57742
38972
44680
47906
46589
46127
55368
54629
4943*
34110
46838
56496
51105
58435
49167
42070
48216
55377
49250
26012
55393
45137
50166
57145
61330
35025
42290
68700
54902
57742
38972
44680
47906
. 46589
46127
55368
54629
46976
14988
19852
26796
25233
43422
17023
22188
33244
32847
19966
20975
41943
23270
22465
54405
61330
49739
39836
63191
45514
39659
35001
36877
34032
35992
4SS4I
56789
43564
41915
249845
507866
463346
284130
422814
1946222
414569
370001
346724
J72777
80261
332971
257204
2176447
47024
11072
305701
339489
61973
76734
131636
33036
376995
156339
357831
8322
112506
295806
2340665
>iei coroon oc
Unrooerved
tloberlend
233764
496990
470466
262322
409126
1694673
413310
364648
348473
372466
36020
322589
243012
2120320
46061
10794
297210
329436
76689
74956
125796
33633
320193
155174
342050
7708
111107
292373
2223398
ooorvod Other foroot
t later lend lend
le font)
7133
10H3
12357
1806
12484
44715
1259
1996
251
201
271
9925
2349
16247
344
83
6664
5294
4766
0
1833
723
51666
2606
11242
167
626
2874
89092
i
6948
162
523
0
1201
6634
0
3363
0
109
23909
457
11843
39680
420
195
1827
4737
316
1796
4005
476
7142
757
4539
433
773
558
28195
Source: Birdsey, 1991 a.
STATES WORKBOOK
10-3
November 1992
-------
Table 10-1 (Continued)
Average and Total Storage of Carbon lit Live Trees in the United States by Region and State, 1987
Hog Ion
ond Alt foroot
toto tond
North Control ond
Itllnolo
Indlono
ION*
Konooo
Mich If on
Nlnnoooto
Nlooourl
Nobrooko
North Ookoto
South Ookoto
Vloconoln
TOTAL
oeky MountoJm
Arliono
Colorodo
Idaho
Montana
Novodo
MOM NoNfco
Utoh
Vyoorim
TOTAL
oelflc Cooott
Aloika
Cotlfornlo
Nona II
Oroton
lloohlntton
TOTAL
Controtf
34241
57178
49258
17670
44462
16168
19179
39549
12586
39006
39929
41983
37918
37695
51749
57642
36212
26011
12648
19927
41615
16968
52670
7791
60677
7651*
46728
ONO coreon o
Unraoarvod
tlotwrtond
. . * *. at I BM
54241
57378
4*42*
3*36*
4438*
. 36883
3*617
40966
34400
39305
40133
42504
47142
40344
35692
62415
42472
11491
16891
41262
49405
61691
65141
16756
66064
61060
67961
itorooo in trooo
ooorvod Othor foroot
tlofcortond tond
i/oe)
541
571
143
176
49429
191
441
161
194
"1
144
19J
461
16*
18*
181
H7
'66
ioo
103
133
42028
47142
40344
55692
62415
42472
51491
16691
41262
46536
611*1
65,41
14754
66864
81068
66*23
18764
11503
1*581
21471
14710
10418
2*785
14686
27614
17016
2*702
10*16
14605
11176
15176
44116
16051
22996
11511
16120
1299*
12109
40247
0
11182
36111
11434
Att foroot
Und
104941
113512
14*00
21127
167474
272051
221686
12*52
67»»
29901
277451
1469814
111122
164825
512118
372658
146612
218604
240194
180492
2369243
2161668
940813
617*
7/4758
7/8451
4664885
101 caroon 01
Unrooorvod
tlBbortond
*9154
111610
12711
21354
151189
227619
215567
9960
5256
25796
268216
116827*
61622
214618
147122
41716*
4256
74685
3158*
61678
12*1001
442317
4*37*4
3320
661771
61*470
2222671
ooorvod Othor foroot
tloiborlond tond
.J_ a> AMA % ^ &
3887
1722
1764
411
12600
1*706
4025
427
0
192
4754
31556
21166
11145
77872
19522
19
19981
57*8
55681
212141
146541
8464*
85*
S12S6
161444
1*1281
»
8
6
463
1141
1464
25305
40*4
2545
1541
3711
445*
47205
228**2
118441
47*44
114147
142353
124414
163095
44311
1024121
1572766
140172
0
39729
57119
2050006
Unltod ftotot Tout 45710
SOW
54065
ssost 15167730 H120744
Mif4f 3200045
Source: Birdsey, 1991 a.
STATES WORKBOOK
10-4
November 1992
-------
Table 10-2. Estimates of Organic Soil Carbon in Relatively Undisturbed, Secondary
Forests in the United States, by Region1
Region
Southeast
South Central
Northeast
Mid Atlantic
North Central
Central
Rocky Mountain
PaciOc Coast
Soil Carbon
(kg/m2)
7.74
738
16.21
11.56
13.09
833
8.02
9.77
(tons/acre)
34.522
33.813
72352
51J87
58396
37.151
35.786
43.5696
1 Data from Post et al. (1982).
Source: Birdsey, 1991a.
Step (5): Calculate Emissions of N2O from Soil Disturbances
Multiply the annual forest area cleared by 0.00154 tons N2O/acre to obtain emissions of
N2O-N due to forest conversion.
Forest Area Cleared (acres) x 0.00154 tons N2O/acre = N2O-N Emissions from Soil (tons)
Step (6): Calculate Total CO2-C Emissions from Conversion of Forests to Permanent
Cropland, Pastures, and Other Uses
Add the net release of aboveground carbon with the carbon emissions from soil
disturbances to obtain the total CO2-C emissions from permanent clearing
Net Release of Aboveground Carbon (tons) + Carbon Released from the Soil (tons)
= CO2-C Emissions from Permanent Clearing (tons)
10B. Emissions Due to Non-sustainable Logging
Step (1): Obtain Required Data
Required Data. The information needed to calculate emissions from logging is forest area
logged non-sustainably and mature or old-growth forest area replaced.
STATES WORKBOOK
10-5
November 1992
-------
Data Source. See the Data Availability discussion at the beginning of this chapter.
Units for Reporting Data. Forest area should be reported in acres.
Step (2): Calculate Carbon Lost by Non-sustainable Logging
Multiply the annual forest area logged non-sustainably and mature or old-growth forest
area replaced'by the appropriate average carbon storage per acre from Table 10-1 and by
0.41 to obtain the amount of carbon contained in aboveground biomass.
Forest Area Logged (ac) x Avg. Carbon Storage. (Ibs/ac) x 0.41 = Aboveground Biomass
Carbon (ibs Q
Divide the result by 2000 to obtain the amount of aboveground biomass carbon released
in tons.
s
IOC Emissions Due to Forest Degradation and Death from Air Pollution
Step (1): Obtain Required Data
Required Data. The information needed to calculate emissions from forest degradation
and decline from air pollution is the annual forest area that has died from air pollution,
the annual forest area that has degraded due to air pollution, and the average
aboveground biomass carbon loss per unit area over a one-year period.
Data Source. See the Data Availability discussion at the beginning of this chapter.
Units for Reporting Data. Forest area should be reported in acres. The average
aboveground biomass carbon loss should be reported in tons/acre.
/
Step (2): Calculate Carbon Released from Forest Area That Has Died from Pollution
Multiply the annual forest area that has died from air pollution by the average carbon
storage per acre from Table 10-1 and by 0.41 to obtain the amount of carbon contained in
aboveground biomass.
Forest Area (acres) x Average Carbon Storage (Ibs/acre) x 0.41 = Aboveground Biomass
Carbon (ibs C)
Divide the result by 2000 to obtain the amount of aboveground biomass carbon released
in tons.
Step (3): Calculate the Annual Loss of Carbon from Degraded Forest Area
Multiply the annual forest area that has degraded due to air pollution by the average
aboveground biomass carbon loss per unit area over a one-year period to obtain the
amount of carbon lost from forest degradation.
STATES WORKBOOK !0.6 November 1992
-------
Forest Area (acres) x Average Aboveground Biomass Carbon Loss (tons/acre)
= Carbon Loss (tons C)
Step (4): Calculate C02-C Emissions from Forest Degradation and Death from Air Pollution
"V.
Add the carbon released from forest area that has died from air pollution to the annual
loss of carbon from forest degradation to obtain total CO2-C emissions.
Carbon Released from Death (tons) + Loss of Carbon from Forest Degradation (tons)
= CO2-C Emissions from Forest Degradation and Death from Air Pollution
10D. Uptake Due to Plantation Establishment and Other Tree-Planting Activities
Step (1): Obtain Required Data
Required Data. The information needed to calculate uptake due to plantation
establishment and other tree-planting activities is: the annual area of plantation
established, the initial aboveground biomass carbon per unit area (before each plantation
was planted), the aboveground biomass carbon per unit area at maturity, and the number
of years required for the plantation to reach maturity for each plantation type; the area of
managed forests that are restocked; the average aboveground biomass carbon added per
unit of area over the lifetime of the restocked trees; the number of years required for the
restocked trees to reach maturity; the area of non-plantation tree planting (e.g., urban
tree planting); the average aboveground biomass carbon added per unit of area over the
lifetime of the non-plantation trees; and the number of years required for the non-
plantation trees to reach maturity.
Data Source. See the Data Availability discussion at the beginning of ihis fhap*T
Units for Reporting Data. Forest area should be reported in acres; the aboveground
biomass carbon should be reported in tons/acre; and the time required to reach maturity
should be reported in years.
Step (2): Calculate the Change in Biomass Carbon Between Initial Plantation
Establishment and Maturity
For each plantation type, subtract the initial aboveground biomass carbon density (before
each plantation was planted) from the aboveground biomass carbon density at plantation
maturity to obtain the expected change in biomass carbon between the initial plantation
establishment and maturity.
Initial Biomass Carbon Density (tons/ac) - Biomass Carbon Density at Maturity (tons/ac)
= Expected Change in Biomass Carbon (tons/acre)
STATES WORKBOOK 10-7 November 1992
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Step (3): Calculate Carbon Uptake Due to Plantation Establishment
For each plantation type, multiply the annual area of plantation established by the change
in biotnass carbon between initial plantation establishment and maturity.
Area of Plantations Established (ac) x Expected Change in Biomass Carbon (tons/ac)
= Total Carbon Uptake (tons)
For each plantation type, divide the total carbon uptake due to plantation establishment
by the number of years required for the plantation to reach maturity to obtain the average
annual net carbon uptake due to plantation establishment
Total Carbon Uptake (tons) + Time Required to Reach Maturity (yean) =
Annual Net Carbon Uptake (tons/yr)
Step (4): Calculate Carbon Uptake Due to Restocking of Managed Forests
Multiply the area of restocking by the average aboveground biomass added per unit area
over the lifetime of the trees planted.
Restocking Area (acres) x Average Aboveground Biomass Carbon Added (tons/acre)
= Total Carbon Added (tons)
Divide the total carbon added by the estimated life of the trees to obtain annual carbon
uptake due to restocking.
Total Carbon Added (tons) + Estimated Life of Trees (years) = Annual Carbon
Uptake (tons/yr)
Step (5): Calculate Carbon Uptake Due to Non-Plantation Tree Planting
Multiply the area of non-plantation tree planting by the average aboveground biomass
added per unit area over the lifetime of the trees planted.
Non-Plantation Area (acres) x Average Aboveground Biomass Carbon Added (tons/acre)
= Total Carbon Added (tons)
Divide the total carbon added by the estimated life of the trees to obtain annual carbon
uptake due to non-plantation tree planting.
Total Carbon Added (tons) + Estimated Life of Trees (years) = Annual Carbon
Uptake (tons/yr)
Step (6): Calculate Total Carbon Uptake
Sum the annual carbon uptake from plantation establishment, restocking of managed
forests, and non-plantation tree planting to obtain total carbon uptake due to plantation
establishment and other tree-planting activities.
STATES WORKBOOK 10-8 November 1992
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Carbon Uptake from Plantation Establishment (tons) + Carbon Uptake from Restocking
(tons) + Carbon Uptake from Non-Plantation Tree Planting (tons) = Total Carbon
Uptake (tons)
10E. Emissions Due to Flooding of Lands
Anthropogenic methane emissions may result when lands are flooded due to changes in
land use (e.g., damming rivers for hydropower). While there has been some research on -
emissions from natural wetlands, little data exist on which to develop emissions coefficients for
methane generated from lands that are newly flooded due to land-use change. Additionally, there
is a large degree of uncertainty associated with estimating emissions from flooded lands because
methane generation would vary significantly depending on temperature, season, characteristics of
the submerged vegetation, and numerous other factors. Accordingly, no methodology for
estimating such emissions is presented here. Though such emissions are not likely to be large in
comparison with other anthropogenic sources of methane, it is recommended that states estimate
the number of acres that have been flooded due to land use change in order to begin to assess
the potential methane emissions from this source.
10F. Methane Emissions Reduction and COj Emissions Due to Wetland Draining
Step (1): Obtain Required Data
Required Data. The information needed to calculate emissions due to wetland draining is
the area of wetland drained, the average daily CH4 emissions rate before and after
draining, the average annual CO2 emissions rate before and after draining, and the
number of days in the year that the wetland was flooded.
Data Source. See the Data Availability discussion at the beginning of this chapter.
Units for Reporting Data. Drained area should be reported in acres. Emissions rates
should be reported in tons CH4-C/acre/day and tons CO2-C/acre/yr. The time the wetland
remained flooded should be reported in days.
Step (2): Calculate the Reduction of CH4 Emissions
Multiply the area drained by the difference in the average daily CH4 emission rate before
and after drainage and by the number of days in a year that the wetland was flooded to
obtain CH4 emissions reduction.
Area Drained (acres) x [CH4 Emission Rate Before Drainage (tons/acre) - CH4 Emissions
Rate After Drainage (tons/acre)] x Days Hooded (days) = CH4-C Reduction (tons)
Step (3): Calculate the Increase in CO2 Emissions
Multiply the area drained by the difference in the average annual CO2 emission rate
before and after drainage to obtain the increase in CO2 emissions due to wetland draining.
STATES WORKBOOK 10-9 November 1992
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Area Drained (acres) x [CO2-C Emission Rate Before Drainage (tons/acre) - CO2-C
Emissions Rate After Drainage (tons/acre)] - CO2-C Emissions Increase (tons)
JOG. CH1 Uptake Reduction and COj Emissions Due to Conversion of Grasslands to
Cultivated Land
Step (1): Obtain Required Data
Required Data. The information needed to calculate CH4 uptake reduction and CO2
emissions due to conversion of grasslands to cultivated land is the area of grassland
converted, the average annual CH4 uptake rate per unit area before conversion, and the
annual CO2-C emissions rates before and after conversion.
Data Source. See the Data Availability discussion at the beginning of this chapter.
Units for Reporting Data. Converted grassland area should be reported in acres.
Emissions rates should be reported in tons CH4-C/acre and tons CO2-C/acre.
Step (2): Calculate CH4-C Uptake Reduction
Multiply the grassland area converted by the average annual CH4 uptake rate per unit
area of the grassland before clearing and by 0.40 to obtain the reduction of CH4 uptake
due to conversion of grasslands to cultivated lands.
Area Converted (acres) x CH4 Emissions Rate Before Conversion (tons CH4-C/ac) x 0.40
= CH4-C Uptake Reduction (tons)
Step (3): Calculate the Net CO2 Release
Multiply the grassland area converted to cultivated land by the difference in annual CO2-C
missions before and after conversion to obtain the net release of CO2.
Area Converted (acres) x [CO2-C Emissions Rate Before Conversion (tons CO2-C/acre) -
CO2-C Emissions Rate After Conversion (tons CO2-C/acre)] = Net CO2-C Release (tons)
10H. Calculation of Net Emissions from Land-Use Change
Step (1): Calculate Annual Net CO2-C Emissions
Add the CO2-C emissions (some of which may be negative indicating a net sink for
carbon) calculated in sections A, B, C, F, and G to obtain total gross emissions.
Subtract the total CO2-C uptake calculated in section D to obtain annual net carbon
dioxide emissions in units of carbon.
STATES WORKBOOK io-io November 1992
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Step (2): Calculate Annual Net CH4-C Emissions
Add the net CH4-C emissions calculated in sections A and E to the CH4 uptake reduction
calculated in section G to obtain total gross emissions.
Subtract the CH4-C emissions reduction calculated in section F from the total gross
emissions to obtain net emissions of CH4-C due to biomass burning, flooding of lands,
wetland drainage, and conversion of grasslands to cultivated lands.
Step (3): Calculate Net Emissions of N2O-N,
Net emissions of N2O-N due to biomass burning are calculated in section A [Step (5)].
Step (4): Convert to Full Molecular Weights
The emissions CO2-C, CH4-C, and N2O-N are multiplied by 44/12, 16/12, and 44/28
respectively, to convert to full molecular weights.1
1 The numbers used to convert NOX emissions to full molecular weight are based on the
assumption that all of the NOX emissions are NO, rather than some combination of NO and NO2,
since NO is the primary form of NOX emitted during biomass combustion (Andreae, 1990).
STATES WORKBOOK I0.n November 1992
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WORKBOOK 11
GREENHOUSE GAS EMISSIONS FROM BURNING OF AGRICULTURAL CROP WASTES
Crop residue burning is a significant source of methane (CH4), carbon monoxide (CO),
nitrogen oxides (NOX), and nitrous oxide (N2O). To estimate emissions from burning of
agricultural wastes, the following general steps are necessary: 1) obtain the required data for crops
whose waste is commonly burned; 2) estimate the total amount of carbon bumed; and 3) calculate
emissions based on the amount of CO2 released and on a range of emission ratios. These steps
are outlined in detail below. A more detailed description of the methodology is provided in the
discussion section on agricultural crop wastes. Greenhouse gas emissions from burning animal
dung and crop residues for energy production should be estimated as part of biomass material
used as energy; this issue is addressed in the CO2 from energy discussion section.
Step (1): Obtain Required Data
Required Data, The information needed to estimate greenhouse gas emissions from
burning of agricultural wastes is the annual production of crops with residues that are
commonly bumed.
Data Source. State agencies responsible for overseeing the agricultural sector should be
consulted first. Additionally, annual crop production can be found in the USDA's Crop
Production or in the U.S. Department of Commerce's Census of Agriculture.
Units for Reporting Data. Annual crop production should be reported in pounds. If
production data are reported in hundred weight (CWT), multiply by 100 to convert to
pounds. If data are reported in bushels, the following conversion factors may be used to
convert to pounds:
Table 11-1. Conversion Factors for Selected Crops
Crop
Wheat
Barley
Corn
Oats
Rye
Rice
Millet
Sorghum
Conversion
Factor
(Ibs/bu)
60
48
56
32
56
45
48-60
60
Crop
Pea
Bean
Soybeans
Potatoes
Feedbeet
Sugarbeet
Artichoke
Peanut
Conversion
Factor
(Ibs/bu)
60
60
60
60
50
50
50
17-25
STATES WORKBOOK
11-1
November 1992
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Example: According to the USDA's Crop Production 1990 Summary, total
U.S. wheat production in 1988 was 1,812,201,000 bushels.
1,812.201,000 bu x eOlbs/bu = 108,732,060,000 pounds
Step (2):
Calculate the Amount of Residue Available for Combustion
For each crop, multiply annual production by the ratio of residue to crop product to
obtain the amount of residue available for combustion. Estimates of residue/crop product
ratios for certain crops are presented in Table 11-2.
Crop Production (Ibs) x Residue/Crop Ratio = Amount of Residue Produced (Ibs)
The amount of residue from U.S. wheat production available
for combustion in 1988 is calculated as follows:
Example:
108,732,060,000 Ibs x 1.3 Ibs residue/lb crop product = 141,351.678,000 Ibs
Step (3):
Calculate the Total Amount of Crop Residue Burned
For each crop, multiply the amount of residue produced by the fraction of residue burned
in the field. If these data are not available, a default factor of 50% may be used.1
Amount of Residue Produced (Ibs) x Residue Burned (%) = Amount of Residue
Burned (Ibs)
Example: The amount of crop residue from U.S. wheat production that
was burned in 1988 is calculated as follows:
141,351,678,000 Ibs. X 50% = 70,675,839,000 Ibs.
1 This default factor is based on 1960 data, and, accordingly, may not be representative of current state
conditions. Therefore, the use of specific state data is recommended, if possible.
STATES WORKBOOK
11-2
November 1992
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Table 11-2. Selected Crop Residue Statistics
Product Residue/Crop Product
Dry Matter Content
(%)
Carbon Content
(%dm)
Cereals
Wheat -
Barley
Maize
Oats
Rye
Rice
Millet
Sorghum
L3
1.2
1
13
1.6
1.4
1.4
1.4
78-88
78-88
30-50
85-95
85-95
78-88
78-88
78-88
4&S3
45.67
47.09
48.53
48J3
41.44
48.53
48.53
Legumes
Pea
Bean
Soya
2.1
2.1
11
85-95
85-95
85-95
45.0
45.0
45.0
Tuber and Root Crops
Potatoes
Feed beet
Sugarbeet
Jerusalem
artichoke .
Peanut
0.4
0.4
0.3
0.8
1.0
30-60
10-202
10-202
30-60
30-60
42.26
40.722
40.722
42.26
42.26
Sugar Cane1
1 Sugar cane data were only available for bagasse as the residue. Bagasse is the dry pulp remaining from
sugar cane after the juice has been extracted; i.e., it is the residue after processing of cane, not the residue
left in the field after harvesting cane. This issue should be researched to try to obtain appropriate data.
2 These statistics are for beet leaves.
Source: Strehler and Stiitzle, 1987.
STATES WORKBOOK
11-3
November 1992
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Step (4):
Convert Crop Residue Burned to Mass of Dry Matter Burned
For each crop, multiply the amount of crop residue burned by the average dry matter
content of the crop. Average dry matter contents for selected crops are presented in
Table 11-2.
Amount of Residue Burned (Ibs) x Dry Matter Content (%) = Dry Matter Burned (Ibs)
Example: According to Table 11-2, wheat has an average dry matter
content of 78% to 88%. The amount of dry matter burned
from U.S. wheat production in 1988 is calculated as follows:
low: 70,675,839,000158 X 78% = 55,127,154,420 Ibs
high: 70,675,839.000 Ibs x 88% = 62,194,738,320 Ibs
Step (5): Calculate Total Carbon Burned
For each crop, multiply the amount of dry matter burned by the carbon content per unit
of dry matter to obtain the total amount of carbon burned. Carbon contents for selected
crop residues are presented in Table 11-2. An average value of 0.45 Ibs C/lb dry matter
can be used if data are not available.
Dry Matter Burned (Ibs) x Carbon Content (Ibs C/lb dm) = Total Carbon Burned (Ibs)
Example: The total amount of carbon burned from U.S. wheat residue in
1988 is calculated as follows:
low: 55,127.2 million Ibs x 48.53 (Ibs C/lb dm)
high: 62,194.7 million tbs x 48.53 (Ibs C/lb dm)
2,675,320.8 million
IbS
3,018,310.7 mllHon
Ibs
Step (6):
Estimate Emissions of CH4 and CO
For each crop, multiply the amount of carbon burned by 90% (to account for the
approximate 10% of the carbon that remains on the ground) to obtain the amount of
carbon dioxide released instantaneously in units of carbon.
Total Carbon Burned (Ibs) x 90% = Amount of CO2 Released (Ibs CO2-C)
STATES WORKBOOK
11-4
November 1992
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For each crop, multiply the amount of CO2 released in units of carbon by the emission
ratios of CH4 and CO relative to CO2 (see Table 11-3) to obtain emissions of CH4 and
CO in units of carbon.
Amount of CO2 Released-low (Ibs CO2-C) x (0.007) = CH4 Emissions-low (Ibs CH4-Q
Amount of CO2 Released-higb (Jbs CO2-C) x (0.013) = CH4 Emissions-high (Ibs CH4-C)
Amount of CO2 Released-low (Ibs CO2-C) x (0.075) = CO Emissions-low (Ibs CO-C)
Amount of CO2 Released-high (Ibs CO2-C) x (0.125) = CO Emissions-high (Ibs CO-C)
Example: CHfC and CO-C emissions from burning of residue from U.S.
wheat production in 1988 is calculated as follows:
(a) low: 2,675,320.8 million Ibs x 90% = 2,407,788.7 million Ibs
high: 3,018,310.7 million Ibs x 90% = 2,716,479.6 million Ibs
(b) low: 2,407,788.7 million Ibs x (0.007) = 16,854.5 million Ibs CH4-C
2,407,788.7 million Ibs x (0.075) = 180,584.1 million Ibs CO-C
high: 2,716,479.6 million Ibs x (0.013) = 35,314.2 million Ibs CH4-C
2,716,479.6 million Ibs X (0.125) = 339,560.0 million Ibs CO-C
Step (7): Estimate Emissions of N2O and NO,
For each crop, multiply the amount of carbon burned by a range of 1-2% (the
nitrogen/carbon ratio by weight) to obtain the total amount of nitrogen released.
Total Carbon Burned-low (Ibs) x 1% = Amount of Nitrogen Released-low (Ibs)
Total Carbon Burned-high (Ibs) x 2% = Amount of Nitrogen Released-high (Ibs)
For each crop, multiply the amount of nitrogen released by the emission ratios of N2O
and NOX relative to the nitrogen content of the residue (see Table 11-3) to obtain
emissions of N2O and NOX in units of N.
Amount of N Released-low (Ibs) x (0.005) = N2O Emissions-low (Ibs N2O-N)
Amount of N Released-high (Jbs) x (0.009) = N2O Emissions-high (Ibs N2O-N)
Amount of N Released-low (Ibs) x (0.094) = NOX Emissions-low (Ibs NOX-N)
Amount of N Released-high (Ibs) x (0.148) = NOX Emissions-high (Ibs NOX-N)
STATES WORKBOOK n.s November 1992
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Example:
low:
high:
tow:
high:
and NOX-N emissions from burning of residue from
U.S. wheat production in 1988 is calculated as follows:
Z675.320.8 million Ibs x 1% =
3,018,310.7 million Ibs x 2% =
26J53.2 tnfllton IbS N X (0.005)
26,753.2 million Ibs N x (0.094)
60,3662 million IbS N x (0.009)
60,366.2 mfflion Ibs N x (0.148)
26,7532 million Ibs N
60,366.2 million Ibs N
133.8 million Ibs
2,514.8 million lb» NO^-N
543.3 million Ibs N2O-N
8,9342 million Ibs NO,-*!
Table 11-3. Emission Ratios for Biomass Burning Calculations
Compound
CH4
CO
N2O
NOX
Ratios1
0.007 - 0.013
0.075 - 0.125
0.005 - 0.009
0.094 - 0.148
1 Ratios for carbon compounds, i.e., CH4 and CO, are mass of carbon compound released (in
units of C) relative to mass of CO2 released from burning (in units of C); those for the
nitrogen compounds are expressed as the ratios of emission relative to the nitrogen content
of the fuel
Step (8): Convert to Full Molecular Weights
. For each crop, multiply the emission estimates of CH4, CO, N2O, and NOX by 16/12,
28/12, 44/28, and 30/14, respectively, to convert to full molecular weights.
For each gas, sum across all crop types to produce total emissions from burning of crop
residues. Divide the results by 2000 to obtain total emissions in tons.
STATES WORKBOOK
11-6
November 1992
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Example: Emissions of CH4 CO, Np, and NOxfrom burning of residue
from U.S. wheat production in 1988 are converted to their full
molecular weights as follows:
low: 16,854.5 million Ibs CH^C x (16/12)
180,584.1 million Ibs CO-C x (28/12)
133.8 million Ibs Np-N x (44/28) =
2,514.8 million Ibs NOX-N x (30/14)
high: 35,314.2 million Ibs CH^C x (16/12)
339,560.0 million IDS CO-C x (28/12)
543.3 million Ibs Np-N x (44/28) =
8,934.2 million Ibs NOX-N x (30/14) '
= 22,472.7 million Ibs CH4
m 421,362.9 million Ibs CO
210.3 million Ibs NgO
= 5,388.9 million Ibs NOX
= 47,085.6 million Ibs CH4
= 792,306.7 million Ibs CO
853.8 million Ibs NjO
= 19,144.7 million Ibs NOX
STATES WORKBOOK
11-7
November 1992
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DISCUSSION CHAPTERS
STATES WORKBOOK November 1992
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DISCUSSION 1
CARBON DIOXIDE EMISSIONS FROM COMBUSTION OF FOSSIL AND BIOMASS FUELS
OVERVIEW
Carbon dioxide (COj) is the most common greenhouse gas produced by anthropogenic
activities, accounting for about 60% of the increase in radiative forcing since pre-industrial times.
By far the largest source of CO2 emissions is from the oxidation of carbon in fossil fuels, accounting
for 70*90% of total anthropogenic CO2 emissions. These emissions occur primarily from combustion
of fossil fuels where most carbon in the fuels is emitted as CO2 immediately during the combustion
process. Some carbon is released as CO, CH4, or as non-methane hydrocarbons which are oxidized
to CO2 within anywhere from a few days to 8 to 12 years. For purposes of this analysis, we include
these emissions as part of CO2 emissions but also estimate and account for these emissions elsewhere
later to avoid double-counting.
Not all carbon in the fuels is oxidized. During the combustion of fossil fuels, a small fraction
of the carbon remains unbumed as soot or ash. Some carbon is not completely oxidized and is
emitted in the form of CH4 or other hydrocarbons, which will oxidize within 10 years. Fossil fuels are
also used for non-energy purposes, primarily as a feedstock for such items as fertilizer, lubricants, and
asphalt. In some cases, as in fertilizer production, the carbon from the fuels is oxidized quickly to
CO2. In other cases, as in asphalt production, the carbon is sequestered in the product, sometimes
for as long as centuries.
The amount of CO2 emitted is directly related to the amount of fuel consumed, the fraction
of the fuel that is oxidized, and the carbon content of the fuel. Coal contains close to twice the
carbon of natural gas and 25% more than crude oil per unit of useful energy. Other reasons for
variations in CO2 emissions include:
1) Given the same energy type, the amount of carbon contained in the fuel per unit of
useful energy produced varies. For example, not all coal types contain the same
proportion of carbon. Generally speaking, the lower the quality of the coal (such as
sub-bituminous coal and lignite), the higher the carbon emission coefficient (i.e.,
carbon per unit of energy). There are similar carbon differences among the different
types of liquids and gases.
2) As mentioned above, when energy is consumed not all of the carbon in the fuel
oxidizes to CO2. Incomplete oxidation occurs due to (1) inefficiencies in the
combustion process that leave some of the carbon unbumed, and (2) non-fuel uses
of the energy, for example, as asphalt, naphtha, and lubricants, among other uses.
For simplicity, the methodology presented in the workbook requires fuel consumption
statistics for eleven fuel types only. The following is a discussion of a more detailed approach, which
requires consumption data for other fuel products in addition to the ones listed in the workbook.
The difference in detail between the two methods should not appreciably affect the resulting emission
estimates.
STATES WORKBOOK DM November 1992
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METHODOLOGY TO ESTIMATE CO2 EMISSIONS
The methodology for estimating CO2 emissions is well-known and straightforward, although
as discussed below, controversy may exist over the appropriate level of detail, data sources, and
carbon content of fuels and products. For this discussion, CO2 emissions include all of the carbon
from the fuels that is either immediately oxidized or oxidized within-a short time period (e.g., less
than 20 years). It includes carbon in the form of gases, like CO and CH4, and carbon in products
that will be burned after use or will decompose quickly. CO2 emissions from gas Oaring and carbon
emissions, in the form of CH4, from coal mining are not included in this section but are discussed
later. The methodology presented here includes four steps that explicitly identify all of the factors
necessary to estimate CO2 emissions by estimating:
1) Consumption of fossil fuels by energy type.
2) Average carbon emission coefficient of fuels and total carbon potentially released
from use of the fuels.
3) Amount of carbon sequestered in products for long periods of time.
4) Amount of carbon not oxidized during combustion.
Each of these steps is discussed in turn.
Estimating Consumption of Fossil Fuels
CO2 is released as carbon-based fossil fuels are consumed. Since carbon content typically
varies by fuel type, the suggested categories for fuel and product types for which data should be
reported are:
A) Liquid Fuels
1) Crude Oil
2) Natural Gas Liquids (NGL)
3) Gasoline
4) Kerosene
5) Jet Fuel
6) Distillate Fuel
7) Residual Oil
8) LPG
9) Naphtha
10) Petroleum coke
11) Refinery feedstocks
12) Other Oil Products
B) Solid Fuels
13) Coking Coal
14) Steam Coal (Anthracite, Bituminous)
15) Sub-bituminous Coal
16) Lignite
17) Peat
18) Coke
19) Other Solid Fuels
STATES WORKBOOK Di_2 November 1992
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C) Gaseous Fuels
20) Natural Gas (Dry)1
Fuel statistics should be provided bo an energy basis (preferably in million Btu). Statistics using other
energy units such as barrels or short tons could be used, but would require additional factors that
permit conversion of these data to million Btu (if other units are used, the conversion factors used
should also be reported). Conversion factors for solid and liquid fuels are contained in Tables Dl-1
and Dl-2. Fuel consumption data should also be disaggregated into the following end-use sectors:
residential, commercial, industrial, transportation, and electric utility.
Estimate Average Carbon Content of the Fuels and Potential Carbon Releases
CO2 emission estimates also need to consider that the amount of carbon per unit of energy
varies considerably both between and among fuel types:
For natural gas, the carbon content depends heavily on the
composition of the gas, which includes methane, ethane, propane,
other hydrocarbons, CO2, and other gases. The relative proportions
of these gases vary from one gas production site to another.
For crude oil, Marland and Rotty (1984) suggest that the API
gravity2 acts as an indicator of the carbon/hydrogen ratio. Carbon
content per unit of energy is usually less for light refined products
such as gasoline than for heavier products such as residual fuel oil.
For coal, carbon emissions per ton vary considerably depending on the
coal's composition of carbon, hydrogen, sulfur, ash, oxygen, and
nitrogen. While variability of carbon emissions on a mass basis can be
considerable, carbon emissions per unit of energy (e.g., per Btu) vary
much less, with lower ranked coals such as sub-bituminous and lignites
usually containing slightly more carbon than higher-ranked coals.
Estimates of carbon emission coefficients for fuels from several studies are summarized in
Table Dl-3. The largest differences in emission coefficients between the studies occur with
bituminous coal and oil, although these differences are relatively minor.
1 Natural gas liquids extracted from the natural gas would be included with liquid fuels.
2 Variations in petroleum are most often expressed in terms of specific gravity at 15 degrees C. The API
gravity, where API gravity = 141.5/specific gravity - 131.5, is an indication of the molecular size,
carboa/hydrogen ratio, and hence carbon content of a crude oil.
STATES WORKBOOK D1_3 November 1992
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Table Dl-1. Conversion Factors for Liquid Petroleum Products
Product
Factors
(million BTU/barrell
Crude Oil
Natural-Gas Liquids (NGL)
Gasoline
Kerosene
Jet Fuel
Distillate Fuel
Residual Oil
LPG
Naphtha
Petroleum coke
Refinery feedstocks
Other Oil Products
5.80
4.620
5.253
5.670
5.670
5.825
6.287
4.011
5.248
6.024
6.00
5.80
Source: DOE/EIA, 1991
Table Dl-2. Conversion Factors for Solid Fuels Products
Product
Factors
(million BTUAhort toirt
Coking Coal
Steam Coal (Anthracite, Bituminous)
Sub-bituminous Coal
Lignite
Peat
Coke
Other Solid Fuels
24.80
21.69
17.00
13.00
N/A
26.80
2133
Source: DOE/EIA, 1991; ICF, Coal & Utilities Information System
STATES WORKBOOK
Dl-4
November 1992
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Table Dl-3
Carbon Emission Coefficients for Fuels from Different Studies
(Ibs C/106 Btu)
Anthra- Bit Sub-Bit
Study cite Coal . Coal Lignite Peat
Marland & Rotty (1984) 563
Grubb(1989) 592 57.0 61.0 63.9
OECD(1991) 57.01
Crude Gaso- Kero- Diesel/ Fuel Natural
Study Oil line sene Gas-Oil Oils Gas
Marland & Rotty (1984) 473 31.8
Grubb (1989) 44.2 41.8 43.1 44.2 46.6 32.0
OECD (1991) 44.2 32.0
1 Average value for all coal: sub-bituminous through anthracite.
Based on these earlier studies, the primary approach for estimating total carbon is outlined
in Table Dl-4. This table illustrates the calculations needed to estimate the total carbon that could
be released from the use of fossil fuels. This approach uses the basic methodology:
Total Carbon (Ibs C) = Primary Energy Consumption (by fuel type in 106 Btu) X
Carbon emission coefficient (by fuel type in Ibs C/106 Btu),
added across all fuel types
The carbon emission coefficient of the fuels are average values. This approach explicitly treats each
major fuel type differently according to its carbon emission coefficient. However, as shown in Table
Dl-3, we do not have carbon emission coefficients for all of the suggested fuel types at this time.
Additional research needs to be undertaken to determine the appropriate factors, and, as these factor
are determined, they can be added to the table.
Estimate Carbon Sequestered in Products
After estimating the total carbon contained in the fuels, the next step is to estimate the
amount of carbon from these fuels that is sequestered in non-energy products and the portion of
this carbon expected to oxidize over a long time period (e.g., greater than 20 years). All of the fossil
fuels are used for non-energy purposes to some degree. Natural gas is used for ammonia production.
LPGs are used for a number of purposes, including production of solvents and synthetic rubber. A
wide variety of products are produced from oil refineries, including asphalt, naphthas, and lubricants.
STATES WORKBOOK Dl-5 November 1992
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Table Dl-4
Approach for Estimating Total Carton in Fuels
(1) (2) (3)
Apparent Emission Total
Cons. Coefficient1 Carbon2
Fuel - fmillion Btul fibs C/10* BtiO fibs O
A) Liquid Fuels
1) Crude Oil input 442 calc
2) N. Gas Liquids input NA calc
3) Gasoline input 41.8 calc
4) Kerosene input 43.1 calc
5) Jet Fuel input NA calc
6) Distillate Fuel input 44.2 calc
- Bunkers input 44.2 calc
7) Residual Oil input 46.6 calc
- Bunkers input 46.6 calc
8)LPG input 38.0 calc
9) Naphtha input NA calc
10) Petroleum Coke input NA calc
11) Refinery F-stocks input NA calc
12) Other Oil input 44.2 calc
- Bunkers input 44.2 calc
B) Solid Fuels
13) Coking Coal input 57.0 calc
14) Steam Coal input 57.0 calc
15) Sub-Bit. Coal input NA calc
16) Lignite input 61.0 calc
17) Peat input 63.9 calc
18) Coke input NA calc
19) Other Solid Fuels input 57.0 calc
C) Gaseous Fuels
20) Natural Gas (Dry) input 32.0 calc
1 NA = carbon emission coefficient not available. All values taken from Grubb (1989), except LPG, which
was taken from Marland and Pippin (in press).
2 calc = calculation to be made by respondent; in this case, Consumption (column 1) is multiplied by A
emission coefficient (column 2).
STATES WORKBOOK D1.6 November 1992
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Coal is used to produce coke; two by-products of the coking process include crude light oil and crude
tar, which are used in the chemical industry. For ease of computation, this step was not included in
the basic inventory methodology presented in the workbook.
Not all non-energy uses of fossil fuels, however, result in the sequestering of carbon. For
example, the carbon from natural gas used in ammonia production is oxidized quickly. Many products
from the chemical and refining industries are burned or decompose within a few years, while the
carbon in coke is oxidized when used.
The approach used by Marland and Rotty (1984) for. estimating the portion of carbon
sequestered in products relied on historical data for determining non-energy applications and varied
depending on fossil fuel type. For natural gas they assume that close to one-third of the carbon used
for non-energy purposes (equivalent to 1% of total carbon from natural gas production) does not
oxidize over long periods of time. For oil products they assume that some portion of LPG, ethane,
naphthas, asphalt, and lubricants do not oxidize quickly. Specifically, they assume that about 50%
of LPG and ethane from gas processing plants is sold for chemical and industrial uses and that 80%
of this amount, or 40% of all LPG and ethane, goes into products that sequester the carbon. About
80% of the carbon in naphthas is assumed to end up in products such as plastics, tires, and fabrics
and oxidize slowly. All of the carbon in asphalt is assumed to remain unoxidized for long periods,
while about 50% of the carbon in lubricants is assumed to remained unoxidized. For coal they
assume that on average 5.91 % of coal going to coke plants ends up as light oil and crude tar, with
75% of the carbon in these products remaining unoxidized for long periods.
The suggested approach for estimating carbon sequestered in products for each state is:
Total Carbon Sequestered = (Non-energy Use, 103 tons) x (% Carbon content) x
(% Sequestered), by product type
These carbon estimates from non-energy uses would be considered "potential" emissions, and are
assigned to the state that produces the products. The suggested categories conform to those used
by Marland and Rotty (1984) and include naphthas, bitumen (asphalt), lubricants, LPG, and crude
light oil and crude tar. Marland and Rotty estimate that 6% of the total energy consumed as coke
produces crude light oil and tar. This suggested approach is illustrated in Table Dl-5. If one is
estimating emissions sector-by-sector, it is suggested that sequestered carbon from these products be
assigned to the industrial sector.
Estimate Carbon Oxidized from Energy Uses
As described earlier, not all carbon is oxidized during the combustion of fossil fuels. The
amount of carbon that falls into this category is usually a small fraction of total carbon, and a large
portion of this carbon oxidizes in the atmosphere shortly after combustion. Marland and Rotty
suggest the following factors:
For natural gas less than 1 % of the carbon in natural gas is unoxidized
during combustion and remains as soot in the burner, stack, or in the
environment.
STATES WORKBOOK Dl-7 November 1992
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Table Dl-5
Estimation of Carbon Sequestered in Products
(1) (2) . (3) (4)
Carbon Carbon Potential
Prod. Content Sequestered Emissions1
Product/Fuel - iftonsl f%1 f%1 (tons Q
Naphthas input 85% 80% calc
Lubricants input 85% 50% calc
Bitumen input 85% 100% calc
Crude Light Oil/
Crude Tar input 85% 75% calc
Gas as Feedstock input2 32.02 33% calc
LPG as Feedstock input 85% 80% calc
1 calc=calculated by respondent Potential emissions (column 4) = production of product, or
LPG used as feedstock (column 1) X carbon content (column 2) X fraction of carbon
sequestered for long periods (column 3).
2 Units of natural gas should be specified in million Btu and carbon content on an energy basis
in Ibs C/106 Btu.
For oil 1.5% ±1% passes through the burners and is deposited in the
environment without being oxidized. This estimate is based on 1976
U.S. ..sffrfefjfs of emissions of hydrocarbons and total suspended
participates.
For coal 1% ±1% of carbon supplied to furnaces is discharged
unon'dized, primarily in the ash.
Table D1-6 illustrates the suggested approach for adjusting for carbon unoxidized during
combustion. In this approach, carbon sequestered in products (see column 4, Table Dl-5) is
subtracted from total carbon in the fuels (see column 3, Table Dl-4) to get net carbon emissions.
These emissions are then multiplied by the fraction of carbon oxidized to determine the amount of
carbon oxidized from the combustion of the fuel.
Carbon Emissions from Fuel Production and Other Activities
Carbon emissions occur from a number of activities associated with the production and
transportation of energy, not all of which are accounted for in energy and non-energy uses of fossil
fuels. These activities include gas venting and flaring, leakages during the transmission and
distribution of natural gas, CH4 leaks from coal mines, SO2 scrubbing at coal plants, and burning in
coal deposits. The first three activities - gas venting and flaring, leakages during the transmission
STATES WORKBOOK w.8 November 1992
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Table Dl-6
Carbon Oxidized During Combustion
(tons C)
(1) (2) (3) (4) (5)
Total Carbon Net Fraction Carbon
- Emissions Seq. Emissions1 Oxidized Oxidized2
Fuel (tons Q ftons C) (tons O _(%) fions C>
Liquid Fuels Table Dl-4 Table Dl-5 calc 99% calc
Solid Fuels Table Dl-4 Table Dl-5 calc 99% calc
Gaseous Fuels Table Dl-4 Table Dl-5 calc 99% calc
calc = calculated by respondent
1 Total carbon emissions (from column 3, Table Dl-4) minus carbon sequestered in products (from
column 4, Table Dl-5).
2 Carbon oxidized from combustion (column 5) equals net emissions (column 3) times fraction oxidized
(column 4).
and distribution of natural gas, and CH4 leaks from coal mines are addressed elsewhere. Emissions
from the burning of coal in coal deposits and waste banks and CO2 emissions from SO2 scrubbing
are highly variable from one state to another and are a very minor portion of total emissions. At this
time, there is no recommended methodology to estimate emissions from these sources.
Consolidation
The previous calculations provide estimates of total carbon in the fossil fuels and carbon
sequestered in non-energy products and energy by-products by sector. Given these estimates, total
carbon emissions from fossil fuel combustion can be determined. Total carbon emissions are equal
to the total carbon estimates in fuel from Table Dl-4 (column 3 summed over all fuel types) minus
carbon sequestered in products (column 4 in Table Dl-5, summed over the different products). The
result is then adjusted for the carbon unoxidized during combustion (column 5, Table Dl-6, summed
over all fuel types). Since these are in units of carbon, they should be multiplied by 44/12 to convert
to the full molecular weight basis of CO2.
DATA SOURCES
Statistics on energy and fossil-fuel production and consumption can be found in a number of
sources, but inconsistencies often exist in how the data are presented, sources of the data, types of
information provided, and reporting units. Information on production of products that sequester
carbon also varies. DOE or EIA data could be used as a starting point, but states should use the
energy data thought to be the most reliable, e.g., from the state energy commission or public utility
STATES WORKBOOK Dl-9 November 1992
-------
commission. If states use in-state sources rather than EIA data, they are strongly urged to provide
thorough documentation on the energy statistics, the reporting procedures, and definitions of sectoral
activities. This would help to ensure consistency and comparability among all state estimates.
CO2 EMISSIONS FROM THE CONSUMPTION OF BIOMASS-BASED FUELS
Overview
The consumption of biomass fuels (biofuels) such as wood, charcoal, crop residues, animal
dung, etc., for energy production for domestic cooking and heating, industrial heat and power, and
the production of industrial charcoal produce carbon dioxide (COj), methane (CH4), carbon
monoxide (CO), nitrogen oxides (NOX), nitrous oxide (N2O), and non-methane volatile organic
compounds (NMVOCs). There are two phases of wood combustion that produce these trace gases:
the flaming stage of combustion and the subsequent smoldering stage (Garrett, personal
communication). During the flaming stage of combustion, which is usually violent and short-lived,
oxygen is consumed as CO2 is produced. The amount of oxygen will decrease unless additional
oxygen is allowed into the combustion chamber. In the smoldering phase, in which less and less
oxygen is available, non-CO2 substances are the primary products emitted.
An important distinction should be made between commercial and non-commercial biofuels
consumption. Commercial consumers typically pay for the biofuels they consume in well-developed
markets on which statistics are often available. On the other hand, non-commercial consumers
typically collect their own fuelwood or purchase it from vendors. Lack of a formal market makes it
difficult to estimate the quantity of biomass consumed in this fashion. As a result, it may be difficult
to estimate the actual amount of biofuels consumed because market statistics may only reflect
commercial consumption. The extent of this problem will vary from state to state. In addition,
limited data are available on the consumption and the relative fuel properties of biofuels. In order
to improve ihe quality of emission estimates from bioenergy consumption, additional research is
needed to refine information on the total quantity of biomass consumed for energy purposes and the
resulting emission factors under various combustion conditions.
It has been argued that CO2 emissions resulting from bioenergy consumption should not be
included in a state's official emission inventory to avoid double counting CO2. This double-counting
would occur either because: (1) biofuels tend to be produced on a sustainable basis such that no net
increase in CO2 occurs, or (2) production of GO2 from biofuels burned on a non-sustainable basis
would be captured as part of emissions resulting from land-use changes (discussed elsewhere). It has
been recognized that such double-counting could occur, but no easy resolution has been made as to
how best to account for net emissions from bioenergy consumption. Bioenergy consumption is low
throughout the United States, but it has been recommended that states estimate CO2 emissions from
bioenergy consumption separately from CO2 emissions from fossil fuel consumption. This would
ensure that total CO2 emissions from energy consumption could be estimated if this is desired. The
issue of double-counting CO2 emissions from biofuels consumption should be given special attention
in the near future to determine the most appropriate methods for accounting for these emissions.
States should note, however, that CO2 emission estimates from biomass consumption may not be the
result of a net increase in total CO2 emissions.
STATES WORKBOOK Dl-10 November 1992
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Data Availability
For ethanol production from biomass, state energy offices (or economic development,
agricultural, or revenue departments) will have production figures. State energy or natural resource
departments may have data on the amount of wood collected for residential use because of the
permits that are required to cut the timber. Agricultural waste data should be.available from state
agriculture offices.
EIA estimates biofuel consumption in the U.S. annually for wood by sector and region and
ethanol by region. The regions included in the EIA analysis (DOE/EIA, 1989) are the South, the
West, the Midwest, and the Northeast; no data are readily available by individual states in a published
format The amount of energy produced from the biomass is also difficult to estimate as it varies
from one type of biomass to the next Additional research will be needed to develop more precise
consumption estimates, including the energy content of the biomass, in order to reduce uncertainties
associated with calculating emissions from fuelwood use. In addition to improving the quality of data
on commercial biomass consumption, this research will need to improve significantly the quality of
data on the amount of biomass that is consumed non-commercially.
Emissions from fuelwood also occur at different rates depending on the particular use of
fuelwood since the technology used and the combustion conditions will vary from one application to
the next (e.g., wood consumed in industrial wood boilers will emit at different emission rates than
wood stoves for residential needs). Additional research will be needed to determine the range of
technology types in which fuelwood is consumed and to characterize the amount of fuelwood that is
consumed by each technology type.
Proposed Methodology
The methodology outlined below is the same methodology used in the workbook. To estimate
CO2 emissions from biomass consumption, the amount of carbon combusted in each biomass type
should be estimated. We can use the following carbon content assumptions to estimate the
percentage of each fuel type that is carbon:
Fuel Carbon Content
Wood . 27.0%
Charcoal 87.0%
Bagasse/Agricultural 22.6%
This result should be adjusted for any carbon that is not oxidized. As a default value, we will assume
that 10% of all carbon in biomass-based fuels is not oxidized (Crutzen and Andreas, 1990); this value
could vary significantly and should be evaluated to determine its appropriateness. The equations for
estimating CO2 emissions are:
Emissions from Wood = Total Wood Consumed (tons) X
Carbon Content (27%) X Amount
Oxidized (90%)
3 Field measurements have yielded some data. See Cofer et al. (1988, 1989) and Hegg et al. (1990).
STATES WORKBOOK Dl-11 November 1992
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Emissions from Charcoal = Total Charcoal Consumed (tons) X
Carbon Content (87%) X Amount
Oxidized (90%)
Emissions from Bagasse
and Agricultural wastes = Total Bagasse and Agricultural Wastes Consumed
(tons) X Carbon Content (22.6%) X Amount Oxidized
(90%)
The resulting values indicate the amount of carbon in tons that is emitted as carbon. To
convert to full molecular weight, these values should be multiplied by 44/12.
REFERENCES
DOE/EIA (Department of Energy/Energy Information Administration). 1989. Estimate ofBiofuels
Consumption in the United States During 2987. CNEAF/NAFD 89-03.
DOE/EIA. 1991. State Energy Data Report. DOE/EIA-021(89).
Grubb, M J. 1989. On Coefficients for Determining Greenhouse Gas Emissions From Fossil Fuel
Production and Consumption. Energy and Environmental Programme, Royal Institute of International
Affairs, London, UK. April. Prepared for OECD/IEA Expert Seminar on Energy Technologies for
Reducing Emissions of Greenhouse Gases, Paris.
Marland, G.t and A. Pippin. 1991. United States emissions of carbon dioxide to the Earth's
atmosphere by economic activity. Energy Systems and Policy 14:101-118.
Marland, G^and JtM. Retry. 1984. Carbon Dioxide Emissions from Fossil Puds; A Procedure for
Estimation and Results for 1950-1982. Tellus 365:232-261.
OECD/IEA. 1990. Energy Balances of OECD Countries, 1987-1988. International Energy Agency,
OECD, Paris.
OECD/IEA. 1991. Greenhouse Gas Emissions. The Energy Division. OECD, Paris. Forthcoming.
Okken, P.A., and T. Kram. 1990. Calculation of Actual CO2 Emissions from Fossil Fuels. Presented
at ETSAP-IV workshop Petten, the Netherlands, 9-12 ApnTl990 and IPCC Preparatory Workshop,
Paris, 22-23 May 1990.
STATES WORKBOOK D1.12 November 1992
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DISCUSSION 2
GREENHOUSE GAS EMISSIONS FROM PRODUCTION PROCESSES
Emissions are often produced as a by-product of various production processes. That is, these
emissions are produced directly from the process itself and are not a result of the energy that may
be consumed during the production process. This discussion on emissions from production processes
is divided into three sections: 1) emissions of ozone depleting compounds from production processes;
2) carbon dioxide emissions from cement manufacturing; and 3) other emissions from production
processes.
The section on ozone depleting compounds (ODCs) provides background information on the
main types and sources of ODCs and provides a method for states to develop their own inventory
of ODC emissions. This method was not included in the workbook section because the use and
emissions of ODCs are already being controlled in the U.S., the calculations are time consuming, and
some of the required data may be difficult to obtain at the state level However, interested users
should read through the suggested method in order to begin to assess state emissions of ODCs.
The section on carbon dioxide emissions from cement manufacturing includes a more detailed
description of the recommended workbook method for estimating these emissions as well as an
alternate method.
The final section identiGes greenhouse gas emissions resulting from a wide range of
production processes. While no emissions estimation methodology is proposed for these processes,
it is recommended that the workbook preparer assess state production levels of each of the sources
listed to gain an understanding of total state emissions from production processes.
1. EMISSIONS OF OZONE-DEPLETING COMPOUNDS
OVERVIEW
' This section describes the issues involved in developing an emissions inventory for ozone-
depleting compounds (ODCs). ODCs are used in eight major end use sectors, which are listed in
Table D2-1 along with the ODCs currently in use and the ODC substitutes that are being considered
to replace the ODCs that are being phased out.
For purposes of this study, ODCs are trace gases emitted from human activities that release
chlorine or bromine into the stratosphere. To release chlorine or bromine into the stratosphere, the
compounds must have two characteristics: (1) they must be sufficiently stable so that they do not
break down in the lower atmosphere; and (2) when they reach the stratosphere, the compounds must
break down and release their chlorine and bromine atoms. As shown in Table D2-1, ODCs are
STATES WORKBOOK D2.i November 1992
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Table DM: ODCs and End Use Sectors
ODCs Currently In Use1
FuUy-balogenated CFCs:
CFC-11: CQ3F
CFC-12: CCIjFj
CFC-113: GC12FCC1F2
CFC-I14: CC1F2CC1F2
CFC-115: CCIF2CF3
Halons:
HaJon 1211: CBrOF2
Halon 1301: CBrF3
Others:
HCFC-22: CHOF2
Methyl Chloroform: CH3CC13
Carbon Tetrachloride: CQ4
HFC-152a:b CH3CHF2
Candidate Substitutes*
Chlorine-containing Compounds:
HCFC-123: CHC12CF3
HCFC-124: CHC1FCF3
HCFC-141b: CH3CC12F
HCFC-142b: CH3CC1F2
Others:
HFC-125: CHF2CF3
HFC-134a CR^CF3
HFC-143a: CH3CF3
Major End Use Sectors
Refrigeration: ODCs are used as refrieerai
refrigeration systems.
Air Conditioning: ODCs are used as refrieerai
automobile and truck) air conditioning sys
Solvent Cleaning: ODCs are used to clean
applications.
Foam Production: ODCs are used ia the produc
Sterilization: ODCs are used in commercial and
Fire Extinguishing: ODCs (halons) are used
electronic equipment
Chemical Intermediates: ODCs are used as che
compounds.
Miscellaneous: ODCs are used in a variety
products and other devices.
its in industrial, commercial, and residential
its in commercial, residential, and mobile (i.e.,
terns.
metal and electronic parts in a variety of
:tion of polyurethane and non-urethane foams.
hospital-based sterilization systems.
in fire extinguisher systems used to protect
:mical intermediates in the production of other
of miscellaneous categories including aerosol
a The chemical formulae are read as follows:
C = Carbon Cl = Chlorine F = Fluorine H = Hydrogen Br = Bromine
b HFC-152a does not contain chlorine or bromine, and hence does not deplete stratospheric
ozone. HFC-152a is used in conjunction with other ODCs.
STATES WORKBOOK
D2-2
November 1992
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divided into three main groups:1
Fullv-halogenated CFCs are the primary chlorofluorocarbons used today. These
compounds contain chlorine and are called "fully-halogenated" because they have no
hydrogen atoms.
Halons are compounds with one or more bromine atoms. Halon 1211 and 1301 are
the two halons used in the U.S.
Other ODCs include: HCFC-22 and methyl chloroform which are partially-
halogenated compounds (they contain hydrogen); and carbon tetrachloride.
To protect stratospheric ozone, the use and emissions of ODCs are being controlled in the
U.S. and globally through international agreements. ODC use and emissions were first controlled in
the late 1970s in the US. Since then, two international agreements, the Vienna Convention of 1985
and the Montreal Protocol of 1987, were negotiated and ratified. Most recently, the U.S. enacted
the Clean Air Act Amendments of 1990 authorizing EPA to promulgate regulations to fulfill the U.S.
obligations under the Montreal Protocol and its subsequent revisions. Throughout this period,
various state and local regulations and ordinances have also been promulgated and enacted to control
ODC emissions.
In response to current and future restrictions on ODC production and use, a variety of
chemical and product substitutes are under development The major chemical substitutes fall into
two categories: partially-halogenated chlorine-containing compounds (HCFCs); and partially-
halogenated compounds that do not contain chlorine (HFCs) (see Table D2-1). Because they contain
chlorine, the HCFCs can deplete stratospheric ozone. However, because they are partially
halogenated, they mostly break down in the lower atmosphere and pose only about one-tenth to one-
one-hundredth the threat posed by CFCs.
How are ODCs Used and Emitted?
Since their invention in the early 1900s, CFC use grew consistently until the middle 1970s in
the U.S. and globally. Initially used as refrigerants in both refrigerators and air conditioners, CFCs
were found to have many desirable properties that made them useful for a variety of applications.
Over time, new uses for CFCs were developed, including as aerosol propellants, foam blowing agents,
sterilant gases, solvents, and chemical intermediates. By the early 1970s, CFCs were commodity
chemicals, produced and traded internationally.
Aerosol propellant uses dominated both CFC-11 and CFC-12 use by the mid-1970s. By 1980,
however, the U.S. had banned the use of the fully-halogenated CFCs in non-essential aerosol
applications. As a result, aerosol use declined to a very low level in the U.S. by 1983.
Throughout the 1980s the non-aerosol uses of the CFCs continued to grow. CFC-113 use
for cleaning electronic components grew significantly. In the late 1980s another significant
1 HFC-152a, listed in Table D2-1, is used in conjunction with ODCs but does not contain chlorine or
bromine.
STATES WORKBOOK D2-3 November 1992
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restructuring of the CFC market was initiated in response to the ratification of the Montreal Protocol
and the promulgation of regulations by the U.S. EPA and others to control CFC use and emissions.
In the last several years CFC production and use in the U.S. has declined approximately 40
percent relative to production levels in the mid- to late 1980s, and all production will be eliminated
before the end of the century. Most of the reductions to date have occurred in the use of CFCs for
manufacturing various types of plastic foams and in solvent applications. In some cases, HCFC-22
has emerged as a substitute chemical, and its use has increased in some areas. These recent
significant shifts in use and emissions must be considered when developing an emissions inventory.
Compared to CFCs, the market for methyl chloroform (MC) has developed more recently.
Used principally as a solvent in a variety of applications, methyl chloroform use began to grow in the
late 1960s when it was viewed as a favorable alternative to trichloroethylene, a suspected carcinogen.
In addition to its solvent uses, methyl chloroform is used in aerosol products, and in inks, adheshres,
and coatings.
The halons are used exclusively as fire extinguishing agents. Halons are valuable fire
extinguishing agents because they are very effective at extinguishing a fire and preventing/suppressing
explosions, while also: being electrically nonconductive; dissipating quickly; leaving no residue; and
posing little harm from human exposure (UNEP, 1991). As a consequence, halons are used to
protect computers and other sensitive equipment from fire.
Based on testing performed in the 1940s, haloc 1301 was selected for military fire protection
applications in the U.S. (UNEP, 1991). Halon 1301 became the halon of choice for total flooding
fire extinguishing systems, and its use has grown significantly since 1966 when it started to be used
to protect computer rooms and command and control centers. Total flooding systems are designed
to "flood" an entire room or area rapidly with the fire extinguishing agent In this case, halon 1301
is flooded into the room to extinguish the fire.
Halon 1211 was also recognized as a suitable fire extinguishing agent in the late 1940s. Halon
1211 has become the halon of choice for portable fire extinguishers, and is found in military and
various commercial applications in museums, art galleries, and computer rooms.
Controls on ODC Use and Emissions
Given the strong U.S. and international policy initiatives of 1990, it is clear that the
production of CFCs will be phased out over the next 10 to 15 years. Recent announcements indicate
that CFCs and MC may be phased out within the next five yean. HCFCs are now generally
considered to be "transition" chemicals, playing a role to help eliminate CFCs, but themselves being
controlled and phased out in the long term.
Considerable uncertainty remains regarding precisely how the transition away from CFCs and,
eventually, HCFCs will unfold. With the exception of the non-essential aerosol propellant ban, to
date the international treaties and federal U.S. role have been solely to limit production and imports
of the controlled chemicals. Restrictions on specific uses or requirements for specific technologies
have not been adopted at the federal or international levels, and the state and local prerogatives in
this area have not been preempted.
STATES WORKBOOK O2-4 November 1992
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With the enactment of the Clean Air Act amendments, the federal role has shifted
significantly. The EPA is implementing programs to control emissions from mobile air conditioners
and other refrigeration and air conditioning equipment Additionally, the EPA has authority to
control other specific uses.
METHOD FOR DEVELOPING STATE ODC EMISSIONS INVENTORY
To develop an emissions inventory for ODCs, the following must be considered:
emissions from the stock of equipment that contains and emits ODCs (e.g.,
automobile air conditioners, refrigerators; freezers, process refrigeration equipment);
emissions from ongoing use of ODCs (e.g., as solvents, foam blowing agents, and
sterilization gas);
the manner in which federal restrictions on ODC production are affecting the mix of
uses to which ODCs are put; and
the effect that federal recycling requirements are having on the use and emissions of
ODCs from refrigeration and air conditioning equipment
As a practical matter, the resources necessary to perform these analyses at a state level will generally
be considerable, particularly because the impacts of the federal controls are national in scope.
Consequently, to develop state-specific emissions inventories, it is recommended that national
emissions estimates be used as a basis for estimating state-specific emissions.
Tables D2-2 and D2-3 present estimates of emissions by ODC and end use for 1990 and 2005.
These two years were chosen to represent emissions in a recent jrear (1990) and in a .year that falls
after the phaseout of the key ODCs (scheduled for 1996). A summary of the ODC use categories
is included at the end of this section on ODC emissions.
Table D2-2 shows estimated emissions for the ODCs commonly in use prior to 1990. By 2005,
a variety of substitute products and chemicals will be used as the production and use of ODCs are
restricted or eliminated. Consequently, in Table D2-3 emissions are listed for a variety of chemical
substitutes, most of which are partially halogenated HCFCs and HFCs. "VOC Substitutes" are listed
to show the potential use of chemicals that are considered volatile organic compounds (VOCs).
"Other Substitutes" refers to a variety of substances, such as: ammonia as a refrigerant; aqueous
cleaners as a solvent; and alternative fire-fighting chemicals such as carbon dioxide.
To develop a state-specific emissions inventory the emissions estimates in these two tables can
be apportioned to individual states using relevant national and state-specific activity factors, as
follows:
State Emissions = National Emissions x State Activity Factor * National Activity Factor.
State and national activity factors should be developed in pairs, so that the state and national data
are comparable. Additionally, activity data specific to each end use should be used when available.
Table D2-4 summarizes suggested activity levels for each of the end uses, and sources that may be
consulted.
STATES WORKBOOK D2-5 November 1992
-------
Table D2-2: Summary of U.S. 1990 ODC Emissions
(Thousands of Kilograms)
Mobile AC
Process Refrigeration
Commercial Refrlg.
Res. Refrig. & Freezers
Res. I Light Com1 I AC
Commercial Chillers
Solvents
Foams
Sterilization
Miscellaneous
Fire Extinguishing
Chem Manufacturing
Total
CFC-11
0
387
0
0
0
7,313
0
35,476
0
3,250
0
89
46,516
CFC-12
60,954
434
18,102
3,668
0
1,736
0
11,586
11.507
6,780
0
390
115,156
CFC-113
0
0
0
0
0
0
44,300
950
0
0
0
226
45,476
CFC-114
0
0
0
0
0
88
0
2,550
0
0
0
13
2,651
CFC-115
0
5
3,695
0
0
0
0
0
0
0
0
12
3,712
HCFC-22
0
2,374
19,679
0
41,311
15,446
0
12,303
0
2,890
0
324
94,327
MC
0
0
0
0
0
0
203,197
0
0
113,803
0
1,585
318,585
H-1211
0
0
0
0
0
0
0
0
0
0
1.043
3
1,046
K-1301
0
0
0
0
0
0
0
0
0
0
1,574
6
1,580
CT
0
0
0
0
0
0
0
0
0
0
0
3,812
3,812
HFC-152A
0
12
41
0
0
142
0
0
0
0
0
1
195
-------
Table D2-3: Summary of U.S. 2005 ODC Emissions
(Thousands of Kilograms)
Mobile AC
Process Refrigeration
Conmercfal Refrig
Res. Refrig. & Freezers
Res. & light Com1 I AC
Commercial Chillers
Solvents
Foams
Sterilization
. Miscellaneous
Fire Extinguishing
ODC Manufacturing
Total
ODC Substitutes:
Mobile AC
Process Refrigeration
Commercial Refrig
Res. Refrig. ft Freezers
Res. 4 Light Com' I AC
Commercial Chillers
Solvents
Foams
Sterilization
Miscellaneous
Fire Extinguishing
Subs Manufacturing
Total
CFC-11
0
29
0
0
0
721
0
28,740
0
0
0
0
29, WO
HCFC- 123
0
67
0
0
0
1,589
23,971
28,097
0
0
0
262
53.986
CFC-12
7,881
170
1,023
144
0
343
0
2,460
0
0
0
0
12,022
HCFC- 124
0
2
34
41
0
40
0
2,489
0
0
0
13
2,619
CFC-113
0
0
0
0
0
0
0
0
0
0
0
0
0
NFC- 125
0
0
5
0
0
0
0
0
0
0
0
0
5
CFC-114
0
D
0
0
0
19
0
0
0
0
0
0
19
HCFC-141B
t>
0
0
0
0
0
6
28,097
0
0
0
140
28,237
CFC-115
0
1
302
0
0
0
0
0
0
0
0
0
303
NCFC-142B
0
0
0
0
0
0
0
7,385
0
0
0
37
7.422
HCFC-22
0
1,442
6,902
82
3,331
3,066
0
17,177
0
29
0
0
32,029
HFC-134A
31,289
150
1,180
0
0
326
0
0
0
0
0
8
32,953
MC
0
0
0
0
0
0
0
0
0
0
0
0
0
HFC-152A
0
7
66
82
0
40
0
0
0
0
0
0
198
H-1211
0
0
0
0
0
0
0
0
0
0
364
0
364
HCFC Subst
0
0
0
0
0
0
0
0
8,399
15
0
42
8,455
H-1301
0
0
0
0
0
0
0
0
0
0
S21
0
521
VOC Subst
0
0
0
0
0
0
5,542
0
0
0
0
0
5,542
Oth Subst
0
0
136
0
0
0
178
0
430
0
1,715
0
49,125
TOTAL
39.170.
1,869
9.651
349
3,331
6,144
29,691
114,445
8,829
44
2.600
502
263,291
STATES WORKBOOK
D2-7
November 1992
-------
Table D2-4: State and National Activity Factors
Use Category
Mobile Air Conditioners
(MACS)
Process Refrigeration
Commercial Refrigeration
Residential Refrigerators
and Freezers
Residential and Light
Commercial Air
Conditioning
Commercial Chillers
Solvent Applications
Foams
Activity Level
Number of registered
automobiles with MACs
Number of establishments
in the chemical, refining,
and pharmaceutical
industries
Number of retail food
establishments (e.g.,
supermarkets) and quantity
of cold storage warehouse
space.
Number of refrigerators and
freezers.
Air conditioned space In
residential buildings.
Square feet of commercial
air conditioned floor space
Number of establishments
In solvent using Industries.
Foams are used in a
complex set of applications.
No single simple activity
level is available.
Population Is
recommended as a
reasonable proxy.
Source
State and national vehicle
registration Information
Department of Commerce Census
ol Manufactures. ON and Qas
Journal (refining establishments)
Dun's Marketing Service
(supermarkets); USDA National
Agricultural Statistics Service (cold
storage warehouse space).
Utility appliance surveys.
Bureau of the Census American
Housing Survey.
U.S. DOE Commercial Building
Characteristics
Department of Commerce Census
of Manufactures
U.S. Bureau of the Census
Comments
Age of vehicle can be considered to Improve
emissions estimates If the age distribution of cars
and trucks Is known.
Emissions estimates can be Improved by dis-
aggregating emissions by retail food store type
(supermarket vs. convenience stores). ,
Number of households or population can be
used as proxy activity levels.
Adjustments of climate can be performed to
Improve the estimates.
Candidate Industries by SIC Code are: industrial
Machinery (SIC 35); Electronic and Electric
Machinery (SIC 36); Transportation Equipment
(SIC 37); and Instruments and Related
Equipment (SIC 38).
bonunuea.
-------
Table D2-4: State and National Activity Factors
(Continued)
Use Category
Sterilization
Miscellaneous
Fire Extinguishing
Chemical Manufacturing
Activity Level
Numbers of hospitals.
ODCs are used In a variety
of miscellaneous uses.
Population is
recommended as a
reasonable proxy.
Halon fire extinguishing
equipment Is found In a
wide variety of applications.
Population Is
recommended as a
reasonable proxy.
ODC use and emissions
Source
American Hospital Association
Hospital Statistics
U.S. Bureau of the Census
U.S. Bureau of the Census
Comments
Emissions occur during the production of ODCs.
STATES WORKBOOK
D2-9
November 1992
-------
UMTTATIONS OF RECOMMENDED METHOD
The method presented here for developing state-specific ODC emissions inventories is very
simplified. The national emissions estimates are based on a complex set of very disaggregated models
that includes national data describing the use and emissions of ODCs from each of its uses.
Theemissions estimates for 1990 are subject to a range of factors that contribute to their uncertainty,
which makes the estimates no better than ±25 percent for each individual end use, although the total
emissions are known with greater precision.
The emissions estimates for 2005 are based on one assessment of the manner in which the
ODC phaseout will unfold. In particular, the emissions estimates for the substitute chemicals, most
of which are still under development or testing, are very sensitive to assumptions regarding the costs
and performance of the substitutes.
Finally, the activity factors listed for apportioning the national emissions to individual states
are themselves imperfect indicators of ODC emissions. For example, the regulations affecting the
use and production of ODCs are rapidly changing usage patterns; over time, many of these activity
factors will no longer be reasonable proxies for potential emission patterns. Consequently, the state-
specific estimates, particularly by end use, must be considered very uncertain.
To improve the state-specific emissions inventory estimates more disaggregate analysis would
be required to apportion the national emissions to the state level Experience indicates that such an
exercise is very data intensive, and not easily performed (ICF, 1992).
SUMMARY OF ODC USES
Mobile Air Conditioning
Mobile air conditioners (MACs) refer to air conditioners used to cool the passenger
compartments of vehicles including automobiles (cars) and light trucks. MACs use CFC-12
exclusively as the refrigerant because it is thermodynamically suitable, chemically stable, non toxic,
non flammable, and non corrosive. The design and production of MACs are controlled by the
automobile manufacturers. However, MACs are serviced at thousands of automobile repair shops
throughout the state.
Currently, approximately 92 percent of new passenger vehicles in the U.S. have MACs
installed in the automobile manufacturing plants. Overall, approximately 90 percent of the passenger
cars and 55 percent of the light trucks in service in 1990 have MACs. All manufacturers supplying
the U.S. market plan to phase out CFC-12 in MACs by the end of the 1995 model year, and will
install air conditioners containing an alternative refrigerant in all new vehicles by that time. At this
time, HFC-134a is anticipated to be the replacement refrigerant
Unfortunately, there is no drop-in substitute refrigerant for existing MACs. HFC-134a and
other substitutes can only be used in existing systems after retrofitting the system at substantial cost
Consequently, CFC-12 will be required to service the existing MACs through their remaining useful
lives. Recognition of this need has led to the implementation of federal CFC-12 recycling
requirements during MAC servicing. Various state and local recycling programs have also been
STATES WORKBOOK D2.io November 1992
-------
initiated. The MAC recycling programs, and ODC recycling in general from other applications, will
play an important role in supplying CFC-12 for MAC servicing in the future.
The following events cause emissions from MACs:
Manufacturing: a small amount of CFC-12 is emitted when MACs are installed.
Normal Leakage: during normal use, CFC-12 leaks out very slowly as the result of
permeation through hoses and around fittings.
Abnormal Leakage: poor-fitting or worn hoses or fittings can result in larger than
normal leakage during normal use.
Accident: a portion of accidents result in the rupture of the MAC system, and
consequently a release of CFC-12.
System Failure with a Rupture: occasionally the MAC system ruptures and fails,
releasing CFC-12; a broken hose may result in this type of system failure.
System Failure without a Rupture: occasionally the MACS system fails without
rupturing and releasing CFC-12; this event prompts the need for servicing. A failed
compressor is an example of this type of system failure.
Servicing: servicing is modeled to occur when normal or abnormal leakage results in
degraded system performance. Servicing is also performed as the result of system
failures and accidents. Emissions during servicing result from the venting of the CFC-
12 remaining in the system (if applicable) and leakage during servicing activities.
Disposal: if CFC-12 remains in a MAC at the time of disposal it is routinely vented
when the MAC is salvaged or the vehicle destroyed.
Because recycling programs are now being put into place, emissions associated with servicing and
disposal are expected to decline relative to the estimates for 1990.
It has been suggested that the national estimates of the frequency of MAC servicing may not
be appropriate for MACs in some states because of differences in climate or miles driven. To date
there is no evidence that miles driven has an impact on the need to service a MAC or expected
emissions. Although there may be differences in the likelihood of accidents, which could lead to
differences in emissions, accident-induced emissions are a relatively small portion of total MAC
emissions (less than 10 percent), so potential biases associated with differential accident rates are not
expected to be serious.
Similarly, climate differences have not been demonstrated to play a role in emissions.
Although MACs may be utilized more hours per year in wanner states, leakage rates are not modeled
as a function of hours of operation. In fact, the mild winters and more frequent MAC usage in a
state may help reduce leakage by keeping valves and fittings well lubricated.
STATES WORKBOOK D2.n , November 1992
-------
Process Refrigeration
Process refrigeration refers to refrigeration equipment used during the manufacture of
products and for other industrial applications. It is used primarily in the chemical, pharmaceutical,
petrochemical, oil and gas, metallurgical and industrial ice making industries. (Refrigeration for cold
storage warehouses is discussed below under commercial refrigeration.)
Ammonia, hydrocarbons, HCFCs and CFCs are the most common types of refrigerants used,
with CFC-based systems comprising only about 15 to 20 percent of the total sector. The choice of
refrigerant depends primarily on the temperature range needed. The major portion of industrial
cooling is required for moderately low temperatures of approximately -20°C and above. CFC-SOO2
is typically used for the low temperature region (-70°C to -4S°Q; ammonia and HCFC-22 and some
CFC-S02* is used above -45°C; CFC-12 is mostly used for -30°C and above; and CFC-11 is used for
water chilling in the range of 5-10°C
Although CFCs are the most expensive refrigerant, they are used for process refrigeration
primarily because they are safe, easy to handle, nontoric and nonflammable. However, as a result
of the specialized needs in the process refrigeration sector, system availability, energy consumption
and refrigerant price are more important concerns than hazards from toxicity and flammability.
CFCs are emitted from process refrigeration equipment during use and disposal. During use,
emissions result from normal leakage, leakage as a result of system damage, and scheduled and
unscheduled servicing. Unscheduled servicing occurs as a result of system failure or external damage.
Because process equipment is typically charged on site after installation, emissions at installation are
estimated and emissions during manufacture are not of concern. It is estimated that from one to two
thirds of emissions occur during servicing, either by deliberate venting or accidental loss.
Commercial Refrigeration
Commercial refrigeration equipment is primarily used for food storage and display. The major
applications are in supermarkets, other retail food establishments, refrigerated warehouses, and
refrigerated transportation equipment (primarily trucks, rail cars, and ships). The equipment ranges
in type from self-contained stand alone equipment display cases to walk-in cold storage rooms.
Emissions result from manufacturing/installation, leakage, servicing, and disposal. The hermetically
sealed equipment (such as vending machines) have .much lower leakage rates and service
requirements than the other major equipment types.
Residential Refrigeration
Domestic refrigerators and freezers are used primarily for food preservation and storage.
During the past 50 years, manufacturers have developed high efficiency systems using the basic vapor
compression refrigeration cycle. Compressors and other system components have been optimized to
be compatible with CFC-12, the existing refrigerant
2 CFC-SOO is a mixture of 74 percent CFC-12 and 26 percent HFC-152a (by weight).
3 CFC-502 is a mixture of 49 percent HCFC-22 and 51 percent CFC-115 (by weight).
STATES WORKBOOK D2.i2 November 1992
-------
The majority of emissions from refrigerators and freezers occur at the point of disposal. Until
recently, appliances were typically disposed similarly to other metal wastes, and refrigerant charges
were emitted as the appliance was stripped of parts or crushed. Refrigerants are also emitted during
servicing, which accounts for the second largest category of emissions from these products.
Residential refrigerators and freezers are designed to be a closed non-leaking system. Typically, they
do not have to be serviced during their expected lifetime of 10 to 15 years, and can last as long as
30 years. It is estimated that only about 1.5 percent of refrigerators and freezers are serviced each
year.
Residential and Light Commercial Air Conditioning
Residential and light commercial air conditioning systems can be divided into three major
types: window units; unitary systems; and packaged terminal systems. All three types use HCFC-22
almost exclusively because it: meets all toxicity and flammability requirements; is therraodvnamically
suited for the temperature ranges typically encountered; and is compatible with all common
construction materials and nearly all commonly used lubricants.
Window units, or "room" air conditioners consist of a single factory-made encased assembly
designed for window or wall mounting. The unit delivers conditioned air into a room without the use
of ducts. In addition to cooling the air, it generally dehumidifies the ah- and may perform other
ventilating or heating functions.
Unitary systems are air conditioners and heat pumps which are commonly referred to as
"central air systems." Unitary systems consist of one or more factory-made assemblies which normally
include an evaporator or cooling coil, a compressor and a condenser. The conditioned air is typically
distributed through the use of ducts. Unitary systems may provide heating as well as cooling.
Packaged terminal air conditioners and heat pumps are self-contained units commonly used
in office buildings and hotels. Similar to window units, these units typically include both heating and
cooling components, and are only mounted through walls. Packaged terminal systems generally have
a higher capacity than window units, but also are designed to serve a single room.
For window units and packaged terminal units, servicing is seldom performed, and
consequently the majority of emissions are associated with disposal. Unitary units require servicing
more often, and it is believed that significant amounts of HCFC-22 are used and emitted during these
service activities.
Commercial Chillers
Chillers are large air conditioning units used primarily for commercial and industrial buildings.
Unlike the unitary systems discussed in the previous section, chillers cool water (or a water/glycol
mixture) which is then passed through a heat exchanger to cool and dehumidify the air being
conditioned.
There are two basic types of water chillers, categorized by compressor types: positive
displacement compressors and centrifugal compressor systems. Positive displacement compressors
(reciprocating and screw) cover the smaller end of the capacity range of commercial chillers. Some
STATES WORKBOOK D2.i3 November 1992
-------
positive displacement chillers use CFC-12 but most use HCFC-22. As CFCs are phased out, it is
expected that HCFC-22 positive displacement chillers will predominate.
Centrifugal compressor systems are usually larger in capacity and primarily use CFC-11 as the
refrigerant CFC-11 is a low pressure refrigerant and is suitable for centrifugal systems only. Some
CFC-12 is used in systems that cover a broad range of capacities. Some centrifugal chillers designed
for CFC-12 are charged with CFC-500 in order to broaden their capacity range. Very large chillers
are often charged with HCFC-22. CFC-114 is used for chilling aboard submarines because it is
capable of being used with reduced levels of vibration.
Nearly all chillers are expected to be in service for many years, usually 25 years or more.
They are generally large pieces of equipment with high capital costs. Chillers are used to cool areas
of high occupancy (such as office buildings), but are usually operated in a relatively remote locations,
such as the roof or a dedicated floor for mechanical equipment
Solvent Applications
CFC-113 and methyl chloroform (MC) are widely used as solvents to clean electronics
assemblies, delicate instruments and surfaces, and metal parts. These substances are also used in a
number of countries for the dry cleaning of clothing, but this use is not significant in the U.S. The
three main types of solvent cleaning in the U.S. are:
Electronics cleaning: Solvents are used extensively in electronics cleaning primarily
to remove flux residue which is left on printed circuit boards after components have
been attached to the board by a soldering operation.
Metal cleaning: Metal cleaning applications of solvents usually consist of the removal
of oil and jjrease from large metal parts such as automobile component. These parts
often do not require a high level of cleanliness.
Precision cleaning: Precision cleaning is performed on products that require an
extremely fine level of cleanliness, including computer disk drives, gyroscopes, and
other high-technology devices.
CFC-113 and MC are two common solvents among a variety of chlorinated and aqueous
solvents in use. The solvents are seldom used in their pure form, but are mixed with various
stabilizers to improve performance. In many cases equipment is used to prevent the emissions of
solvent vapors. Because some solvents are considered volatile organic compounds (VOCs), emission
control requirements have been implemented to limit emissions. These requirements generally
exempt CFC-113 and MC, which are not considered VOCs.
Solvent recycling has recently become increasingly popular as the cost of used solvent storage
and disposal have increased. Recycling is performed on site if large quantities are used. More
commonly, off site recycling is more cost effective.
STATES WORKBOOK D2>14 / November 1992
-------
Foam Production
CFCs have been used to make a variety of different types of foams. CFCs are used primarily
as blowing agents, which cause the expansion of the resin or other materials from which the foam is
made. As a result of this expansion, bubbles or "cells" are created in the resin that hardens to form
the foam material. CFCs also serve other important functions, such as increasing the foam's
insulating properties, softening the foam, absorbing some of the heat generated during production
so that the foam does not scorch or burn, and reducing the foam's density.
Foams are used to produce a variety of foam plastic products, including building and appliance
insulation, cushioning materials, packaging, and flotation devices. CFC-11, CFC-12, CFC-113 and
CFC-114 are used in the manufacture of the four main types of foam:
Flexible Polvurethane Foam is made from a polyurethane resin and is primarily used
in cushioning products such as furniture, carpet padding, and packaging. CFC-11 is
used to produce this foam.
Rigid Polvurethane Foam is made from a polyurethane resin in a manner that
produces a very stiff material. This foam can be factory-produced as boards with
laminated sealants on the outside for use as building insulation. This foam can also
be sprayed or poured onto surfaces or into molds. Rigid polyurethane foam is
currently used in the walls of refrigerators and freezers to supply both structural
strength and insulation. CFC-11 and CFC-12 are used to produce this foam.
Extruded polystyrene fEPS) foam is produced in two forms: sheet and boardstock.
EPS sheet has been used for food service and packaging applications. EPS
boardstock has been used almost exclusively for insulation in buildings. EPS has been
produced primarily with CFC-12.
Other Foam "Products Include polyolefin foams made from polypropylene or
polyethylene resins and phenolic foams made from phenol-based resins. These foams
are used in a wide variety of products, including building insulation, flotation devices,
aircraft seating, automotive bumper systems, packaging, and other applications. CFC-
11, CFC-12, CFC-113, and CFC-114 are used in the production of these foams.
Since the signing of the Montreal Protocol and the implementation of federal regulations
restricting CFC production, the use of CFCs in making foam has declined significantly. Restrictions
on the use of CFCs in foam production in some states and localities have also played a role.
HCFC-22 is being used in increasing amounts as a substitute blowing agent in many foam
formulations. Additionally, MC is being used in some areas, as are alternative foam production
systems and non-foam substitute products.
There are two basic types of emissions rates from foams:
Prompt emitters release their CFCs during or shortly following foam production.
CFCs are not stored in these foams for an extended period of time.
Delayed emitters store CFCs in the foam material, for example to enhance the
insulating properties of the foam. These CFCs are released slowly over a period of
many years.
STATES WORKBOOK D2.is November 1992
-------
Flexible polyurethane foam, EPS sheet, and other foam products are prompt emitters. For these
foams, emissions in each year are equal to the amount of CFCs used in the production of the foams
in the year. Rigid polyurethane foams and EPS board store CFCs for a range of 12 to 30 years.
A variety of studies have been performed on the rate of CFC release from the delayed
emitting foams. The emissions rate is very variable depending on how the foam was manufactured,
sealed (if at all) and used. The release rate tends to be on the order of 2 to 5 percent per year.
However, these foams are used in a variety of products, and the point at which the product is
disposed often determines when the bulk of the CFCs are released. In all cases, the CFCs will be
emitted eventually.
Sterilization
CFC-12 with ethylene oxide (EO) is widely used for sterilization of medical equipment and
devices by medical device manufacturers and contract sterilization services, as well as by hospitals.
EO, the main cleaning ingredient of sterilization solutions, is used for its ability to penetrate a wide
variety of packaging materials to destroy microorganisms on medical products and devices. Due to
the high flammability and explosion risk associated with EO, it is often diluted with CFC-12 to a
mixture of 12 percent EO and 88 percent CFC-12 (by weight), a combination commonly referred to
as "12/88". CFC-12 is emitted when the 12/88 mixture is exhausted from the sterilization equipment
Miscellaneous Uses
ODCs are used in a variety of miscellaneous applications, including the following.
Adhesives. Methyl chloroform (MC) is used as an adhesive solvent because it is
nonflammable, dries rapidly, and performs well in many applications, particularly foam
bonding. Adhesives are used in a very wide range of industries and consumer
applications.
Coatings and inks. MC is used alone or combined with other solvents in coatings and
inks applications and is preferred for its low flammability and its fast evaporation rate.
In coatings, MC can be used to solubilize a binding substance due to its good solvency
power. These properties also make MC especially favorable in the manufacture of
inks which are used to print items such as wallpaper and beverage bottles or cartons.
Aerosols. MC, CFC-11, and CFC-12 are used in aerosol product applications. MC
functions principally as a solvent in these products. CFCs can be used as propellants
or as active ingredients. In the U.S., the use of CFCs in nonessential aerosol
propellant applications was banned in 1978. Some medical devices were found to be
essential and were exempt from the ban, as were aerosol products in which CFCs
were an active ingredient
Other Miscellaneous Uses. CFCs and MC are used in a variety of other applications
and products. For example, CFC-12 is used in warning devices, boat horns,
pressurizes blowers, and drain cleaners (Hammitt et al. 1986). MC is used in semi-
conductor fabrication, film cleaning, and fabric manufacturing applications.
STATES WORKBOOK D2-16 November 1992
-------
When ODCs are used in these miscellaneous applications they are generally emitted in the year in
which they are used.
Fire Extinguishers
Halons are used in specialized fire extinguisher applications. Halons are very effective in fire
Gghting and explosion prevention/suppression and have valuable characteristics including: (1) they
are electrically nonconductive; (2) they dissipate quickly and leave no residue; and (3) they are
relatively safe for human exposure (UNEP, 1991).
Halon 1301 is used principally in total flooding systems to protect electronic equipment rooms.
Upon detection of a Ore, the total flooding system discharges halon 1301 very rapidly, extinguishing
the fire. The total flooding systems are designed to produce a sufficient concentration of halon in
the room in order to be effective in Gghting the fire. Total flooding systems are also used in areas
where flammable liquids are stored or handled, in military applications, and other miscellaneous
situations.
Halon 1211 is used principally in portable fire extinguishers. These systems are used to
protect the same types of areas that use total flooding systems, with electronic equipment and military
applications being the largest uses. Halon 1211 hand-held fire extinguishers have also been marketed
to consumers for home use. A small amount of halon 1301 is used in portable systems, and small
amounts of both halon 1211 and 1301 are used in locally applied systems which are similar to total
flooding systems, but only are effective in a portion of the room.
Halons are emitted as the result of several emissions events, including: manufacturing and
installation; discharge during a fire; unwanted (i.e., accidental) discharge; leakage and servicing;
training; and disposal.
Chemical Manufacturing
Fugitive emissions from manufacturing facilities may result in ODC emissions. Only carbon
tetrachloride (CT) emissions from this source have been estimated to date. Fugitive emissions of the
CFCs, halons, and MC themselves also likely occur during the manufacture of the chemicals.
2. CARBON DIOXIDE EMISSIONS FROM CEMENT PRODUCTION
OVERVIEW
Carbon dioxide emitted during the cement production process represents the only major non-
energy source of industrial carbon dioxide emissions. In cement kilns, calcium carbonate (CaCO3)
from limestone, chalk, or other calcium-rich materials are heated to form lime (CaO) and carbon
dioxide. The process is known as calcination or calcining:
CaCO3 + Heat - CaO + CO2
STATES WORKBOOK m~n November 1992
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The lime thus produced combines with silica-containing materials, provided to the kiln as clays or
shales, to form dicalcium or tricalcium silicates, two of the four major compounds in powdered
cement (or "clinker") (Griffin, 1987).
Carbon dioxide emissions from cement production are essentially directly proportional to lime
content, so production of cements lower in lime yield less CO2. Most of the structural cement
currently produced in the world is of the "Portland" cement type, which contains 60 to 67 percent
lime by weight Other specialty cements are lower in lime, but are typically used in small quantities.
Research is underway on cement formulations that have similar structural properties to Portland
cement, but require less lime (Tresouthick and Mishulovich, 1990).
WORKBOOK METHODOLOGY
The recommended method for estimating CO2 emissions from cement production involves
multiplying the most reliable figures available for tons of cement produced by an emission factor of
0.4985 tons CO2/ton of cement produced.
Cement production data by state, published by the U.S. Bureau of Mines, are currently
reported in thousand short tons. The estimation of CO2 emissions from cement production is
accomplished by applying an emission factor, in tons of CO2 released per ton of cement produced,
to the annual cement output4 The emission factor is the product of the fraction of lime used in
the cement clinker (clinker is the intermediate material produced in a cement kfln from which cement
is produced) and a constant reflecting the mass of CO2 released per unit lime:
EFcemem = Fraction CaO x (44 g/raole CO2 / 56.08 g/mole CaO), or
EFcement = Fract'°n CaO x 0.785
There are two methods for calculating this emission factor (EF). The first is to assume an average
CaO fraction in cement This approach has been followed by Marland et al. (1989), who took the
average CaO content of cement to be 63.5%, yielding an emission factor of 0.4985 tons CO2/ton of
cement produced (0.136 tons C/ton of cement).
EFcemcm = 0.635 '.0.785
= 0.4985
Therefore, for every ton of cement produced, it has been estimated that 0.4985 tons are
emitted as CO2 during the process of calcination. U.S. cement production totaled 73,272 thousand
tons in 1988 (U.S. Bureau of Mines, 1988). Thus, by applying the suggested methodology, it can be
estimated that U.S. CO2 emissions from cement production were equivalent to 36,526.1 thousand tons
CO,5.
4 Note that the CO2 generated by energy use during cement production is accounted for as emissions from
energy consumption, which are discussed in the energy chapter.
5 Estimate was calculated using the methodology proposed by Marland et al., 1988.
STATES WORKBOOK D2.i8 November 1992
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ALTERNATE METHODOLOGY
A second method is to assemble state data on cement production by type and cement CaO
content by type, then calculate a weighted average for cement lime content in the state. These data
are not readily available in published sources. In most states, the difference in the results of these
two methods is likely to be small; any error in the lime content assumption is likely to be smaller than
the uncertainty in cement production figures (Griffin, 1987).
The methodology presented here does not take into account that some cement produced in
a state may be made from clinker imported from other states or countries (CO2 is actually released
during the production of clinker, which is an intermediate product of finished cement) or that finished
cement may contain some lime that is not attributable to clinker production. As a result, the most
accurate estimates of CO2 emissions from cement production would be based on clinker production,
not the production of finished cement Since the data are not easily obtainable, this method is not
recommended here.
Because clinker is mixed with gypsum (which has a lower lime content) to make cement,
clinker has a higher lime percentage than finished cement The clinker lime percentage was found
to be 64.6%6. This number was multiplied by the molecular weight ratio of CO^CaO (0.785) to
achieve a clinker emissions factor of 0.5071 tons of COj/ton of clinker produced.
Masonry Cement requires additional lime, over and above the lime used in its clinker. As a
generic formula, the following was developed to account for this activity:
a(All Cement Production) x ((1-1/(1 +b)) x c) x 0.785 = tons CO2 from CaO added to masonry
cement
where
a = fraction of all cement produced that is masonry cement (e.g. 0.1, 0.2)
b = fraction of weight added to masonry cement by non-plasticizer additives limp- slag, and shale g
0.03, 0.05)
c = fraction of weight of non-plasticizer additives that is lime (e.g. 0.6, 0.8)
a(All Cement Production) = Masonry Cement Production
((l-l/(l+b)) x c) = fraction of lime in masonry cement not attributable to clinker
((!-!/(!+b)) x c) x 0.785 = an emissions factor of CO2 from masonry cement additives
For simplicity, states could use the recommended methodology since it requires less data and
fewer calculations and should be reasonably accurate.
DATA SOURCES
State cement production data are available from the U.S. Bureau of Mines (1988). In some
states, data may be available from appropriate government offices.
6 Gregg Marland, ORNL, Personal communication.
STATES WORKBOOK D2.19 November 1992
-------
3. EMISSIONS FROM OTHER PRODUCTION
Several processes that produce greenhouse gas emissions are listed in Table D2-5. This is not
a definitive list since other activities do generate process emissions, although the major processes are
identified here. Each state may have additional categories that need to be identified in the future.
There are currently no recommended emission estimation methodologies for these sources.
Table D2-5
Emissions From Production Processes
PROCESS
Cement Production
Limestone Production
Agricultural Liming
Aluminum Production
Ferro-alloy Production
Silisium Carbid
Production
Coke Production
Nitric Acid Production
Nitrogen Fertilizer
Production
Petroleum Product
Processing (including
FOC)
Sulphur Recovery
Plants
Storage of Petroleum
Products in a Refinery
Colliery Coke
Production
Metallurgical Coke
Production
Steel Plant (electric,
EOF, etc.)
POLLUTANTS
NOX
X
X
X
X
NM
voc
X
X
X
X
CH4
«
X
X
X
X
CO
X
X
X
C02
X
X
X
X
X
X
X
X
X
N2O
X
X
STATES WORKBOOK
D2-20
November 1992
-------
PROCESS
Sulfuric Acid
Production
Nitric Acid Production
Ammonia Production
Sodium Carbonate
Urea Production
Carbon Black
Titanium Dioxide
NH3 Based Chemical
Production
Etbylene Production
Propylene Production
1,2 Dichlorothane
Production
Vinylchloride
Production
Polyethylene Low
Density Production
Polyethylene High
Density Production
Polyvinylchloride
Production
Polypropylene
Production
Styrene Butadiene
ABS Resins
Ethylene Oxide
Formaldehyde
Production
Ethylbenzene
Production
POLLUTANTS
NOX
X
X
NM
voc
X
X
X
X
X
X
X
X
X
X
X
X
X
X
CH4
X
CO
C02
X
N2O
STATES WORKBOOK
D2-21
November 1992
-------
PROCESS
Styrene Butadiene
Latex
Styrene Butadiene
Rubber
Phtalic Anhydride
Production
Aciylonitrile
Production
Chipboard Production
Paper Pulp Production
Bread Production
Wine Production
Beer Production
Spirits Production
Paint Applications:
manufacture of
automobiles
Paint Applications:
ship building
Paint Applications:
manufactures of metal
articles
Paint Applications:
wood products
Paint Applications:
construction and
buildings
Paint Applications:
vehicles refinishing
Paint Applications:
domestic use
Metal Degreasing
Dry Cleaning
POLLUTANTS
NO*
NM
voc
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
CH4
CO
C02
X
X
X
X
X
N2O
STATES WORKBOOK
D2-22
November 1992
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PROCESS
Polymers Processing
Elastomers Processing
Rubber Processing
Plastics Processing
Pharmaceutical
Processing
Paints Processing
Inks Processing
Glues Processing
Printing Industry
(solvent use only)
Domestic Solvent Use
POLLUTANTS
NO,
NM
voc
X
X
X
X
X
X
X
X
X
X
CH4
CO
C02
N2O
REFERENCES
ICE, Inc. 199Z Study of Emissions and Control of Stratospheric Ozone-Depleting Compounds in
California, prepared for the California Air Resources Board, Sacramento, California.
Hammitt, J.K. et al. 1986. Product uses and Market Trends for Ozone-Depleting Substances. 1985-
2000. Prepared for the U.S. Environmental Protection Agency by the Rand Corporation, Santa
Monica, California.
Griffin, R.C. 1987. CO2 release from cement production, 1950-1985, In Marland, G., T.A. Boden,
R.C. Griffin, S.F. Huang, P. Kanciruk, and T.R. Nelson. Estimates ofCO2 Emissions from Fossil Fuel
Burning and Cement Manufacturing, Based on the United Nations Energy Statistics and the U.S. Bureau
of Mines Cement Manufacturing Data. Report #ORNL/CDIAC-25, Carbon Dioxide Information
Analysis Center, Oak Ridge National Laboratory, Oak Ridge, Tennessee. May 1989. 643-680.
Marland, G., T.A. Boden, R.C Griffin, S.F. Huang, P. Kanciruk, and T.R. Nelson. 1989. Estimates
of CO2 Emissions from Fossil Fuel Burning and Cement Manufacturing, Based on the United Nations
Energy Statistics and the U.S. Bureau of Mines Cement Manufacturing Data. Report #ORNL/CDIAC-
25, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge,
Tennessee. May.
Tresouthick, S.W., and A. Mishulovich. 1990. Energy and environment considerations for the
cement industry. In conference proceedings Energy and Environment in the 21st Century.
STATES WORKBOOK
D2-23
November 1992
-------
Massachusetts Institute of Technology, Cambridge, Massachusetts. March 26-28,1990. B-110 to B-
123
UNEP (United Nations Environment Program) 1991. Montreal Protocol Assessment n. Report of
the Halons Technical Options Committee.
U.S. Bureau of the Mines. 1988. Cement Minerals Yearbook, authored by Wilton Johnson. U.S.
Bureau of the Mines, U.S. Department of the Interior, Washington, D.C
STATES WORKBOOK D2-24 November 1992
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DISCUSSION 3
METHANE AND CARBON DIOXIDE EMISSIONS FROM
NATURAL GAS AND OIL SYSTEMS
OVERVIEW
Emissions from natural gas and oil systems are primarily methane, although smaller quantities
of non-methane VOCs, carbon dioxide, and carbon monoxide can be emitted It is important to
account for emissions from oil and natural gas systems separately in order to differentiate between
emissions associated with particular fuels. For purposes of the workbook, the terms natural gas or
gas are used to refer to both natural gas (extracted from the ground), and "synthetic" or "substitute"
natural gas (comprised mostly of methane) produced from other petroleum-based products or sources.
Depending on its origin and how it is processed, commercially distributed natural gas also will include
various amounts of non-methane hydrocarbons (e.g., ethane, butane, propane, and pentane), carbon
monoxide, carbon dioxide, and nitrogen. Oil is used to refer to both oil extracted directly from the
ground and various synthetic processes such as oil shale or tar sands.
Natural gas and oil systems and resultant emissions vary greatly from system to system. In
order to understand these differences better, these systems could be defined by several components,
specifically: pre-production; production and central processing; venting and flaring during processing;
transmission; and distribution:
Pre-production: The drilling of wells and related activities prior to oil
or natural gas production.
Production and Central Processing: The production of oil and natural
gas and subsequent processing of the fossil resource to prepare it for
market generate emissions that depends on the number of operating
wells, the quantity of fossil energy produced, the composition of the
fossil resource, and any emission controls that may be used. During
the production phase, natural gas or oil is usually extracted from
underground formations through natural gas or oil wells. Both
onshore and offshore facilities are used to produce gas or oil. During
the processing phase, the oil or natural gas is refined into various
product types. For example, natural gas is usually processed in a gas
plant to produce a gas product that has specified characteristics.
Depending on the composition of the unprocessed gas, a variety of
processes may be used to remove most of the heavier hydrocarbons
from the gas. These hydrocarbons, often referred to as "condensates,"
may be marketed separately from the gas. The processed gas is then
ready for transmission and distribution to customers. A similar
rationale is followed at oil refineries in order to produce a variety of
products for end use.
* Venting and Flaring: Carbon dioxide (COj) and methane (CH4)
emissions occur when natural gas is flared or vented at crude oil and
STATES WORKBOOK D3-1 November 1992
-------
natural gas production wells. Gas flaring occurs at oQ wells where
there are no markets to sell the gas or the market value of the gas is
well below the additional development and transportation costs of the
gas. The venting of natural gas occurs during well drilling and well
maintenance operations.
Transmission: After refining the oil or natural gas is ready for sale.
For natural gas the processed natural gas is often transported long
distances in high-pressure pipelines. Reciprocal and turbine
compressors are used to pressurize the gas, which then flows within
large diameter pipes. Metering stations, maintenance facilities, and
additional compressor stations are located along the pipelines.
Although used less frequently, gas is also transported in liquid form in
specially built tanker trucks or ships. This gas is commonly referred
to as "liquified natural gas" or LNG. For oil the refined oil products
are typically transported long distances via tanker or pipeline. Many
of the basic components (at least for pipeline transport) are similar in
nature to components of gas transmission systems.
Distribution: Once the oil or natural gas are transported to major
demand centers, they are distributed to various end-users for
consumption. Natural gas is distributed to commercial, industrial, and
residential customers through distribution systems. These systems
include metering stations, compressor stations, and maintenance
facilities. A variety of pipe types and sizes are used. The gas is
usually obtained from a transmission pipeline, and the pressure is
reduced for distribution within a city or town. For oil perhaps the
most common method for distribution is through the use of tanker
trucks, -which typically transport the oil to the end-user where it is
stored until needed.
Each of these components may produce methane or other emissions, either deliberately or
inadvertently. The major types of emissions are the following:
Fugitive Emissions: Fugitive emissions are the inadvertent leakage of gas
from equipment on an ongoing basis. Valves, connections, meters, and other
components develop small leaks during use. Depending on the inspection and
maintenance procedures used, these small leaks will be identified and
corrected periodically.
Maintenance-related Emissions: Maintenance activities result in emissions
when equipment or pipelines are opened to the atmosphere. Emissions are
often minimized by reducing the pressure in the equipment or pipeline prior
to the maintenance activity being undertaken. The emissions that result from
maintenance activities are generally considered to be deliberate because they
are planned and controlled.
STATES WORKBOOK D3-2 November 1992
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Equipment Exhaust: Several types of equipment commonly found in natural gas
systems emit methane on a regular ongoing basis. A variety of pressure-activated
devices are used to control valves and other control equipment Some of these
devices operate off of the pressurized gas stream, and consequently emit gas into the
atmosphere. Additionally, there are various gas-powered engines used for gas
compression and other purposes. These emissions should be accounted for in the
energy section since they are a direct result of energy consumption, but should also
be identified separately in order to determine total emissions from natural gas systems.
Upsets and Mishaps; Unintended pressure surges or inadvertent breeches of
pipelines or other equipment are referred to as upsets and mishaps. These
events, which occur at irregular intervals, often result in unintended emissions.
Emissions associated with upsets at production and processing facilities are
generally included in estimates of venting and flaring that are discussed above.
The relative importance of these various types of emissions will depend on the individual system
design and operation. It is likely that there will be considerable variation depending on how and
when the oil and gas systems were built
WORKBOOK METHODOLOGY
The workbook methodology is limited to estimating CO2 and methane emissions from venting
and flaring. As discussed in the section on CO2 emissions from fossil fuel use, the carbon from CH4
emissions due to flaring and venting were not accounted for in the CO2 discussion since this portion
of energy production and consumption is typically not included in aggregate energy statistics.
Nevertheless, these carbon emissions should be included in total CO2 emissions since the carbon is
oxidized immediately if flared or oxidizes fairly rapidly in the atmosphere (within about 10 years) if
vented as CH4. To be consistent with the accounting followed for other energy categories, separate
estimates should be calculated for (1) total carbon vented or flared as CO2, and (2) the subset of
carbon vented as CH4.
The proposed methodology to determine total CO2 emissions involves: (1) estimating the
total quantity of natural gas flared or vented, (2) estimating the carbon content of the gas, and 3)
multiplying the gas quantity by carbon content to get carbon emissions.
Marland and Rotty (1984) estimated that the carbon content of natural gas flared was 0.0328
pounds of C per cubic foot which is somewhat higher than their estimate of 0.0319 Ibs C/ft3 used for
dry gas. This is due to the fact that natural gas liquids have not been extracted from the flared gas
as is commonly done with the dry gas sold to markets.
To determine the subset of total carbon that is vented as CH4, these additional steps should
be followed: (4) since practices vary, of the total carbon flared and vented [from step 3 above], each
state should estimate the percentage that is vented, (5) estimate total carbon vented by multiplying
total carbon emissions from step 3 times the percentage vented from step 4, (6) estimate the
proportion of vented gas that is methane (value is about 90% for the U.S.), and (7) calculate total
CH4 emissions from venting by multiplying total carbon vented times the proportion of the vented
carbon that is methane times 16/12 to convert carbon to the molecular weight of CH4.
STATES WORKBOOK D3-3 November 1992
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Table D3-1 illustrates the calculations required to estimate total carbon emissions (as C02
and CH4) from gas flaring and venting and the portion of this carbon that is vented as CH4. The
estimate of the quantity of gas flared and vented (10* rr) is multiplied by the carbon content of the
gas (Ibs C/ft3) to get emissions (million tons C). This value is multiplied by the estimate of the
percentage that is vented times the methane content of the natural gas times 16/12 to determine the
amount of methane that is vented. Since the CO2 emission estimate is determined in units of carbon,
it should be multiplied by 44/12 to convert to a full molecular basis.
Table D3-1
Emission Calculations for
Gas Flaring and Venting
Total CQ2 Emissions
1) Gas Vented or Flared (106 ft3) calc
2) Carbon Content of Gas (Ibs C/106 ft3) 32,800
3) Carbon Emissions (tons Q (1) x (2) + 2000
Subset of Emissions as CH1
4) Percentage Vented (%) calc
5) Total Carbon Vented (tons C) (3) X (4)
6) Percentage Methane <%) 90%
7) CH4 Emissions (tons CH4) (5) X (6) x (16/12)
calc = calculated by respondent
Units: 106 ft3 = million cubic feet; Ibs C/ft3 = pounds carbon per cubic
foot; t C = million tons carbon; t CH4 = million tons methane.
The emission estimation approach summarized in Table D3-1 provides a minimum
methodological framework for estimating emissions from oil and natural gas systems. As more
information is developed on emission pathways and emission factors, the estimation approaches
should be altered accordingly to reflect any recent developments.
STATES WORKBOOK D3-4 November 1992
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ALTERNATE METHODOLOGIES
To estimate emissions from oil and gas systems, it would be preferred to measure emissions
with precise instruments. However, due to the diverse nature of the various types of emissions and
the fact that many emissions occur periodically or unexpectedly, precise measurements are not
practical in most cases. Additionally, no single method for estimating emissions will be appropriate
for all the different types of emissions. Although much of the information required to estimate
emissions from oil and gas systems is not readily-available at this time, the data may be available for
the U.S. or will shortly be available from ongoing work. A minimum data set should be established
separately for oil and natural gas systems based on this information to estimate the various types of
emissions from the different system components identified above. These two minimum data sets are
summarized in Table D3-2.
The data sets summarized in Table D3-2 indicate the minimum amount of activity data needed
to estimate emissions. For each of the data items, an emission factor(s) would need to be developed.
It may be preferable to develop emission estimates for various aspects of oil and natural gas systems
by determining an emission factor by dividing their emission estimate by the activity data item listed
in Table D3-2 to determine an appropriate emission factor. For example, if a state estimated its
methane emissions for pre-production activities at 10 tons and the number of successfully drilled wells
at 1000, then the estimated emission factor would be 0.01 tons of methane per well. To be most
useful, the states providing these emission factors could characterize the most important aspects of
their systems and management practices in order to provide guidance to other states as to which
emission factors are most appropriate for each system component if state emission inventories are
ever shared. In this manner a series of emission factors could be developed from which states wishing
to use the basic method summarized here could select an emission factor that most closely
corresponds to their system design.
To assist in the development of appropriate emission factors, emission data are currently being
generated for a detailed U.S. inventory. This inventory could be used to develop the range of
emission factors for the activity data identified in Table D3-2. Information about the systems
examined in the detailed studies would need to be assembled, described, compared, and contrasted
so that states could identify pieces of these systems that are similar to theirs and select appropriate
emission factors.
In addition to the approach discussed above for estimating CO2 and CH4 emissions from
venting and flaring, there are other approaches that could be employed to determine oil and natural
gas system emissions more accurately. The following section discusses the range of methods identified
and the types of emissions for which they may be useful; the discussion focuses primarily on
applications for natural gas systems. These more complex approaches could be used for refining
emission estimates derived from the minimum methodology.
Gas Accounting Data
Gas accounting data are routinely developed during the operation of gas systems. These data
describe the quantities of gas that are produced, transported, injected, withdrawn, used, or sold at
various stages. Various instrumentation is used to measure pressure, temperatures, and flow rates
to develop these accounting data.
STATES WORKBOOK D3-5 November 1992
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Table D3-2
Minimum Data Sets for Oil and Natural Gas Systems*
System Component
Pre-production
Production and Centra]
Processing
Venting and Flaring
Transportation
Distribution
Oil System Data Set
Activity Data Needed
Number of successfully drilled wells
Number of operating wells
Level of oil production
Composition of the oil
Type of emission controls
Flaring losses
Composition of the oil
Quantity of oil transported by
ocean tanker or large pipeline
Quantity of oil distributed
Emission Factor
To be determined
To be determined
To be determined
To be determined
To be determined
To be determined
To be determined
To be determined
To be determined
System Component
Pre-production
Production and Central
Processing
Venting and Flaring
Transmission
Distribution
Natural Gas System Data Set
Activity Data Needed
Number of successfully drilled wells
Number of operating wells
Quantity of natural gas produced
Composition of the natural gas
Type of emission controls
Flaring losses
Composition of the gas (% methane)
Length of transmission piping
Length of pipe designed for town
(or wet) gas
Length of pipe designed for dry gas
To be determined
To be determined
To be determined
To be determined
To be determined
To be determined
To be determined
To be determined
To be determined
To be determined
a For each system component,
factor to determine emissions
the activity data level would be multiplied by the appropriate emission
for that component
STATES WORKBOOK
D3-6
November 1992
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Gas accounting data can be very useful for estimating emissions associated with routine
maintenance activities. The quantities of gas released during these activities are often estimated and
recorded using standardized procedures. For example, these data can be used to estimate gas that
is released during scheduled repair of transmission pipelines.
Routinely collected accounting data are often not adequate for estimating fugitive emissions
from production and distribution faculties (i.e., leaks from pipes and equipment). These emissions
are usually too small to be estimated because the meters and instruments used to measure gas flows
are not adequately precise to measure the small leaks that are generally encountered. Additionally,
other factors that are not well quantified (such as theft) reduce the precision of the routinely
collected gas accounting data.
Unlike routinely-collected accounting data, specially conducted measurement studies using
specially-designed and operated meters and instruments may be useful for estimating fugitive
emissions from designated sections of distribution systems. In conducting such studies, care must be
taken to ensure that the segments of distribution systems that are studied are representative of the
overall distribution system.
Emission Factors
Emission factors provide estimates of emissions on specific types of equipment over specified
periods of time, such as 0.1 cubic meter (m3) per hour from a given type of valve. Emission factors
are most commonly used to estimate fugitive emissions from equipment The most important
consideration in using emission factors is that care must be taken to ensure that the emission factors
are appropriate for the type of equipment in question, including the maintenance-practices that are
performed.
As described below there are several major sources of -emission factors for the types of
equipment commonly found in gas production fields and gas processing plants. To use the emission
factors, the numbers of components of various types are multiplied by the emission factors specific
to those types. The aggregate is an estimate of the total emissions.
Procedures have been developed to estimate emission factors quantitatively (Radian 1982).
These procedures generally require that:
the numbers of each type of component be counted (e.g., the number
of valves, etc.);
a sample of components be selected randomly (this selection is often
stratified using an indication of the leak rate);
the actual emission rates (e.g., in m3 per hour) from the individual
components selected are measured;
the measurements are used to develop emission factors that consider
the different component types and represent the weighting of the
strata; and
STATES WORKBOOK D3-7 November 1992
-------
the emission factors are applied to the total number of components
identified.
The advantage of developing emission factors is that the approach reflects the condition of equipment
and maintenance practices at specific facilities and is based on actual physical measurements and
reliable statistical techniques. The drawback of the approach is that it can be costly and can produce
estimates that have associated with them a wide range of uncertainty. Currently, U.S. EPA and GRI
(Gas Research Institute) are developing a system that will attempt to quantify uncertainty, especially
on areas where emissions and uncertainty appear to be high. This is part of a larger study by EPA
and GRI to be completed in 1992 to develop methodology for measuring and estimating methane
emissions from the U.S. natural gas industry.
Emission factors can also be used to estimate emissions from instruments powered by
compressed gas. The number of various types of instruments must be obtained, and the emissions
associated with the use of each type must be estimated or obtained from manufacturers.
Leak Repair Data
Leak detection and repair programs generally produce data on the number of various types
of leaks detected and repaired in distribution systems. Unfortunately, the quantity of gas emitted
front individual leaks is generally not available. Therefore, based on the data that are usually
collected, emissions from line leaks are not quantifiable.
A recent study by Pacific Gas and Electric developed methods to quantify such leaks (Cowgill
and Waller, 1990). During the study, 20 leak measurements were performed and used as a basis to
quantify the gas emitted from pipe leaks. Additional measurements, however, are needed to improve
the basis for using this technique.
Activity Studies
The frequency with which specific activities are undertaken can be used with estimates of
emissions from those activities to estimate emissions. For example, the emissions associated with
starting a compressor or scraping a given length of pipeline can be estimated based on the procedures
used. The frequency with which these activities are undertaken for, say, a typical compressor station,
can be estimated based on operating records. The total emissions can then be estimated as the
emissions per occurrence times the number of occurrences per year.
Table D3-3 identifies the approaches that are most appropriate for the source/emissions type
combinations. Of the entries in the table, fugitive emissions from distribution systems will likely be
the most difficult to estimate. Very little data are available, and these emissions may be significant,
depending on the system. All the methods described here must consider the portion of the gas that
is actually methane. Additionally, all estimates must be careful to consider and report the
temperatures and pressures at which the volumes are estimated. All estimates should therefore be
reported on a mass basis (e.g.t in metric tons) as well as a volume basis.
STATES WORKBOOK D3-8 November 1992
-------
DATA SOURCES
No individual data source will provide all the information needed to estimate emissions from
oil and gas systems. Several studies have been undertaken that use one or more of the techniques
described above including: Cowgill and Waller (1990), PSI (1989), PSI (1990), INGAA (1989), AGA
(1989), Barns and Edmonds (1990), Radian (1982), Rockwell (1980), Lillie (1989), ADL (1989),
Schneider-Fresenius et aL (1989), and Thorell -I- VBB (1989). These studies are instructive
regarding how emissions can be estimated and the uncertainties inherent in the exercise.
Radian (1982) and Rockwell (1980) both involve the development and application of
emissions factors for estimating fugitive emissions. EPA (1985) has a summary of emissions factors
for various types of equipment, including compressors. Detailed emissions data from equipment that
can contribute to the development of emissions factors have been collected and reported in a variety
of studies including: Martin and Thring (1989), Leslie et al. (1989), and EPA (1979).
The studies by PSI (1989 and 1990) include assessments of activity levels and emissions per
activity occurrence. Cowgill and Waller (1990) is by far the most detailed and complete analysis of
the fate of gas in a system. This study goes into considerable depth in applying a wide variety of
approaches discussed above.
In addition to these various emissions-related studies, gas companies and agencies responsible
for gas safety and operations are important sources of data. Finally, McAllister (1988) is a useful
source of methods for estimating gas flows under various circumstances.
EVALUATION
The ability to estimate emissions from oil and gas systems will be hampered by the general
Jack of data on the factors that lead to emissions. These systems are very diverse and variable.
Emissions generally cannot be estimated with simple assumptions or rules of thumb. Nevertheless,
recent studies indicate that the main types of emissions can be assessed with fairly straightforward
approaches.
The most difficult emissions to estimate will likely be fugitive emissions from distribution
systems. Gas accounting data are generally not adequately precise in order to estimate these
emissions. Because these emissions can be quite important, specially-conducted measurement studies
using specially-designed and operated meters and instruments may be required. Such studies would
improve considerably the basis for estimating emissions from this source.
Additional observations have been offered, including:
Emissions from fuel used in compressor stations and related
equipment for providing the pressure to transport the fuel over land
were not included in the fuel production category but part of the
stationary combustion category. Each state should ensure that the
energy consumed for these activities is accounted for accordingly.
STATES WORKBOOK D3-9 November 1992
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TABLE J33-3
ESTIMATING TECHNIQUES BY SOURCE AND EMISSIONS TYPE
SOURCE
PRE-
PRODUCTION
PRODUCTION
AND CENTRAL
PROCESSING
VENTING AND
FLARING
TRANSMISSION
DISTRIBUTION
FUGITIVE
EMISSIONS
Emission Factors
Emission Factors
Gas Accounting
Data
Emission Factors
Gas Accounting
Data
Bn^on Facto*
Specially-conducted
Measurement
Studies
Leak Repair Data
MAINTENANCE
EMISSIONS
Gas Accounting
Data
Activity Studies
Gas Accounting
Data
Activity Studies
Gas Accounting
Data
Activity Studies
Gas Accounting
Data
Activity Studies
Activity Studies
EQUIPMENT
EXHAUST
Emission Factors
Gas Accounting
Data
Emission Factors
Gas Accounting
Data
Emission Factors
Gas Accounting
Data
^nissionPaoor,
Gas Accounting
Data
Emission Factors
UPSETS AND
MISHAPS
Gas Accounting
Data
Gas Accounting
Data
Activity Studies
Gas Accounting
Data
Activity Studies
Gas Accounting
Data
Activity Studies
Leak Repair Data
Activity Studies
STATES WORKBOOK
D3-10
November 1992
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More information is needed on non-methane VOC emission factors.
CO2 emissions from certain gas and oil fields could be significant but
are not well characterized Additional effort is needed to understand
these emissions.
The factors for methane emitted during incomplete combustion should
be reviewed.
Sources of information for obtaining the data outlined above need to
be investigated in greater detail.
REFERENCES
ADL (Arthur D. Little). 1989. Methane Emissions from the Oil and Gas Production Industries.
Prepared for Ruhrgas A.G. July.
AGA (American Gas Association). 1989. Survey of member interstate natural gas pipelines.
Arlington, Virginia.
Bams, D.W., and J.A. Edmonds. 1990. An Evaluation of the Relationship Between the Production
and Use of Energy and Atmospheric Methane Emissions. Prepared for the Office of Energy Research,
U.S. Department of Energy, Washington, D.C
Cowgill, R.M., and R.L. Waller. 1990. Unaccounted-for Gas Project. Pacific Gas and Electric
Company (PG&E), San Ramon, California.
Crutzen, PJ. 1987. Role of the tropics in atmospheric chemistry. In R. Dickenson (ed.)
Geophysiology of Amazonia. John Wiley and Sons, New York. 107-132.
DOE/EIA (Department.of Energy/Energy Information Administration). 1988. International Energy
Annual 1987. Energy Information Administration, Office of Energy Markets and End Use, U.S.
Department of Energy, Washington, D.C.
Ehhalt, D.H. 1974. The Atmospheric cycle of methane. Tellus 26(1-2)^8-70.
Hitchcock, D.R., and A.E. Wechsler. 1972. Biological Cycling of Atmospheric Trace Gases. Final
Report (NASW-2128) prepared by Arthur D. Little, Cambridge, Massachusetts.
INGAA (Interstates Natural Gas Association of America). 1989. Survey of member interstate
natural gas pipelines. Washington, D.C.
STATES WORKBOOK D3-11 November 1992
-------
Leslie, NJP., P.O. Gbassan, and EK. Knig. 1989. Baseline Characterization of Combustion Products
at the CR1Conventional Research House. Prepared for the Gas Research Institute, Chicago, Illinois.
August
Lillie, I.W. 1989. Emission of Methane by the Gas and Oil Producing Industry in the Federal Republic
of Germany in 1988. German Society for Petroleum Sources and Coal Chemistry, Hamburg,
Germany.
Marland, G., and R.M. Rotty. 1984. Carbon dioxide emissions from fossil fuels: A Procedure for
estimation and results for 1950-1981 Telius 36(6)^32-261.
Martin, N.L., and RJL Taring. 1989. Computer Database of Emission Data for Stationary
Reciprocating Natural Gas Engines and Gas Turbines in Use by the Gas Pipeline Transmission Industry,
Users Manual. Prepared for the Gas Research Institute, Chicago, Illinois. February.
McAllister, E.W. (ed). 1988. Pipe Line Rules of Thumb Handbook. Gulf Publishing Company,
Houston, Texas.
PSI (Pipeline Systems Incorporated). 1989. Annual Methane Emission Estimate of the Natural Gas
and Petroleum Systems in the United States. Prepared for the Global Change Division of the U.S.
EPA, Washington, D.C
PSI. 1990. Annual Methane Emission Estimate of the Natural Gas Systems in the United States: Phase
2. Prepared for the Global Change Division of the U.S. EPA, Washington, D.C
Radian Corporation. 1982. Frequency of Leak Occurrence and Emission Factors for Natural Gas
Liquid Plants. Prepared for the U.S. EPA (EMB Report No. 80-FOL-l). July.
Rockwell International. 1980. Fugitive Hydrocarbon Emissionsjrom Petroleum Production Operations.
Prepared for the American Petroleum Institute. March.
Schneider-Fresenius, W., R.A Hintz, U. Hoffman-Meienbrock, W. Klopffer, and J. Wittekind. 1989.
Determination of Methane Emission into the Atmosphere due to Losses in the Natural Gas Supply
System of the Federal Republic of Germany. Battelle-Institut c.V., Frankfurt, Germany. August
Seeliger, W., and G. Zimmermeyer. 1989. Private correspondence in ICF Resources (1990).
Seiler, W. 1984. Contribution of biological processes to the global budget'of CH4 in the atmosphere.
In Current Perspectives in Microbial Ecology. American Society for Microbiology, Washington, D.C
Selzer, H. 1990. .Anthropogenic methane emissions. In Proceedings from the International
Workshop on Methane Emissions from Natural Gas Systems, Coal Mining, and Waste Management
Systems. April 9-13, 1990. Washington, D.C
Thorell + VBB Energikonsulter AB. 1989. Releases of methane from Natural Gas Activity in
Sweden. Prepared for SwedeGas AB, Stockholm, Sweden. August
STATES WORKBOOK D3-12 November 1992
-------
U.S. EPA (U.S. Environmental Protection Agency). 1979. Emissions Assessment of Conventional
Stationary Combustion Systems. Industrial Environmental Research Laboratory (EPA-600/7-79-029b).
May.
U.S. EPA. 1985. Compilation of Air Pollutant Emission Factors. Office of Air Quality, Planning and
Standards (AP-42). September.
STATES WORKBOOK D3-13 November 1992
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-------
DISCUSSION 4
METHANE EMISSIONS FROM COAL MINING
OVERVIEW
The process of coal formation, commonly called coalification, inherently generates methane
and other byproducts. The formation of coal is a complex physio-chemical process occurring over
millions of years. The degree of coalification (defined by the rank of the coal) determines the
quantity of methane generated and, once generated, the amount of methane stored in coal is
controlled by the pressure and temperature of the coal seam and other, less well-defined
characteristics of the coal The methane will remain stored in the coal until the pressure on the coal
is reduced, which can occur through the erosion of overlying strata or the process of coal mining.
Once the methane has been released, it flows through the coal toward a pressure sink (such as a coal
mine) and into the atmosphere (ICF Resources, 1990). Global methane emissions from coal mining
account for an estimated 25 to 50 Tg of anthropogenic methane emissions (IPCC 1992).
The amount of CH4 generated during coal mining is primarily a function of coal rank and
depth, as well as other factors such as moisture. Coal rank represents the differences in the stages
of coal formation and is dependent on pressure and temperature of the coal seam; high coal ranks,
such as bituminous, contain more CH4 than low coal ranks, such as lignite. Depth is important
because it affects the pressure and temperature of the coal seam, which in turn determines how much
CH4 is generated during coal formation. If two coal seams have the same rank, the deeper seam will
hold larger amounts of CH4 because the pressure is greater at lower depths, all other things being
equal As a result, most methane released to the atmosphere from coal mining comes from
underground rather than surface mining.
In most underground coal mines, methane is removed by ventilating large quantities of air
through the mine and exhausting this air (typically containing a concentration of 1 % methane or less)
into the atmosphere. In some mines, however, more advance methane recovery systems may be used
to supplement the ventilation systems and ensure mine safety. These recovery systems typically
produce a higher concentration product, ranging from 35 to over 95 percent methane. Although this
recovered methane could be used as an energy source, historically very little has been collected and
used as fuel. Recent technological innovations are increasing the amount of methane that can be
recovered during coal mining and the options available to use it Thus, methane emissions could be
reduced from this source in the future.
A portion of the CH4 emitted from coal mining comes from post-mining activities such as coal
processing, transportation, and utilization. Coal processing involves the breaking, crushing, and
thermal drying of coal, making it acceptable for sale. Methane is released mainly because the
increased surface area allows more CH4 to desorb from the coal. Transportation of the coal
contributes to CH4 emissions, because CH4 desorbs directly from the coal to the atmosphere while
in transit (e.g., in railroad cars). Utilization of metallurgical coal also emits methane. For instance,
in metallurgical coke production coal is crushed to a particle size of less than 5 mm, vastly increasing
the surface area of the coal and allowing more CH4 to desorb. During the coking process, methane,
carbon dioxide, and other volatile gases are released. In modern coke ovens, this gas is typically
collected and utilized as a fuel source, but in older coke ovens, coke gas is vented to the atmosphere
(ICF Resources, 1990).
STATES WORKBOOK D4-1 November 1992
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WORKBOOK METHODOLOGY
The approach suggested for the workbook calculations is based on the coal mining emissions
estimates in the report Anthropogenic Methane Emissions in the U.S. (U.S. EPA 1992). The method
develops different methane emissions coefficients per ton of coal produced from underground mines
and surface mines and for post-mining emissions.
Underground Mines
The emissions coefficients for underground mines were developed by using emissions data
from 1988. Methane emissions from underground mining includes: (1) measured methane emissions
in the ventilation air at the gassiest underground mines; (2) estimated ventilation emissions from
mines for which measurements were not made; and, (3) estimated degasification system emissions.
Ventilation Emissions
Measured Ventilation Emissions. Methane emissions in ventilation air are available from
the Mine Safety and Health Administration (MSHA) for about 200 of the gassiest U.S. underground
coal mines. A database compiled from 1988 MSHA inspection data by the US. Bureau of Mines
(USBM) reports the emissions of methane from each mine with emissions exceeding 100,000 cubic
feet per day in ventilation air.1 About one-third of all active U.S. underground mines are included
in the USBM database. The reported methane emissions were used for ventilation air estimates for
those mines included in the USBM database.
Estimated Ventilation Emissions. Methane emissions from ventilation systems were
estimated for the underground mines not included in the USBM database. These other mines were
classified into three categories: Active Mines with Detectable Methane Emissions; Active Mines with
Non-Detectable Methane Emissions; and Inactive or Abandoned Mines. Estimation methodologies
were developed based on information provided by USBM and MSHA about their characteristics and
regulatory treatment. The estimated ventilation emissions for these mines represented less than 2
percent of measured ventilation emissions in 1988. This factor was applied to the actual ventilation
emissions for each coal basin.
Degasification System Emissions
Specific information on methane emissions from the degasification systems in place at U.S.
coal mines is not currently available because coal mine owner/operators are not required to report
emissions from these systems. In fact, without close examination of the mine ventilation plans
provided to MSHA for each mine, it is difficult to confirm which mines have degasification systems
in place.
Degasification system emissions were estimated for mines known or believed to have such
1 Trevits, Finfinger, and Lascola. 1991. "Evaluation of U.S. Coal Mine Emissions," in Proceedings
of the Fifth U.S. Mine Ventilation Symposium Society for Muling, Metallurgy and Exploration, Inc.
Littleton, Colorado.
STATES WORKBOOK D4-2 November 1992
-------
systems in place.2 Low and high estimates were developed based on information about likely coal
mine degasification strategies and on conditions in various coal basins. The percentage of methane
liberations assumed to be recovered by degasification systems at mines in different basins is: Northern
Appalachian and Illinois (30% to 65%) and Central Appalachian, Black Warrior, and Western (40%
to 65%). Known recovery factors were applied to those mines that reported the methane recovery
from their degasification systems (i.e., those mines that sold the gas to pipelines). The recovery
factors were applied to the measured ventilation emissions of the estimated 32 mines with
degasification systems in place in 1988 to estimate total emissions.
Once ventilation and degasification emissions were calculated for individual mines, total
emissions for each basin could be calculated. These emissions were then divided by total coal
production in 1988 for each basin to determine the estimated emissions factor per ton of coal mined.
The emissions factors arc:
Northern Appalachian: 450 to 780 cf/ton
Central Appalachian: 220 to 330 cf/ton
Black Warrior: 2,500 cf/ton
Illinois Basin: 160 to 190 cf/ton
Rockies & Southwest: 410 to 570 cf/ton
These emissions factors are recommended in the workbook, except that emissions factors for the
Northern and Central Appalachian basin are combined because a few states are part of both basins.
Surface Mines
Measurements of methane emissions from surface mines arc currently unavailable, although
a field measurement study is underway to better quantify emissions from this source.3 In the
absence of measurements, emissions were estimated using reported methane contents for the surface
coals mined jn each coal basin. For each coal basin, the estimated methane content of the coal was
multiplied by an emission factor and by the basin's surface coal production. In the low case, an
emission factor of 1 was used; that is, it was assumed that only the methane actually contained in the
mined coal seams would be emitted. In the high case, however, it was assumed that actual emissions
would be 3 times greater than the methane content of the target coal seam due to the release of
methane from the surrounding strata.4
In the suggested workbook methodology, average, rather than basin specific, surface mining
emissions coefficients are recommended. These emissions coefficients are 15 cubic feet per ton (low)
and 150 cubic feet per ton (high).
2 This list was developed based on discussions with USBM and MSHA officials, industry
representatives and literature review.
3 This study is being done by the U.S. EPA*s Office of Research and Development
* This assumption is consistent with the methodology developed by Environment Canada in their
report on greenhouse gas emissions. (Environment Canada 1992.) Preliminary results of the U.S.
EPA study indicate that the factor could be as high as five (Kirchgessner et al 1992).
STATES WORKBOOK D4-3 November 1992
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Post-Mining Emissions
The methane emitted during the post-mining transportation and handling of coal has not been
systematically measured or evaluated. Previous analyses have estimated that 25 to 40 percent of the
in-situ methane content of extracted coal would be released to the atmosphere after the coal leaves
the mine. British Coal, for example, estimates that post-mining emissions are 40 percent of the in-situ
content because their coals have low permeability and the gas desorbs slowly.5 Similarly,
Environment Canada estimates that only 54 percent of the methane contained in their surface mined
coals is released during mining.6
In the absence of actual measurements for U.S. .coals, post-mining emissions were estimated
to range from 25 to 40 percent The low case estimate of 25 percent represents a conservative
assumption, while the high case is more consistent with experience in other countries. For each coal
basin, these emissions factors were applied to the methane contents reported for surface and
underground coals. For the recommended workbook coefficients, average post-mining emissions for
all basins were developed based on the calculated basin-specific estimates. These average emissions
coefficients are 3 cubic feet per ton (low) and 30 cubic feet per ton (high).
ALTERNATE METHODOLOGY
The most precise method for estimating state methane emissions from coal mining is to
estimate emissions from underground mines on a mine by mine basis. This approach is possible
because methane emissions from ventilation systems at underground mines are measured by the Mine
Safety and Health Administration (MSHA). However, MSHA is not required to monitor emissions
from degasification systems and states would need to determine the number of mines in their state
with such systems in place. Furthermore, emissions from surface mines and post-mining emissions
would still need to be estimated using basin specific emissions factors for each ton of coal mined.
The steps for calculating emissions using this more detailed approach are as follows:
1. Ventilation System Emissions from Underground Mines. States would need to consult
an in-state source or the U.S. Bureau of Mines, which periodically provides reports
on underground coal mines that emit more than 100,000 cf of methane per day from
their ventilation systems (the USBM reports are based on MSHA data). For mines
that do not emit less than this amount, ventilation emissions should be assumed to be
negligible.
2. Degasification Emissions from Underground Mines. States would need to identify
mines with these systems in place. However, mine owners are not required to report
whether they have such systems. U.S. EPA (1992) contains a list of 32 mines that are
either known or believed to have degasification systems in place in 1988. States could
5 Quantification of Methane Emissions from British Coal Mine Sources, report produced for the
Working Group on Methane Emissions, the Watt Committee on Energy (1991).
6 Canada's Greenhouse Gas Emissions Estimates for 1990. Draft April, 1992.
STATES WORKBOOK D4-4 November 1992
-------
use this list in order to estimate the number of mines using degasification systems.
Emissions from degasification systems can be assumed to represent from about 35 to
60 percent of total emissions. Accordingly, for those mines with degasification systems
in place, total emissions may be calculated by dividing ventilation emissions (step 1
above) by (1 35) = .65 for a low estimate or by (1 - .6) = .4 for a high estimate.
3. Surface Mine Emissions. Emissions from surface mines would be estimated by
multiplying surface coal production by the methane content shown in Table D4-1.
States that have coal seams located in more than one basin would need to determine
the portion of production accounted for by each basin and then multiply production
from each basin by the appropriate methane content coefficient. To account for the
uncertainty associated with estimated surface mine emissions, a range of from 1 (low
estimate) to 3 (high estimate) times the methane content should be used.
4. Post-mining Emissions. Post mining emissions may be calculated by multiplying
surface and underground coal production by the appropriate average methane content
shown in Table D4-1. This value should then be multiplied by 20 percent (for a low
estimate) and 40 percent (for a high estimate).
These calculations require more data and will be more time-consuming to complete than the
recommended workbook method. However, they should lead to more precise estimates of state coal
mine methane emissions.
Table D4-1
Average Methane Contents of Underground and Surface Coal
Underground Coal
Basin
Northern Appalachian
Central Appalachian
'Warrior
Piceance
San Juan
Illinois
Uinta
Green River
Pennsylvania Anthracite Fields
CPlon1
173
333
32.1
25.6
22.8
5.8
4.2
4.2
14.1
Surface Coal
Basin or State
Appalachian (including Warrior)
Illinois
Powder River
Artoma
San Juan
Alaska
Arizona
California
Louisiana
North Dakota
Texas
Washington
Of/ton1
5.0
3.9
03
10.9
1.5
03
1.6
3.9
03
03
03
03
Source: U.S. EPA (1990).
STATES WORKBOOK
D4-5
November 1992
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Other methodologies for estimating coal mine methane emissions have focussed primarily on
estimating ventilation system emissions. In general, methane emissions have been calculated based
on total coal production and an emissions coefficent that described the total amount of methane
released per ton of coal T"""*** This amount of methane is greater than the amount contained in
the coal since methane is released from the surrounding strata. Specifically, this approach is defined
by:
Total CH4 Emissions (tons) = CH4 Emissions Coefficient (m3 CH4/ton coal mined) x Coal
production (ton) x Conversion Factor7
This general methodology is used in a number of approaches, which are shown in Table D4-2.
However, the approaches vary by the level of detail on CH4 content by coal type and by the depth
of the coal extracted Also, since estimation approaches applied to date have often relied on data
from ventilation systems to determine emission releases, these methods are most appropriately viewed
as representative of ventilation system emissions only. Note that several studies are based on data
from countries other than the U.S.
Table D4-2 summarizes key variables from the available studies and the total CH4 emissions
estimates determined by each study. The earlier approaches typically assume one CH4 emissions
coefficient for all coal produced, while more recent approaches provide more detail on how the rate
of CH4 emissions may vary. For example, Koyaraa (1963) uses a single assumption on methane
content and applied it to all hard coal production for 1960 to obtain a global methane emission
estimate. ICF Resources (1990), on the other hand, modifies an approach originally developed by
Kissell et al. (1973) to determine the total amount of methane released from coal mining (including
from the surrounding rock strata). ICF Resources uses data on in-situ methane contents (i.e., the
methane content within the coal seam only) for different coal basins in the U.S. from underground-
and surface-mining operations and estimates the relationship between in-situ methane content and
methane emissions from ventilation systems to determine an emission coefficient that can be varied
ty coal seam.*
The studies summarized in Table D4-2 vary due to differences in emission coefficients. For
example, Koyama (1963) applies a single coal-field gas production rate of 21 cm3 of gas/g coal to
global hard coal production in 1960, and assumes that 93% of the gas is methane. Seiler (1984) uses
Koyama's methodology but applies more recent coal production data. Hitchcock and Wechsler (1972)
consider a range of methane production rates (i.e., emission coefficients) of 5-17.7 m3/ton of coal.
These studies do not, however, distinguish among different coal types or mining methods (i.e.,
underground or surface mines as does the ICF Resources (1990) report
7To convert the volume of CH4 to a weight measurement, the density of CH4 is required. The
density of CH4 is 1.49 x 109 cubic meters of CH4 per 1 million metric tons.
8In-situ methane content is the actual amount of methane in the coal in the coal seam, defined
in units of m3 CH4/ton coal. These data may not be included in standard coal quality analyses,
although measurements may be made prior to mining.
STATES WORKBOOK D4-6 November 1992
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Table D4-2. Comparison of Previous Coal Mining-Related Emission Estimates
Study Authors
Koyama (1963)
Hitchcock and Wecbsler (1972)
Ehhalt (1974)2
Seller (1984)1
Crutzen (1987)
Seeliger and Zimmenneyer (1989)
Selzer (1990)
ICF Resources (1990)
Total CH«
Emissions
(10*
tonsjyr)
20
8-28
8-28
30
34
24
29
33-64
Average CH«
Content
(tnVton)
17.7
5-17.7
5-17.7
17.7
18-19
14
14
Z5 Surface;
27.1
Underground
Type of Coal
Included in
FfftimfltfE
Hard Coal
only1
Hard &. Brown
Coal
Hard & Brown
Coal
Hard Coal
only1
Hard Coal
only1
Hard Coal
only1
Hard Coal
only1
Hard & Brown
Coal
Year
of Coal
Pro-
duction
Data
1960
1967
1967
1967
N/A
1987
N/A
1987
Source of
Emission
Factor Data
Original
Original; Uses
Koyama for
upper range
From
Hitchcock &
Wechsler
(thus Koyama)
Koyama
From Noack,
D.K., private
communication
Original
Original
Original
1 Coal type not specified. Coal tonnage values approximately match hard coal (bituminous and anthracite) production only.
2 These studies did not conduct original research on coalbcd methane emissions, but relied on data in the other studies to
estimate emissions.
Source: ICF Resources, 1990, except Sclzcr, 1990.
REFERENCES
Environment Canada. 1992. Canada's Greenhouse Gas Emissions Estimates for 1990. Draft April,
1992.
Grau ID, R.H. 1987. An Overview of Methane Liberations from U.S. Coal Mines in the Last 15 Years.
Third U.S. Mine Ventilation Symposium, October 12-14,1987. University Park, Pennsylvania. 251-
255.
STATES WORKBOOK
D4-7
November 1992
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Grau m, RJJ., and J.C LaScoIa. 1980. Methane Emissions from U.S. Coal Mines in 1980. U.S.
Bureau of Mines Circular 8987, U.S. Department of the Interior, Washington, D.C 13 pp.
ICF Resources (Boyer H, CM., J.R. Kelafant, V.A. Kuuskraa, and K.C Manger). 1990. Methane
Emissions from Coal Mining: Issues and Opportunities for Reduction. ICF Resources, Fairfax.
Virginia. September.
Kirchgessner, D.A., S.D. Piccot, and A. Chadha. 1992. Estimation of Methane Emissions from a
Surface Coal Mine Using Open-Path FT1R Spectroscopy and Modeling Techniques. Chemosphere.
(In Press).
Kissell, F.N., CM. McCulloch, and CH. Elder. 1973. The Direct Method for Determining Methane
Content of Coalbeds for Ventilation Design. U.S. Bureau of Mines Information Circular 7767, U.S.
Department of the Interior, Washington, D.C 17 pp.
Koyama, T. 1963. Gaseous metabolism in lake sediment and paddy soils and the production of
atmospheric methane and hydrogen. Journal of Geophysical Research 68(13)^971-3973.
Selzer, H. 1990. Anthropogen methane emissions. In Proceedings from the International Workshop
on Methane Emissions from Natural Gas Systems, Coal Mining, and Waste Management Systems.
April 9-13, 1990. Washington, D.C
USEPA (U.S. Environmental Protection Agency). 1990. Methane Emissions From Coal Mining:
Issues and Opportunities for Reduction. Prepared by ICF Resources Incorporated for Office of Air
and Radiation, USEPA, Washington, DC
U.S. Environmental Protection Agency. 1992. Anthropogenic Methane Emissions in the United States.
Office of Air and Radiation. Draft Report. October 1992.
STATES WORKBOOK D4-8 November 1992
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DISCUSSION 5
METHANE AND CARBON DIOXIDE EMISSIONS FROM LANDFILLS
This discussion chapter primarily focusses on estimating methane emissions from landfills.
While landfill gas contains roughly equal amounts of methane and CO2, landfill CO2 emissions are
small compared to emissions from other sources discussed in this report However, landfills represent
one of the major anthropogenic sources of methane emissions in the U.S. and globally. Moreover,
methane is a more potent greenhouse gas than CO2 (see, for example, discussion on the relative
GWPs in the Introduction to this report).1 Therefore, relatively small quantities of methane
emissions have large implications for global warming.
OVERVIEW i
Methane (CH4) and Carbon Dioxide (CO^ are produced from anaerobic decomposition of
organic matter in landfills by methanogenic bacteria. Organic waste first decomposes aerobically (in
the presence of oxygen) and is then attacked by anaerobic non-methanogenic bacteria, which convert
organic material to simpler forms like cellulose, amino acids, sugars, and fats. These simple
substances are further broken down to gases and short-chain organic compounds (H2, COj,
CH3COOH, HCOOH, and CH3OH), which form the substrates for methanogenic bacteria. The
resulting biogas consists of approximately 50% CO2 and 50% CH4 by volume, although the
percentage of CO2 may be smaller because some CO2 dissolves in landfill water (Bingemer and
Crutzen, 1987). Additionally, some landfills practice flare recovered landfill gas, which converts the
methane portion of the gas to
Numerous factors affect the amount of CH4 and CO2 produced in landfills. The factors may
be divided into two general categories: management practices and physical factors.
Management Practices Physical Factors
Waste Management Type (portion of waste Waste Composition
that is landfilled) Moisture Content
Density of Refuse Leachate pH
Particle Size of Refuse Nutrients
Landfill Temperature
Municipal solid waste (MSW) constitutes a significant portion of all types of waste produced in the
United States and also the waste deposited in landfills that produce methane.2 The two types of
1 For example, on a gram for gram basis, methane's direct impact on global warming is about 11
times greater than CO2 over a 100 year time period (IPCC, 1992).
2Other types of waste that may produce methane in landfills are hazardous and industrial solid
waste and agricultural waste. Hazardous and industrial waste landfills may contain compounds that
will result in a low pH environment toxic to the methanogenic bacteria. Agricultural waste, if
landfilled, could potentially be a significant source of methane emissions but is typically not deposited
where anaerobic conditions develop (see Bingemer and Crutzen, 1987).
STATES WORKBOOK D5-1 November 1992
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waste management practices that lead to methane production are open dumping and sanitary
landGlling. Since CH4 production from open dumping, or waste piles, is highly uncertain and based
on anecdotal evidence, and since the amount of waste that is openly dumped in the United States
is negligible, CH4 emissions from open dumping is assumed to be zero.
In sanitary landfills, a tightly packed, anaerobic environment favorable for landfill gas
production is created as compacted waste is spread evenly over the active area of the landfill and
covered with some type of nonporous soil (e.g., clay). In order to avoid discrepancies over the term
"landfill," this definition of sanitary landfill will be used in the methodology described below.
Other variables of management practices that affect CH4 and CO2 generation are density and
particle size of refuse. By increasing density, a greater mass can be placed into a specified volume.
As density increases, the degree of saturation (t.e., the ability to absorb water) will increase due to
greater mass, which can lead to more gas production per unit volume (Pacey and DeGier, 1986).
One way to increase density is by shredding refuse. Shredding not only increases density, but also
reduces particle size, which results in a greater surface area exposed to bacterial activity, moisture,
and nutrients. In addition, if shredded refuse is spread evenly in thin layers (30 cm) and then
compacted, size could be further reduced. Extremely dense refuse (i.e., baled refuse), however,
cannot be penetrated by water, and consequently, may produce less gas (Pacey and DeGier, 1986).
Other variables that may affect emissions are the design and size of the landfill and the use of cover
soils.
The actual composition of the municipal solid waste is very important in determining the
amount of landfilled gas produced. Municipal solid waste supplies the necessary starting material for
methane generation in landfills by providing degradable organic carbon (DOC) with which
methanogenic bacteria interact to produce landGll gas.3 For the most part, the majority of waste in
the United States is paper and paper products, which contain a higher carbon content than food, for
example (40% by weight in Bingemer and Crutzen, 1987), and will therefore produce more CH4.
One of the foremost physical factors influencing landfill gas production, aside from the waste
itself, is the moisture content of the landfill environment (Pacey and DeGier, 1986). Moisture is
essential to anaerobic decomposition and the life of methanogenic bacteria. Water serves as a
transport medium for nutrients, bacteria, and alkaline substances within the refuse (Pacey and
DeGier, 1986). In a sanitary landfill the moisture content will affect the rate at which landfill gas is
produced because wastes are exposed to more bacteria as moisture increases. Unfortunately, no
explicit functional relationship exists between moisture content of the landfill environment and gas
production estimates (Emcon Associates, 1982).4 The moisture of the refuse, however, can be
3DOC is biochemically decomposed to form substrates and can be divided into two parts:
dissimilated and assimilated. The dissimilated fraction is the portion of carbon in substrates that is
converted to landOil gas (i.e., CO2 and CH4), and the assimilated fraction is the remainder of carbon
that is used to produce new microbial cell material (Tabasaran, 1981).
4In a study recently conducted by U.S. EPA's Office of Solid Waste, a correlation between landfill
gas generation rate and precipitation rate was obtained (no correlation between precipitation rate
and moisture content in the landOli was evaluated). Based on data from 12 "wet" landfills
(precipitation of 0.58 m or more) and data from 8 "dry" landfills (precipitation of less than 0.58 m),
STATES WORKBOOK D5-2 November 1992
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determined by analyzing the composition of the landfilled MSW and determining the percentage of
Vet refuse" (i.e., food wastes) and "dry refuse" (i.e., paper waste). Ahuja (1990) attempts to include
the percentage of dry refuse in the total amount of MSW landfilled, which contains the DOC
available for methane production, in his methodology to estimate methane emissions; this
methodology is discussed in the next section.
Other factors that are important but have not been factored into any emission estimate due
to the lack of data include the leachate pH and nutrient availability. "The optimal pH for gas
production is near neutral, between 6.8 and 12, which is not usually reached for several years (Pacey
and DeGier, 1986). Methane generation is not inhibited unless the environment is very acidic (pH
<6.0). Alkaline substances, transported in water, help to balance the pH level and hinder the
formation of organic acids, which in large concentrations decrease methane production. Nutrients
are essential to the life and growth of bacteria.
Temperature, unlike leachate pH, can be related to the amount of degradable organic carbon
that will generate landfill gas (i.e., the fraction of DOC dissimilated). At temperatures below 10-15°C,
methane production is drastically reduced (Pacey and DeGier, 1986). Because the majority of
methane production occurs in the deeper layers of the landfill, where heat is generated from
anaerobic decomposition, temperatures typically range between 25-40°C An average of 35°C can be
expected within the anaerobic zone (2-4 m) (Gunnerson and Stuckey, 1986, in Bingemer and Crutzen,
1987) and will result in 77% dissimilated DOC5 At extremely high temperatures (above 60°C)
methane generation usually ceases (Pacey and DeGier, 1986).
Landfill gas recovery can be an important factor in reducing CH4 emissions from landfills as
well as provide a source of renewable energy. Landfill gas recovery systems are increasing, especially
in the United States, and the CH4 generated from landfills is being captured as an energy source.
Currently, there are 242 sites in 20 nations where landfill gas is captured and its energy contents
exploited (Richards, 1989). The U.S. is by far the biggest collector and user of landfill gas, with the
UK and Germany following. It would be beneficial to estimate the amount of CH4 casting in the
recovered landfill gas in order to subtract it from total CH4 emitted from that state's landfills. This
has been accounted for in the methodology, although each state will need to estimate its own quantity
of CH4 capture from biogas recovery sites.
Refuse may be disposed of by other management practices that do not produce methane such
as incineration, materials recovery/recycling, and composting. These alternative methods of disposal
may be more attractive than sanitary landfilling without gas recovery systems as land availability
declines and potential health and environmental risks of landfilling increases in the U.S. For example,
Japan prefers incineration over landfilling; about 73% of Japan's waste is disposed of by incineration
and only 23% by sanitary landfilling (Hayakawa, 1990, in Thorneloe, 1991).
landfill gas emissions from "wet" landfills were -2.6 times greater than emissions from "dry" landfills
(Thorneloe, 1990).
5Landfill temperature is related to the amount of DOC that is dissimilated to produce biogas by
the relationship: Cc / Cp = (0.014 T + 0.28), where Cc = carbon converted to biogas, Cj = total
carbon compounds in substrates, and T = landfill temperature (Tabasaran, 1981). From this
relationship, as temperature increases, so does the rate of gas formation.
STATES WORKBOOK D5-3 November 1992
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DESCRIPTION OF WORKBOOK METHODOLOGY
The simplest methodology for estimating CH4 emissions from landfills is based on a mass
balance approach, where an instantaneous release of methane is assumed to enter the atmosphere
during the same year that refuse is placed in the landfill (Bingemer and Crutzen, 1987). Furthermore,
Bingemer and Crutzen do not consider subsequent releases of CH4 to the atmosphere from the MSW
placed in a landfill that will be emitted in future years nor from previous years since their approach
implicitly assumes that all waste placed into a landfill during the year emits all potential methane
immediately. Bingemer and Crutzen use four economic regions: U.S7Canada/Australia, Other
OECD, USSR/E. Europe, and Developing Countries. Then they determine how much MSW is
produced for each region and how much of that MSW is degradable organic carbon. To calculate
the annual emissions from MSW, Bingemer and Crutzen used the following equation:
(1) Methane Emissions
Total MSW generated (Ibs/yr) x MSW landfilled (%) x DOC
in MSW (%) x Fraction Dissimilated DOC (%) x 0.5 Ibs
CH4/lb biogas x Conversion factor (16 Ibs CH4/12 Ib C) -
Recovered CH4 (Ibs/yr).
The MSW generation rates and composition data for the U.S. can be used to calculate methane
emissions instead of the regional factors for U.S./Canada/AustraIia (see Table 5-1). Currently, no
state-specific data are available, but each state can estimate its annual MSW generation rate and
percentage of MSW landfilied. MSW generation rates and percentage of MSW landfilled for the
U.S. have been estimated by the OECD (1989) as well and are presented in Table 5-1. Bingemer
and Crutzen's regional estimates are for 1980 and are outdated somewhat; the country-specific
estimates presented by OECD (1989) were taken from 1988 data or the nearest year to 1988 for
which data were available. The U.S. EPA's Office of Solid Waste has also provided MSW and MSW
landfilled figures.
Table 5-1
The United States' Waste Disposal, Composition, and Waste Generation
Source
Bingemer and Crutzen (1987)
EPA (1988)
OECD (1989)
Piccot et al. (1990)
WRI(1990)
Year
1980
1986
1985
1988
1983
% MSW
Landfilled
91
83.2
62
85
NA
% DOC of
MSW
22
NA
NA
21
NA
Waste Generation
(Ibs/cap/yr)
4.0
4.0
4.4
1.9
4.6
In another recent study country-level data were collected for 31 countries, representing 67%
of the global population, through literature review and personal communication (Piccot et al., 1990).
Piccot et al. determined country-specific 'factors of MSW generation rate per capita, waste
STATES WORKBOOK
D5-4
November 1992
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composition (used to calculate percentage of degradable organic carbon), and disposal practice
(percentage of waste landfilled) for the United States as well (Table 5-1).
While the method described was developed to estimate methane emissions from landfills, it
can also be used to approximate CO2 emissions because landfill gas contains roughly equal portions
of Ct>2 and methane. Assuming that the quantity of CO2 and methane in landfill gas are roughly
equal, CO2 emissions can be calculated by multiplying methane emissions by 44/16 to convert to tons
of CO2. Additional CO2 emissions may result when landfill methane is flared. In order to calculate
CO2 emissions from this source, the amount of landfill methane that is flared must be estimated.
Next, methane flared should be multiplied by 0.98 (an estimated 98% of methane flared will be
converted to CO^ and then by 44/16 to convert to
ALTERNATE METHODOLOGY
The methodology outlined by Ahuja (1990) is based on Bingemer and Crutzen's assumptions
but is more detailed due to the addition of a new variable - percentage of MSW that is dry refuse.
Using assumptions by Bingemer and Crutzen (1987), % MSW as dry refuse, and an average landfill
temperature of 35°C to derive the fraction of dissimulated DOC, methane emissions can be calculated
as follows (Ahuja, 1990):
(2) Methane Emissions = Total MSW generated (Ibs/yr) x MSW landfilled (%) x DOC
in MSW (%) x Dry Refuse (%) x Fraction dissimilated DOC
(%) x (0.5 Ibs CH4/Ib biogas) x Conversion factor (16 Ibs
CH4/12 Ib C) - Recovered CH4 (Ibs/yr).
A more complex method for estimating methane emissions from landfills is based on a first-
order kinetic model, the Scboll Canyon model, which considers timed releases of methane to the
atmosphere (Thorneloe, 1990). Best results are usually obtained when the model is applied to
individual landfills, but it can be applied to an entire country such as the U.S. Estimates have been
made for the U.S. using this model (e.g., Colt et al., 1990). Detailed information, such as waste
generation and composition, moisture content, pH, temperature, available nutrients, landfill's age,
size, type, and time since closure, is required to calculate emissions (Thoraeloe, 1990). This method
assumes that gas production will be highest upon initial placement of waste in the landfill, after a
certain negligible lag period during which anaerobic conditions are formed. The rate then decreases
exponentially (i.e., undergoes first-order decay) as the degradable organic carbon available decreases
(U.S. EPA, 1990). The model requires that MSW rates over the life of the landfill, or extended
period of time (e.g., 1960-1990), be used to estimate methane emissions more accurately. The model
equation and variables are described briefly below:
QCH4 = kxL0xRxe-kl
where, QcH4 = methane generation rate at year t (ft3AT),
L0 = potential methane generation capacity (ft3/tons of refuse),
R = quantity of waste landfilled (tons/yr),
k = methane generation rate constant (yr"1),
t = time since initial refuse placement (yr).
STATES WORKBOOK D5-5 November 1992
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Theoretically, Lg depends on the type of refuse only and is based on the chemical composition of
refuse and its biodegradability. The methane generation constant, k, determines how quickly the
methane generation rate decreases (U.S. EPA, 1990). The rate constant and the generation rate are
related; the higher the value of k, the faster the methane generation rate decreases over time. The
rate constant is affected by the same factors that affect L^, with the addition of temperature. Some
of these variables themselves, such as Lp and k, need to be calculated even before the equation can
be used, although some values have been determined (see, e.&, Barlaz and Ham, 1988, or EPA,
1990). To date no functional relationships have been determined among these key factors and a
better understanding of these factors is needed to more accurately calculate methane emissions from
landfills with this approach.
Given the lack of supporting data about most landfills (e.g., MSW generation rates dating
back to 1960, etc.) and the level of uncertainty associated with some of the variables, such as Lj, and
K, the detailed method of estimating emissions using the first-order kinetic analysis (Scholl Canyon
model) seems premature for state-level estimates at this time. If, on the other hand, the necessary
data were available to an individual state, CH4 emissions could be estimated using the Scholl Canyon
model. For the majority of states, therefore, the methodology expressed in either Equation (1) or
(2) is the recommended approach for estimating CH4 emissions from landfills.
Other sources of uncertainty in estimating CH4 emissions are the effects of climate on
methane emission rates and the impact of landfill design characteristics and maintenance procedures
(Piccot et al., 1990). Landfill gas collection facilities provide an opportunity to study the generation
of landfill gas in similarly operated facilities, with the goal of developing quantifiable relationships
between climate, waste quantity and composition, and gas generation. These relationships would be
developed by characterizing the waste streams (especially regarding quantity and composition), design,
and climate of these facilities, then correlating these data with facility landfill gas output (Piccot et
al., 1990).
AVAILABILITY OF DATA
In-state sources should be consulted to obtain data on total MSW generated and the amount
of methane recovered from landfills. Ideally, in-state data sources should also be used for waste
characteristics and waste management practices (e.g., percent of MSW that is landfilled; percent of
DOC contained in the MSW; and percent of DOC that is dissimilated). However, in many states,
such data may not be readily available. In such cases, the average default values indicated in the
workbook section should be used. Additionally, some data on waste generation, waste composition,
and waste disposal are available from EPA (1988).
REFERENCES
Ahuja, D. 1990. Estimating Regional Anthropogenic Emissions of Greenhouse Gases. Forthcoming
in The Indian Geosphere Biosphere Programme. Tata Energy Research Institute, New Delhi, and The
Bruce Co., Washington, D.C.
STATES WORKBOOK DS-6 November 1992
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Bhide, A.D., and B.B. Sundaresan. 1981. Solid Waste Management in Developing Countries. National
Environmental Engineering Research Institute, Nadpur, India. 210 pp.
Bingemer, H-G, and P J. Cnitzen. 1987. The production of methane from solid wastes. Journal of
Geophysical Research 92(D2)2181-2187.
Cointreau, S. J. 1984. Solid waste collection practice and planning in developing countries. In
Holmes, J.R. (ed.), Managing Solid Wastes in Developing Countries. John Wiley: New York. 151-
182.
Colt, J., R. Harvey, M. Lochhead, S. Mayer, L. Boccuti, and K. Hogan. Methane Emissions from
Municipal Soh'd Waste Landfills in the United States. ICF/U.S. EPA, Washington, D.C 23 pp.
Emcon Associates. 1982. Methane Generation and Recovery From Landfills. Ann Arbor Science:
Ann Arbor, Michigan.
Gunnerson, CO., and D.C. Stuckey. 1986. Integrated Resource Recovery: Anaerobic Digestion
Principles and Practices for Biogas Systems. World Bank Technical Paper Number 49, Washington,
D.C
OECD/IEA (Organization for Economic Cooperation and Development/International Energy
Agency). 1989. Environmental Data Compendium 1989. OECD/IEA, Paris.
OECD/IEA (Organization for Economic Cooperation and Development/International Energy
Agency). 1991. Environmental Indicators: A Preliminary Set. OECD/IEA, Paris.
Orlich, J. 1990. Methane emissions from landfill sites and waste water lagoons. In International
Workshop on Methane Emissions from Natural Gas Systems, Coal Mining and Waste Management
Systems. April 9-13, 1990, Washington, D.C Funded by the Environment Agency of Japan, UJ3L
Agency for International Development, and the U.S. Environmental Protection Agency.
Pacey, J.G., and J.P. DeGier. 1986. The factors influencing Landfill gas production. In Energy
From Landfill Gas, proceedings of a conference sponsored by the U.K. Department of Energy and
the U.S. Department of Energy. 51-59.
Piccot, S.D., A. Chadha, J. DeWaters, T. Lynch, P. Marsosudiro, W. Tax, S. Walata, and J.D.
Winkler. 1990. Evaluation of Significant Anthropogenic Sources of Radiatively Important Trace
Gases. Prepared for the Office of Research and Development, U.S. EPA, Washington, D.C
Richards, K.M. 1990. Landfill gas: Working with Gaia. In Biodeterioration Abstracts 3(4) 317-331.
In International Workshop on Methane Emissions from Natural Gas Systems, Coal Mining and Waste
Management Systems. April 9-13, 1990, Washington, D.C. Funded by the Environment Agency of
Japan, U.S. Agency for International Development, and the U.S. Environmental Protection Agency.
Tabasaran, O. 1981. Gas production from Landfill. In Bridgewater, A.V., and K Lidgren (eds.),
Household Waste Management in Europe, Economics and Techniques. Van Nostrand Reinhold Co.,
New York. 159-175.
STATES WORKBOOK D5-7 November 1992
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Thomeloe, S.A. 1990. Landfill Gas and the Greenhouse Effect Paper presented at the
International Conference on Landfill Gas: Energy and Environment October 17.
Thomeloe, S.A., R.L. Peer, D.L. Campbell, and K.S. Kepford. 1991. Proposed Methodology for
Estimating Global Landfill Methane Emissions. January 28. U.S. EPA and Radian Corporation,
Research Triangle Park, North Carolina.
U.S. EPA. 1988. Solid Waste Disposal in the United States. Volume II. Office of Solid Waste and
Emergency Response, U.S. EPA, Washington, D.C
U.S. EPA. 1990. Air Emissions from Municipal Solid Waste Landfills-Background Information for
Proposed Standards and Guidelines. Office of Air Quality, Planning and Standards, U.S. EPA,
Washington, D.C
World Resources Institute. 1990. World Resources 1990-91. WRI, Washington, D.C
STATES WORKBOOK D5-8 November 1992
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DISCUSSION 6
METHANE EMISSIONS FROM DOMESTICATED ANIMALS
OVERVIEW
This section covers methane emissions from animals. Only animals managed by humans for
production of animal products, including meat, milk, hides and fiber, and draft power are included.1
Among livestock, the ruminant animals (i.e., cattle, buffalo, sheep, and goats) are the major emitters
of methane. The rumen, a large "fore-stomach," is the unique physiological characteristic of ruminant
animals that causes methane to be created within the animal
Non-ruminant domestic animals, such as swine, bones, and mules also contribute to methane
emissions. The digestive physiology of these animals precludes them from having large methane
emissions. To produce a complete inventory for methane emissions from animals, these animals are
included here.
Two areas have been identified on which agreement has not been reached on whether they
should be included in this section on methane emissions from animals:
Wild Animals. The need to develop methane emissions inventories for wild animate
has been recognized. The fact is that the populations of some wild animals are
controlled in some areas for conservation or other reasons.
Controlled populations often generate economic returns, e.g.,through tourism.
Experts have suggested that the emissions from these animals should be estimated,
for they may be important for some states. State methane emissions inventories
that include natural sources should assess the importance of methane emissions
from wild animals and estimate the emissions if appropriate.
Termites. It has been recognized that termites produce methane emissions and that
termite populations may be affected by animal husbandry activities. Some experts feel
that emissions from termites should be included in the emissions inventory. It has
been recommended that follow-up work on land use activities should elicit
information useful for evaluating changes in termite emissions associated with animal
management activities.
In addition to the methane created by and emitted from the digestive tracts of animals, animal
wastes (manure) also contribute to methane emissions. Emissions from animal wastes are discussed
in a separate section.
1 Wild animab also produce methane emissions. The principal wild animals that contribute to U.S.
emissions are wild ruminant animals such as anielope, caribou, deer, elk, and moose. Termites have been
identified as a potentially imponant source of emissions and are generally examined separately from other wild
animals.
STATES WORKBOOK D6-1 November 1992
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Methanogenesis in Ruminant Animals
'-vT*
The production of methane is part of the normal digestive process of ruminant animals.
Under normal conditions, ruminant animals consume plant material or roughage that are composed
mostly of cellulosic carbohydrates (cellulose). The rumination process that takes place in ruminant
animals provides an opportunity for microorganisms to break down the cellulose into products that
can be digested and used by the animal Within the rumen, over 200 species and strains of organisms
have been identified to date, although a smaller number dominate (Baldwin and Allison, 1983).
These organisms form a complex ecology that includes both competition and cooperation or
symbiosis. The population mix of the organisms is strongly influenced by the composition of the diet
consumed by the animal.
Rumen methanogenic bacteria, or methanogens, are the source of methane produced in
ruminant animals. Although these bacteria are a very small fraction of the total population of
microorganisms in the rumen, they play an important role in the complex rumen ecology. The
conversion of hydrogen or formate and carbon dioxide (produced by other fermentative bacteria) is
believed to be the primary mechanism by which methanogenic bacteria produce methane in ruminant
animals. The methane produced in the rumen is emitted through eructation and exhalation.
Because methane is produced as a result of digestive processes, the amount of methane
produced will vary with the animal type, the type, amount, and digestibility of the feed consumed by
the animal, and the production level of the animal.
There is a vast scientific literature on the digestive processes and proper feeding of domestic
ruminant animals that can be used to estimate methane emissions (see, for example, NRC (1989],
Jurgens [1988], Van Soest [1982], and ARC [1980]). This literature, developed principally over the
last SO years, includes several systems for defining the feeding requirements of domestic ruminant
animals. Equations have been developed that describe the energy requirements of ruminant animals
at various levels of production. Common feeds have been evaluated to define the level of energy that
they provide. These equations and feed data provide useful information for estimating methane
emissions.
The approach discussed here is to estimate the amount of methane emitted from individual
ruminant animals as a percentage of the amount of feed energy that the animal consumes. This
percentage varies depending on the amount and type of feed consumed by the animal, and will often
range from 4 to 9 percent of the gross energy consumed: Furthermore, the amount of feed energy
consumed by ruminant animals can be estimated directly if the feed consumption is known, or
indirectly if the level of production is known. This discussion is much more detailed than the
recommended method, which is a simplification of the calculations described below.
Methanogenesis in Non-Ruminant Herbivores
Methane is produced as part of the digestive processes of non-ruminant herbivores. As in
ruminant animals, microorganisms produce the methane while breaking down basic feed components,
and the methane production can be expressed as a percentage of the energy consumed by the animal
Because non-ruminant animals lack a rumen, the percent of feed energy converted to
methane is much smaller than the percent for ruminant animals. At the low end, swine convert about
STATES WORKBOOK D6-2 November 1992
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one percent of their gross energy intake to methane, depending on their diet Horses, with their
enlarged cecum acting as a site for the fermentation of cellulose, convert about 3 to 4 percent of
their gross energy intake to methane.
DESCRIPTION OF WORKBOOK METHODOLOGY
The emission coefficients presented in the workbook were calculated using the following
approach:
estimate the percentage of feed energy that is converted to methane
by the animal;
estimate the total feed energy intake by the animal; and
multiply the conversion percentage by the feed intake.
Each of these steps requires a complex series of calculations and a relatively large data set For
simplicity, default assumptions were taken from Crutzen, et al. (1986) to calculate emissions factors
for the workbook. A more detailed discussion of the method is presented in the following section.
Given the assumptions from Crutzen, et al., annual methane emission coefficients were
calculated using the following equation:
M = GE x Ym x 365 x 1/6
where:
- GE = the gross energy intake by the animal per day (Megacalories);
YJU = the methane yield of the gross energy intake (%);
* 365 is used to convert daily values to annual values;
1/6 is the conversion factor from Megacalories to pounds of methane; and
M = methane emissions in pounds per year for each animal.
Table D6-1 presents the data used for each animal type. Total methane emissions are calculated by
multiplying animal populations by the appropriate methane emissions coefficient (M), and then
summing across animal types.
STATES WORKBOOK D6-3 November 1992
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Table D6-1. Estimates of Annual Methane Emissions for Selected Livestock in the U.S.
Daily Average
Energy Intake
(Megacalories)
Methane Yield
of Gross Energy
Intake (%)
CH4 Production
(Ibsfrr)
Cattle
Dairy
55
53%
184
Beef
36
65%
142
Range
26
15%
119
Horses
26
25%
40
Mules/
Asses
NA
NA
22
Sheep
4.8
6%
17.6
Goaf
33
55%
11
Swine
9
0.6%
3J
Note: NA = Not Available
ALTERNATE METHODOLOGY
To estimate methane emissions from animals, the following general steps are required:
1. Enumerate the number of animals of the various types.
2. Characterize the populations of animals into separate categories with the
available dati. At a minimum, the animals must be divided by species and
production systems. Further divisions based on animal size, feeding, and
production levels are desired if data are available with which to make the
estimates. A representative animal should be adopted to represent each
category.
3. Estimate methane emissions for each representative animal type.
4. Estimate total methane emissions by multiplying the emissions for each
representative animal times the population that it represents, and then by
summing across the animal categories.
These basic steps can be performed at various levels of detail. Each of these steps is discussed in
turn. The discussion focuses on the more accurate methods for estimating emissions, but simplifying
approaches are presented as alternatives to the more detailed approach.
STATES WORKBOOK
D6-4
November 1992
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Enumerate the Number of Animals
It is straightforward to enumerate the number of animals. Several data sources that can be
used are described below. Because animal populations fluctuate within the year or across years for
various reasons, it is important to adopt a population that is representative of the study year.
Characterize the Populations of Animals
The populations of animals must be characterized so that they can be divided into categories
that are individually relatively homogeneous. These categories should differ along dimensions that
most influence the level of methane emissions, subject.to the availability of data. .When data are
lacking, detailed characterization will not be possible.
The best definitions of categories will likely vary depending on the animal production systems
that are employed in individual states and the data that are available. The following is recommended
as an example of the hierarchy of categories that is desired:
Species
The animals should be divided by species because the species (e.g., dairy cow, beef cattle,
goat, sheep, etc.) have different digestion processes that result in different levels of methane
emissions.
Livestock Management System
The livestock management system, or production system, employed has a strong influence on
methane emissions per animal. The livestock management system is also indicative of other
characteristics of the animals that are relevant, including size and feeding. There are a wide variety
of livestock management systems, many of which depend on -vegetation or crops for their feed base,
and are heavily influenced by the agro-ecological conditions that exist (FAO, 1980; Reuss et al., 1990;
and Vaidyanathan, 1988).
Within the cattle industry, for example, there are large differences among regions in the U.S.
in the way animals are managed. There are several distinct dairy regions in the U.S. with distinctly
different practices. Historically, the Lake States have been the dominant dairy producers (WI, MN,
IL, IN, OH, PA, and NY). These areas are characterized by small family farms with average herd
sizes of 30 to 60 cows per farm. The forage of the feed is often produced on the farm (Gibbs, 1991).
As a contrast, the growth area for dairying has been in the West (CA principally, but also TX
and NM). These areas are characterized by large herds, averaging in the hundreds, with many herds
in the thousands. The feed for these herds is entirely purchased, i.e., none is grown on the farm
locally. These large herd operations are very mechanized and highly productive. The large dairies
are often referred to as "businesses", as distinct from "family farms" found in the Lake States (Gibbs,
1991).
Although there are many differences among dairy regions, there are also many similarities.
Over 90% of all milk cows are holsteins or holstein crosses. All milking is automated, and careful
attention is paid to sanitation and milk quality (Gibbs, 1991).
STATES WORKBOOK D6-5 November 1992
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The U.S. beef industry is much more fragmented than the U.S. daily industry. The beef cattle
industry is comprised of four main players. They are:
Cow/calf operations: Generally "extensive" or grazing systems, these groups produce calves.
When cows do not get pregnant, they are culled (sent for slaughter). The calf production is
very seasonal Over 75% of the calves are bom in the spring. Most of the operators are very
small, producing under 50 calves per day.
Stocker operation: Stackers purchase calves and grow them for 6 months to a year, usually
on pasture or rangeland.
Feedlots; Feedlots take over after the stocker phase. The steers and heifers will be in the
feedlot for 100 to 200 days depending on their initial weight and prices of feed and cattle.
The objective is to grow the cattle quickly into a form that will get the right grade when
slaughtered (e.g., choice). The feedlot industry is very centralized. A small number of
feedlots account for over 50% of the fed beef produced in the U.S. The feedlot phase is
based principally on the use of grains as feeds (com, sorghum, wheat). In fact, the feedlot
system is a mechanism for translating grain into beef. The large grain companies often have
Gnancial interests in large feedlot operations (Gibbs, 1991).
Packers: The packers purchase the live cattle from the feedlot. The feedlot organizes the
sale even if it does not own the cattle. The packers slaughter the animals and sell to
wholesalers and retailers.
Animal size, feeding, and production
Size, feeding, and production are helpful for making the best estimates of methane emissions.
To the extent that data are available, they should be used. In many cases, "rules of thumb" may be
needed based on the production systems identified. At a minimum two size categories should be
used: young and adult animals. Feeding characteristics include amount, type, and digestibility of feed.
Some production characteristics are milk produced per day, weight gain per day, and for draft animals,
work per day.
Within each of the categories, a "representative animal" should be defined. The category will
then be assumed to be homogeneous with the characteristics of the representative animal. The
characteristics of the representative animal can then be used to estimate methane emissions.
Estimating Methane Emissions
Data on CH4 emissions from animals are very limited. The most precise method for
estimating methane emissions is to measure emissions from individual animals in the Geld that
represent the categories of animals defined above. Due to variations among individual animals, many
measurements would be required to define a "representative" animal. Undertaking such
measurements is not practical at this time. Alternatively, existing laboratory measurements could be
used as a basis for estimating emissions from those animals that have been measured. In most cases,
these experimental data are also not readily available.
STATES WORKBOOK D6-6 . November 1992
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Assuming that direct measurements are not available, methods of estimating emissions based
on models and equations are required. Hie most detailed models will be those that consider the
complex digestive processes of ruminant and non-ruminant animals. For example, such a model has
been developed for cattle, and can probably be applied to sheep and buffalo as well (Baldwin et aL,
1987). In cases where very detailed data are available to describe the animals and the diets they
consume, such a model can be implemented In most cases, such data are not readily available
(Baldwin, personal communication).
When less detailed data are available, simplified summary relationships can be used to
estimate methane emissions. The approach proposed here is to;
estimate the percentage of feed energy that is converted to methane
by the animal;
estimate the total feed energy intake by the animal; and
multiply the conversion percentage by the feed intake.
An equation that estimates the percentage of the total feed intake of the animal that is converted
to methane has been developed for ruminant animals by Blaxter and Oapperton (1965). As part of
the feeding systems discussed above, equations have been developed to describe the energy intake
of the animals.
Estimating Proportion of Feed Converted to Methane
Blaxter and Clapperton (1965) reviewed the results of 615 closed-circuit respiration indirect
calorimetry experiments on sheep and cattle performed over a period of 10 years. Based on an
analysis of the results for 48 different diets in 391 different experiments on 4-5 sheep for various
levels of feeding, Blaxter and Clappcrton identified feed digestibility and level of intake to be
important factors influencing the extent of methanogenesis in the rumen. Using statistical techniques,
Blaxter and Clapperton developed the following equation to describe methane production:
Ym = 130 + (0.112 x D) + L x (137 - 0.050 x D) (1)
where Ym is the methane yield (Megacalories of methane produced per 100 Megacalories of gross
energy feed intake), L is the ratio of energy intake to maintenance energy requirements (e.g., two
times maintenance),2 and D is the percent digestibility of the feed (e.g., 50 percent). The methane
yield estimated with this equation can be interpreted as the percent of gross energy intake that is
converted to methane within the animal. The digestibility of the diets represented in the data used
to develop this equation ranged from poor hay (54 percent digestible at maintenance) to sugar-beet
pulp (87.2 percent digestible at maintenance). The levels of the diets ranged from one to three times
maintenance.
2 Maintenance is defined as the condition where the animal neither gains nor loses weight In practice,
the "maintenance" condition is rarely observed for any significant period of time. Consequently, it is
principally a concept that is used in the energy-based feeding systems to describe the energy requirements of
ruminant animals.
STATES WORKBOOK D6-7 November 1992
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To use this equation for ruminant animals, information is needed to specify D, the digestibility
of the feed, and L, the level of feeding. In the absence of specific information about individual
production systems, rules of thumb will be required. Examples of rules of thumb that may be
appropriate include the following: '
Digestibility: Intensive high-production systems generally rely on
grains and other high-energy feeds in addition to forages. The feeds
will have an overall digestibility of 70 to 80 percent. Well managed
grazing systems with high levels of production will likely have feeds
that are in the range of 60 to 70 percent Subsistence agriculture
situations with poor feed resources will likely have digestibilities in the
50 to 60 percent range.
Level of Feeding: As described below, the level of feeding should be
estimated from the level of production that is attained. However, in
the absence of such data, feeding levels for intensive high-production
systems will generally be on the order of 2.5 to 4.5 times maintenance.
Well managed grazing systems with high levels of production will likely
have feeding levels of about 1.5 to 2.5 times maintenance; the higher
level occurring when energy supplements are provided to the grazing
animals. Subsistence agriculture situations with poor feed resources
will likely have levels of feeding of about 1.0 to 1.5 times
maintenance.
Estimating Total Energy Intake
The result from equation 1 must be multiplied by an estimate of the total energy intake of
the animal. In general, feed intake will be a function of animal size and production. Larger animals
require "more fecJ intake than smaller aniuials, and high producing animals require more feed intake
than low producing animals. Under the energy-based systems of animal feeding described above,
several equations have been developed to estimate energy intake as a function of animal size and
production. Other characteristics such as breed, sex, and age have also been incorporated into the
feeding systems. These factors can be used, but for simplicity they are omitted from this presentation.
To estimate the feed energy intake, first estimate the actual amount of feed energy used by
the animal; this quantity is generally referred to as the "net energy" utilized by the animal (NRC,
1989). This net energy value will then be "scaled up" to reflect the fact that the animal utilizes only
a portion of the total feed energy consumed. In cases where the feed consumption of the animals
is well known (e.g., based on data from agricultural census), the energy intake can be estimated
directly from the feed data. The energy content of various feeds have been estimated (see, e.g., NRC
[1989] or Jurgens [1988]).
In cases where feeding data are not available, feed energy intake can be estimated based on
animal production data. As shown in the following example for cattle, if adequate data are available,
the net energy estimate can be built up with the following equations:3
3 Similar equations have been developed for sheep and goats. See NRC (1985) and NRC (1981).
STATES WORKBOOK D6-8 November 1992
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NE,,, = 0322 W°-75 x activity factor (2)
NEg = 4.18 x (0.035 W°-75 x WGUH> + WG) (3)
NEj = 3.1 x milk production in pounds per day (4)
where:
is the net energy required for maintenance, in Megacalories;
NE, is the net energy required for growth in Megacalories;
NE, is the net energy required for lactation (Le^ milk production) in
Megacalories;
W is the weight of the animal in pounds;
WG is the daily weight gain in pounds;
activity factor represents an adjustment for the energy required to
graze for food;
milk production per day is the amount of 4 percent fat corrected milk
produced daily in pounds.4
The total net energy required for the representative animal can be estimated by applying
these equations and summing the individual net energy estimates. Care must be taken in adding the
work-related energy values because animal power is usually used seasonally.
Rules of thumb for the activity factor are as follows (Reuss et al., 1990):
confined animals that are stall fed: 1.125;
animals grazing on good quality pasture: 1.25; and
extensively managed animals that graze over very large areas: 1.50.
The total net energy required for the representative animal can be estimated by applying
these equations and summing the individual net energy estimates. Energy requirements for the work
performed by draft animals also need to be added. These energy requirements are separate from the
activity factor that is related to the energy required to graze for food. Care must be taken in adding
the work-related energy values because animal power is usually seasonal.
By applying these equations, the net energy intake that is consistent with the size and
performance of the animals is estimated. The level of the feeding can be estimated from these data
by dividing the total by the net energy required for maintenance, assuming an activity level of 1.0.
This estimate of the level of feeding can be used in equation 1 above to estimate the methane yield.
The estimate should be compared with the general rules of thumb for feeding levels discussed above
to test for the reasonableness of the estimate.
4 The formula presented for NE, assumes that the milk production is corrected to a 4% milk fat content
Higher (lower) milk fat levels require more (less) ME, per pound of milk produced. See NRC (1989).
STATES WORKBOOK D6-9 November 1992
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The estimate of net energy must be translated into gross energy in order to be used with the
methane yield estimated above. This translation depends on the type of feed consumed and the
efficiency with which its energy is used by the animal. Although there are a wide range of values that
can be used based on the specific characteristics of individual feed types, the following rule of thumb
can be used for simplicit
GE = ((NE,,, + NE, + WE) * 0.492 + (NEg * 0328)] * (digestibility) (5)
where:
NE is as defined above in Megacalories;
WE is the work energy per day as defined above;
digestibility is expressed as a fraction (e.g^ 65% digestibility is
expressed as 0.65); and
GE is gross energy intake in Megacalories.
To check the reasonableness of this estimate of gross energy intake, the approximate dry matter
equivalent of this intake can be estimated by assuming that 1 pound of feed has about 2 Megacalories
of energy.6 Dearly, feeds differ substantially in their energy content, and this value is used here only
as a check. The intake implied by the gross energy estimate is then estimated as:
DMj = GE + 2 (6)
where DM; is daily dry matter intake in pounds. This value should be about 2.0 to 3.0 percent of the
weight of the animal, and slightly higher in intensive management situations. If the gross energy
estimate produces dry matter intake estimates that fall outside this range, a careful review of the
assumptions and data used may be warranted.
With the gross energy and methane yield estimates, the annual methane emissions for the
representative animal can be estimated as:
M = GE x (Yffl * 100) x 365 x 1/6 (7)
where M is the methane emissions in pounds per year and Ym is the methane yield estimated from
equation I.7
5 The specific food types will have a range of gross energy values in relation to their net energy values.
Emissions estimates will be improved if the characteristics of actual feeds are used.
6 Higher energy values for feeds in North America and Europe may be appropriate due to the use of feed
grains in high-production dairy and feedlot operations. See, e.g., Reuss et al. (1990).
7 Ym is divided by 100 to put it into a fraction form, e.g., 5 percent equals O.OS. The factor of 365 is used
to convert daily values to yearly values. The factor of 1/6 is used to convert Me|acalories to pounds of
methane.
STATES WORKBOOK D6-10 November 1992
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Similar analyses could be used to estimate methane emissions from non-ruminant animals.
However, the equations and feed characteristics would be quite different from those presented above
for ruminant animals. Because the non-ruminant animals are relatively less important than the
ruminant animals in terms of methane emissions, simple emissions factors per head may be
appropriate. Crutzen et al. (1986) derive the following emissions factors:
swine in the U.S.: 33 Ibs/head per year;
horses: 40 Ibs/bead per year, and
mules and asses: 22 Ibs/bead per year.
These estimates may be modified in individual cases when unusual feeding or animal management
practices are found.
Estimate Total Methane Emissions
Total methane emissions are estimated by multiplying the annual emissions for the
representative animals by the number of animals in the categories, and then summing across the
categories.
DATA SOURCES
A wealth of unpublished information is available from practitioners in individual states.
Departments within each state responsible for conducting agricultural research and that oversee the
agriculture sector should be consulted. State animal populations can be found in the Census of
Agriculture, Volume 1: Geographic Area Series, published by the Bureau of the Census. Also, the
USDA can produce state by state inventories on domesticated animal populations.
Data on feed characteristics have been compiled in NRC (1989), ARC (1980), and Jnrgens
(1988). These, and similar, sources may be consulted to evaluate the feed consumption of specific
categories of ruminant animals.
Lerner et al. (1988) and Reuss et ai. (1990) have compiled statistics about animals in order
to estimate global methane emissions. These sources can be examined to provide an indication of
the data sources that have been used in initial assessments of animal methane emissions.
EVALUATION
The methods described above for estimating methane emissions from animals are based on
sound scientific data and experimental evidence. To the extent possible, emissions should be
estimated with as much information about levels of feeding and feed characteristics as possible. This
information is particularly important for high-producing animals fed high-energy feeds.
The rules of thumb and emissions factors presented above for ruminant animals in subsistence
or extensive grazing situations will likely be required due to a lack of data needed to implement the
more ambitious method. The use of these simplified approaches adds to the uncertainty of the
estimates, but the extent of the inaccuracies introduced cannot be quantified at this time.
STATES WORKBOOK D6-11 November 1992
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Overall, a lack of data will likely limit the precision with which methane emissions from
animals can be estimated. With additional data more precise methods may be implemented because
the understanding of the factors that control methane emissions b ruminant animals is fairly
advanced. To improve future estimates, systemic collection of data on feeding and feed
characteristics should be initiated.
REFERENCES
ARC (Agriculture Research Council). 1980. The Nutrient Requirements of Ruminant Livestock.
Commonwealth Agricultural Bureaux, Farnham Royal, England.
Baldwin, R.L., and MJ. Allison. 1983. Rumen metabolism. Journal of Animal Science 57:461-477.
Baldwin, R.L., J.H.M. Thornley, and D.E. Beever. 1987. Metabolism of the lactating cow. n.
Digestive elements of a mechanistic model. Journal of Dairy Research 54:107-131.
Blaxter, K.L., and J.L. Clapperton. 1965. Prediction of the amount of methane produced by
ruminants. British Journal of Nutrition 19:511-522.
Crutzen, PJ., I. Aseimann, and W. Seller. 1986. Methane production by domestic animals, wild
ruminants, other herbivorous fauna, and humans. Tellus 386:271-284.
FAO (Food and Agriculture Organization of the United Nations). 1980. The Classification of World
Livestock Systems. FAO, Rome. 37+ pp.
FAO (Food and Agriculture Organization of the United Nations). 1989. 1988 FAO Production
Yearbook. Volume 42. FAO, Rome.
Gibbs, M. 1991. Memorandum to Barbara Braatz, ICF Inc. 1991.
Jurgens, M.H. 1988. Animal Feeding and Nutrition. Kendall/Hunt Publishing Company, Dubuque,
Iowa.
Leng, R.A. 1990. Improving Ruminant Production and Reducing Methane Emissions From
Ruminants by Strategic Supplementation. Draft report prepared for the Global Change Division,
U.S. Environmental Protection Agency, Washington, D.C., January.
Lerner, J., E Matthews, and I. Fung. 1988. Methane emission from animals: A Global high-
resolution database. Global Biogeochemical Cycles 2:139-156.
NRC (National Research Council). 1981. Nutrient Requirements of Coats. National Academy Press,
Washington, D.C.
NRC (National Research Council). 1985. Nutrient Requirements of Sheep. National Academy Press
(Sixth Revised Edition), Washington, D.C.
STATES WORKBOOK D6-12 November 1992
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NRC (National Research Council). 1989. Nutrient Requirements of Daily Cattle. National Academy
Press (Sixth Revised Edition), Washington, D.C
Preston, T.R., and R-A. Leng. 1987. Matching Ruminant Production Systems with Available Resources
in the Tropics and Sub-tropics. Penambul Books, Armidale, New South Wales, Australia.
Reuss, S.K., D.M. Swift, G. Ward, and JJE. Ellis. 1990. Global Ruminant Livestock Production
Systems: Estimated 1988 Methane Emissions. Draft report prepared for the Global Change Division,
U.S. Environmental Protection Agency, Washington, D.C
USD A (United States Department of Agriculture). 1987. Agricultural Statistics 1987.
United States Government Printing Office, Washington D.C 1987.
USD A. 1990. Agricultural Statistics 1990. United States Government Printing Office, Washington,
D.C. 1990.
Vaidyanathan, A. 1988. Bovine Economy in India. Center for Development Studies, Trivandrum.
Van Soest, PJ. 1982. Nutritional Ecology of the Ruminant. Cornell University Press, Ithica, New
York.
STATES WORKBOOK D6-13 November 1992
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DISCUSSION 7
METHANE EMISSIONS FROM ANIMAL MANURE
OVERVIEW
Manure decomposition is a process in which microorganisms derive energy and material for
cellular growth by metabolizing organic material in the manure. When decomposition occurs without
oxygen present (anaerobically), methane is an end-product of the process. This section will describe
the fundamentals of anaerobic decomposition; the methane producing capacity of livestock manure;
and the factors that influence methane production from livestock manure.1
The Fundamentals of Anaerobic Decomposition
Livestock manure is primarily composed of organic material and water. Under anaerobic
conditions, the organic material is decomposed by anaerobic and facultative (living in the presence
or absence of oxygen) bacteria. The end products of anaerobic decomposition are methane, carbon
dioxide, and stabilized organic material
The anaerobic decomposition process can be represented in three stages: hydrolytic; acid
forming; and methanogenic. Carbohydrates decomposition can be illustrated as follows:2
Stage 1: Hvdrolvtic. In the first stage, complex organic materials in the manure
substrate are broken down through the hydrolytic action of enzymes. Enzymes are
proteins formed by living cells that act as catalysts in metabolic reactions. The amount
and rate of breakdown can vary substantially and depend on the enzymes present, the
characteristics of the manure, and environmental factors such as pH and temperature.
Stage 2: Acid Forming. Anaerobic and facultative bacteria reduce (ferment) the
simple sugars produced in Stage 1 to simple organic acids. Acetic acid is the primary
product of the breakdown of carbohydrates, though other organic acids such as
propionic acid and butyric acid can be formed. In addition, metabolic hydrogen and
carbon dioxide are produced. With acetic acid as an end product, the breakdown of
a simple sugar molecule (glucose) in Stage 2 can be represented as:
C6H12°6 + 2H2° > 2CH3COOH + 2CO2 + 4H2
glucose + water acetic acid carbon dioxide metabolic
hydrogen
1 Background information on animal wastes is taken from Safley et al. (1992).
2 This discussion focuses on the decomposition of carbohydrates because carbohydrate decomposition
accounts for the majority of the methane produced from livestock manure and because the process of methane
production from the decomposition of carbohydrates is best understood. By weight, the volatile solids portion
of cattle and swine manure is approximately 40 percent carbohydrate, 15 to 20 percent protein, and up to 10
to 20 percent fat with the remainder composed of other material (Hrubant, Rftodts, and Stoneker, 1978).
STATES WORKBOOK D7-1 November 1992
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Stage 3: Methanopenic. Methane producing bacteria (methanogens) convert the
simple organic acids, metabolic hydrogen, and carbon dioxide from Stage 2 into
methane and carbon dioxide. Methanogens are strict anaerobes and cannot tolerate
the presence of molecular oxygen. Methanogens multiply slowly and are very
sensitive to temperature, pH, and substrate composition. With acetic acid, metabolic
hydrogen and carbon dioxide as substrate, the reactions producing methane can be
expressed as:
2CH3COOH > 2CH4 + 2CO2
acetic acid > methane + carbon dioxide
4H2 + COj >
metabolic + carbon dioxide > methane + water
hydrogen
Methane Producing Capacity of Livestock Manure
In general, livestock manure is highly conducive to methane generation due to its high organic
content and the presence of useful bacteria. However, the specific methane producing capacity of
livestock manure depends on the specific composition of the manure which in turn depends on the
composition and digestibility of the animal diet The greater the energy content and digestibility of
the feed, the greater the methane producing capacity of the resulting manure. For example, feedlot
cattle eating a high energy grain diet produce a highly biodegradable manure with a high methane
producing capacity. Range cattle eating a low energy forage diet produce a less biodegradable
manure with only half the methane producing capacity of feedlot cattle manure. ,
In principal, the ultimate methane producing capacity of a quantity of manure can be
predicted from the gross elemental composition of the manure. In practice, however, insufficient
information f.a'srs .to implement this approach and the methane producing capacity is determined
through direct laboratory measurement The methane producing capacity of livestock manure is
generally expressed in terms of the quantity of methane that can be produced per kilogram of volatile
solids (VS) in the manure.3 This quantity is commonly referred to as B0 with units of cubic feet of
methane (CH4) per pound VS (ft3 CH4 / Ib VS). Representative B0 values for a number of livestock
manure types are presented later in this discussion.
Factors Influencing Methane Production
While a particular quantity of manure may have a certain potential to produce methane based
on its volatile solids content, the management of the livestock manure and the environment in which
the manure is managed are the major factors influencing the amount of methane actually produced
during manure decomposition.
3 Volatile solids (VS) are defined as the organic fraction of the total solids (TS) in manure that will
oxidize and be driven off as gas at a temperature of 600°C Total solids (TS) are deOned as the material that
remains after evaporation of water at a temperature between 103° and 105'C
STATES WORKBOOK D7-2 November 1992
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The characteristics of the manure management systems and environmental conditions can be
expressed in a methane conversion factor (MCF) which represents the extent to which the potential
for emitting methane is actually realized. Manure systems and climate conditions that promote
methane production will have an MCF near 1 and manure systems and climate conditions that do not
promote methane production will have an MCF near 0. The primary characteristics determining the
MCF are:
Livestock Manure Management System Factors
Contact with Oxygen. Under aerobic conditions where oxygen is in contact with the
manure, there is no potential for methaqe production.
Water Content. Liquid based systems promote an oxygen-free environment and
anaerobic decomposition. In addition, water is required for bacterial cell production
and metabolism and acts as a buffer to stabilize pH. Moist conditions increase the
potential for methane production.
pH. Methane producing bacteria are sensitive to changes in pH. The optimal pH is
near 7.0 but methane can be produced in a range between 6.6 and 8.0.
Nutrients. Bacterial growth depends on the availability of nutrients such as nitrogen,
phosphorus, and sulfur. Deficiency in one or more of these nutrients will inhibit
bacterial growth and methane formation. Animal diets typically contain sufficient
nutrients to sustain bacterial growth. Therefore, nutrient availability is not a limiting
factor in methane production under most circumstances.
Climate Factors
* Temperature. Methanogenesis in livestock manure has been observed between 4° C
and 75° C Temperature is one of the major factors affecting the growth of the
bacteria responsible for methane formation (Chawla, 1986). The rate of methane
production generally increases with rising temperature.
Moisture.. For non-liquid based manure systems, the moisture content of the manure
is determined by rainfall and humidity. The moisture content of the manure will
determine the rate of bacteria) growth and decomposition. Moist conditions promote
methane production.
These factors can be combined into the following expression for estimating realized methane
emissions from livestock manure:
Realized Emissions = B0 MCF (7.1)
where B0 = the maximum methane producing capacity of the manure determined
by animal type and diet (ft3 CH4 / Ib VS).
MCF = Methane Conversion Factor (MCF) that represents the extent to
which the BQ is realized for a given livestock manure management
system and environmental conditions. Note: 0 s MCF s 1.
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DESCRIPTION OF WORKBOOK METHODOLOGY
Methane emissions from livestock manure depend on the type of manure, the characteristics
of the manure management system, and the climatic conditions in which the manure decomposes.
While limited data are available on which to base emission estimates, a study recently prepared for
the USEPA provides an adequate basis for making initial estimates (Safley et al., 1992). Additional
analysis is ongoing to provide additional data for estimating these emissions.
Based on the Safley et al. (1992) approach, emission estimates are developed by:
identifying the manure management systems in use in the United States and their
methane producing potential;
estimating the amount and type of manure managed by each system; and
estimating emissions by multiplying the amount of manure managed in each system
by the estimated emission rate per unit of manure in the system.
Information can be obtained from a variety of sources, including:
the U.S. Census of Agriculture;
USDA agriculture statistics;
livestock manure management experts throughout the U.S.; and
scientific literature.
Total emissions will equal the quantity of volatile solids managed in each system times
emissions per kilogram of volatile solids (VS) for that system. Safley et al. (1992) used the following
procedure to estimate total emissions:
Collect data on: (1) the populations of the major animal types in each state of the
U.S. (AT); and (2) their typical animal mass (TAM).
Collect information on the characteristics of the manure produced by each of the
animal populations in each state, including: (1) the amount of volatile solids (VS)
produced; and (2) the methane producing capacity (B0) of the manure. The amount
of volatile solids produced depends on the number of animals in the state and their
mass:
VSik - Nit TAMt vst (7-2)
where:
NJ k = number of animals of type i in state k.
TAMi = typical animal mass in pounds of animal /'; and
vsl - the average annual volatile solids production per unit of
animal mass (pounds per pound) for animal i.
STATES WORKBOOK D7-4 November 1992
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Identify the livestock manure management systems used in each state and the
percentage of manure managed by each (WS%).
Estimate the methane producing potential (MCF) of each manure management
system in each state based on the average monthly temperature in the state.
Estimate methane emissions for each animal and manure system in each state (TM)
by multiplying the amount of volatile solids (VS) produced by the methane producing
capacity of the manure (B0) times the methane producing potential (MCF) of the
manure system in each state,
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In the U.S., considerable data are available to allow the populations of animals to be analyzed
by. species, production system, and (for cattle) age. Six main categories of animals were defined:
feedlot beef cattle;4 other beef cattle; dairy cattle; swine; poultry, and other. These main categories
were further divided into 20 subcategories. For each subcategory, VS production was estimated using
data on: the animal population; the typical animal mass (TAM); and the VS production per unit of
animal mass. Table D7-1 lists the data obtained for the 20 subcategories.
Maximum Methane Producing Capacity
The maximum amount of methane that can be produced per pound of VS (Bg) varies by
animal type and diet Measured B0 values for beef manure range from 2.72 cubic feet of methane
per pound of VS (f^/lb-VS) for a com silage diet to 5.29 ft^/lb-VS for a corn-based high energy diet
that is typical of feedlots. Table D7-2 summarizes these values.
Appropriate B0 values were selected depending on the typical diet of each animal type and
category. For animal types without B0 measurements, the B0 was estimated based on similarities with
other animals and the authors' experience. Ruminants for which there were no literature values were
assumed generally to have the same values as cattle, except for sheep, which were assumed to have
B0 values 10 percent higher than cattle (Jain et al. 1981). Table D7-3 lists the values selected for
the analysis.
4 Feedlot cattle are animals fed a ration of grain, silage, hay and protein supplements for the slaughter
market (ASB, 1991).
STATES WORKBOOK D7-6 November 1992
-------
Table D7-1
US, Animal Populations, Avenge Size, and VS Prodaction
Animal Type
Feedlot Beef Cattle
Other Beef Cattle
Dairy Cattle
Swine
Poultiyc
Other
Steers
Heifers
Cows/Other
Total
Calves
Heifers
Steers
Cows
Bulls
Total
Heifers
Cows
Total
Market
Breeding
Total
Layers
Broilers
Ducks
Turkeys
Sheep
Goats
Donkeys
Horses and Mules
Population*3
Ni
7367,000
3,785,000
87,000
11,239,000
20,248,000
13447,000
8,430,000
33483,000
2^21,000
78,029,000
4,199,000
10,217,000
14,416,000
48,259,000
7,040,000
55299,000
355.469,000
951,914,000
7,000,000
53,783,000
10,639,000
2396,000
4,000
2,405,000
Typical
Animal
Mass
(TAMJ0
It*
915
915
1102.
397
794
794
1102
1587
903
1345
101
399
35
15
3.1
7.5
154
141
661
992
Manure per day°
(Ibs/day per 1000 Ite mass)
Total
Manure
58
58
58
58
58
58
58
58
86
86
84
84
64
85
107
47
40
41
51
51
Volatile
Solids
**i
12
12
12
12
12
12
12
12
10
10
8.5
S.5
12
17
18.5
9.1
92
95
10
10
A Population data for animals except goats and horses from ASB (1989a-f). Goat and none population
data from Bureau of Census (1987). Population data as of January 1, 1988 for cattle, poultry, and sheep
and as of December 1, 1987 for swine, goats, and horses.
B BroilerAurkey populations estimated yearly based on number of Docks per year (North 1978; Carter
1989).
c Source: Taiganides and Stroshioe (1971).
D Source: ASAE (1988).
STATES WORKBOOK
D7-7
November 1992
-------
Table D7-2
Maximum Methane Producing Capacity for VS. Livestock Manure
Animal
T>pe
Beef
Beef
Beef
Beef
Beef
Daily
Daily
Dairy
Dairy
Horse
Poultry
Poultry
Poultry
Poultry
Swine
Swine
Swine
Swine
Swine
Swine
Swine
Swine
Diet
7% corn silage, 87.6% corn
Corn-based high energy
91.5% com silage, 0% corn
58-68% silage
72% roughage
Roughage, poor quality
Grain-based ration
<
Barley-based ration
Corn-based high energy
Cora-based high energy
Corn-based high energy
Corn-based high energy
Cora-based high energy
Cora-based high energy
(m3 CH<°/kg-VS)
4.65
5.29
2.72
3.68
5.29
3.84
2.72
224
1.60
5.29
6.25
5.77
3.84
3.84
5.77
7.69
5.13
833
7.69
7.53
7.05
7.21
Reference
Hashimoto et al. (1981)
Hashimoto et al (1981)
Hashimoto et aL (1981)
Hill (1984)
Chen, et aL (1980)
Morris (1976)
Bryant et aL (1976)
Hill (1984)
Chen, et aL (1988)
Ghosh (1984)
Hill (1982)
Hill (1984)
Webb & Hawkes (1985)
Hawkes & Young (1980)
Summers & Bousfield (1980)
Hashimoto (1984)
Hill (1984)
Kroeker et al. (1984)
Stevens & Schulte (1979)
Chen (1983)
lannotti et aL (1979)
Fischer et al. (1975)
Table D7-3
Maximum Methane Producing Capacity Adopted For U.S. Estimates
Animal Type,
Cattle:
Swine:
Poultry:
Sheep:
Goats:
Horses and Mules:
Category
Beef in Feed lots
Beef Not in
Feedlots
Dairy
Breeder
Market
Layers
Broilers
Turkeys
In Feedlots
Not in Feedlots
Maximum Potential
Emissions (B0)
5.29
172
3.84
5.77
7.53
5.45
4.81
4.81
5.77
3.04
2.72
5.29
Reference
Hashimoto et al. (1981)
Hashimoto et al. (1981)
Morris (1976)
Summers & Bousfield (1980)
Chen (1983)
Hill (1982 & 1984)
Safley et al. (1992)
Safley et al. (1992)
Safley et al. (1992)
Safley et al. (1992)
Safley et al. (1992)
Ghosh (1984)
STATES WORKBOOK
D7-8
November 1992
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Manure Management Systems Definitions
A variety of manure management practices are in use throughout the U.S. The following is
a brief description of the major livestock manure management systems in use.
PASTURE/RANGE
DAILY SPREAD
SOLID STORAGE
DRYLOT
DEEP PIT STACKS
LITTER
PADDOCK
LIQUID/SLURRY
ANAEROBIC LAGOON
Animals that are grazing on pasture are not on any true manure handling
system. The manure from these animals is allowed to lie as is, and is not
managed at alL
With the daily spread system the manure is collected in solid form, with
or without bedding, by some means such as scraping. The collected
manure is stored until applied to fields on a regular basis.
In a solid storage system the solid manure is collected as in the daily
spread system, but this collected manure is stored in bulk for a long
period of time (months) before any disposal.
In dry climates animals may be kept on unpaved feedlots where the
manure is allowed to dry until it is periodically removed. Upon removal
the manure may be spread on fields.
With caged layers the manure may be allowed to collect in solid form in
deep pits (several feet deep) below the cages. The manure in the pits
may only be removed once a year. This manure generally stays dry.
Broilers and young turkeys may be grown on beds of litter such as
shavings, sawdust, or peanut hulls, and the manure/litter pack is removed
periodically between flocks. This manure will not generally be as dry as
with deep pits, but will still be in solid form.
Horses are frequently kept in paddocks where they are confined to a
limited area, but not entirely confined to their stalls. This manure will be
essentially the same as manure on pasture or drylot
These systems are generally characterized by large concrete lined tanks
built into the ground. Manure is stored in the tank for six or more
months until it can be applied to fields. To facilitate handling as a liquid,
water usually must be added to the manure, reducing its total solids
concentration to less than 12 percent Slurry systems may or may not
require addition of water.
Anaerobic lagoon systems are generally characterized by automated flush
systems that use water to transport the manure to treatment lagoons that
are usually greater than six feet deep. The manure resides in the lagoon
for periods ranging from 30 days to over 200 days depending on the
lagoon design and other local conditions. The water from the lagoon is
often recycled as flush water. Periodically the lagoon water may be used
for irrigation on fields with the treated manure providing fertilizer value.
STATES WORKBOOK
D7-9
November 1992
-------
PIT STORAGE Liquid swine manure may be stored in a pit while awaiting final disposal.
The pits are often constructed beneath the swine building. The length of
storage time varies, and for this analysis is divided into two categories:
less than one month or greater than one month.
Methane Conversion Factors fMCFsl
The extent to which the maximum methane producing capacity (B^ is realized for a given
livestock manure management system and environmental conditions is defined as the Methane
Conversion Factor (MCF) for the manure system. For example, a manure system that produces no
methane emissions will have an MCF of 0. A manure system that achieves full potential methane
emissions would have an MCF of 1.
To assess the MCF values for a wide range of livestock manure management systems, two
broad classifications of livestock manure handling systems can be defined based on the total solids
content of the manure:
Solid systems have a total solids content greater than about 20 percent
Liquid/slurry systems have a total solids content less than 20 percent
Manure as excreted may have a total solids content from 9 to 30 percent (Taiganides 1987).
This solids content may be modified by adding an absorbent bedding material to increase the total
solids content for easier handling. Alternatively, water may be added to lower the total solids to allow
for liquid transport and handling.
These classifications of systems are particularly important to the potential for methane
production from the manure. Liquid and slurry systems will typically cause anaerobic conditions to
develop, which result in methane production. Solid systems promote conditions that limit methane
production even if anaerobic conditions may exist
Safley et al. (1992) reviewed the literature to investigate the appropriate range of MCF values
for U.S. manure management systems. Although some data were available, MCF values were
estimated for many systems. To improve the MCF estimates, the U.S. Environmental Protection
Agency is sponsoring analysis to better estimate the MCF for several key livestock manure systems.
Preliminary findings from this analysis indicate that:
The estimated MCF value of dry in sou pasture, range, paddock, and solid storage
manure is 1 to 2 percent The estimated MCF for drylot manure is 1 to 5 percent
However, the analysis has not yet considered the effect of moisture or emissions that
may result when the manure is washed into streams, rivers, and lakes or incorporated
into the soil (Hashimoto 1992).
The MCF value liquid/slurry and pit storage varies greatly by temperature and is on
the order of 10 percent at 10°C to 65 percent at 30°C (Hashimoto 1992).
The MCF value for daily spread is less than 1 percent (Hashimoto 1992).
STATES WORKBOOK D7-10 November 1992
-------
The MCF value for anaerobic lagoons is on the order of 90 percent This estimate
is based on continuous methane measurements taken over a two and one-half year
period at a North Carolina dairy farm (Safley 1991).
The MCFs values for each system are listed in Table D7-4. The MCF for an individual state will
depend on the average monthly temperature and are calculated by:
estimating the average monthly temperature in each climate division;5
estimating the MCF value for each month using the average temperature data and the
MCF values listed in Table D7-4;
estimating the annual MCF by averaging the monthly division estimates; and
estimating the state-wide MCF by weighting the average MCF for each division by the
fraction of the state's dairy population represented in each division.6
Table D7-5 summarizes the MCF estimates for each state.
Livestock Manure Manaement System Usage
Livestock manure management system usage in the United States was determined by obtaining
information from Extension Service personnel in each state. The U.S. was divided into eleven
geographic regions based on similarities of climate and livestock production as shown in Table D7-6.
For states that did not provide information, the regional average manure system usage was assumed.
Some states did not give data for all animal types and a regional average was used in these cases.
Table D7-7 lists the percentage of manure managed by the major systems in the United
States. The important manure management characteristics in the U.S. are:
Approximately one-third of dairy manure is managed as a liquid and approximately
one-third is spread directly to cropland.
Seventy-five percent of swine manure is managed as a liquid.
Poultry manure is primarily managed by deep pit stacking or litter and is included in
"other systems" in Table D7-7.
5 The average temperature in each climate division of each state was calculated for the normal period of
1951 to 1980 using the National Climatic Data Center (NCDC) time-bias corrected Historical Climatological
Series Divisional Data (NCDC 1991).
6 The dairy population in each climate division were estimated using the dairy population in each county
(Bureau of the Census 1987) and detailed county and climate division maps (NCDC 1991). Using the dairy
population as a weighting factor may slightly over or underestimate the MCFs for other livestock populations.
STATES WORKBOOK D7-11 November 1992
-------
Table D7-4
Methane Conversion Factors for U.S. Livestock Manure Systems
MCFs based on
laboratory measurement
Pasture, Range, PaddocksA
Liquid/Slunj^
Pit Storage < 30 daysA
Pit Storage > 30 daysA
Drylot8
Solid StorageA
Daily SpreadA
MCF measured by
long term field monitoring
Anaerobic Lagoonsc
MCFs estimated by Safley et aL
MCFat30°C
2%
65%
33%
65%
5%
2%
1 %
MCF at 20°C
1.5%
35%
18%
35%
1.5%
1.5%
0.5%
MCF at 10°C
1%
10%
5%-
10%
1 %
1 %
0.1 %
Average Annual MCF
90%
Average Annual MCF
Litter0 10 %
Deep Pit Stacking0 5 %
A Hashimoto (1992)
B Based on Hashimoto (1992).
C Safley et al. (1992) and Safley and Westerman (1992).
D Safley et at. (1992).
STATES WORKBOOK
D7-12
November 1992
-------
Table D7-5
Methane Conversion Factors for U.S. livestock Manure
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Other Systems: Pit Storage for
Pasture,
Range &
Paddocks
1.4%
1.4%
13%
1.2%
0.9%
0.9%
12%
1.5%
1.4%
0.8%
1.1%
1.0%
0.9%
1.1%
1.2%
1.4%
0.8%
1.1%
0.9%
0.8%
0.8%
1.4%
1.1%
0.7%
1.0%
1.2%
0.8%
1.0%
1.2%
0.9%
13%
0.7%
1.0%
1.4%
1.1%
0.9%
1.0%
13%
0.8%
13%
1.4%
0.9%
0.8%
1.2%
1.0%
1.2%
0.8%
0.8%
less than 30 davs
Liquid/Slurry. Pit Storage for more than 30 days
Drytot
\9%
1.9%
\&%
1.4%
1.0%
1.0%
1.4%
2.4%
1.8%
0.8%
13%
1.2%
1.1%
1.5%
13%
2.1%
0.8%
1.2%
1.0%
0.9%
0.8%
1.9%
1.4%
0.8%
1.1%
1.4%
0.8%
1.1%
13%
0.9%
15%
0.7%
1.1%
1.9%
1.1%
1.0%
1.1%
1.7%
0.9%
1.6%
2.1%
1.0%
0.8%
1.4%
1.0%
13%
0.8%
0.8%
is assumed to
is assumed to
Solid
Storage
1.4%
1.4%
13%
\2%
0.9%
0.9%
12%
1.5%
1.4%
0.8%
1.1%
1.0%
0.9%
1.1%
12%
1.4%
0.8%
1.1%
0.9%
0.8%
0.8%
1.4%
1.1%
0.7%
1.0%
\2%
0.8%
1.0%
1.2%
0.9%
13%
0.7%
1.0%
1.4%
1.1%
0.9%
1.0%
13%
0.8%
13%
1.4%
0.9%
0.8%
1.2%
1.0%
1.2%
0.6%
0.8%
have an MCF equal
have an MCF equal
lagoons are assumed to have an MCF of 90%; litter and deep pit stacks an MCF of
Systems
Dafly
Spread
0.4%
0.4%
o:4%
03%
02%
02%
03%
0.6%
0.4%
02%
03%
03%
0.2%
03%
03%
05%
0-2%
03%
02%
02%
02%
0.4%
03%
0.2%
0.2%
03%
02%
03%
03%
02%
03%
0.2%
0.2%
0.4%
0.2%
02%
02%
0.4%
0.2%
03%
05%
0.2%
0.2%
03%
0.2%
03%
02%
02%
Liquid/
Slurry
29.0%
28.9%
27.6%
21.9%
187%
185%
22.6%
38.6%
29.0%
155%
22J%
215%
20.7%
24.7%
23.8%
325%
155%
21.0%
1&1%
17.0%
18.0%
293%
24.1%
15.8%
20.8%
22.1%
163%
20.6%
213%
18.1%
245%
16.8%
20.2%
28.7%
16.2%
18.7%
18.7%
273%
19.1%
24.8%
31.7%
17.4%
16.6%
225%
155%
21.4%
17.0%
15.9%
to 50% of the MCF for
to liquid/slurry.
10%.
Anaerobic
STATES WORKBOOK
D7-13
November 1992
-------
Table D7-6
Regions of the ILS. for Manure Management Characterization
North East
South East
Plains
South
South West
Mid West
North West
Far West
Pacific West
North Pacific
Pacific Islands
Connecticut, Maine, Massachusetts, 'New Hampshire, New Jersey, 'New York,
Pennsylvania, Rhode Island, Vermont
Delaware, 'Florida, 'Georgia, Maryland, 'North Carolina, 'South Carolina,
Virginia, 'West Virginia.
Colorado, 'Kansas, 'Montana, 'Nebraska, 'North Dakota, 'South Dakota,
Wyoming.
Alabama, 'Arkansas, Kentucky, 'Louisiana, 'Mississippi, 'Tennessee
New Mexico, 'Oklahoma, 'Texas.
'Illinois, 'Indiana, Michigan, 'Ohio, 'Wisconsin, 'Iowa, 'Minnesota, 'Missouri.
'Idaho, 'Oregon, 'Washington
'Arizona, Nevada, 'Utah
'California
Alaska
'Hawaii
States that have supplied estimates of their percent use of manure management
Livestock
Table D7-7
Manure System Usage for the U.S.
Liquid/Slurry
Anaerobic and Pit
Animal Lagoons Storage
Non-Dairy Cattle 0%
Dairy 10%
Poultry8 5%
Sheep 0%
Swine 25%
Other Animals0 0%
A Includes liquid/slurry storage and pit storage.
B Includes chickens, turkeys, and ducks.
C includes goats, horses, mules, and donkeys.
1%
23%
4%
0%
50%
0%
Daily
Spread
0%
37%
0%
0%
0%
0%
Solid
Storage
& Drylot
14%
23%
0%
2%
18%
0%
Pasture,
Range &
Paddock
84%
0%
1%
88%
0%
92%
Litter,
Deep Pit
Stacks and
Other
1%
7%
90%
10%
6%
8%
Totals may not add due to rounding.
Source: SaOey et a]. (199Z).
STATES WORKBOOK
D7-14
November 1992
-------
DATA SOURCES
Many states may have their own agricultural census that includes data on animal populations
and production levels. Animal population data can be found from a variety of other sources,
including the U.S. Census of Agriculture, USDA agriculture statistics, and from livestock manure
management experts throughout the U.S. Safley et al. (1992) include animal populations and also
estimate CH4 emitted from their wastes in their report
EVALUATION
The method described above for estimating methane emissions from animal manure is based
on sound scientific data and experimental evidence. To the extent possible, emissions should be
estimated with as much information as possible about the conditions under which animal manure is
managed. This is particularly important when manure is managed under anaerobic conditions, such
as lagoons or other liquid/slurry systems.
The estimates and assumptions used by Safley et al. (1992) are instructive for identifying the
potential magnitude of emissions and the relative importance of various animals and manure
management systems. However, to the extent possible, information that is specific to the individual
state should be used because manure management systems and practices may vary in different states.
The weakest link in the method presented here is the estimates of the methane conversion
factors (MCFs) for the individual management systems. Very few field measurements are available
upon which to base these estimates, particularly for "dry" management systems such as dry lots,
pastures, and paddocks. The MCFs for the "wet" management systems such as lagoons and slurry
storage have a much stronger foundation. The inaccuracy in the emissions estimates due to this lack
of data cannot be quantified. Emissions estimates can be improved significantly once comprehensive
field measurements are performed.
This discussion has focused only on emissions of methane from animal manure. It has been
mentioned, however, that animal waste decomposition also has the potential to produce nitrous oxide.
At this time no information is available on the potential for N2O emissions; this should be
investigated in the future.
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ASAE (American Society of Agricultural Engineers). 1988. Manure Production and
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14 pp.
STATES WORKBOOK O7-15 / November 1992
-------
ASB (Agriculture Statistics Board). 1989c. Hogs and Pigs, Released: January 6,1989.
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ASB (Agriculture Statistics Board). 1989d. Layers and Egg Production, 1988 Summary. January,
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Carter, T. A. 1989. Personal communication with Dr. Thomas A. Carter. Extension Professor of
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Chawla, O.P. 1986. Advances in Biogas Technology. Indian Council of Agricultural Research: New
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Chen, T. H., D. L. Day, and M. P. Steinberg. 1988. Methane production -from fresh versus dry
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STATES WORKBOOK D7-16 November 1992
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Hashimoto, A. G. 1992. Personal communication with Dr. Andrew Hashimoto. Professor and
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lannotti, E. L., J. H. Porter, J. R. Fischer, and D. M. Sievers. 1979. Developments in Industrial
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Jain, M. K., R. Singh, and P. Tauro. 1981. Anaerobic digestion of cattle and sheep waste.
Agricultural Wastes. 3:65-73.
Kroeker, E J., D. D. Schulte, A. B. Sparling, and J. T. Chieng. 1984. Anaerobic-treatment
process stability. Journal of the Water Pollution Control Federation. 51:718-727.
Morris, G. R. 1976. Anaerobic fermentation of animal wastes: a kinetic and empirical design
fermentation. M. S. Thesis. Cornell University.
NCDC (National Climatic Data Center) 1991. Historical Clirnatological Series Divisional Data.
National Oceanic and Atmospheric Administration. Ashville, NC.
North, M. O. 1978. Commercial Chicken Production Manual. AVI. Westport, Connecticut
SaQey, L.M. 1991. Personal communication with Dr. Lawson SaQey. Professor of Biological and
Agricultural Engineering. North Carolina State University. Raleigh, North Carolina,
January 1991.
Safley, L.M., M.E. Casada, J.W. Woodbury, and K.F. Roos (1992). "Global Methane Emissions
from Livestock and Poultry Manure." EPA/400/1091/048. U.S. Environmental Protection
Agency. Washington, D.C. February 1992.
Safley, L.M., Jr. and P.W. Westerman 1992. "Performance of a Low Temperature Lagoon
Digester." Bioresource Technology. 41:167-175.
Stevens, M. A and D. D. Schulte. 1979. Low temperature digestion of swine manure. Journal of
the Environmental Engineering Division, ASCE. 105(EE1): 33-42.
STATES WORKBOOK D7-17 November 1992
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Summers, R. and S. Bousfield. 1980. A detailed study of piggery-waste anaerobic digestion.
Agricultural Wastes. 2:61-78.
Taiganides, E. P. 1987. Animal waste management and wastewater treatment pp. 91-153, In: D.
Straucb (ed.). Animal Production and Environmental Health. Elsevier. New York.
Taiganides, E, P. and R. L. Stroshine. 1971. Impacts of farm animal production and processing
on the total environment pp. 95-98. In: Livestock Waste Management and Pollution
Abatement The Proceedings of the International Symposium on Livestock Wastes, April
19-22,1971, Columbus, Ohio. ASAE. St Joseph, ML
USDA (United States Department of Agriculture). 1990. Agricultural Statistics 1990. U.S.
Department of Agriculture. Washington, DC
Webb, A. R. and F. R. Hawkes. 1985. Laboratory scale anaerobic digestion of poultry litter, gas
yield-loading rate relationships. Agricultural Wastes. 1331-49.
STATES WORKBOOK D7-18 ' November 1992
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DISCUSSION 8
METHANE EMISSIONS FROM FLOODED RICE FIELDS
OVERVIEW
Globally, flooded rice fields are the primary anthropogenic source of methane. However,
flooded rice fields account for only a small portion of U.S. anthropogenic methane emissions.
Methane is produced through anaerobic decomposition of organic material in flooded rice fields. The
CH4 escapes into the atmosphere primarily by diffusive transport through rice plants during the
growing season. It should be noted that drv upland rice fields, which are not flooded, do not produce
significant quantities of CH4.
The USDA reported that 2,887,000 acres of wetland rice, consisting of irrigated, rainfed, and
deepwater rice, were planted in 1990, while overall rice production for 1990 was reported as 154,919
CWT (pounds, hundred weight)1. However, deepwater, floating rice is not believed to produce
significant quantities of CH4 either. This is due to the fact that the lower stems and roots of the
floating rice plants are dead, and are therefore effectively blocking the primary CH4 transport
pathway to the atmosphere.
Experiments have shown that the CH4 flux from flooded rice fields varies with soil type,
temperature, redox2 potential, and pH; the type, timing, application method, and amount of fertilizer
applied; water management technique; and cultivar type (e.g., Schutz et aL, 1990; Matthews et al.,
1990). Understanding bow these variables control emissions requires understanding how they control
the three processes that together determine emissions. These three processes are CH4 production,
CH4 oxidation, and CH4 transport
Methane production in flooded rice fields is the result of decomposition of organic material
by methanogenic bacteria, which begins only after anoxic, reduced soil conditions have been
established in the paddies. There are three primary sources of the organic material from which CH4
is produced: (1) root exudates and sloughed-off root cells from the rice plants,
(2) organic material such as rice straw that was incorporated into the soil during field preparation,
and (3) floodwater biomass (i.e., algae). Pan of the methane that is produced does not reach the
atmosphere, as it is oxidized by aerobic methanotropic bacteria that are present in the oxic surface
layer of the submerged paddy soil and in the rhizosphere where oxygen is available around the rice
roots. Averaged over a growing season, as much as 60-80% of the produced CH4 is oxidized
(Holzapfel-Pschorn et al., 1985; Sass et aL, 1990). Transport of the remaining, non-oxidized methane
from the submerged soil to the atmosphere occurs by diffusion through the floodwater, by ebullition
(i.e., bubbling), and by plant-mediated transport. The most important pathway of escape is diffusive
1 Both production and planting statistics include all varieties of rice: short grain, medium grain, and long
grain.
2 Redox refers to oxidation-reduction, two processes that take place simultaneously. Oxidation is the loss
of an electron by an atom, and reduction is the gain of an electron by an atom.
STATES WORKBOOK D8-1 November 1992
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flow through the intercellular gas space system of the rice plant (e.g., Holzapfel-Pschorn and Seiler,
1986). Figure D8-1 graphically depicts the process of CH4 production and its emission.
Certain soil characteristics have been found to affect CH4 production. Since the bacteria
responsible for CH4 production are strict aerobes which cannot function in the presence of oxygen
or other inorganic electron acceptors, CH4 formation usually occurs only after prolonged flooding of
soils that have sufficient carbonaceous substrate to reduce these electron acceptors. Electron
acceptor reduction is generally sequential, with oxygen being reduced first, followed in order by
nitrate, manganic manganese compounds, ferric iron compounds, sulfate, and lastly carbon dioxide
(CO£. The production of CH4 from the reduction of CO2 does not occur until the sulfate has been
reduced and Eh values have declined to less than about -200 mV (Patrick and Delaune, 1977).
Methane formation is also favored at near neutral pH values. Rice soils that are most likely to show
high methane production are Entisols, Histosols, Inceptisols, Alfisols, Vertisols, and Mollisols.
Experiments in Italy (Holzapfel-Pschorn and Seiler, 1986; Schutz et al., 1989a) have found
consistent diurnal fluctuations in CH4 emissions, with maximum values during the afternoon and
minimum values during the early morning, indicating that CH4 production is strongly dependent on
the temperature of the upper soil layer. In these experiments, CH4 emissions approximately doubled
when soil temperature rose from 20° to 25°C A similar dependence of CH4 emissions on
temperature was found by Koyama (1964) in laboratory experiments using anaerobically
incubated paddy soil samples. However, experiments in California (Cicerone and Shetter, 1981;
Cicerone et al., 1983), under climatic conditions similar to those in Italy, found no clear relationship
between CH4 flux and soil temperature, and experiments in China found that diurnal patterns of
emissions varied seasonally and were not related to soil temperature (Schutz et al., 1990). Two
maximum daily emissions occurred during the early vegetation period in China, one at noon and one
during the night, while only one daily maximum occurred (at night) in the late vegetation stage.
Application of either of the commercial nitrogen fertilizers ammonium sulfate or urea has
generally been found to reduce CH4 emissions, especially if the fertflvw is deeply incorporated into
the soil. This is believed to be due to suppression of CH4 production as a result of the addition of
sulfate or ammonium ions. In continuous measurements over three years in Italy, Schutz et al.
(I989a) found that deep incorporation of either fertilizer resulted in a reduction in methane emissions
averaged over a growing season, relative to unfertilized plots, of about 50%. Surface application of
ammonium sulfate resulted in slightly reduced emissions; surface application of urea resulted in
slightly enhanced emissions. On the other hand, an experiment in California (Cicerone and Shelter,
1981) found that application of ammonium sulfate increased CH4 emissions almost five-fold.
However, these results from California are based on late summer measurements, rather than
continuous measurements over an entire growing season.
Application of organic fertilizers (e.g., rice straw, composted rice straw, animal wastes)
whether or not in combination with mineral fertilizers, has been found, in most cases, to enhance
CH4 emissions. The organic fertilizers provide an additional carbon source for the production of CH4
in the paddy soil. Both Schutz et al. (1989a) and Yagi and Minami (1990) found that increasing
applications of dried and chopped rice straw resulted in increasing enhancement of CH4 emissions,
relative to unfertilized paddies and paddies fertilized with mineral fertilizer. Schutz et al. (1989a)
found that application of composted rice straw also enhanced CH4 emissions, while Yagi and Minami
(1990) found that additions of composted rice straw only slightly enhanced emissions. However,
STATES WORKBOOK D8-2 November 1992
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Figure D8-1
WATER-AIR
EXCHANGE
CH4
til
[WATER
111
ANOXIC
SEDIMENTS
CO,
t h
CH4-oxidatton by
methanotrophlc
bacteria
CH4
CH4
00° EBULLITION
n
CH«- production by
tnethanogenic
bacteria
Source: SchOtz el at, 1991.
STATES WORKBOOK
D8-3
November 1992
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preliminary experiments in China (Schutz et al, 1990) found that application of organic fertilizers
(animal manure, rape seed cake) did not affect emissions.
Water management practices also influence CH4 emissions since it is only through continuous
flooding that the paddy soil remains sufficiently reduced for methane production to occur. When
water is drained from fields during the growing season or between crops, the soil redox potential in
the surface soil layer increases, and CH4 emissions decline (Yagi and Minami, 1990; Sass et at, 1990).
This is probably due to both a reduction in CH4 formation (due to increased redox potentials) and
to an increase in CH4 oxidation (due to increased input of oxygen into the soils).
Cultivar selection is likely to affect CH4 emissions through two mechanisms: (1) root
exudation, and (2) gas transport Many studies have observed two or three maxima in CH4 emissions
during the growing season with the last one or two peaks occurring during the reproductive stage of
the rice plants. These latter emission peak(s) may be due to peaks in CH4 production that result
from the plants providing soil organic bacteria with organic root exudates or root litter at this time
(Schutz et al., 1989a). The degree of root exudation and soughing off of root cells that occurs is
believed to vary between cultivar types. The rice plant also affects CH4 emissions through gas
transport mechanisms. Downward oxygen transport through the plant (and subsequent oxidation of
CH4 in the rhizosphere) and upward methane transport probably varies between cultivars. Gas
transport mechanisms may also play a role in controlling the latter emission peaks, e.g., methane
transport may be more efficient during the reproductive stage of rice plants than at other
developmental stages (Sass et al., 1990). Sporadic measurements at four sites in India (Parashar et
ah, 1991) indicate that CH4 emissions vary between cultivars, but continuous measurements of
emissions from different cultivars over an entire growing season, and with all other variables held
constant, have yet to be made. Experiments are also needed to determine the relative importance
of the rice plant mechanisms that affect CH4 emissions, i.e., the relative importance of organic input
versus that of gas transport.
Large seasonal variations in CH4 flux from paddies have been observed in most experiments,
although the magnitude and liming of the seasonal peaks vary greatly between studies. In studies in
Italian rice fields, two to three emission peaks have been observed (Schutz et al., 1989a). The first,
occurring during tillering of the rice, is believed to be due to mineralization of organic material in
the soil prior to flooding, since the timing and magnitude of this peak in planted fields has been
found to be similar to that in unplanted fields. The second peak, occurring during the reproductive
stage of the rice plant, is believed to be due to root exudation, and the third to degradation of dying
plant materials and plant litter. Three peaks in emissions were observed in field experiments in Texas
rice fields, but the early season emission peak was missing. This was probably because there was not
much organic material present in the soil since the fields had been fallow for the previous two years,
and the sparse native material that was tilled into the soil was allowed to decompose for several
months before planting and flooding (Sass et al., 1990). The three peaks that were observed occurred
immediately prior to panicle differentiation, just before heading, and during grain filling and
maturation.
DESCRIPTION OF WORKBOOK METHODOLOGY
Because of the variability of measured emissions and the uncertainty in the effects of factors
that control methane emissions from flooded rice fields, only two variables are included in the first
STATES WORKBOOK D8-4 November 1992
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methodology that we recommend. These two variables are rice ecology, Lex, upland, deepwater, or
other wetland type, and growing season length. In this methodology, a daily emission rate ranpe is
applied to the number of non-deepwater, wetland acre-days harvested annually3 to obtain annual
emissions from this source. By employing an emission range, this methodology captures some of the
variability described above without requiring the detail in calculations that would be necessary to
account for factors such as soil characteristics and fertilizer regime (if the data permitted such an
accounting). We recommend using average daily emission rates (pounds CH4 per acre per day, or
Ibs CH4/acre/day), rather than seasonal emission rates (Ibs CH4/acre/growing season), to account for
the variability in growing season lengths both within and between states. The rice growing season
is usually about four months, but can vary from about 80 to 180 days. The daily emission rate range,
however, should be a seasonally-averaged range, Le^ based on emission measurements taken over an
entire season, so that the seasonal fluctuations described above are averaged Using a daily emission
rate range based on a few measurements during a growing season, rather than semi-continuous
measurements over an entire growing season, could yield misleading results.
The recommended range for daily emission fluxes is based on recent field measurements in
Texas (Sass, 1991):
135 - 4.04 Ibs CH4/acre/day.
Sass measured methane emissions from several experimental plots in Texas over the 1990
growing season, and calculated an average daily emission rate of 2.69 (± 50%) Ibs CH4/acre/day. We
recommend this range for two reasons: 1) it is based on experiments in the U.S., and 2) it is
reasonable given the range in emission estimates from other studies. For comparison, measurements
in Italian rice fields over a three-year period yielded seasonally-averaged daily emission rates of 1.44-
3.41 Ibs CH4/acre/day for unfertilized fields, and of 2J1-539 Ibs CH4/acre/day for fields fertilized with
organic or mineral fertilizers (Schiitz et al., 1989a). Recent field measurements in China yielded a
range of daily emission rates of 1.71 - 630 Ibs CH4/acre/day (Schutz, eL al., I989b). In California,
the seasonally-averaged daily emission rate for fields fertilized with mineral fertilizers was 225 Ibs
CH4/acre/day (Cicerone et al., 1983).
States may wish to develop their own emission coefficients, especially if wetland rice is a major
crop. As discussed above, because of the great variability in methane emissions over a growing
season, seasonally-averaged daily emission coefficients (i.e., the seasonal average of average daily
emission coefficients based on semi-continuous measurements [2-12 per day] taken over an entire
growing season) should be used (see Braatz and Hogan, 1991, for a description of appropriate
emission measurement techniques).
The daily harvested area, to which an emission range is applied, should not include upland
areas or deepwater. floating rice areas because these areas are not believed to release significant
quantities of methane. Also, it is recommended that a three-year average, centered on 1988, of
annual acre-days harvested be used. Because agricultural activities typically fluctuate from year to
3 The number of acre-days harvested annually is equal to: (the number of acres with a certain cropping
length x the number of days in that cropping cycle) + (the number of acres with another cropping cycle length
x the number of days in that cropping cycle ) +..... The workbook assumes that there is only one cropping
cycle for all states.
STATES WORKBOOK D8-5 November 1992
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year due to economic, climatic, and other variables, estimation of CH4 emissions based on one
specific year of data on rice area harvested could lead to misleading or misrepresentative results.
Ideally, only the harvested rice area that represents an anthropogenic increase in methane
emissions above natural levels would be included in the emissions inventory. For example, if a
freshwater wetland, which is a natural source of methane, is converted to a flooded rice field, and the
annual CH4 emissions from the former land use are equivalent to those of the latter land use, then
this rice area should not be included in the inventory. However, it is difficult, if not impossible, to
know what the annual methane emissions might have been in the past (or even what the original land
use was). Also, conversion of an area that naturally emits CH^ such as a freshwater wetland, to a
flooded rice field may not necessarily result in reduced annual CH4 emissions. For example, some
wetlands are flooded for only part of the year. Conversion of such a wetland to an intensively
managed rice field may result in longer periods of continuous flooding and therefore greater
production of methane over an annual cycle. Similarly, the soils of intensively cultivated rice fields
may receive more organic inputs (e.g., organic fertilizers, root exudates) than natural wetlands, which
would also result in greater methane production. For these reasons, no attempt to account for this
issue is made in the methodology described here.
In summary, to estimate a state-specific annual CH4 emissions range from rice cultivation
using the first methodology, the three-year average of the number of (non-deepwater, wetland) acre-
days harvested annually in the state would be multiplied by the endpoints of the recommended range,
Low estimate (Ibs CH4) = (average # of acre-days harvested annually) x
(135 Ibs CH4/acre/day)
High estimate (Ibs CH4) (average # of acre-days harvested annually) x
(4.04 Ibs CH4/acre/day)
For any users interested in converting CH4 emissions to CH4-C emissions, each estimate would then
be multiplied by 12/16.
A complete example of how to apply the recommended approach is shown in Table D8-1.
DATA AV AILABIIJTY
Because variables such as soil properties (type, pH, Eh), fertilizer practices, water
management practices, and cultivar type have been shown to affect CH4 emissions from rice fields,
a state may want to collect these data at the same time as harvested area data are collected.
Therefore, when the effects of these variables on emissions are sufficiently understood to include
them in an emissions inventory methodology, the data will have already been collected.
STATES WORKBOOK D8-6 November 1992
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Table DM
Sample Calculation for Workbook Methodology
Hypothetical state statistics for year 1987:
10 million acres of rice growing cultivated area that is double-cropped, for 120 days during the first growing
season and for 110 days during the second growing season, and 2 million acres that is triple-cropped, with
growing seasons of 120 days, 110 days, and 80 days (This cultivated acreage would translate into
(10x2)+(2x3) = 26 million acres harvested annually}
To calculate annual emissions, the following calculations would be made:
Low estimate:
1) Estimate number of acre-days in year 1987:
(10 million acres x 120 days) + (10 million acres x 110 days) + (2 million acres x 120 days) + (2
million acres x 110 days) + (2 million acres x 80 days)
a 2,920 million acre-days
2) Estimate number of acre-days for 1988 and 1989.
3) Average the acre-days for 1987, 1988, and 1989.
(For this example, assume the 3-year average is 2,900 million acre-days)
4) Multiply the average number of acre-days by the low emission estimate:
(2,900 million acre-days) x (135 Ibs CHJacre/day)« 3,915 million IDS CH,
or 1.96 million tons CH4
5) Convert to mass of carbon:
(1.96 million tons CH4) x (12 tons C/16 tons CH«) = 1.47 million tons CH4-C
High Estimate:
Same as above, except the high emission estimate (4.04 Ibs CH«fecre/day) would be used instead of the low
emission estimate (135 Ibs CHVacre/day):
(2,900 million acre-days) x (4.04 Ibs CH/acre/day) = 11,716 million Ibs CH<
or 5.86 million tons CH«
or 439 million tons CH<-C
Result: This hypothetical state emits 1.96-5.86 million tons CH4 (IA7-439 million tons CH4-C) each year due to rice
cultivation.
STATES WORKBOOK D8-7 November 1992
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SUMMARY
Methane emissions from flooded rice fields vary significantly over hourly, daily, and
seasonal cycles, and are affected by a wide range of factors. Research to date, most of which has
been undertaken in temperate regions where less than 10% of the world's rice is grown, has not
provided consistent enough results to allow researchers to quantify the effects of many of these
factors on CH4 emissions.
The methodology outlined above for use in estimating national CH4 emissions from rice
cultivation is meant to include some of this variability, without being too complex and therefore
impractical. The required data (i.e., number of acre-days harvested annually in each rice-
producing state) is readily available, while the methodology captures some of the observed
emissions variability without requiring extrapolation of relationships between factors and emissions
that are not yet completely understood.
The characterization of CH4 emissions from flooded rice fields is a rapidly evolving
research area, likely to yield results in the near future that can be used to refine the suggested
methodologies. For example, it may be possible to tie methane emissions to soil type and
cropping cycle (Yagi and Minami, 1990; Schutz et al. 1991) so that a state's calculated emissions
will be dependent upon not only the rice area harvested, but also these two other factors as well.
A recent study by Neue et al. (1990), using soil characteristics and water regimes, found that only
198 million acres of harvested wetland rice lands worldwide (about 65% of the total harvested
wetland area, or about 55% of the total (wetland + upland) harvested area) are likely to be
potential sources of CH4. Although a particular methodology has been recommended here, the
process of estimating emissions should remain flexible enough for new research results, such as
those of Yagi and Minami (1990), Neue et al. (1990), and Schutz et al. (1991), to be incorporated
when appropriate.
REFERENCES
Braatz, B.V., and K.B. Hogan, (eds.). 1991. Sustainable Rice Productivity and Methane Reduction
Research Plan, U.S. Environmental Protection Agency, Washington, D.C.
Cicerone, RJ., and J.D. Shelter. 1981. Sources of atmospheric methane: Measurements in rice
paddies and a discussion. Journal of Geophysical Research 86:7203-7209.
Cicerone, RJ., J.D. Shelter, and C.C. DeKviche. 1983. Seasonal variation of methane flux from a
California rice paddy. Journal of Geophysical Research 88:11022-11024.
Holzapfel-Pschorn, A., R. Conrad, and W. Seiler. 1985. Production, oxidation, and emission of
methane in rice paddies. FEMS Microbiology Ecology 31:343-351.
Holzapfel-Pschorn, A., and W. Seiler. 1986. Methane emission during a cultivation period from
an Italian rice paddy. Journal of Geophysical Research 91:11803-11814.
STATES WORKBOOK D8-8 November 1992
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Koyama, T. 1964. Biogeochemical studies on lake sediments and paddy soils and the production
of atmospheric methane and hydrogen. In: Miyake, Y., and T. Koyama, eds. Recent Researches
in the Fields of Hydrosphere, Atmosphere and Nuclear Geochemistry. Muruzen, Tokyo, 143-177.
Matthews, £., L Fung, and J. Lerner. 1991. Methane emission from rice cultivation: Geographic
and seasonal distribution of cultivated areas and emissions. Global Biogeochemical Cycles 53-24.
Neue, H.U., P. Becker-Heidmann, and H.W. Scharpenseel. 1990. Organic matter dynamics, soil
properties, and cultural practices in rice lands and their relationship to methane production. In:
Bouwman, AJ., ed. Soils and the Greenhouse Effect. John Wiley & Sons, Chichester, 457-466.
Parashar, D.C, J. Rai, P.K. Gupta, and N. Singh. 1991. Parameters affecting methane emission
from paddy Gelds. Indian Journal of Radio and Space Physics 20:12-17.
Patrick, Jr., W.H., and R.D Delaune. 1977. Chemical and biological redox systems affecting
nutrient availability in the coastal wetlands. Geosdences and Man 28:131-137.
Sass, R.L., F.M. Fisher, P.A. Harcombe, and F.T. Turner. 1990. Methane production and
emission in a Texas rice field. Global Biogeochemical Cycles 4:47-68.
Sass, R.L. 1991. Personal Communication.
Schiitz, H., A. Holzapfel-Pschora, R. Conrad, H. Rennenberg, and W. Seiler. 1989a. A 3-year
continuous record of the influence of daytime, season, and fertiliser treatment on methane
emission rates from an Italian rice paddy. Journal of Geophysical Research 94:16405-16416.
Schutz, H., P. Schroder, and H. Rennenberg. 1991. Role of plants in regulating the methane flux
to the atmosphere. In Sharkey, T.D., E.A. Holland, and H.A. Mooney, eds. Trace Gas Emission
from Plants. Academic Press, New York, in press.
Schiitz, H., W. Seiler, and H. Rennenberg. 1989b. Presentation (by Rennenberg) at the
International Conference on Soils and the Greenhouse Effect, 14-18 August 1989. Wageningen,
The Netherlands.
Schiitz, H., W. Seiler, and H. Rennenberg. 1990. Soil and land use related sources and sinks of
methane (CH4) in the context of the global methane budget In Bouwman, A.F., ed. Soils and
the Greenhouse Effect. John Wiley & Sons, Chichester. 269-285.
Yagi, K-, and K. Minami. 1990. Effects of organic matter applications on methane emission from
Japanese paddy fields. In Bouwman, A.F., ed. Soils and the Greenhouse Effect. John Wiley &
Sons, Chichester. 467-473.
STATES WORKBOOK D8-9 November 1992
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DISCUSSION 5
METHANE AND CARBON DIOXIDE EMISSIONS FROM LANDFILLS
This discussion chapter primarily focusses on estimating methane emissions from landfills.
While landfill gas contains roughly equal amounts of methane and CO2, landfill CO2 emissions are
small compared to emissions from other sources discussed in this report. However, landfills represent
one of the major anthropogenic sources of methane emissions in the U.S. and globally. Moreover,
methane is a more potent greenhouse gas than CO2 (see, for example, discussion on the relative
GWPs b the Introduction to this report).1 Therefore, relatively small quantities of methane
emissions have large implications for global wanning.
OVERVIEW :
Methane (CH4) and Carbon Dioxide (COj) are produced from anaerobic decomposition of
organic matter in landfills by methanogenic bacteria. Organic waste first decomposes aerobicaUy (in
the presence of oxygen) and is then attacked by anaerobic non-methanogenic bacteria, which convert
organic material to simpler forms like cellulose, amino acids, sugars, and fats. These simple
substances are further broken down to gases and short-chain organic compounds (Hj, COj,
CH3COOH, HCOOH, and CH3OH), which form the substrates for methanogenic bacteria. The
resulting biogas consists of approximately 50% CO2 and 50% CH4 by volume, although the
percentage of CO2 may be smaller because some CO2 dissolves in landfill water (Bingemer and
Crutzen, 1987). Additionally, some landfills practice flare recovered landfill gas, which converts the
methane portion of the gas to CO2-
Numerous factors affect the amount of CH4 and CO2 produced in landfills. The factors may
be divided into two general categories: management practices and physical factors.
Management Practices Physical Factors
Waste Management Type (portion of waste Waste Composition
that is landfilled) Moisture Content
Density of Refuse Leachate pH
Particle Size of Refuse Nutrients
Landfill Temperature
Municipal solid waste (MSW) constitutes a significant portion of all types of waste produced in the
United States and also the waste deposited in landfills that produce methane.2 The two types of
1 For example, on a gram for gram basis, methane's direct impact on global warming is about 11
times greater than CO2 over a 100 year time period (IPCC, 1992).
2Other types of waste that may produce methane in landfills are hazardous and industrial solid
waste and agricultural waste. Hazardous and industrial waste landfills may contain compounds that
will result in a low pH environment toxic to the methanogenic bacteria. Agricultural waste, if
landfilled, could potentially be a significant source of methane emissions but is typically not deposited
where anaerobic conditions develop (see Bingemer and Crutzen, 1987).
STATES WORKBOOK D5-1 November 1992
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waste management practices that lead to methane production are open dumping and sanitary
landfilling. Since CH4 production from open dumping, or waste piles, is highly uncertain and based
on anecdotal evidence, and since the amount of waste that is openly dumped in the United States
is negligible, CH4 emissions from open dumping is assumed to be zero.
In sanitary landfills, a tightly packed, anaerobic environment favorable for landfill gas
production is created as compacted waste is spread evenly over the active area of the landfill and
covered with some type of nonporous soil (e.g., clay). In order to avoid discrepancies over the term
"landfill," this definition of sanitary landfill will be used in the methodology described below.
Other variables of management practices that affect CH4 and CO2 generation are density and
particle size of refuse. By increasing density, a greater mass can be placed into a specified volume.
As density increases, the degree of saturation (i.e., the ability to absorb water) will increase due to
greater mass, which can lead to more gas production per unit volume (Facey and DeGier, 1986).
One way to increase density is by shredding refuse. Shredding not only increases density, but also
reduces panicle size, which results in a greater surface area exposed to bacterial activity, moisture,
and nutrients. In addition, if shredded refuse is spread evenly in thin layers (30 cm) and then
compacted, size could be further reduced. Extremely dense refuse (i.e., baled refuse), however,
cannot be penetrated by water, and consequently, may produce less gas (Pacey and DeGier, 1986).
Other variables that may affect emissions are the design and size of the landfill and the use of cover
soils.
The actual composition of the municipal solid waste is very important in determining the
amount of landfilled gas produced. Municipal solid waste supplies the necessary starting material for
methane generation in landfills by providing degradable organic carbon (DOC) with which
methanogenic bacteria interact to produce landfill gas.3 For the most part, the majority of waste in
the United States is paper and paper products, which contain a higher carbon content than food, for
example (40% by weight in Bingemer and Crutzen, 1987), and will therefore produce more CH4.
One of the foremost physical factors influencing landfill gas production, aside from the waste
itself, is the moisture content of the landfill environment (Pacey and DeGier, 1986). Moisture is
essential to anaerobic decomposition and the life of methanogenic bacteria. Water serves as a
transport medium for nutrients, bacteria, and alkaline substances within the refuse (Pacey and
DeGier, 1986). In a sanitary landfill the moisture content will affect the rate at which landfill gas is
produced because wastes are exposed to more bacteria as moisture increases. Unfortunately, no
explicit functional relationship exists between moisture content of the landfill environment and gas
production estimates (Emcon Associates, 1982).4 The moisture of the refuse, however, can be
3DOC is biochemically decomposed to form substrates and can be divided into two parts:
dissimulated and assimilated. The dissimulated fraction is the portion of carbon in substrates that is
converted to landfill gas (i.e., CO2 and CH4), and the assimilated fraction is the remainder of carbon
that is used to produce new microbial cell material (Tabasaran, 1981).
4In a study recently conducted by U.S. EPA's Office of Solid Waste, a correlation between landfill
gas generation rate and precipitation rate was obtained (no correlation between precipitation rate
and moisture content in the landfill was evaluated). Based on data from 12 Vet" landfills
(precipitation of 0.58 m or more) and data from 8 "dry" landfills (precipitation of less than 0.58 m),
STATES WORKBOOK D5-2 November 1992
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determined by analyzing the composition of the landfiUed MSW and determining the percentage of
"wet refuse* (Le., food wastes) and "dry refuse" (i.e., paper waste). Ahuja (1990) attempts to include
the percentage of dry refuse in the total amount of MSW landfiUed, which contains the DOC
available for methane production, in his methodology to estimate methane emissions; this
methodology is discussed in the next section.
Other factors that are important but have not been factored into any emission estimate due
to the lack of data include the leachate pH and nutrient availability. The optimal pH for gas
production is near neutral, between 6.8 and 7.2, which is not usually reached for several years (Facey
and DeGier, 1986). Methane generation is not inhibited unless the environment is very acidic (pH
<6.0). Alkaline substances, transported in water, help to balance the pH level and hinder the
formation of organic acids, which in large concentrations decrease methane production. Nutrients
are essential to the life and growth of bacteria.
Temperature, unlike leachate pH, can be related to the amount of degradable organic carbon
that will generate landfill gas (i.e., the fraction of DOC dissimilated). At temperatures below 10-15°C,
methane production is drastically reduced (Pacey and DeGier, 1986). Because the majority of
methane production occurs in the deeper layers of the landfill, where heat is generated from
anaerobic decomposition, temperatures typically range between 2S-40°C An average of 35°C can be
expected within the anaerobic zone (2-4 m) (Gunnerson and Stuckey, 1986, in Bingemer and Crutzen,
1987) and will result in -77% dissimilated DOC.5 At extremely high temperatures (above 60°C)
methane generation usually ceases (Pacey and DeGier, 1986).
Landfill gas recovery can be an important factor in reducing CH4 emissions from landfills as
well as provide a source of renewable energy. Landfill gas recovery systems are increasing, especially
in the United States, and the CH4 generated from landfills is being captured as an energy source.
Currently, there are 242 sites in 20 nations where landfill gas is captured and its energy contents
exploited (Richards, 1989). The U-S. is by far the biggest collector and user of landfill gas, with the
UK and Germany following. It would be beneficial to estimate the amount of CH4 existing in the
recovered landfill gas in order to subtract it from total CH4 emitted from that state's landfills. This
has been accounted for in the methodology, although each state will need to estimate its own quantity
of CH4 capture from biogas recovery sites.
Refuse may be disposed of by other management practices that do not produce methane such
as incineration, materials recovery/recycling, and composting. These alternative methods of disposal
may be more attractive than sanitary landfilling without gas recovery systems as land availability
declines and potential health and environmental risks of landGlling increases in the U.S. For example,
Japan prefers incineration over landfilling; about 73% of Japan's waste is'disposed of by incineration
and only 23% by sanitary landfilling (Hayakawa, 1990, in Thorneloe, 1991).
landfill gas emissions from "wet" landfills were 2.6 times greater than emissions from "dry" landfills
(Thorneloe, 1990).
5Landfill temperature is related to the amount of DOC that is dissimilated to produce biogas by
the relationship: Cc / Cj = (0.014 T + 0.28), where Cc = carbon converted to biogas, Cj- = total
carbon compounds in substrates, and T = landfill temperature (Tabasaran, 1981). From this
relationship, as temperature increases, so does the rate of gas formation.
STATES WORKBOOK D5-3 November 1992
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DESCRIPTION OF WORKBOOK METHODOLOGY
The simplest methodology for estimating CH4 emissions from landfills is based on a mass
balance approach, where an instantaneous release of methane is assumed to enter the atmosphere
during the same year that refuse is placed in the landfill (Bingemer and Crutzen, 1987). Furthermore,
Bingemer and Crutzen do not consider subsequent releases of CH4 to the atmosphere from the MSW
placed in a landfill that will be emitted in future years nor from previous years since their approach
implicitly assumes that all waste placed into a landfill during the year emits all potential methane
immediately. Bingemer and Crutzen use four economic regions: ILSJCanada/Austraiia, Other
OECD, USSR/E. Europe, and Developing Countries. Then they determine how much MSW is
produced for each region and how much of that MSW is degradable organic carbon. To calculate
the annual emissions from MSW, Bingemer and Crutzen used the following equation:
(1) Methane Emissions = Total MSW generated (Ibsyyr) x MSW landfilled (%) x DOC
in MSW (%) x Fraction Dissimilated DOC (%) x 0.5 Ibs
CH4/lb biogas x Conversion factor (16 Ibs CH4/12 Ib C) -
Recovered CH4 (Ibs/yr).
The MSW generation rates and composition data for the U.S. can be used to calculate methane
emissions instead of the regional factors for U.S./Canada/Australia (see Table 5-1). Currently, no
state-specific data are available, but each state can estimate its annual MSW generation rate and
percentage of MSW landfilled. MSW generation rates and percentage of MSW landfilled for the
U.S. have been estimated by the OECD (1989) as well and are presented in Table 5-1. Bingemer
and Crutzen's regional estimates are for 1980 and are outdated somewhat; the country-specific
estimates presented by OECD (1989) were taken from 1988 data or the nearest year to 1988 for
which data were available. The U.S. EPA's Office of Solid Waste has also provided MSW and MSW
landfilled figures.
Table 5-1
The United States' Waste Disposal, Composition, and Waste Generation
Source
Bingemer and Crutzen (1987)
EPA (1988)
OECD (1989)
Piccot et al. (1990)
WRI (1990)
Year
1980
1986
1985
1988
1983
%MSW
Landfilled
91
83.2
62
85
NA
% DOC of
MSW
22
NA
NA
21
NA
Waste Generation
(Ibs/cap/yr)
4.0
4.0
4.4
1.9
4.6
In another recent study country-level data were collected for 31 countries, representing 67%
of the global population, through literature review and personal communication (Piccot et al., 1990).
Piccot et al. determined country-specific factors of MSW generation rate per capita^ waste
STATES WORKBOOK
D5-4
November 1992
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composition (used to calculate percentage of degradable organic carbon), and disposal practice
(percentage of waste landfilled) for the United States as well (Table 5-1).
While the method described was developed to estimate methane emissions from landfills, it
can also be used to approximate CO2 emissions because landfill gas contains roughly equal portions
of Ct>2 and methane. Assuming that the quantity of CO2 and methane in landfill gas are roughly
equal, CO2 emissions can be calculated by multiplying methane emissions by 44/16 to convert to tons
of CO2- Additional CO2 emissions may result when landfill methane is flared. In order to calculate
CO2 emissions from this source, the amount of landfill methane that is flared must be estimated.
Next, methane flared should be multiplied by 0.98 (an estimated 98% of methane flared will be
converted to CO^ and then by 44/16 to convert to
ALTERNATE METHODOLOGY
The methodology outlined by Ahuja (1990) is based on Bingemer and Crutzen's assumptions
but is more detailed due to the addition of a new variable - percentage of MSW that is dry refuse.
Using assumptions by Bingemer and Crutzen (1987), % MSW as dry refuse, and an average landfill
temperature of 35°C to derive the fraction of dissimulated DOC, methane emissions can be calculated
as follows (Ahuja, 1990):
(2) Methane Emissions = Total MSW generated (Ibs/yr) x MSW landfilled (%) x DOC
in MSW (%) x Dry Refuse (%) x Fraction dissimilated DOC
(%) x (0.5 Ibs CH4/lb biogas) x Conversion factor (16 Ibs
CH4/12 Ib C) - Recovered CH4 (Ibs/yr).
A more complex method for estimating methane emissions from landfills is based on a first-
order kinetic model, the SchoU Canyon model, which considers timed releases of methane to the
atmosphere (Thomeloe, 1990). Best results are usually obtained when the model is applied to
individual landfills, but it can be applied to an entire country such as the U.S. Estimates have been
made for the U.S. using this model (e.g., Colt et al., 1990). Detailed information, such as waste
generation and composition, moisture content, pH, temperature, available nutrients, landfill's age,
size, type, and time since closure, is required to calculate emissions (Thorneloe, 1990). This method
assumes that gas production will be highest upon initial placement of waste in the landfill, after a
certain negligible lag period during which anaerobic conditions are formed. The rate then decreases
exponentially (i.e., undergoes first-order decay) as the degradable organic carbon available decreases
(U.S. EPA, 1990). The model requires that MSW rates over the life of the landfill, or extended
period of time (e.g., 1960-1990), be used to estimate methane emissions more accurately. The model
equation and variables are described briefly below:
QCH4 = kxL0xRxe-lrt
where, QcH4 = methane generation rate at year t (ft3/yr),
L0 = potential methane generation capacity (ft^/tons of refuse),
R = quantity of waste landfilled (tonsyyr),
k = methane generation rate constant (yr"1),
t = time since initial refuse placement (yr).
STATES WORKBOOK DS-5 November 1992
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Theoretically, LQ depends on the type of refuse only and is based on the chemical composition of
refuse and its biodegradability. The methane generation constant, k, determines how quickly the
methane generation rate decreases (U.S. EPA, 1990). The rate constant and the generation rate are
related; the higher the value of k, the faster the methane generation rate decreases over time. The
rate constant is affected by the same factors that affect LQ, with the addition of temperature. Some
of these variables themselves, such as LQ and k, need to be calculated even before the equation can
be used, although some values have been determined (see, e.gn Barlaz and Ham, 1988, or EPA,
1990). To date no functional relationships have been determined among these key factors and a
better understanding of these factors is needed to more accurately calculate methane emissions from
landfills with this approach.
Given the lack of supporting data about most landfills (e.g., MSW generation rates dating
back to 1960, etc.) and the level of uncertainty associated with some of the variables, such as L^ and
K, the detailed method of estimating emissions using the first-order kinetic analysis (Scholl Canyon
model) seems premature for state-level estimates at this time. If, on the other hand, the necessary
data were available to an individual state, CH4 emissions could be estimated using the Scholl Canyon
model. For the majority of states, therefore, the methodology expressed in either Equation (1) or
(2) is the recommended approach for estimating CH4 emissions from landfills.
Other sources of uncertainty in estimating CH4 emissions are the effects of climate on
methane emission rates and the impact of landfill design characteristics and maintenance procedures
(Piccot et at, 1990). Landfill gas collection facilities provide an opportunity to study the generation
of landfill gas in similarly operated facilities, with the goal of developing quantifiable relationships
between climate, waste quantity and composition, and gas generation. These relationships would be
developed by characterizing the waste streams (especially regarding quantity and composition), design,
and climate of these facilities, then correlating these data with facility landfill gas output (Piccot et
al., 1990).
AVAILABILITY OF DATA
In-state sources should be consulted to obtain data on total MSW generated and the amount
of methane recovered from landfills. Ideally, in-state data sources should also be used for waste
characteristics and waste management practices (e.g., percent of MSW that is landfilled; percent of
DOC contained in the MSW; and percent of DOC that is dissimilated). However, in many states,
such data may not be readily available. In such cases, the average default values indicated in the
workbook section should be used. Additionally, some data on waste generation, waste composition,
and waste disposal are available from EPA (1988).
REFERENCES
Ahuja, D. 1990. Estimating Regional Anthropogenic Emissions of Greenhouse Gases. Forthcoming
in The Indian Geosphere Biosphere Programme. Tata Energy Research Institute, New Delhi, and The
Bruce Co., Washington, D.C.
STATES WORKBOOK D5-6 November 1992
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Bhide, AJX, and B.B. Sundaresan. 1981. Solid Waste Management in Developing Countries. National
Environmental Engineering Research Institute, Nadpur, India. 210 pp.
Bingemer, H.G., and PJ. Crutzen. 1987. The production of methane from solid wastes. Journal of
Geophysical Research 92(D2)2181-2187.
Cointreau, S. J. 1984. Solid waste collection practice and planning in developing countries. In
Holmes, J.R. (ed), Managing Solid Wastes in Developing Countries. John Wiley: New York. 151-
182.
Colt, J., R. Harvey, M. Lochhead, S. Mayer, L. Boccuti, and K Hogan. Methane Emissions from
Municipal Solid Waste Landfills in the United States. ICF/U.S. EPA, Washington, D.C 23 pp.
Emcon Associates. 1982. Methane Generation and Recovery From Landfills. Ann Arbor Science:
Ann Arbor, Michigan.
Gunnerson, C.G., and D.C. Stuckey. 1986. Inte&uted Resource Recovery: Anaerobic Digestion
Principles and Practices for Biogas Systems. World Bank Technical Paper Number 49, Washington,
D.C.
OECD/IEA (Organization for Economic Cooperation and Development/International Energy
Agency). 1989. Environmental Data Compendium 1989. OECD/IEA, Paris.
OECD/IEA (Organization for Economic Cooperation and Development/International Energy
Agency). 1991. Environmental Indicators: A Preliminary Set. OECD/IEA, Paris.
Orlich, J. 1990. Methane emissions from landfill sites and waste water lagoons. In International
Workshop on Methane Emissions from Natural Gas Systems, Coal Mining and Waste Management
Systems. April 9-13, 1990, Washington, D.C Funded by the Environment Agency of Japan, IIS.
Agency for International Development, and the U.S. Environmental Protection Agency.
Pacey, J.G., and J.P. DeGier. 1986. The factors influencing Landfill gas production. In Energy
From Landfill Gas, proceedings of a conference sponsored by the U.K. Department of Energy and
the U.S. Department of Energy. 51-59.
Piccot, S.D., A. Chadha, J. DeWaters, T. Lynch, P. Marsosudiro, W. Tax, S. Walata, and JJD.
Winkler. 1990. Evaluation of Significant Anthropogenic Sources of Radiativeiy Important Trace
Gases. Prepared for the Office of Research and Development, U.S. EPA, Washington, D.C
Richards, K.M. 1990. Landfill gas: Working with Gaia. In Biodeterioration Abstracts 3(4) 317-331.
In International Workshop on Methane Emissions from Natural Gas Systems, Coal Mining and Waste
Management Systems. April 9-13, 1990, Washington, D.C. Funded by the Environment Agency of
Japan, U.S. Agency for International Development, and the U.S. Environmental Protection Agency.
Tabasaran, O. 1981. Gas production from Landfill. In Bridgewater, A.V., and K. Lidgren (eds.),
Household Waste Management in Europe, Economics and Techniques. Van Nostrand Reinhold Co.,
New York. 159-175.
STATES WORKBOOK D5-7 November 199Z
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Thoraeloe, S.A. 1990. Landfill Gas and the Greenhouse Effect Paper presented at the
International Conference on Landfill Gas: Energy and Environment October 17.
Thoraeloe, S.A., R.L. Peer, D.L. Campbell, and K.S. Kepford. 1991. Proposed Methodology for
Estimating Global Landfill Methane Emissions. January 28. U.S. EPA and Radian Corporation,
Research Triangle Park, North Carolina.
U.S. EPA. 1988. Solid Waste Disposal in the United States. Volume II. Office of Solid Waste and
Emergency Response, U.S. EPA, Washington, D.C.
U.S. EPA 1990. Air Emissions from Municipal Solid Waste Landfills-Background Information for
Proposed Standards and Guidelines. Office of Air Quality, Planning and Standards, U.S. EPA,
Washington, D.C.
World Resources Institute. 1990. World Resources 1990-91. WRI, Washington, D.C
STATES WORKBOOK DS-8 November 1992
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DISCUSSION 6
METHANE EMISSIONS FROM DOMESTICATED ANIMALS
OVERVIEW
This section covers methane emissions from animals. Only animals managed by humans for
production of animal products, including meat, milk, hides and fiber, and draft power are included.1
Among livestock, the ruminant animals (i.e., cattle, buffalo, sheep, and goats) are the major emitters
of methane. The rumen, a large "fore-stomach," is the unique physiological characteristic of ruminant
animals that causes methane to be created within the animal.
Non-ruminant domestic animals, such as swine, horses, and mules also contribute to methane
emissions. The digestive physiology of these animals precludes them from having large methane
emissions. To produce a complete inventory for methane emissions from animals, these animals are
included here.
Two areas have been identified on which agreement has not been reached on whether they
should be included in this section on methane emissions from animals:
Wild Animals. The need to develop methane emissions inventories for wild animals
has been recognized. The fact is that the populations of some wild animals are
controlled in some areas for conservation or other reasons.
Controlled populations often generate economic returns, e.g.,through tourism.
Experts have suggested that the emissions from these animals should be estimated,
for they may be important for some states. State methane emissions inventories
that include natural sources should assess the importance of methane emissions
from wild animals and estimate the emissions if appropriate.
Termites. It has been recognized that termites produce methane emissions and that
termite populations may be affected by animal husbandry activities. Some experts feel
that emissions from termites should be included in the emissions inventory. It has
been recommended that follow-up work on land use activities should elicit
information useful for evaluating changes in termite emissions associated with animal
management activities.
In addition to the methane created by and emitted from the digestive tracts of animals, animal
wastes (manure) also contribute to methane emissions. Emissions from animal wastes are discussed
in a separate section.
1 Wild animals also produce methane emissions. The principal wild animals that contribute to U.S.
emissions are wild ruminant animals such as antelope, caribou, deer, elk, and moose. Termites have been
identified as a potentially important source of emissions and are generally examined separately from other wild
animals.
STATES WORKBOOK D6-1 November 1992
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Methanogenesis in Ruminant Animals
The production of methane is part of the normal digestive process of ruminant animals.
Under normal conditions, ruminant animals consume plant material or roughage that are composed
mostly of cellulosic carbohydrates (cellulose). The rumination process that takes place in ruminant
animals provides an opportunity for microorganisms to break down the cellulose into products that
can be digested and used by the animal Within the rumen, over 200 species and strains of organisms
have been identified to date, although a smaller number dominate (Baldwin and Allison, 1983).
These organisms form a complex ecology that includes both competition and cooperation or
symbiosis. The population mix of the organisms is strongly influenced by the composition of the diet
consumed by the animal.
Rumen methanogenic bacteria, or methanogens, are the source of methane produced in
ruminant animals. Although these bacteria are a very small fraction of the total population of
microorganisms in the rumen, they play an important role in the complex rumen ecology. The
conversion of hydrogen or formate and carbon dioxide (produced by other fermentative bacteria) is
believed to be the primary mechanism by which methanogenic bacteria produce methane in ruminant
animals. The methane produced in the rumen is emitted through eructation and exhalation.
Because methane is produced as a result of digestive processes, the amount of methane
produced will vary with the animal type, the type, amount, and digestibility of the feed consumed by
the animal, and the production level of the animal.
There is a vast scientific literature on the digestive processes and proper feeding of domestic
ruminant animals that can be used to estimate methane emissions (see, for example, NRC [1989],
Jurgens [1988], Van Soest [1982], and ARC [1980]). This literature, developed principally over the
last 50 years, includes several systems for defining the feeding requirements of domestic ruminant
animals. Equations have been developed that describe the energy requirements of ruminant animals
at various levels of production. Common feeds Tiave"been evaluated to define the level of energy that
they provide. These equations and feed data provide useful information for estimating methane
emissions.
The approach discussed here is to estimate the amount of methane emitted from individual
ruminant animals as a percentage of the amount of feed energy that the animal consumes. This
percentage varies depending on the amount and type of feed consumed by the animal, and will often
range from 4 to 9 percent of the gross energy consumed. Furthermore, the amount of feed energy
consumed by ruminant animals can be estimated directly if the feed consumption is known, or
indirectly if the level of production is known. This discussion is much more detailed than the
recommended method, which is a simplification of the calculations described below.
Methanogenesis in Non-Ruminant Herbivores
Methane is produced as pan of the digestive processes of non-ruminant herbivores. As in
ruminant animals, microorganisms produce the methane while breaking down basic feed components,
and the methane production can be expressed as a percentage of the energy consumed by the animal
Because non-ruminant animals lack a rumen, the percent of feed energy converted to
methane is much smaller than the percent for ruminant animals. At the low end, swine convert about
STATES WORKBOOK D6-2 November 1992
-------
one percent of their gross energy intake to methane, depending on their diet Horses, with their
enlarged cccum acting as a site for the fermentation of cellulose, convert about 3 to 4 percent of
their gross energy intake to methane.
DESCRIPTION OF WORKBOOK METHODOLOGY
The emission coefficients presented in the workbook were calculated using the following
approach:
estimate the percentage of feed energy that is converted to methane
by the animal;
* estimate the total feed energy intake by the animal; and
multiply the conversion percentage by the feed intake.
Each of these steps requires a complex series of calculations and a relatively large data set. For
simplicity, default assumptions were taken from Crutzen, et al. (1986) to calculate emissions factors
for the workbook. A more detailed discussion of the method is presented in the following section.
Given the assumptions from Crutzen, et al., annual methane emission coefficients were
calculated using the following equation:
M - GE x Ym x 365 x 1/6
where:
GE = the gross energy intake by the animal per day (Megacalories);
Ym = the methane yield of the gross energy intake (%);
365 is used to convert daily values to annual values;
1/6 is the conversion factor from Megacalories to pounds of methane; and
M = methane emissions in pounds per year for each animal.
Table D6-1 presents the data used for each animal type. Total methane emissions are calculated by
multiplying animal populations by the appropriate methane emissions coefficient (M), and then
summing across animal types.
STATES WORKBOOK D6-3 November 1992
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Table D6-1. Estimates of Annual Methane Emissions for Selected Livestock in the U.S.
Daily Average
Energy Intake
(Megacalories)
Methane Yield
of Gross Energy
Intake (%)
CH4 Production
(Ibsftr)
Cattle
Dairy
55
53%
184
Beef
36
63%
142
Range
26
73%
119
Horses
26
23%
40
Mules/
Asses
NA
NA
22
Sheep
4.8
6%
17.6
Goat
33
53%
11
Swine
9
0.6%
3.3
Note NA = Not Available
ALTERNATE METHODOLOGY
To estimate methane emissions from animals, the following general steps are required:
1.
2.
3.
4.
Enumerate the number of animals of the various types.
Characterize the populations of animals into separate categories with the
available data. At a minimum, the animals must be divided by species and
production systems. Further divisions based on animal size, feeding, and
production levels are desired if data are available with which to make the
estimates. A representative animal should be adopted to represent each
category.
Estimate methane emissions for each representative animal type.
Estimate total methane emissions by multiplying the emissions for each
representative animal times the population that it represents, and then by
summing across the animal categories.
These basic steps can be performed at various levels of detail. Each of these steps is discussed in
turn. The discussion focuses on the more accurate methods for estimating emissions, but simplifying
approaches are presented as alternatives to the more detailed approach.
STATES WORKBOOK
D6-4
November 1992
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Enumerate the Number of Animals
It is straightforward to enumerate the number of animals. Several data sources that can be
used are described below. Because animal populations fluctuate within the year or across years for
various reasons, it is important to adopt a population that is representative of the study year.
Characterize the Populations of Animals
The populations of animals must be characterized so that they can be divided into categories
that are individually relatively homogeneous. These categories should differ along dimensions that
most influence the level of methane emissions, subject.to the availability of data. .When data are
lacking, detailed characterization will not be possible.
The best definitions of categories will likely vary depending on the animal production systems
that are employed in individual states and the data that are available. The following is recommended
as an example of the hierarchy of categories that is desired:
Species
The animals should be divided by species because the species (e.g., dairy cow, beef cattle,
goat, sheep, etc.) have different digestion processes that result in different levels of methane
emissions.
Livestock Management System
The livestock management system, or production system, employed has a strong influence on
methane emissions per animal. The livestock management system is also indicative of other
characteristics of the animals that are relevant, including size and feeding. There are a wide variety
of livestock management systems, many of which depend on vegetation or crops for their feed base,
and are heavily influenced by the agro-ecological conditions that exist (FAO, 1980; Reuss et al., 1990;
and Vaidyanathan, 1988).
Within the cattle industry, for example, there are large differences among regions in the U.S.
in the way animals are managed. There are several distinct dairy regions in the U.S. with distinctly
different practices. Historically, the Lake States have been the dominant dairy producers (WI, MN,
IL, IN, OH, PA, and NY). These areas are characterized by small family farms with average herd
sizes of 30 to 60 cows per farm. The forage of the feed is often produced on the farm (Gibbs, 1991).
As a contrast, the growth area for dairying has been in the West (CA principally, but also TX
and NM). These areas are characterized by large herds, averaging in the hundreds, with many herds
in the thousands. The feed for these herds is entirely purchased, i.e., none is grown on the farm
locally. These large herd operations are very mechanized and highly productive. The large dairies
are often referred to as "businesses", as distinct from "family farms" found in the Lake States (Gibbs,
1991).
Although there are many differences among dairy regions, there are also many similarities.
Over 90% of all milk cows are holsteins or holstein crosses. All milking is automated, and careful
attention is paid to sanitation and milk quality (Gibbs, 1991).
STATES WORKBOOK D6-5 November 1992
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The U.S. beef industry is much more fragmented than the U.S. daily industry. The beef cattle
industry is comprised of four main players. They are:
Cow/calf operations: Generally "extensive" or grazing systems, these groups produce calves.
When cows do not get pregnant, they are culled (sent for slaughter). The calf production is
very seasonal Over 75% of the carves are born in the spring. Most of the operators are very
small, producing under 50 calves per day.
Stocker operation: Stackers purchase calves and grow them for 6 months to a year, usually
on pasture or rangeland.
Feedlots: Feedlots take over after the stocker phase. The steers and heifers will be in the
feedlot for 100 to 200 days depending on their initial weight and prices of feed and cattle.
The objective is to grow the cattle quickly into a form that will get the right grade when
slaughtered (e.g., choice). The feedlot industry is very centralized. A small number of
feedlots account for over 50% of the fed beef produced in the U.S. The feedlot phase is
based principally on the use of grains as feeds (corn, sorghum, wheat). In fact, the feedlot
system is a mechanism for translating grain into beef. The large grain companies often have
financial interests in large feedlot operations (Gibbs, 1991).
Packers: The packers purchase the live cattle from the feedlot The feedlot organizes the
sale even if it does not own the cattle. The packers slaughter the animals and sell to
wholesalers and retailers.
Animal size, feeding, and production
Size, feeding, and production are helpful for making the best estimates of methane emissions.
To the extent that data are available, they should be used. In many cases, "rules of thumb* may be
needed based on the production systems identified. At a minimum two size categories should be
used: young and adult animals. Feeding characteristics include amount, type, and digestibility of feed.
Some production characteristics are milk produced per day, weight gain per day, and for draft animals,
work per day.
Within each of the categories, a "representative animal" should be defined. The category will
then be assumed to be homogeneous with the characteristics of the representative animal. The
characteristics of the representative animal can then be used to estimate methane emissions.
Estimating Methane Emissions
Data on CH4 emissions from animals are very limited. The most precise method for
estimating methane emissions is to measure emissions from individual animals in the field that
represent the categories of animals defined above. Due to variations among individual animals, many
measurements would be required to define a "representative" animal. Undertaking such
measurements is not practical at this time. Alternatively, existing laboratory measurements could be
used as a basis for estimating emissions from those animals that have been measured. In most cases,
these experimental data are also not readily available.
STATES WORKBOOK D6-6 . November 1992
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Assuming that direct measurements are not available, methods of estimating emissions based
on models and equations are required. The most detailed models wfll be those that consider the
complex digestive processes of ruminant and non-ruminant animals. For example, such a model has
been developed for cattle, and can probably be applied to sheep and buffalo as well (Baldwin et aL,
1987). In cases where very detailed data are available to describe the animals and the diets they
consume, such a model can be implemented. In most cases, such data are not readily available
(Baldwin, personal communication).
When less detailed data are available, simplified summary relationships can be used to
estimate methane emissions. The approach proposed here is to;
estimate the percentage of feed energy that is convened to methane
by the animal;
estimate the total feed energy intake by the animal; and
multiply the conversion percentage by the feed intake.
An equation that estimates the percentage of the total feed intake of the animal that is converted
to methane has been developed for ruminant animals by Blaxter and Gapperton (1965). As part of
the feeding systems discussed above, equations have been developed to describe the energy intake
of the animals.
Estimating Proportion of Feed Converted to Methane
Blaxter and Clapperton (1965) reviewed the results of 615 closed-circuit respiration indirect
calorimetry experiments on sheep and cattle performed over a period of 10 years. Based on an
analysis of the results for -48 different diets in 391 different experiments on 4-5 sheep for various
levels of feeding, Blaxter and Clapperton identified feed digestibility and level of intake to be
important factors influencing the extent of metbanogenesis in the rumen. Using statistical techniques,
Blaxter and Gapperton developed the following equation to describe methane production:
Ym = 130 + (0.112 x D) + L x (2.37 - 0.050 x D) (1)
where Ym is the methane yield (Megacalories of methane produced per 100 Megacalories of gross
energy feed intake), L is the ratio of energy intake to maintenance energy requirements (e.g.r two
times maintenance),2 and D is the percent digestibility of the feed (e.g., 50 percent). The methane
yield estimated with this equation can be interpreted as the percent of gross energy intake that is
converted to methane within the animal. The digestibility of the diets represented in the data used
to develop this equation ranged from poor hay (54 percent digestible at maintenance) to sugar-beet
pulp (87.2 percent digestible at maintenance). The levels of the diets ranged from one to three times
maintenance.
2 Maintenance is defined as the condition where the animal neither gains nor loses weight In practice,
the "maintenance" condition is rarely observed for any significant period of time. Consequently, it is
principally a concept that is used in the energy-based feeding systems to describe the energy requirements of
ruminant animals.
STATES WORKBOOK D6-7 November 1992
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To use this equation for ruminant animals, information is needed to specify D, the digestibility
of the feed, and L, the level of feeding. In the absence of specific information about individual
production systems, rules of thumb will be required. Examples of rules of thumb that may be
appropriate include the following:
Digestibility. Intensive high-production systems generally rely on
grains and other high-energy feeds in addition tq forages. The feeds
will have an overall digestibility of 70 to 80 percent Well managed
grazing systems with high levels of production will likely have feeds
that are in the range of 60 to 70 percent Subsistence agriculture
situations with poor feed resources will likely have digestibilities in the
50 to 60 percent range.
Level of Feeding: As described below, the level of feeding should be
estimated from the level of production that is attained. However, in
the absence of such data, feeding levels for intensive high-production
systems will generally be on the order of 15 to 4.5 times maintenance.
Well managed grazing systems with high levels of production will likely
have feeding levels of about 1.5 to 25 times maintenance; the higher
level occurring when energy supplements are provided to the grazing
animals. Subsistence agriculture situations with poor feed resources
will likely have levels of feeding of about 1.0 to 1.5 times
maintenance.
Estimating Total Energy Intake
The result from equation 1 must be multiplied by an estimate of the total energy intake of
the animal. In general, feed intake will be a function of animal size and production. Larger animals
require more feed intake than smaller animals, and high producing animals require more feed intake
than low producing animals. Under the energy-based systems of animal feeding described above,
several equations have been developed to estimate energy intake as a function of animal size and
production. Other characteristics such as breed, sex, and age have also been incorporated into the
feeding systems. These factors can be used, but for simplicity they are omitted from this presentation.
To estimate the feed energy intake, first estimate the actual amount of feed energy used by
the animal; this quantity is generally referred to as the "net energy" utilized by the animal (NRC,
1989). This net energy value will then be "scaled up" to reflect the fact that the animal utilizes only
a portion of the total feed energy consumed. In cases where the feed consumption of the animals
is well known (e.g., based on data from agricultural census), the energy intake can be estimated
directly from the feed data. The energy content of various feeds have been estimated (see, e.g., NRC
[1989] or Jurgens [1988]).
In cases where feeding data are not available, feed energy intake can be estimated based on
animal production data. As shown in the following example for cattle, if adequate data are available,
the net energy estimate can be built up with the following equations:3
3 Similar equations have been developed for sheep and goats. See NRC (1985) and NRC (1981).
STATES WORKBOOK D6-8 November 1992
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= 0322 W°-75 x activity factor (2)
NEg = 4.18 x (0.035 W°-75xWGul9 + WG) (3)
NEj = 3.1 x milk production in pounds per day (4)
where:
is the net energy required for maintenance, in Megacalories;
NE- is the net energy required for growth in Megacalories;
NE] is the net energy required for lactation (i.e., milk production) in
Megacalories;
W is the weight of the animal in pounds;
WG is the daily weight gain in pounds;
activity factor represents an adjustment for the energy required to
graze for food;
milk production per day is the amount of 4 percent fat corrected milk
produced daily in pounds.4
The total net energy required for the representative animal can be estimated by applying
these equations and summing the individual net energy estimates. Care must he taken in adding the
work-related energy values because animal power is usually used seasonally.
Rules of thumb for the activity factor are as follows (Reuss et at, 1990):
confined animals that are stall fed: 1.125;
ammak grazing on good quality pasture: 1.25; and
extensively managed animals that graze over very large areas: 1.50.
The total net energy required for the representative animal can be estimated by applying
these equations and summing the individual net energy estimates. Energy requirements for the work
performed by draft animals also need to be added. These energy requirements are separate from the
activity factor that is related to the energy required to graze for food. Care must be taken in adding
the work-related energy values because animal power is usually seasonal.
By applying these equations, the net energy intake that is consistent with the size and
performance of the animals is estimated. The level of the feeding can be estimated from these data
by dividing the total by the net energy required for maintenance, assuming an activity level of 1.0.
This estimate of the level of feeding can be used in equation 1 above to estimate the methane yield.
The estimate should be compared with the general rules of thumb for feeding levels discussed above
to test for the reasonableness of the estimate.
4 The formula presented for NE, assumes that the milk production is corrected to a 4% milk fat content
Higher (lower) milk fat levels require more (less) NE, per pound of milk produced. See NRC (1989).
STATES WORKBOOK D6-9 November 1992
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The estimate of net energy must be translated into gross energy in order to be used with the
methane yield estimated above. This translation depends on the type of feed consumed and the
efficiency with which its energy is used by the animal. Although there are a wide range of values that
can be used based on the specific characteristics of individual feed types, the following rule of thumb
can be used for simplicity:5
GE = [(ME,,, + NEj + WE) * 0.492 + (NEg * 0328)] + (digestibility) (5)
where:
ME is as defined above in Megacalories;
WE is the work energy per day as defined above;
digestibility is expressed as a fraction (e.g., 65% digestibility is
expressed as 0.65); and
GE is gross energy intake in Megacalories.
To check the reasonableness of this estimate of gross energy intake, the approximate dry matter
equivalent of this intake can be estimated by assuming that 1 pound of feed has about 2 Megacalories
of energy.6 Gearly, feeds differ substantially in their energy content, and this value is used here only
as a check. The intake implied by the gross energy estimate is then estimated as:
DM; = GE + 2 (6)
where DM; is daily dry matter intake in pounds. This value should be about 2.0 to 3.0 percent of the
weight of the animal, and slightly Higher in intensive management situations. If the gross energy
estimate produces dry matter intake estimates that fall outside this range, a careful review of the
assumptions and data used may be warranted.
With the gross energy and methane yield estimates, the annual methane emissions for the
representative animal can be estimated as:
M = GE x (Ym * 100) x 365 x 1/6 (7)
where M is the methane emissions in pounds per year and Ym is the methane yield estimated from
equation I.7
5 The specific food types will have a range of gross energy values in relation to their net energy values.
Emissions estimates will be improved if the characteristics of actual feeds are used.
6 Higher energy values for feeds in North America and Europe may be appropriate due to the use of feed
grains in high-production dairy and feedlot operations. See, e.g., Reuss et al. (1990).
7 Y,,, is divided by 100 to put it into a fraction form, e.g., 5 percent equals 0.05. The factor of 365 is used
to convert daily values to yearly values. The factor of 1/6 is used to convert Megacalories to. pounds of
methane.
STATES WORKBOOK D6-10 November 1992
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Similar analyses could be used to estimate methane emissions from non-ruminant animate.
However, the equations and feed characteristics would be quite different from those presented above
for ruminant animals. Because the non-ruminant animals are relatively less important than the
ruminant animals in terms of methane emissions, simple emissions factors per head may be
appropriate. Crutzen et at (1986) derive the following emissions factors:
swine in the U.S.: 33 Ibs/head per year;
horses: 40 Ibs/head per year; and
mules and asses: 22 Ibs/head per year.
These estimates may be modified in individual cases when unusual feeding or animal management
practices are found.
Estimate Total Methane Emissions
Total methane emissions are estimated by multiplying the annual emissions for the
representative animals by the number of animals in the categories, and then summing across the
categories.
DATA SOURCES
A wealth of unpublished information is available from practitioners in individual states.
Departments within each state responsible for conducting agricultural research and that oversee the
agriculture sector should be consulted. State animal populations can be found in the Census of
Agriculture, Volume 1: Geographic Area Series, published by the Bureau of the Census. Also, the
USDA can produce state by state inventories on domesticated animal populations.
Data on feed characteristics have been compiled in NRC (1989), ARC (1980), and Jurgens
(1988). These, and similar, sources may be consulted to evaluate the feed consumption of specific
categories of ruminant animals.
Lerner et al. (1988) and Reuss et al. (1990) have compiled statistics about animals in order
to estimate global methane emissions. These sources can be examined to provide an indication of
the data sources that have been used in initial assessments of animal methane emissions.
EVALUATION
The methods described above for estimating methane emissions from animals are based on
sound scientific data and experimental evidence. To the extent possible, emissions should be
estimated with as much information about levels of feeding and feed characteristics as possible. This
information is particularly important for high-producing animals fed high-energy feeds.
The rules of thumb and emissions factors presented above for ruminant animals in subsistence
or extensive grazing situations will likely be required due to a lack of data needed to implement the
more ambitious method. The use of these simplified approaches adds to the uncertainty of the
estimates, but the extent of the inaccuracies introduced cannot be quantified at this time.
STATES WORKBOOK D6-11 November 1992
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Overall, a lack of data will likely limit the precision with which methane emissions from
animals can be estimated. With additional data more precise methods may be implemented because
the understanding of the factors that control methane emissions in ruminant animals is fairly
advanced. To improve future estimates, systemic collection of data on feeding and feed
characteristics should be initiated.
REFERENCES
ARC (Agriculture Research Council). 1980. The Nutrient Requirements of Ruminant Livestock.
Commonwealth Agricultural Bureaux, Famham Royal, England.
Baldwin, R.L., and MJ. Allison. 1983. Rumen metabolism. Journal of Animal Science 57:461-477.
Baldwin, R.L., J.H.M. Thomley, and D.E. Beever. 1987. Metabolism of the lactating cow. IL
Digestive elements of a mechanistic model. Journal of Dairy Research 54:107-131.
Blaxter, K.L., and J.L. Clapperton. 1965. Prediction of the amount of methane produced by
ruminants. British Journal of Nutrition 19:511-522.
Crutzen, PJ., I. Aselmann, and W. Seiler. 1986. Methane production by domestic animals, wild
ruminants, other herbivorous fauna, and humans. TeUits 388:271-284.
FAO (Food and Agriculture Organization of the United Nations). 1980. The Classification of World
Livestock Systems. FAO, Rome. 37+ pp.
FAO (Food and Agriculture Organization of the United Nations). 1989. 1988 FAO Production
Yearbook. Volume 42. FAO, Rome.
Gibbs, M. 1991. Memorandum to Barbara Braatz, ICF Inc. 1991.
Jurgens, M.H. 1988. Animal Feeding and Nutrition. Kendall/Hunt Publishing Company, Dubuque,
Iowa.
Leng, R_A. 1990. Improving Ruminant Production and Reducing Methane Emissions From
Ruminants by Strategic Supplementation. Draft report prepared for the Global Change Division,
U.S. Environmental Protection Agency, Washington, D.C., January.
Lerner, J., E. Matthews, and I. Fung. 1988. Methane emission from animals: A Global high-
resolution database. Global Biogeochemical Cycles 2:139-156.
NRC (National Research Council). 1981. Nutrient Requirements of Goats. National Academy Press,
Washington, D.C
NRC (National Research Council). 1985. Nutrient Requirements of Sheep. National Academy Press
(Sixth Revised Edition), Washington, D.C
STATES WORKBOOK D6-12 November 1992
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NRC (National Research Council). 1989. Nutrient Requirements of Dairy Cattle. National Academy
Press (Sixth Revised Edition), Washington, D.C
Preston, T.R., and R~A.Leng. 1987. Matching Riiminant Production Systems with Available Resources
in the Tropics and Sub-tropics. Penambul Books, Armidale, New South Wales, Australia.
Reuss, S.K., D.M. Swift, G. Ward, and J.E. Ellis. 1990. Global Ruminant Livestock Production
Systems: Estimated 1988 Methane Emissions. Draft report prepared for the Global Change Division,
U.S. Environmental Protection Agency, Washington, D.C
USDA (United States Department of Agriculture). 1987. Agricultural Statistics 1987.
United States Government Printing Office, Washington D.C. 1987.
USDA. 1990. Agricultural Statistics 1990. United States Government Printing Office, Washington,
D.C. 1990.
Vaidyanathan, A. 1988. Bovine Economy in India. Center for Development Studies, Trivandrum.
Van Soest, P J. 1982. Nutritional Ecology of the Ruminant. Cornell University Press, Ithica, New
York.
STATES WORKBOOK D6-13 November 1992
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DISCUSSION 7
METHANE EMISSIONS FROM ANIMAL MANURE
OVERVIEW
Manure decomposition is a process in which microorganisms derive energy and material for
cellular growth by metabolizing organic material in the manure. When decomposition occurs without
oxygen present (anaerobically), methane is an end-product of the process. This section will describe
the fundamentals of anaerobic decomposition; the methane producing capacity of livestock manure;
and the factors that influence methane production from livestock manure.1
The Fundamentals of Anaerobic Decomposition
Livestock manure is primarily composed of organic material and water. Under anaerobic
conditions, the organic material is decomposed by anaerobic and facultative (living in the presence
or absence of oxygen) bacteria. The end products of anaerobic decomposition are methane, carbon
dioxide, and stabilized organic material.
The anaerobic decomposition process can be represented in three stages: hydrolytic; acid
forming; and methanogenic. Carbohydrates decomposition can be illustrated as follows:2
Stage 1: Hvdrolvtic. In the first stage, complex organic materials in the manure
substrate are broken down through the hydrolytic action of enzymes. Enzymes are
proteins formed by living cells that act as catalysts in metabolic reactions. The amount
and rate of breakdown can vary substantially and depend on the enzymes present, the
characteristics of the manure, and environmental factors such as pH and temperature.
Stage 2: Acid Forming. Anaerobic and facultative bacteria reduce (ferment) the
simple sugars produced in Stage 1 to simple organic acids. Acetic acid is the primary
product of the breakdown of carbohydrates, though other organic acids such as
propionic acid and butyric acid can be formed. In addition, metabolic hydrogen and
carbon dioxide are produced. With acetic acid as an end product, the breakdown of
a simple sugar molecule (glucose) in Stage 2 can be represented as:
C6H12°6 + 2H2° > 2CH3COOH + 2CO2 + 4H2
glucose + water acetic acid carbon dioxide metabolic
hydrogen
1 Background information on animal wastes is taken from Safley et al. (1992).
2 This discussion focuses on the decomposition of carbohydrates because carbohydrate decomposition
accounts for ihe majority of the methane produced from livestock manure and because the process of methane
production from the decomposition of carbohydrates is best understood. By weight, the volatile solids portion
of cattle and swine manure is approximately 40 percent carbohydrate, 15 to 20 percent protein, and up to 10
to 20 percent fat with the remainder composed of other material (Hrubant, Rhodes, and Sloneker, 1978).
STATES WORKBOOK D7-1 November 1992
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Stage 3: Methanogenic. Methane producing bacteria (methanogens) convert the
simple organic acids, metabolic hydrogen, and carbon dioxide from Stage 2 into
methane and carbon dioxide. Methanogens are strict anaerobes and cannot tolerate
the presence of molecular oxygen. Methanogens multiply slowly and are very
sensitive to temperature, pH, and substrate composition. With acetic acid, metabolic
hydrogen and carbon dioxide as substrate, the reactions producing methane can be
expressed as:
2CH3COOH > 2CH4 + 2CO2
acetic acid > methane + carbon dioxide
4H2 + C02 > CH, + 2H20
metabolic + carbon dioxide > methane + water
hydrogen
Methane Producing Capacity of Livestock Manure
In general, livestock manure is highly conducive to methane generation due to its high organic
content and the presence of useful bacteria. However, the specific methane producing capacity of
livestock manure depends on the specific composition of the manure which in turn depends on the
composition and digestibility of the animal diet The greater the energy content and digestibility of
the feed, the greater the methane producing capacity of the resulting manure. For example, feedlot
cattle eating a high energy grain diet produce a highly biodegradable manure with a high methane
producing capacity. Range cattle eating a low energy forage diet produce a less biodegradable
manure with only half the methane producing capacity of feedlot cattle manure.
In principal, the ultimate methane producing capacity of a quantity of manure can be
predicted from the gross elemental composition of the manure. In practice, however, insufficient
information exists to implement this approach and the methane producing capacity is determined
through direct laboratory measurement The methane producing capacity of livestock manure is
generally expressed in terms of the quantity of methane that can be produced per kilogram of volatile
solids (VS) in the manure.3 This quantity is commonly referred to as B0 with units of cubic feet of
methane (CH4) per pound VS (ft3 CH4 / Ib VS). Representative B0 values for a number of livestock
manure types are presented later in this discussion.
Factors Influencing Methane Production
While a particular quantity of manure may have a certain potential to produce methane based
on its volatile solids content, the management of the livestock manure and the environment in which
the manure is managed are the major factors influencing the amount of methane actually produced
during manure decomposition.
Volatile solids (VS) are defined as the organic fraction of the total solids (TS) in manure that will
oxidize and be driven off as gas at a temperature of 600°C Total solids (TS) are defined as the material that
remains after evaporation of water at a temperature between 103" and 105eC
STATES WORKBOOK D7-2 November 1992
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The characteristics of the manure management systems and environmental conditions can be
expressed in a methane conversion factor (MCF) which represents the extent to which the potential
for emitting methane is actually realized. Manure systems and climate conditions that promote
methane production will have an MCF near 1 and manure systems and climate conditions that do not
promote methane production will have an MCF near 0. The primary characteristics determining the
MCF are:
Livestock Manure Management System Factors
Contact with Oxygen. Under aerobic conditions where oxygen is in contact with the
manure, there is no potential for methane production.
Water Content Liquid based systems promote an oxygen-free environment and
anaerobic decomposition. In addition, water is required for bacterial cell production
and metabolism and acts as a buffer to stabilize pH. Moist conditions increase the
potential for methane production.
pH. Methane producing bacteria are sensitive to changes in pH. The optimal pH is
near 7.0 but methane can be produced in a range between 6.6 and 8.0.
* Nutrients. Bacterial growth depends on the availability of nutrients such as nitrogen,
phosphorus, and sulfur. Deficiency in one or more of these nutrients will inhibit
bacterial growth and methane formation. Animal diets typically contain sufficient
nutrients to sustain bacterial growth. Therefore, nutrient availability is not a limiting
factor in methane production under most circumstances.
Climate Factors
Temperature. Metbanojenesis in livestock manure has been observed between 4' C
and 75° C. Temperature is one of the major factors affecting the growth of the
bacteria responsible for methane formation (Chawla, 1986). The rate of methane
production generally increases with rising temperature.
Moisture.. For non-liquid based manure systems, the moisture content of the manure
is determined by rainfall and humidity. The moisture content of the manure will
determine the rate of bacterial growth and decomposition. Moist conditions promote
methane production.
These factors can be combined into the following expression for estimating realized methane
emissions from livestock manure:
Realized Emissions = B0 MCF (7.1)
where B0 = the maximum methane producing capacity of the manure determined
by animal type and diet (ft3 CH4 / Ib VS).
MCF = Methane Conversion Factor (MCF) that represents the extent to
which the B0 is realized for a given livestock manure management
system and environmental conditions. Note: 0 & MCF s 1.
STATES WORKBOOK D7-3 November 1992
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DESCRIPTION OF WORKBOOK METHODOLOGY
Methane emissions from livestock manure depend on the type of manure, the characteristics
of the manure management system, and the climatic conditions in which the manure decomposes.
While limited data are available on which to base emission estimates, a study recently prepared for
the USEPA provides an adequate basis for making initial estimates (Safley et al., 1992). Additional
analysis is ongoing to provide additional data for estimating these emissions.
Based on the Safley et al. (1992) approach, emission estimates are developed by:
identifying the manure management systems in use in the United States and their
methane producing potential;
estimating the amount and type of manure managed by each system; and
estimating emissions by multiplying the amount of manure managed in each system
by the estimated emission rate per unit of manure in the system.
Information can be obtained from a variety of sources, including:
the U.S. Census of Agriculture;
USDA agriculture statistics;
livestock manure management experts throughout the U.S.; and
scientific literature.
*
Total emissions wfl] equal the quantity of volatile solids managed in each system times
emissions per kilogram of volatile solids (VS) for that system. Safley et al. (1992) used the following
procedure to estimate total emissions:
Collect data on: (1) the populations of the major animal types in each state of the
U.S. (N); and (2) their typical animal mass (TAM).
Collect information on the characteristics of the manure produced by each of the
animal populations in each state, including: (1) the amount of volatile solids (VS)
produced; and (2) the methane producing capacity (B0) of the manure. The amount
of volatile solids produced depends on the number of animals in the state and their
mass:
VSit = Nik TAMt vs{ (7-2)
where:
jVj k = number of animals of type i in state L
TAMX ~ typical animal mass in pounds of animal i; and
w, = the average annual volatile solids production per unit of
animal mass (pounds per pound) for animal i.
STATES WORKBOOK D7-4 November 1992
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Identify the livestock manure management systems used in each state and the
percentage of manure managed by each (WS%).
Estimate the methane producing potential (MCF) of each manure management
system in each state based on the average monthly temperature in the state.
Estimate methane emissions for each animal and manure system in each state (TM)
by multiplying the amount of volatile solids (VS) produced by the methane producing
capacity of the manure (B^ times the methane producing potential (MCF) of the
manure system in each state.
where:
K?} k = total volatile solids produced (Ibs/yr) for animal i in
state k, .
B0 j = maximum methane producing capacity per pound of
VS for .animal i;
MCF: k = methane conversion factor for each manure system ;' in
state Jk;
TKS%j:k = percent of animal fs manure managed in manure
system j instated.
Estimate total annual methane emissions (TM) for animal i as the sum of annual
emissions over all applicable manure management systems ;":
TMt - £ TM{j (7.4)
j
Estimate total annual methane emissions from all animals (TM) as the sum over all
animal types i as follows:
TM - TMt (15)
These equations show that methane emissions are driven by four main factors: the quantity
if VS produced; the B0 values for the manure; the MCFs for the manure management systems; and
:he portion of the manure bandied by each manure management system (WS%). The following
sections describe the data collected to implement this method.
Volatile Solids Production (VS}
Methane emissions from livestock manure are directly related to the amount of volatile solids
(VS) produced. The data required to estimate total VS production are the number of animals
average size (TAA fj), and average VS production per unit of animal size (vjj).
STATES WORKBOOK D7-5 . November 1992
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In the U.S., considerable data are available to allow the populations of animals to be analyzed
by: species, production system, and (for cattle) age. Six main categories of animals were defined:
feedlot beef cattle;4 other beef cattle; dairy cattle; swine; poultry; and other. These main categories
were further divided into 20 subcategories. For each subcategory, VS production was estimated using
data on: the animal population; the typical animal mass (TAM); and the VS production per unit of
animal mass. Table D7-1 lists the data obtained for the 20 subcategories.
Maximum Methane Producing Capacity CBQ)
The maximum amount of methane that can be produced per pound of VS (BJ varies by
animal type and diet Measured BQ values for beef manure range from 2.72 cubic feet of methane
per pound of VS (frVlb-VS) for a com silage diet to 5.29 ftVlb-VS for a corn-based high energy diet
that is typical of feedlots. Table D7-2 summarizes these values.
Appropriate B0 values were selected depending on the typical diet of each animal type and
category. For animal types without B0 measurements, the B0 was estimated based on similarities with
other animals and the authors' experience. Ruminants for which there were no literature values were
assumed generally to have the same values as cattle, except for sheep, which were assumed to have
B0 values 10 percent higher than cattle (Jain et al. 1981). Table D7-3 lists the values selected for
the analysis.
4 Feedlot cattle are animals fed a ration of grain, silage, hay and protein supplements for the slaughter
market (ASB, 1991).
STATES WORKBOOK D7-6 November 1992
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US. Animal Popu
Animal Type
Feedlot Beef Cattle
Other Beef Cattle
Dairy Cattle
Swine
Poultry0
Other
Steers
Heifen
Cows/Other
Total
Calves
Heifers
Steers
Cows
Bulls
Total
Heifers
Cows
Total
Market
Breeding
Total
Layers
Broilers
Ducks
Turkeys
Sheep
Goats
Donkeys
Horses and Mules
Table D7-1
latinnt Amwp fiEtt. anr* V£ iHvwln^
Population**
Nj
7367,000
3,785,000
87,000
11^39,000
20,248,000
13447,000
8,430,000
33483.000
2^21,000
78,029,000
4,199,000
10,217,000
14.416,000
48,259,000
7,040,000
55.299,000
355.469,000
951,914,000
7,000,000
53,783,000
10,639,000
2396.000
4,000
2,405,000
Typical
Animal
Mass
(TAMJC
Ibs
915
915
1102
397
794
794
1102
1587
903
1345
101
399
34
15
3.1
74
154
141
661
992
Ion
Manure per day0
(Ibs/day per 1000 Ibs mass)
Total
Manure
58
58
58
58
58
58
58
58
86
86
84
84
64
85
107
47
40
41
51
51
Volatile
Solids
«i
7.2
Z2
7.2
7.2
7.2
7.2
7.2
72
10
10
8.5
8.5
12
17
18.5
9.1
92
93
10
10
A Population data for animals except goats and horses from ASB (1989a>f). Goat and horse population
data from Bureau of Census (1987). Population data as of January 1, 1988 for cattle, poultry, and sheep
and as of December 1, 1987 for swine, goats, and horses.
B Broiler/turkey populations estimated yearly based on number of flocks per year (North 1978; Carter
1989).
c Source: Taiganides and Stroshine (1971).
D Source: ASAE ( 1988).
STATES WORKBOOK
D7-7
November 1992
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Table D7>2
Maximum Methane Producing Capacity for U.S. Livestock Manure
Animal
Type
Beef
Beef
Beef
Beef
Beef
Dairy
Dairy
Dairy
Dairy
Horse
Poultry
Poultry
Poultry
Poultry
Swine
Swine
Swine
Swine
Swine
Swine
Swine
Swine
Diet
7% corn silage, 87.6% corn
Corn-based high energy
91.5% corn silage, 0% corn
58-68% silage
72% roughage
Roughage, poor quality
Grain-based ration
Barley-based ration
Corn-based high energy
Corn-based high energy
Corn-based high energy
Corn-based high energy
Corn-based high energy
Corn-based high energy
. BO
(m3 CH4 /kg-VS)
4.65
5.29
2.72
3.68
5.29
3.84
2.72
124
1.60
5.29
6.25
5.77
3.84
3.84
5.77
7.69
5.13
833
7.69
753
7.05
7.21
Reference
Hashimoto et al. (1981)
Hashimoto et al. (1981)
Hashimoto et aL (1981)
Hill (1984)
Chen, et aL (1980)
Morris (1976)
Bryant et aL (1976)
Hill (1984)
Chen, et aL (1988)
Ghosh (1984)
Hill (1982)
Hill (1984)
Webb & Hawkes (1985)
Hawkes & Young (1980)
Summers & Bousfield (1980)
Hashimoto (1984)
Hill (1984)
Kroeker et al. (1984)
Stevens & Schulte (1979)
Chen (1983)
lannotti et al. (1979)
Fischer et al. (1975)
Table D7-3
Maximum Methane Producing Capacity Adopted For U.S. Estimates
Cattle:
Swine:
Poultry:
Animal Type, Category
Beef in Feedlots
Beef Not in
Feedlots
Dairy
Breeder
Market
Layers
Broilers
Turkeys
Sheep: In Feedlots
Not in Feedlots
Goats:
Horses and Mules:
Maximum Potential
Emissions (B0)
5.29
2.72
3.84
5.77
7.53
5.45
4.81
4.81
5.77
3.04
2.72
5.29
Reference
Hashimoto et al. (1981)
Hashimoto et aL (1981)
Morris (1976)
Summers & Bousfield (1980)
Chen (1983)
Hill (1982 & 1984)
Safky et aL (1992)
Safley et al. (1992)
Safley et aL (1992)
Safley et al. (1992)
Safley et al. (1992)
Ghosh (1984)
STATES WORKBOOK
D7-8
November 1992
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Manure Management Svstems Definitions
A variety of manure management practices are in use throughout the U.S. The following is
a brief description of the major livestock manure management systems in use.
PASTUR^RANGE
DAILY SPREAD
SOLID STORAGE
DRYLOT
DEEP PIT STACKS
LITTER
PADDOCK
LIQUID/SLURRY
ANAEROBIC LAGOON
Animals that are grazing on pasture are not on any true manure handling
system. The manure from these animals is allowed to lie as is, and is not
managed at all
With the daily spread system the manure is collected in solid form, with
or without bedding, by some means such as scraping. The collected
manure is stored until applied to fields on a regular basis.
In a solid storage system the solid manure is collected as in the daily
spread system, but this collected manure is stored in bulk for a long
period of time (months) before any disposal.
In dry climates animals may be kept on unpaved feedlots where the
manure is allowed to dry until it is periodically removed. Upon removal
the manure may be spread on fields.
With caged layers the manure may be allowed to collect in solid form in
deep pits (several feet deep) below the cages. The manure in the pits
may only be removed once a year. This manure generally stays dry.
Broilers and young turkeys may be grown on beds of Utter such as
shavings, sawdust, or peanut hulls, and the manure/litter pack is removed
periodically between Docks. This manure will not generally be as dry as
with deep pits, but -will still be In solid form.
Horses are frequently kept in paddocks where they are confined to a
limited area, but not entirely confined to their stalls. This manure will be
essentially the same as manure on pasture or drylot
These systems are generally characterized by large concrete lined tanks
built into the ground. Manure is. stored in the tank for six or more
months until it can be applied to fields. To facilitate handling as a liquid,
water usually must be added to the manure, reducing its total solids
concentration to less than 12 percent Slurry systems may or may not
require addition of water.
Anaerobic lagoon systems are generally characterized by automated flush
systems that use water to transport the manure to treatment lagoons that
are usually greater than six feet deep. The manure resides in the lagoon
for periods ranging from 30 days to over 200 days depending on the
lagoon design and other local conditions. The water from the lagoon is
often recycled as flush water. Periodically the lagoon water may be used
for irrigation on fields with the treated manure providing fertilizer value.
STATES WORKBOOK
D7-9
November 1992
-------
PIT STORAGE Liquid swine manure may be stored in a pit while awaiting final disposal.
The pits are often constructed beneath the swine building. The length of
storage time varies, and for this analysis is divided into two categories:
less than one month or greater than one month.
Methane Conversion Factors (MCFst
The extent to which the maximum methane producing capacity (Bg) is realized for a given
livestock manure management system and environmental conditions is defined as the Methane
Conversion Factor (MCF) for the manure system. For example, a manure system that produces no
methane emissions will have an MCF of 0. A manure system that achieves full potential methane
emissions would have an MCF of 1.
To assess the MCF values for a wide range of livestock manure management systems, two
broad classifications of livestock manure handling systems can be defined based on the total solids
content of the manure:
Solid systems have a total solids content greater than about 20 percent
* Liquid/slurry systems have a total solids content less than 20 percent.
Manure as excreted may have a total solids content from 9 to 30 percent (Taiganides 1987).
This solids content may be modified by adding an absorbent bedding material to increase the total
solids content for easier handling. Alternatively, water may be added to lower the total solids to allow
for liquid transport and handling.
These classifications of systems are particularly important to the potential for methane
production from the manure. Liquid and slurry systems will typically cause anaerobic conditions to
develop, which result in methane production. Solid systems promote conditions that limit methane
production even if anaerobic conditions may exist
Safley et al. (1992) reviewed the literature to investigate the appropriate range of MCF values
for U.S. manure management systems. Although some data were available, MCF values were
estimated for many systems. To improve the MCF estimates, the U.S. Environmental Protection
Agency is sponsoring analysis to better estimate the MCF for several key livestock manure systems.
Preliminary findings from this analysis indicate that:
The estimated MCF value of dry in situ pasture, range, paddock, and solid storage
manure is 1 to 2 percent The estimated MCF for drylot manure is 1 to 5 percent
However, the analysis has not yet considered the effect of moisture or emissions that
may result when the manure is washed into streams, rivers, and lakes or incorporated
into the soil (Hashimoto 1992).
The MCF value liquid/slurry and pit storage varies greatly by temperature and is on
the order of 10 percent at 10°C to 65 percent at 30eC (Hashimoto 1992).
The MCF value for daily spread is less than 1 percent (Hashimoto 1992).
STATES WORKBOOK D7-10 November 1992
-------
The MCF value for anaerobic lagoons is on the order of 90 percent This estimate
is based on continuous methane measurements taken over a two and one-half year
period at a North Carolina dairy farm (Safley 1991).
The MCFs values for each system are listed in Table D7-4. The MCF for an individual state will
depend on the average monthly temperature and are calculated by:
estimating the average monthly temperature in each climate division;5
estimating the MCF value for each month using the average temperature data and the
MCF values listed in Table D7-4;
estimating the annual MCF by averaging the monthly division estimates; and
estimating the state-wide MCF by weighting the average MCF for each division by the
fraction of the state's dairy population represented in each division.6
Table D7-5 summarizes the MCF estimates for each state.
Livestock Manure Management System Usae
Livestock manure management system usage in the United States was determined by obtaining
information from Extension Service personnel in each state. The U.S. was divided into eleven
geographic regions based on similarities of climate and livestock production as shown in Table D7-6.
For states that did not provide information, the regional average manure system usage was assumed.
Some states did not give data for all animal types and a regional average was used in these cases.
Table D7-7 lists the percentage of manure managed by the major systems in the United
States. The important manure management characteristics in the U.S. are:
Approximately one-third of dairy manure is managed as a liquid and approximately
one-third is spread directly to cropland.
Seventy-five percent of swine manure is managed as a liquid.
Poultry manure is primarily managed by deep pit stacking or litter and is included in
"other systems" in Table D7-7.
5 The average temperature in each climate division of each state was calculated for the normal period of
1951 to 1980 using the National Climatic Data Center (NCDC) time-bias corrected Historical Climatological
Series Divisional Data (NCDC 1991).
6 The dairy population in each climate division were estimated using the dairy population in each county
(Bureau of the Census 1987) and detailed county and climate division maps (NCDC 1991). Using the dairy
population as a weighting factor may slightly over or underestimate the MCFs for other livestock populations.
STATES WORKBOOK D7-11 November 1992
-------
Table D7-4
Methane Conversion Factors for U.S. Livestock Manure Systems
MCFs based on
laboratoiy measurement
Pasture, Range, PaddocksA
Liquid/Slur^
Pit Storage < 30 daysA
Pit Storage > 30 daysA
Drylot8
Solid StorageA
Daily SpreadA
MCF measured by
long term field monitoring
Anaerobic Lagoonsc
MCFs estimated by Safley et aL
V:;.; :<,:-::;,::::, . ^
MGFat30°C
2%
65%
33%
65%
5%
2%
1%
MCFat20*C
1.5%
35%
18%
35%
1.5%
1.5%
0.5%
MCFatlO°C
1 %
10%
5%
10%
1%
1 %
0.1 %
Average Annual MCF
90%
Average Annual MCF
Litter0 10 %
Deep Pit Stacking0 5 %
A Hashimoto (1992)
B Based on Hashimoto (1992).
C Safley et al. (1992) and Safley and Westerman (1992).
D Safley ct al. (1992).
STATES WORKBOOK
D7-12
November 1992
-------
Table D7-5
Methane Coimnioo Factor* for U.S. Livestock Maoare
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Other Systems: Pit Storage for
Pasture,
Range &
Paddocks
1.4%
1.4%
13%
12%
0.9%
0.9%
12%
1.5%
1.4%
0.8%
1.1%
1.0%
0.9%
1.1%
1.2%
1.4%
0.8%
1.1%
0.9%
0.8%
0.8%
1.4%
1.1%
0.7%
1.0%
12%
0.8%
1.0%
1.2%
0.9%
13%
0.7%
1.0%
1.4%
1.1%
0.9%
1.0%
13%
0.8%
13%
1.4%
0.9%
0.8%
12%
1.0%
1.2%
0.8%
0.8%
less than 30 davs
Liquid/Slurry. Pit Storage for more than 30 days
Drylot
L9%
1.9%
1.8%
1.4%
1.0%
1.0%
1.4%
2.4%
1.8%
0.8%
13%
1.2%
1.1%
1.5%
1.5%
2.1%
.. 0.8%
12%
1.0%
0.9%
0.8%
1.9%
1.4%
0.8%
1.1%
1.4%
O8%
1.1%
13%
0.9%
1.5%
0.7%
1.1%
1.9%
1.1%
1.0%
1.1%
1.7%
0.9%
1.6%
2.1%
1.0%
0.8%
1.4%
1.0%
13%
0.8%
0.8%
is assumed to have
is assumed to have
Solid
Storage
1.4%
1.4%
13%
12%
0.9%
0.9%
12%
1.5%
1.4%
0.8%
1.1%
1.0%
0.9%
1.1%
12%
1.4%
0.8%
1.1%
0.9%
0.8%
0.8%
1.4%
1.1%
0.7%
1.0%
12%
0.8%
1.0%
12%
0.9%
13%
0.7%
1.0%
1.4%
1.1%
0.9%
1.0%
13%
0.8%
13%
1.4%
0.9%
0.8%
12%
1.0%
12%
0.8%
0.8%
an MCF equal
an MCF equal
lagoons are assumed to have an MCF of 90%; litter and deep pit stacks an MCF of
System
Dafly
Spread
0.4%
0.4%
0.4%
03%
02%
02%
03%
0,6%
0.4%
02%
03%
03%
02%
03%
03%
0.5%
02%
03%
02%
02%
02%
0.4%
03%
02%
02%
03%
02%
03%
03%
02%
03%
02%
02%
0.4%
02%
02%
02%
0.4%
02%
03%
0.5%
02%
0.2%
03%
02%
03%
02%
02%
Liquid/
Slurry
29.0%
28.9%
27.6%
21.9%
182%
IS5%
22.6%
38.6%
29.0%
15.5%
218%
21.5%
20.7%
24.7%
23.8%
325%
155%
21.0%
18.1%
17.0%
18.0%
293%
24.1%
15.8%
20.8%
22.1%
163%
20.6%
213%
18.1%
24.5%
16.8%
202%
28.7%
162%
18.7%
18.7%
273%
19.1%
24.8%
31.7%
17.4%
16.6%
225%
155%
21.4%
17.0%
15.9%
to 50% of the MCF for
to liquid/slurry.
10%.
Anaerobic
STATES WORKBOOK
D7-13
November 1992
-------
Table D7-6
Regions of the U.S. for Manure Management Characterization
Nonh East 'Connecticut, Maine, Massachusetts, *New Hampshire, New Jersey. 'New York,
Pennsylvania, Rhode Island, Vermont
South East 'Delaware, 'Florida, 'Georgia, Maryland, 'North Carolina, 'South Carolina,
Virginia, 'West Virginia.
Plains 'Colorado, 'Kansas, 'Montana, 'Nebraska, 'North Dakota, 'South Dakota,
Wyoming.
South 'Alabama, 'Arkansas, Kentucky, 'Louisiana, 'Mississippi, 'Tennessee
South West 'New Mexico, 'Oklahoma, 'Texas.
Mid West 'Illinois, 'Indiana, Michigan, 'Ohio, 'Wisconsin, 'Iowa, 'Minnesota, 'Missouri
North West 'Idaho, 'Oregon, 'Washington
Far West 'Arizona, Nevada, 'Utah
Pacific West 'California
North Pacific 'Alaska
Pacific Islands 'Hawaii
States that have supplied estimates of their percent use of manure management.
Animal
Non-Dairy Cattle
Dairy
Poultry8
Sheep
Swine
Other Anima|$c
Livestock
Table D7-7
Manure System Usage for the U.S,
Liquid/Slurry
Anaerobic and Pit
Lagoons Storage
0%
10%
5%
0%
25%
0%
1%
23%
4%
0%
50%
0%
Daily
Spread
0%
37%
0%
0%
0%
0%
Solid
Storage
& Drylot
14%
23%
0%
2%
18%
0%
A Includes liquid&lurry storage and pit storage.
B Includes chicken*, turkeys, and duck*.
C Includes goiu, horses, mules, and donkeys.
Pasture,
Range &
Paddock
84%
0%
1%
88%
0%
92%
Liner,
Deep Pit
Stacks and
Other
1%
7%
90%
10%
6%
8%
Totals nay not idd due to rounding.
Source SaOey et al. (1992).
STATES WORKBOOK
D7-14
November 1992
-------
DATA SOURCES
Many states may have their own agricultural census that includes data on animal populations
and production levels. Animal population data can be found from a variety of other sources,
including the U.S. Census of Agriculture, USDA agriculture statistics, and from livestock manure
management experts throughout the U.S. Safley et ah (1992) include animal populations and also
estimate CH4 emitted from their wastes in their report
EVALUATION
The method described above for estimating methane emissions from animal manure is based
on sound scientific data and experimental evidence. To the extent possible, emissions should be
estimated with as much information as possible about the conditions under which animal manure is
managed. This is particularly important when manure is managed under anaerobic conditions, such
as lagoons or other liquid/slurry systems.
The estimates and assumptions used by Safley et al. (1992) are instructive for identifying the
potential magnitude of emissions and the relative importance of various animals and manure
management systems. However, to the extent possible, information that is specific to the individual
state should be used because manure management systems and practices may vary in different states.
The weakest link in the method presented here is the estimates of the methane conversion
factors (MCFs) for the individual management systems. Very few field measurements are available
upon which to base these estimates, particularly for "dry" management systems such as dry lots,
pastures, and paddocks. The MCFs for the Vet" management systems such as lagoons and slurry
storage have a much stronger foundation. The inaccuracy in the emissions estimates due to this lack
of data cannot be quantified. Emissions estimates can be improved significantly once comprehensive
Geld measurements are performed.
This discussion lias focused only on emissions of methane from animal manure. It has been
mentioned, however, that animal waste decomposition also has the potential to produce nitrous oxide.
At this time no information is available on the potential for N2O emissions; this should be
investigated in the future.
REFERENCES
AMI (American Meat Institute). 1991. Meat Facts. American Meat Institute. Washington, D.C
ASAE (American Society of Agricultural Engineers). 1988. Manure Production and
Characteristics, ASAE Data: ASAE D384.1. American Society of Agricultural Engineers.
St. Joseph, ML
ASB (Agriculture Statistics Board). 1989a. Cattle. Released: February 8, 1989. Agricultural
Statistics Board. ERS-NASS, USDA, P.O. Box 1608, Rockville, MD 20850. 15 pp.
ASB (Agriculture Statistics Board). 1989b. Cattle on Feed. Released: January 26, 1989.
Agricultural Statistics Board. ERS-NASS, USDA, P.O. Box 1608, Rockville, MD 20850.
14 pp.
STATES WORKBOOK D7-15 November 1992
-------
ASB (Agriculture Statistics Board). 1989c. Hogs and Pigs. Released: January 6,1989.
Agricultural Statistics Board. ERS-NASS, USDA, P.O. Box 1608, Rockville, MD 20850.
20 pp.
ASB (Agriculture Statistics Board). 1989d. Layers and Egg Production, 1988 Summary. January,
1989. Agricultural Statistics Board. ERS-NASS, USDA, P.O. Box 1608, Rockville, MD
20850. 40pp.
ASB (Agriculture Statistics Board). 1989e. Poultry, Production and Value, 1988 Summary. April,
1989. Agricultural Statistics Board. ERS-NASS, USDA, P.O. Box 1608, Rockville, MD
20850.
ASB (Agriculture Statistics Board). 1989f. Sheep and Goats. Released: February 8,1989.
Agricultural Statistics Board. ERS-NASS, USDA, P.O. Box 1608, Rockvflle, MD 20850.
llpp-
Bryant, M. P., V. H. Varel, R. A. Frobish, and H. R. Isaacson. 1976. 347 pp. In: H. G. Schlegel
(ed). Seminar on Microbial Energy Conversion. E. Goltz KG. Gottingen, Germany.
Bureau of Census. 1987. Census of Agriculture. United States Department of Commerce. U.S.
Government Printing Office. Washington, DC 20402.
Carter, T. A. 1989. Personal communication with Dr. Thomas A. Carter. Extension Professor of
Poultry Science. Poultry Science Department North Carolina State University. Box
7608. Raleigh, NC 27695-7608.
Chawla, O.P. 1986. Advances in Biogas Technology. Indian Council of Agricultural Research: New
Delhi.
Chen, T. H., D. L_ Day, and M. P. Steinberg. 1988. Methane production from fresh versus dry
dairy manure. Biological Wastes. 24:297-306.
Chen, Y. R. 1983. Kinetic analysis.of anaerobic digestion of pig manure and its implications.
Agricultural Wastes. 8:65-81.
Fischer, J. R., D. M. Servers, and D. C. Fulhage. 1975. Anaerobic digestion in swine wastes, pp.
307-316. In: W. J. Jewell (ed.). Energy, Agriculture and Waste Management. Ann Arbor
Science. Ann Arbor, MI.
Ghosh, S. 1984. Methane production from farm waste, pp. 372-380. In: M. M. El-Halwagi
(ed.). Biogas Technology, Transfer and Diffusion. Elsevier. New York.
Hashimoto, A. G., V. H. Varel, and Y. R. Chen. 1981. Ultimate methane yield from beef cattle
manure; effect of temperature, ration constituents, antibiotics and manure age. Agricultural
Wastes. 3:241- 256.
Hashimoto, A. G. 1984. Methane from swine manure: effect of temperature and influent
substrate composition on kinetic parameter (k). Agricultural Wastes. 9:299-308.
STATES WORKBOOK D7-16 ', November 1992
-------
Hashimoto, A. G. 1992. Personal communication with Dr. Andrew Hashimoto. Professor and
Department Chairman. Bioresource Engineering Department Oregon State University.
Corvallis, OR. July 1992.
Hawkes, F. R. and B. V. Young. 1980. Design and operation of laboratory- scale anaerobic
digesters: operating experience with poultry litter. Agricultural Wastes, 2:119-133.
Hawkes, F. R. and B. V. Young, 1980. Design and operation of laboratory-scale anaerobic
digesters: operating experience with poultry litter. Agricultural Wastes. 2:119-133.
Hill, D. T. 1982. Design of digestion systems for maximum methane production. Transactions of
theASAE. 25(1)^26-230.
Hill, D. T. 1984. Methane productivity of the major animal types. Transactions of the ASAE,
27(2)^30-540.
Hrubant, G.R., R.A. Rhodes, and J.H. Sloneker, "Specific Composition of Representative Feedlot
Wastes: A Chemical and Microbial Profile," SEA-NC-59. Northern Regional Research
Center, U.S. Department of Agriculture, Peoria, Illinois, 1978.
lannotti, E. L., J. H. Porter, J. R. Fischer, and D. M. Sievers. 1979. Developments in Industrial
Microbiology. 20(49)^19-520.
Jain, M. K., R. Singh, and P. Tauro. 1981. Anaerobic digestion of cattle and sheep waste.
Agricultural Wastes. 3:65-73.
Kroeker, E J., D. D. Scbulte, A. B. Sparling, and J. T. Chieng. 1984. Anaerobic-treatment
process stability. Journal of the Water Pollution Control Federation. 51:718-727.
Morris, G. R, 1976. Anaerobic fermentation of animal wastes: a kinetic and empirical design
fermentation. M. S. Thesis. Cornell University.
NCDC (National Climatic Data Center) 1991. Historical Climatological Series Divisional Data.
National Oceanic and Atmospheric Administration. Ashvilie, NC.
North, M. O. 1978. Commercial Chicken Production Manual. AVI. Westport, Connecticut
Safley, L.M. 1991. Personal communication with Dr. Lawson Safley. Professor of Biological and
Agricultural Engineering. North Carolina State University. Raleigh, North Carolina,
January 1991.
Safley, L.M., M.E. Casada, J.W. Woodbury, and K.F. Roos (1992). "Global Methane Emissions
from Livestock and Poultry Manure." EPA/400/1091/048. U.S. Environmental Protection
Agency. Washington, D.C. February 1992.
Safley, L.M., Jr. and P.W. Westerman 1992. "Performance of a Low Temperature Lagoon
Digester." Bioresource Technology. 41:167-175.
Stevens, M. A. and D. D. Schulte. 1979. Low temperature digestion of swine manure. Journal of
the Environmental Engineering Division, ASCE. 105(EE1): 33-42.
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Summers, R. and S. Bousfield. 1980. A detailed study of piggery-waste anaerobic digestion.
Agricultural Wastes. 2:61-78.
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Strauch (ed.). Animal Production and Environmental Health. Elsevier. New York.
Taiganides, E. P. and F- L. Stroshine. 1971. Impacts of farm animal production and processing
on the total environment pp. 95-98. In: Livestock Waste Management and Pollution
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STATES WORKBOOK D7-J8 November 1992
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DISCUSSION 8
METHANE EMISSIONS FROM FLOODED RICE FIELDS
OVERVIEW
Globally, flooded rice fields are the primaiy anthropogenic source of methane. However,
flooded rice fields account for only a small portion of U.S. anthropogenic methane emissions.
Methane is produced through anaerobic decomposition of organic material in flooded rice fields. The
CH4 escapes into the atmosphere primarily by diffusive transport through rice plants during the
growing season. It should be noted that drv upland rice fields, which are not flooded, do not produce
significant quantities of CH4.
The USDA reported that 2,887,000 acres of wetland rice, consisting of irrigated, rainfed, and
deepwater rice, were planted in 1990, while overall rice production for 1990 was reported as 154,919
CWT (pounds, hundred weight)1. However, deepwater, floating rice is not believed to produce
significant quantities of CH4 either. This is due to the fact that the lower stems and roots of the
floating rice plants are dead, and are therefore effectively blocking the primaiy CH4 transport
pathway to the atmosphere.
Experiments have shown that the CH4 flux from flooded rice fields varies with soil type,
temperature, redox2 potential, and pH; the type, timing, application method, and amount of fertilizer
applied; water management technique; and cultivar type (e.g., Schutz et al., 1990; Matthews et al.,
1990). Understanding how these variables control emissions requires understanding how they control
the three processes that together determine emissions. These three processes are CH4 production,
CH4 oxidation, and CH4 transport
Methane production in flooded rice fields is the result of decomposition of organic material
by methanogenic bacteria, which begins only after anoric, reduced soil conditions have been
established in the paddies. There are three primary sources of the organic material from which CH4
is produced: (1) root exudates and sloughed-off root cells from the rice plants,
(2) organic material such as rice straw that was incorporated into the soil during field preparation,
and (3) floodwater biomass (i.e., algae). Part of the methane that is produced does not reach the
atmosphere, as it is oxidized by aerobic methanotropic bacteria that are present in the oxic surface
layer of the submerged paddy soil and in the rhizosphere where oxygen is available around the rice
roots. Averaged over a growing season, as much as 60-80% of the produced CH4 is oxidized
(Holzapfel-Pschorn et al., 1985; Sass et al., 1990). Transport of the remaining, non-oxidized methane
from the submerged soil to the atmosphere occurs by diffusion through the floodwater, by ebullition
(i.e., bubbling), and by plant-mediated transport The most important pathway of escape is diffusive
1 Both production and planting statistics include all varieties of rice: short grain, medium grain, and long
grain.
2 Redox refers to oxidation-reduction, two processes that take place simultaneously. Oxidation is the loss
of an electron by an atom, and reduction is the gain of an electron by an atom.
STATES WORKBOOK D8-1 November 1992
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flow through the intercellular gas space system of the rice plant (e.g., Holzapfel-Pschorn and Seller,
1986). Figure D8-1 graphically depicts the process of CH4 production and its emission.
Certain soil characteristics have been found to affect CH4 production. Since the bacteria
responsible for CH4 production are strict aerobes which cannot function in the presence of oxygen
or other inorganic electron acceptors, CH4 formation usually occurs only after prolonged flooding of
soils that have sufficient carbonaceous substrate to reduce these electron acceptors. Electron
acceptor reduction is generally sequential, with oxygen being reduced first, followed in order by
nitrate, manganic manganese compounds, ferric iron compounds, sulfate, and lastly carbon dioxide
(CC>2). The production of CH4 from the reduction of CO2 does not occur until the sulfate has been
reduced and Eh values have declined to less than about -200 mV (Patrick and Delaune, 1977).
Methane formation is also favored at near neutral pH values. Rice soils that are most likely to show
high methane production are Entisols, Histosols, foceptisols, Alfisols, Vertisols, and Mollisols.
Experiments in Italy (Holzapfel-Pschorn and Seiler, 1986; Scbutz et al., I989a) have found
consistent diumal fluctuations in CH4 emissions, with maximum values during the afternoon and
minimum values during the early morning, indicating that CH4 production is strongly dependent on
the temperature of the upper soil layer. In these experiments, CH4 emissions approximately doubled
when soil temperature rose from 20° to 2S°C A similar dependence of CH4 emissions on
temperature was found by Koyama (1964) in laboratory experiments using anaerobicatly
incubated paddy soil samples. However, experiments in California (Cicerone and Shelter, 1981;
Cicerone et al., 1983), under climatic conditions similar to those in Italy, found no clear relationship
between CH4 flux and soil temperature, and experiments in China found that diurnal patterns of
emissions varied seasonally and were not related to soil temperature (Schutz et al., 1990), Two
maximum daily emissions occurred during the early vegetation period in China, one at noon and one
during the night, while only one daily maximum occurred (at night) in the late vegetation stage.
Application of either of the commercial nitrogen fertilizers ammonium sulfate or urea has
generally been found to reduce CH4 emissions, especially if the fertilizer is deeply incorporated into
the soil. This is believed to be due to suppression of CH4 production as a result of the addition of
sulfate or ammonium ions. In continuous measurements over three years in Italy, Schutz et al.
(1989a) found that deep incorporation of either fertilizer resulted in a reduction in methane emissions
averaged over a growing season, relative to unfertilized plots, of about 50%. Surface application of
ammonium sulfate resulted in slightly reduced emissions; surface application of urea resulted in
slightly enhanced emissions. On the other hand, an experiment in California (Cicerone and Shelter,
1981) found that application of ammonium sulfate increased CH4 emissions almost five-fold.
However, these results from California are based on late summer measurements, rather than
continuous measurements over an entire growing season.
Application of organic fertilizers (e.g., rice straw, composted rice straw, animal wastes)
whether or not in combination with mineral fertilizers, has been found, in most cases, to enhance
CH4 emissions. The organic fertilizers provide an additional carbon source for the production of CH4
in the paddy soil. Both Schutz et al. (1989a) and Yagi and Minami (1990) found that increasing
applications of dried and chopped rice straw resulted in increasing enhancement of CH4 emissions,
relative to unfertilized paddies and paddies fertilized with mineral fertilizer. Schutz et al. (1989a)
found that application of composted rice straw also enhanced CH4 emissions, while Yagi and Minami
(1990) found that additions of composted rice straw only slightly enhanced emissions. However,
STATES WORKBOOK D8-2 November 1992
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Figure D8-1
WATER-AIR
EXCHANGE
CH4
ttl
WATEBJ
W
444
ANOXIC
SEDIMENTS
CO,
t h
CH4-oxidatlon by
fnethanotrophic
bacteria
CH4
0° EBULLITION
o°o°
n
CH4- production by
methanogente
bacteria
Source: Schdtz et aL, 199L
STATES WORKBOOK
D8-3
November 1992
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preliminary experiments in China (Schutz et al, 1990) found that application of organic fertilizers
(animal manure, rape seed cake) did not affect emissions.
Water management practices also influence CH4 emissions since it is only through continuous
flooding that the paddy soil remains sufficiently reduced for methane production to occur. When
water is drained from fields during the growing season or between crops, the soil redox potential in
the surface soil layer increases, and CH4 emissions decline (Yagi and Minami, 1990; Sass et al, 1990).
Tliis is probably due to both a reduction in CH4 formation (due to increased redox potentials) and
to an increase in CH4 oxidation (due to increased input of oxygen into the soils).
Cultivar selection is likely to affect CH4 emissions through two mechanisms: (1) root
exudation, and (2) gas transport Many studies have observed two or three maxima in CH4 emissions
during the growing season with the last one or two peaks occurring during the reproductive stage of
the rice plants. These latter emission peak(s) may be due to peaks in CH4 production that result
from the plants providing soil organic bacteria with organic root exudates or root litter at this time
(Schutz et al., 1989a). The degree of root exudation and soughing off of root cells that occurs is
believed to vary between cultivar types. The rice plant also affects CH4 emissions through gas
transport mechanisms. Downward oxygen transport through the plant (and subsequent oxidation of
CH4 in the rhizosphere) and upward methane transport probably varies between cultivars. Gas
transport mechanisms may also play a role in controlling the latter emission peaks, e.g., methane
transport may be more efficient during the reproductive stage of rice plants than at other
developmental stages (Sass et al., 1990). Sporadic measurements at four sites in India (Parashar et
al., 1991) indicate that CH4 emissions vary between cultivars, but continuous measurements of
emissions from different cultivars over an entire growing season, and with all other variables held
constant, have yet to be made. Experiments are also needed to determine the relative importance
of the rice plant mechanisms that affect CH4 emissions, i.e., the relative importance of organic input
versus that of gas transport.
Large seasonal variations in CH4 Dux from paddies have been observed In most experiments,
although the magnitude and timing of the seasonal peaks vary greatly between studies. In studies in
Italian rice fields, two to three emission peaks have been observed (Schutz et al, 1989a). The first,
occurring during tillering of the rice, is believed to be due to mineralization of organic material in
the soil prior to flooding, since the timing and magnitude of this peak in planted fields has been
found to be similar to that in unplanted fields. The second peak, occurring during the reproductive
stage of the rice plant, is believed to be due to root exudation, and the third to degradation of dying
plant materials and plant litter. Three peaks in emissions were observed in field experiments in Texas
rice fields, but the early season emission peak was missing. This was probably because there was not
much organic material present in the soil since the fields had been fallow for the previous two years,
and the sparse native material that was tilled into the soil was allowed to decompose for several
months before planting and flooding (Sass et al, 1990). The three peaks that were observed occurred
immediately prior to panicle differentiation, just before heading, and during grain filling and
maturation.
DESCRIPTION OF WORKBOOK METHODOLOGY
Because of the variability of measured emissions and the uncertainty in the effects of factors
that control methane emissions from flooded rice fields, only two variables are included in the first
STATES WORKBOOK DS-4 November 1992
-------
methodology that we recommend. These two variables are rice ecology, Le, upland, deepwater, or
other wetland type, and growing season length. In this methodology, a daily emission rate range is
applied to the number of non-deepwater, wetland acre-days harvested annually3 to obtain annual
emissions from this source. By employing an emission range, this methodology captures some of the
variability described above without requiring the detail in calculations that would be necessary to
account for factors such as soil characteristics and fertilizer regime (if the data permitted such an
accounting). We recommend using average daily emission rates (pounds CH4 'per acre per day, or
Ibs CH4/acre/day), rather than seasonal emission rates (Ibs CH4/acre/growing season), to account for
the variability in growing season lengths both within and between states. The rice growing season
is usually about four months, but can vary from about 80 to 180 days. The daily emission rate range,
however, should be a seasonally-averaged range, Le., based on emission measurements taken over an
entire season, so that the seasonal fluctuations described above are averaged. Using a daily emission
rate range based on a few measurements during a growing season, rather than semi-continuous
measurements over an entire growing season, could yield misleading results.
The recommended range for daily emission fluxes is based on recent field measurements in
Texas (Sass, 1991):
135 - 4.04 Ibs CH4/acre/day.
Sass measured methane emissions from several experimental plots in Texas over the 1990
growing season, and calculated an average daily emission rate of 2.69 (± 50%) Ibs CH4/acre/day. We
recommend this range for two reasons: 1) it is based on experiments in the U.S., and 2) it is
reasonable given the range in emission estimates from other studies. For comparison, measurements
in Italian rice fields over a three-year period yielded seasonally-averaged daily emission rates of 1.44-
3.41 Ibs CH4/acre/day for unfertilized fields, and of 2J1-5J9 Ibs CH4/acre/day for fields fertilized with
organic or mineral fertilizers (Schutz et al., 1989a). Recent field measurements in China yielded a
range of daily emission rates of 1.71 - 620 Jbs CH4/acre/day (Schutz, et aL, 1989b). In California,
the seasonally-averaged daily emission rate for fields fertilized with mineral fertilizers was 2.25 Ibs
CH4/acre/day (Cicerone et al, 1983).
States may wish to develop their own emission coefficients, especially if wetland rice is a major
crop. As discussed above, because of the great variability in methane emissions over a growing
season, seasonally-averaged daily emission coefficients (i.e., the seasonal average of average daily
emission coefficients based on semi-continuous measurements [2-12 per day] taken over an entire
growing season) should be used (see Braatz and Hogan, 1991, for a description of appropriate
emission measurement techniques).
The daily harvested area, to which an emission range is applied, should not include upland
areas or deepwater. floating rice areas because these areas are not believed to release significant
quantities of methane. Also, it is recommended that a three-year average, centered on 1988, of
annual acre-days harvested be used. Because agricultural activities typically fluctuate from year to
3 The number of acre-days harvested annually is equal to: (the number of acres with a certain cropping
length x the number of days in that cropping cycle) + (the number of acres with another cropping cycle length
x the number of days in that cropping cycle )+.._. The workbook assumes that there is only one cropping
cycle for all states.
STATES WORKBOOK D8-5 November 1992
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year due to economic, climatic, and other variables, estimation of CH4 emissions based on one
specific year of data on rice area harvested could lead to misleading or misreprcsentative results.
Ideally, only the harvested rice area that represents an anthropogenic increase in methane
emissions above natural levels would be included in the emissions inventory. For example, if a
freshwater wetland, which is a natural source of methane, is converted to a flooded rice field, and the
annual CH4 emissions from the former land use are equivalent to those of the latter land use, then
this rice area should not be included in the inventory. However, it is difficult, if not impossible, to
know what the annual methane emissions might have been in the past (or even what the original land
use was). Also, conversion of an area that naturally emits CH*, such as a freshwater wetland, to a
flooded rice field may not necessarily result in reduced annual CH4 emissions. For example, some
wetlands are flooded for only pan of the year. Conversion of such a wetland to an intensively
managed rice field may result in longer periods of continuous flooding and therefore greater
production of methane over an annual cycle. Similarly, the soils of intensively cultivated rice fields
may receive more organic inputs (e.g., organic fertilizers, root exudates) than natural wetlands, which
would also result in greater methane production. For these reasons, no attempt to account for this
issue is made in the methodology described here.
In summary, to estimate a state-specific annual CH4 emissions range from rice cultivation
using the first methodology, the three-year average of the number of (non-deepwater, wetland) acre-
days harvested annually in the state would be multiplied by the endpoints of the recommended range,
i.e.:
Low estimate (Ibs CH^ = (average # of acre-days harvested annually) x
(1.35 Ibs CH4/acre/day)
High estimate (Ibs CH4) = (average # of acre-days harvested annually) x
(4.04 Ibs CH4/acre/day)
For any users interested in converting CH4 emissions to CH4-C emissions, each estimate would then
be multiplied by 12/16.
A complete example of how to apply the recommended approach is shown in Table D8-1.
DATA AVAILABILITY
Because variables such as soil properties (type, pH, Eh), fertilizer practices, water
management practices, and cultivar type have been shown to affect CH4 emissions from rice fields,
a state may want to collect these data at the same time as harvested area data are collected.
Therefore, when the effects of these variables on emissions are sufficiently understood to include
them in an emissions inventory methodology, the data will have already been collected.
STATES WORKBOOK D8-6 November 1992
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Table DS-1
Sample Calculation for Workbook Methodology
Hypothetical state statistics for year 196*7:
10 million acres of rice growing cultivated area that is double-cropped, for 120 days during the first growing
. season and for 110 days during the second growing season, and 2 million acres that is triple-cropped, with
growing seasons of 120 days, 110 days, and 80 days (This cultivated acreage would translate into
(10x2)+(2x3) = 26 million acres harvested annually)
To calculate annual emissions, the following calculations would be made:
Low estimate: .
1) Estimate number of acre-days in year 1987:
(10 million acres x 120 days) + (10 million acres x 110 days) + (2 million acres x 120 days) + (2
million acres x 110 days) + (2 million acres x 80 days)
= 2,920 million acre-days
2) Estimate number of acre-days for 1988 and 1989.
3) Average the acre-days for 1987, 1988, and 1989.
(For this example, assume the 3-year average is 2,900 million acre-days)
4) Multiply the average number of acre-days by the low emission estimate:
(2,900 million acre-days) x (135 Ibs OT«/acre/iJay)« 3,915 million Ibs CH,
or 1.96 million tons CH4
5) Convert to mass of carbon:
(1.96 million tons CH4) x (12 tons C/16 tons CH4) « 1.47 million tons CH4-C
High Estimate:
Same as above, except the nigh emission estimate (4.04 Ibs CHj/acre/day) would be used instead of the low
emission estimate (135 Ibs CHj/acre/day):
(2,900 million acre-days) x (4.04 Ibs.CH/acre/day) = 11,716 million Ibs CH4
or 5.86 million tons CH4
or 439 million tons CHj-C
Result: This hypothetical state emits 1.96-5.86 million tons CH4 (1.47-439 million tons CH4-C) each year due to rice
cultivation.
STATES WORKBOOK D8-7 November 1992
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SUMMARY
Methane emissions from flooded rice fields vary significantly over hourly, daily, and
seasonal cycles, and are affected by a wide range of factors. Research to date, most of which has
been undertaken in temperate regions where less than 10% of the world's rice is grown, has not
provided consistent enough results to allow researchers to quantify the effects of many of these
factors on CH4 emissions.
The methodology outlined above for use in estimating national CH4 emissions from rice
cultivation is meant to include some of this variability, without being too complex and therefore
impractical The required data (i.e., number of acre-days harvested annually in each rice-
producing state) is readily available, while the methodology captures some of the observed
emissions variability without requiring extrapolation of relationships between factors and emissions
that are not yet completely understood.
The characterization of CH4 emissions from flooded rice fields is a rapidly evolving
research area, likely to yield results in the near future that can be used to refine the suggested
methodologies. For example, it may be possible to tie methane emissions to soil type and
cropping cycle (Yagi and Minami, 1990; Schutz et al 1991) so that a state's calculated emissions
will be dependent upon not only the rice area harvested, but also these two other factors as well
A recent study by Neue et al. (1990), using soil characteristics and water regimes, found that only
198 million acres of harvested wetland rice lands worldwide (about 65% of the total harvested
wetland area, or about 55% of the total (wetland + upland) harvested area) are likely to be
potential sources of CH4. Although a particular methodology has been recommended here, the
process of estimating emissions should remain flexible enough for new research results, such as
those of Yagi and Minami (1990), Neue et al. (1990), and Schutz et al. (1991), to be incorporated
when appropriate.
REFERENCES
Braatz, B.V., and K.B. Hogan, (eds.). 1991. Sustainable Rice Productivity and Methane Reduction
Research Plan. U.S. Environmental Protection Agency, Washington, D.C
Cicerone, RJ., and J.D. Shelter. 1981. Sources of atmospheric methane: Measurements in rice
paddies and a discussion. Journal of Geophysical Research 86:7203-7209.
4
Cicerone, RJ.. J.D. Shelter, and CC. Delwiche. 1983. Seasonal variation of methane flux from a
California rice paddy. Journal of Geophysical Research 88:11022-11024.
Holzapfel-Pschorn, A., R. Conrad, and W. Seiler. 1985. Production, oxidation, and emission of
methane in rice paddies. FEMS Microbiology Ecology 31343-351.
Holzapfel-Pschorn, A., and W. Seller. 1986. Methane emission during a cultivation period from
an Italian rice paddy. Journal of Geophysical Research 91:11803-11814.
STATES WORKBOOK D8-8 November 1992
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Koyama, T. 1964. Biogeochemical studies on lake sediments and paddy soils and the production
of atmospheric methane and hydrogen. In: Miyake, Y., and T. Koyama, eds. Recent Researches
in the Fields of Hydrosphere, Atmosphere and Nuclear Geochemistry. Muruzen, Tokyo, 143-177.
Matthews, IL, I. Fung, and J. Lemer. 1991. Methane emission from rice cultivation: Geographic
and seasonal distribution of cultivated areas and emissions. Global Biogeochemical Cycles 5:3-24.
Neue, H.U., P. Becker-Heidmann, and H.W. ScharpenseeL 1990. Organic matter dynamics, soil
properties, and cultural practices in rice lands and their relationship to methane production. In:
Bouwman, A.F., ed. Soils and the Greenhouse Effect. John Wiley & Sons, Chichester, 457-466.
Parashar, D.C, J. Rai, PJC Gupta, and N. Singh. 1991. Parameters affecting methane emission
from paddy fields. Indian Journal of Radio and Space Physics 20:12-17.
Patrick, Jr., W.H., and R.D Delaune. 1977. Chemical and biological redox systems affecting
nutrient availability in the coastal wetlands. Geosciences and Man 28:131-137.
Sass, R.L., F.M. Fisher, P.A. Harcombe, and F.T. Turner. 1990. Methane production and
emission in a Texas rice field. Global Biogeochemical Cycles 4:47-68.
Sass, R.L. 1991. Personal Communication.
Schiitz, H., A. Holzapfel-Pschora, R. Conrad, H. Rennenberg, and W. Seiler. 1989a. A 3-year
continuous record of the influence of daytime, season, and fertilizer treatment on methane
emission rates from an Italian rice paddy. Journal of Geophysical Research 94:16405-16416.
Schiitz, H., P. Schroder, and H. Rennenberg. 1991. Role of plants in regulating the methane flux
to the atmosphere. In Sharkey, T.D., E.A. Holland, and H.A. Mooney, eds. Trace Gas Emission
from Plants. Academic Press, New York, in press.
Schiitz, H., W. Seiler, and H. Rennenberg. 1989b. Presentation (by Rennenberg) at the
International Conference on Soils and the Greenhouse Effect, 14-18 August 1989. Wageningen,
The Netherlands.
Schiitz, H., W. Seiler, and H. Rennenberg. 1990. Soil and land use related sources and sinks of
methane (CH4) in the context of the global methane budget In Bouwman, A.F., ed. Soils and
the Greenhouse Effect. John Wiley & Sons, Chichester. 269-285.
Yagi, K., and K. Minami. 1990. Effects of organic matter applications on methane emission from
Japanese paddy fields. In Bouwman, A.F., ed. Soils and the Greenhouse Effect. John Wiley &
Sons, Chichester. 467-473.
STATES WORKBOOK D8-9 November 1992
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DISCUSSION 9
NITROUS OXIDE EMISSIONS FROM FERTILIZER USE
OVERVIEW
Nitrous oxide (N2O), which has been found to contribute to global wanning and the
destruction of stratospheric ozone, is naturally produced in soils by microbial processes during
denitrification and nitrification1. Commercial nitrogen fertilizers provide an additional nitrogen
source and therefore increase the emissions of nitrous oxide from the soil. Fertilizer nitrogen
generally enters the nitrous oxide producing mechanisms in two fundamental forms: ammonium
(NH4) or nitrate (NO3). Nitrous oxide is produced as an intermediate when nitrate is reduced to
nitrogen gas (N^ in a multistep denitrification reaction under aerobic conditions yielding nitrous
oxides as a byproduct (Crutzen, 1977).
The denitrification of the fixed nitrogen from soils and waters under anaerobic or almost
anaerobic conditions causes a reduction in ozone. This leads to an increased production not only of
inert N2 but also of N2O, the oxidation of which then lead to a larger build up of ozone destroying
oxides of nitrogen into the stratosphere as a consequence of the reaction (Crutzen, 1977):
0 (D) + N20 -> 2NO
In order to increase food production, industrially fixed nitrogen fertilizer has been used in
increasing amounts in modern agriculture. Increased burdens of nitrous oxide gas in the atmosphere,
following growing inputs of fixed nitrogen in the environment, leads to larger concentrations of nitric
oxide and nitrogen dioxide in the stratosphere.
Global estimates of N2O emissions due to nitrogen fertilizer use based on estimates of the
amount and type of fertilizers consumed and the fraction of fertilizer nitrogen released to the
atmosphere as N2O vary widely: 6-20 Tg N2O-N (9.4-31.4 Tg N2O Hahn and Junge, 1977); <3 Tg
N2O-N (<4.7 Tg N2O Crutzen et al., 1983); 0.6-Z3 Tg N2O-N (0.9-3.6 Tg N2O Bolle et al., 1986);
and 0.2-2.4 Tg N2O-N (03-3.8 Tg N2O U.S. EPA, 1990).2 Despite the uncertainty in N2O emissions
from this source, the importance of nitrogen fertilizer use, relative to other anthropogenic sources
of N2O, may be growing. The World Bank (1988) estimates that nitrogen fertilizer use is increasing
at a rate of 1.3% per year in industrial countries, and by 4.1% per year in developing countries.
Contamination of surface and groundwater from leaching and runoff of nutrients from
1 Penitrification: the process by which nitrates or nitrites are reduced by bacteria and which results in the
escape of nitrogen into the air.
Nitrification: the oxidation of ammonium salts 10 nitrites and the further oxidation of nitrites to nitrates.
2 Emissions of N2O are usually expressed in the literature in mass units of nitrogen (N), i.e., as N2O-N.
In this section emissions are first expressed in units of N (N2O-N), then in full molecular weight units (N2O-
N2O, or N2O). N2O-N is convened to molecular N2O by a conversion factor (44/28), representing the ratio
of the molecular weight of N2O to the atomic weight of N.
STATES WORKBOOK D9.! November 1992
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agricultural systems and from sewage systems may be an even more significant source of nitrous oxide
than fertilizer use alone. It is estimated that approximately 5-30% of applied fertilizer nitrogen
leaches or runs off (Breitenbeck, 1988).
Application of organic fertilizers and use of leguminous crops may also result in elevated
levels of nitrous oxide emissions (above background levels) (Breitenbeck, 1990), although the
magnitude of this anthropogenic source is highly uncertain. No-till systems employing leguminous
crops as a nitrogen source may have higher N2O emissions than no-till systems using nitrogen
fertilizers, but again, this is highly uncertain as definitive measurements have not been made
(Hargrove, 1988).
Because of the uncertainty in emissions from nutrient leaching and runoff and from organic
fertilizers and leguminous crops, as well as the lack of data and emission coefficients for each
contributing "activity" (e.g., the amount of human and animal waste nitrogen that contaminates
aquifers and the fraction of N released as N2O to the atmosphere from the N in these wastes), these
emissions of N2O will not be included in the methodologies outlined below. However, because of
the potential relative importance of these N2O emissions, they should be included in the future as
data availability and scientific understanding permit
Numerous factors influence the biological processes of the soil microorganisms that determine
nitrous oxide emissions from nitrogen fertilizer use. The factors can be divided into two general
categories: natural processes and management practices.
Natural Processes
temperature precipitation soil moisture content oxygen available
porosity pH organic carbon content thaw cycle
microorganisms soil type
Management Practices
fertilizer type application rate application technique
crop type timing of application tillage practices
use of other chemicals irrigation residual N and C from crop/fertilizer
While it is relatively well known how the natural processes individually affect N2O
emissions, it is not well understood how the interaction of the processes affects N2O emissions.
Experiments have shown that in some cases increases in each of the following factors
(individually) enhance N2O emissions: pH, soil temperature, soil moisture, organic carbon content,
and oxygen supply (Bouwman, 1990; Eichner, 1990). However, how soil moisture, organic carbon
content, and microbial population together, for example, affect N2O emissions, is not readily
predictable.
Management practices may also affect N2O emissions, although these relationships have
not been well quantified. Levels of N2O emissions may be dependent on the type of fertilizer
used, although the extent of the effect is not clear, as demonstrated by the wide range of emission
STATES WORKBOOK D9.2 November 1992
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coefficients for individual fertiliser types derived in experiments (Bouwman, 1990). Although high
application rates for fertilizer may cause higher N2O emission rates, the relationship between
fertilizer application rate and nitrous oxide emission is not well understood. Deep placement of
fertilizer as an application technique will result in lower N2O emissions than broadcasting or hand
placement (Stangel, 1988). Bremner et al. (1981) found that emissions from fertilizer applied in
the fall were higner than emissions from the same fertilizer applied in the spring, indicating that
the timing of fertilizer application can affect N2O emissions. Tillage practices can also affect K2O
emissions. Tilling tends to decrease N2O emissions; no tfll and use of herbicides may increase
N2O emissions (Groffman, 1987; Breitenbeck, 1988). However, limited research at unique sites
under specific conditions has not been able to account for the complex interaction of the factors,
making the effects of combinations of factors difficult to predict
Application of nitrogen fertilizer may also decrease the natural rate of methane uptake by
both tropical and temperate soils, and thereby contribute to the increase in atmospheric methane
concentrations (Mosier et al., 1991). The magnitude of this effect, however, is highly uncertain
and will not be addressed in the methodology outlined below.
DESCRIPTION OF WORKBOOK METHODOLOGY
The methodology for calculating N2O emissions from nitrogen fertilizers used in the
workbook is based on the total amount of nitrogen in the fertilizer consumed (in mass units of
nitrogen), an emission coefficient describing the amount N2O-N released per unit of nitrogen
applied, and a factor used to convert the emission from N2O-N to N2O (Equation 1).
(1) Total N2O-N Emissions (tons N2O-N) = Total Nitrogen Content of Fertilizer Applied
(tons N) x Emission Coefficient (tons N2O-N
released/ton N applied)
Total N2O Emissions (tons N2O) = Total N2O-N Emissions (tons N2O-N) x 44/28
There may be instances in which fertilizer consumption is given as a total mass of
fertilizer, not as N content In such cases total mass may be converted to N content using the
percentages in Table D9-1.
Because agricultural activities typically fluctuate from year to year due to economic,
climatic, and other variables, emission estimates based on a specific year of fertilizer consumption
data could result in misleading or misrepresentattve estimates. Therefore, it is suggested that an
average of three years of fertilizer consumption (e.g., centered on 1988 if 1988 is the target year)
be used in the methodology.
Emission coefficients can vary by level of detail, e.g., by fertilizer type or by both fertilizer
type and type of crop system to which the fertilizer is applied. Two different types of emission
coefficients have been identified and are presented here in increasing order of detail.
STATES WORKBOOK D9-3 November 1992
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Table D9-1
NITROGEN CONTENT OF PRINCIPAL FERTILIZER MATERIALS
1 MATERIAL
Nitrogen
Ammonia, Anhydrous
Ammonia, Aqua
Ammonium nitrate
Ammonium nitrate-limestone mixtures
Ammonium sulfate
Ammonium sulfate-nitrate
Calcium cyanamide
Calcium nitrate
Nitrogen solutions
Sodium nitrate
Urea
Urea-form
Phosphate
Basic slag, Open hearth
Bone meal
Phosphoric acid
Rock phosphate
Superphosphate, Normal
Superphosphate, Concentrated
Superphosphoric acid
Potash
Potassium chloride (muriate)
Potassium magnesium sulfate
Potassium sulfate
% NITROGEN
82
16-25
33J
20.5
21
26
21
15
21-49
16
46
38
-
2-4.5
-
-
-
-
-
-
-
-
STATES WORKBOOK
D9-4
November 1992
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Table D9-1 (continued)
1 MATERIAL
1 Multiple Nutrient
Ammoniated superphosphate
Ammonium phosphate-nitrate
Ammonium phosphate-sulfate
Diammonium phosphate
Monoammonium phosphate
Nitric phosphates
Nitrate of soda-potash
Potassium nitrate
Wood ashes
Blast furnace slag
Dolomite
Gypsum
Kieserite (emjeo)
Limestone
Lime-sulfur solution
Magnesium sulfate (Epsom salt)
Sulfur
% NITROGEN
3-6
27
13-16
16-21
11
14-22
15
13
-
-
-
-
-
-
-
-
-
Note: A dash (-) indicates no, or a negligible amount of,
nitrogen present
Source: The Fertilizer Institute, 1982.
Differences in Emission Coefficients
The simplest emission coefficient is based on fertilizer type. For this approach the
emission coefficients are represented as a range and the median of the range (both taken from a
literature review by Eichner, 1990). The emission coefficients are expressed as the percentage of
STATES WORKBOOK
D9-5
November 1992
-------
total nitrogen in the fertilizer that evolves as N2O-N (Table D9-2). Because research is lacking it
is difficult to draw definitive conclusions about which emission coefficients are best to use. For
the purposes of this analysis, we will use the median estimate as a first choice, but given the
uncertainties associated with the point estimate (Bouwman, 1990) we will also present the results
in a range. The TTnited States as a whole is used as an example for calculating the N2O emissions
using the median and range of emission coefficients by fertilizer type (Table D9-3). Consumption
of nitrogen fertiliser in the United States is based on an average of 3 years of data, starting with
the 1986/87 seasons and ending with the 1988/89 seasons. The figures used were taken from the
Tennessee Valley Authority's Fertilizer Summary Data and Commercial Fertilizers (TVA/NFERC,
1991).
Table D9-2
Fertilizer Derived N2O Emissions By Fertilizer Type*
Fertilizer Type
ANHYDROUS AMMONIA
AQUA AMMONIA
AMMONIUM NITRATE
Ammonium Sulfate Nitrate
Calcium Ammonium Nitrate
AMMONIUM TYPE
Ammonium Sulfate
Ammonium Phosphate
UREA
NITRATE
Calcium Nitrate
Potassium Nitrate
Sodium Nitrate
OTHER NITROGEN
FERTILIZERS
OTHER COMPLEX
FERTILIZERS
% N2O-N produced
(Median)
1.63
-------
Table D9-3
Nitrous Oxide Emissions From Nitrogen Fertilizer Use In The United States Calculated Using
Emission Coefficients By Fertilizer Type
Fertilizer
Sulphite
Ammonium
Nitnie
Sodium Nitrate
Urea
Diammomum
Phosphate
Monoammonium
Phosphate
Anhydrous
Ammonia
Aqua Ammonia
Other Nitrogen
Fenilizen
TOTAL
Average
Amount
(tottN)
154493.6
593,0912
5,446.0
1.539.533.2
616,735.4
84,5410
3,754,406.1
96.128.9
3,587,0183
10,431.0953
NjO-N
Emissions
(ions) -
Median
185.03
1.542.04
1.63
1,693.49
740.06
101.45
61.196.82
U 66.90
3.945.72
70.973.17
Low
30.84
23704
0.05
1,077.67
12335
16.91
32287.89
826.71
35.87
34 .636.53
High
2312.90
10,141.88
2703
23,093.00
9051.03
1068.12
25630138
6.57SO2
245352.09
554.822.84
NjO
Emotions
(ion.)
Median
290.76
2,42301
2J7
2,661.19
1.162.99
159.42
96.166.43
2.462O7
6O00.42
11U29.26
Low
48,46
37180
0.09
1.693.49
19333
26J7
50,738.12
1099.11
5637
54/428.83
Hifb
3.63456
15,93703
4Z79
36089.00
14,53733
1.992.76
403^45.02
10332.49
385^5308
871,864.46
ALTERNATE METHODOLOGY
An alternate approach accounts for differences in the crop types to which fertilizer is applied
(Table D9-4). Equation 2 summarizes this approach.
(2) Total N2O-N Emissions (Sum by Type & Crop) =
Total N2O Emissions
.Emission Coefficient by Fertilizer
Type and Crop System x Amount of
Fertilizer Consumed by Type & Crop
(tons N)
Total N2O-N Emissions x 44/28
STATES WORKBOOK
D9-7
November 1992
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Table D9-4
Fertilizer-Derived Emissions From Soil Systems and Fertilizer Types
Fertilizer Type
V
Anhydrous
Ammonia
Ammonium Type
Ammonium Nitrate
Ca, K, Na Nitrate
Urea
Crop System
Soil"
Corn
Grass
Soil
Plant
Grass
Plant
Grains
Grass
Soil
Plant
Grass
Soil
% N2O-N
Produced
0.860-6.84
0.000-1.80
0.030-0.70
0.040-0.18
0.090-0.90
0.040-1.71
0.05
0.040-0.70
0.001-0.50
0.010-0.04
0.007-0.10
0.18
0.017-0.14
* Soil u used here refer* to the experimental condition} under which fertilizer
was applied to toil with no crops planted.
Source: Eichner, 1990.
Equation (1) is the preferred method for estimating N2O-N emissions from nitrogen
fertilizer. Basing emission estimates solely on fertilizer type avoids the complexities and
uncertainties of the second methodology involving dependence on the type of crop system.
However, the numerous factors tbat affect fertilizer-derived N emissions need to be better
understood in order to provide more accurate emissions estimates from commercial nitrogen
fertilizers. Predicting the effects of these factors is difficult because their influence on emissions
is complicated and little research has been done to quantify the interactive effects between them.
In addition, neither methodology takes into account the leaching of fertilizer into ground
and surface water and the subsequent release of N2O from the aquatic sources, the use of
fertilizers and leguminous crops, or the effect of fertilizer use on the methane budget More
research is needed before these potentially important effects can be quantified and included in an
emissions inventory.
STATES WORKBOOK
D9-8
November 1992
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AVAILABILITY AND QUALITY OF DATA
The most apparent data limitation may be information relating to fertilizer use and crop
system. State statistics on amount and type of fertilizer used are available from a variety of
sources. The Fertilizer Institute, headquartered in Washington, D.C, produces an annual
publication entitled Fertilizer Facts and Figures. In this document, The Fertilizer Institute presents
information pertaining to the supply and distribution of fertilizers in the United States, as well as
consumption and demand by state. The United States Department of Agriculture (USDA), in its
publications Fertilizer Use and Price Statistics and Agricultural Statistics, breaks down fertilizer
consumption by state and includes information on fertilizer consumption by plant nutrient and
major micronutrient It also gives statistics on fertilizer use per acre by nutrient in the major
corn, cotton, soybean, and wheat producing states. Also, the Tennessee Valley Authority's
National Fertilizer and Environmental Research Center, in its publications Commercial Fertilizers
and Fertilizer Summary Data, presents state statistics on nitrogen fertilizer consumption.
REFERENCES
Bouwman, A.F. 1990. Background - Exchange of Greenhouse Gases Between Terrestrial
Ecosystems and the Atmosphere. In Bouwman, A.F., ed. Soils and the Greenhouse Effect, John
Wiley and Sons, Chichester. 61-126.
Breitenbeck, G.A. 1988. Presentation at U.S. EPA Workshop on Agriculture and Climate
Change, February 29-March 1, 1988, Washington, D.C.
Breitenbeck, GjV 1990. Best management practices and technologies for limiting nitrous oxide
fluxes from fertilizer management systems and leguminous crop rotations. In Proceedings of the
Workshop on Greenhouse Gas Emissions from Agricultural Systems, Summary Report, U.S. EPA,
Washington, D.C September. VII-6-YII-1Q.
Bremner, J.M., G.A Breitenbeck, and A.M. Blackmer. 1981. Effect of anhydrous ammonia
fertilization on emission of nitrous oxide from the soil. Journal of Environmental Quality 10:77-80.
Crutzen, PJ., and D.H. EhhalL 1977. Effects of nitrogen fertilizers and combustion on the
stratospheric ozone layer. Ambio, Vol. 6 No. 2-3. 1977. pp 112-117.
Eichner, M J. 1990. Nitrous oxide emissions from fertilized soils: Summary of available data.
Journal of Environmental Quality 19:272-190.
FAO (Food and Agriculture Organization). 1987. FAO 1986 Fertilizer Yearbook. FAO, Rome.
Fertilizer Institute, The. 1982. The Fertilizer Handbook. The Fertilizer Institute, Washington,
D.C.
Groffman, P., P. Kendrix, D. Crossley. 1987. Nitrogen dynamics in conventional and no-tillage
agroecosystems with inorganic fertilizer or legume nitrogen inputs. Plant and Soil 97-315-332.
STATES WORKBOOK D9-9 November 1992
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Hargrove, W. 1988. Nitrogen Fixing Crops. Presented at the U.S. EPA Workshop on
Agricultural and Climate Change, February 29-March 1,1988, Washington, D.C
Mosier, A.R., and K.F. Branson. 1990. Effect of encapsulated calcium carbide and nitrapyrin on
N2O emissions from irrigated corn. Agronomy Abstracts 82276.
Mosier, A.R., W.D. Guenzi, and RE. Schweizer. 1986. Sofl losses of dinitrogen and nitrous
oxide from irrigated crops in northeastern Colorado. Soil Science Society of America Journal
5
-------
DISCUSSION 10
GREENHOUSE GAS EMISSIONS FROM LAND-USE CHANGE
OVERVIEW
Land-use changes that alter the amount of biomass1 on that land produce a net exchange
of greenhouse gases between the atmosphere and the land surface. Various land-use change activities
that contribute to anthropogenic emissions and uptake include:
Forest clearing for permanent conversion to other uses, including crops, pasture,
roads, and suburban development,
Prescribed forest burning,
Logging,
Forest degradation due to and air pollution,
Timber stand management that increases CO2 uptake.
Establishment of plantations, and other tree planting practices,
Flooding of lands,
Wetland drainage,
Conversion of grasslands to cultivated lands.
Biomass is approximately 45% carbon (C) by weight (measured in dry matter mass units)
(Whittaker, 1975). Although the carbon loading of any acre of forest can vary greatly, depending on
the species, stand age and composition, and other factors, an average forested acre in the U.S. is
estimated to hold almost 80 tons of biomass; more than half of which (59%) is in the soil, rather than
the tree (Trexler, 1991). Recent research has found that for the U.S. as a whole, the average percent
of carbon for softwood tree species is 52.1 percent, and for hardwoods, 49.1 (Koch, as cited in Birdsey
1991b).
Clearing of forest vegetation by burning results in immediate emissions of CO2. Mechanical
clearing, on the other hand, generally results in delayed release of CO2; from biomass left on the site
to decay, from biomass removed to landfills, from biomass used as fuel, or from biomass converted
into wood products. When the biomass is converted into wood products, the length of time before
the CO2 it contains is released will depend on the particular production process and end-product
involved.
If the forest is allowed to regrow, atmospheric carbon dioxide is absorbed by the growing
vegetation, and over time CO2 uptake can equal CO2 emissions (i.e., the net exchange of CO2 is
zero). However, if forests are not allowed to regrow to their original level of biomass density
(biomass/unit area), net CO2 emissions to the atmosphere will occur. Similarly, forest degradation
due to air pollution (e.g., acid rain and tropospheric ozone), result in net CO2 emissions. If
Biomass is a shorthand term for organic material, both aboveground and belowground and both living
and dead, e.g., trees, crops, grasses, tree litter, roots, etc
STATES WORKBOOK D10-1 November 1992
-------
accumulation of biomass on land (through natural regeneration, forest restocking, and/or
establishment of plantations)2 is greater than biomass removal, net uptake of CO2 will occur.
In addition to CO2, biomass burning releases other gases which are by-products of incomplete
combustion. These include methane (CH4), carbon monoxide (CO), nitrous oxide (N2O), and oxides
of nitrogen (NO,., i.e., NO + NOj), among others. Unlike CO2 emissions from land clearing, which
may or may not imply a net release of CO2 to the atmosphere (depending on whether or not the
vegetation is allowed to regrow), emissions of these other gases from biomass burning are net
transfers from the biosphere to the atmosphere.
Forest conversion also results in greenhouse gas emissions through soil disturbance. When
forests are converted to croplands, on average about 25-50% of the soil carbon is released as CO2,
primarily through oxidation of organic matter (Houghton et at, 1983; Schlesinger, 1984; Houghton,
1991). Gearcutting and other forms of forest disturbance stimulate loss of soil nitrogen (primarily in
the form of nitrate) (e.g., Likens et al., 1970; Matsen and Vitousek, 1981). The amount of nitrogen
lost as N2O is uncertain. An experiment in New Hampshire found that clearcutting resulted in
enhanced N2O flux to the atmosphere via dissolution of N2O in the soil water, transport to surface
waters, and degassing from solution (Bowden and Bonnann, 1986). Loss of forest area may also
result in increased net CH4 emissions to the atmosphere. Soils are a natural sink of CH4 (Le., soils
absorb atmospheric CH^), and various experiments indicate that conversion of forests to agricultural
lands diminishes this absorptive capacity of soils (Keller et al., 1990; Scharffe et al., 1990).
Gearing by burning may also stimulate soil nutrient loss. Measurements in temperate
ecosystems indicate that surface biomass burning enhances emissions of N2O and NOX from the soils
for up to 6 months following the burn (Anderson et al., 1988; Levine et aL, 1988).
Other land-use changes that result in net greenhouse gas emissions include changes in areas
of wetland, grassland, and cultivated land. Freshwater wetlands are a natural source of CH4,
estimated to release 110-220 million tons CH4 (KL5-165 minion tons CH4-C)3 per year due to
anaerobic decomposition of organic material in the wetland soils (Cicerone and Oremland, 1988).
Destruction of freshwater wetlands, through drainage or filling, would result in a reduction of CH4
emissions, and an increase in CO2 emissions due to increased oxidation of soil organic material
(Moore and Knowles, 1989). The magnitude of these effects is largely a function of soil temperature
and the extent of drainage (i.e., the water content of the soil). Also, since dryland soils are a sink
of CH4, drainage and drying of a wetland could eventually result in the wetland area changing from
a source to a sink of CH4 (e.g., Harriss et al., 1982). Similarly, flooding of a dryland area would result
2 Plantations are forest stands that have been established artificially, either on lands that previously have
not supported forests for more than SO years (afforestation), or on lands that have supported forests within
the last 50 years (reforestation) and where the original crop has been replaced with a different one (Brown
et al., 1986).
3 Emission estimates in this chapter are Gist expressed in full molecular mass units, e.g., tons CO2, tons
CH4, tons CO, tons N2O, and tons NOP and second in element (carbon or nitrogen) mass units, e.g., tons
CO2-C, tons CH4-C, tons CO-C, tons N2O-N, and tons NOX-N. To convert from the former to the latter, the
former is multiplied by the ratio of the elemental weight of the element to the molecular weight of the gas,
e.g., ions CO2 is multiplied by 12/44 to convert to tons CjO-C
STATES WORKBOOK D10.2 November 1992
-------
in an increase in net CH^ emissions to the atmosphere. The magnitude of emissions would vary
depending on depth of flooding, length of flooding (e.g., intermittent or continuous), as well as
vegetation and soil types of the flooded area.
Cain and loss of wetland area could also affect net N2O and CO fluxes, although both the
direction and magnitude of the effect is highly uncertain.
Conversion of a grassland to cultivated land could result in net CO2 emissions to the
atmosphere due to soil disturbance and resultant oxidation of soil carbon, and to oxidation of carbon
in the vegetation if there is a net reduction in standing biomass. Similarly, abandonment of cultivated
land and subsequent regrowth of natural vegetation could result in net uptake of atmospheric CO^
Such activities could also affect net N2O and CO fluxes, although as discussed above, both the
direction and magnitude of the effect are highly uncertain.
LAND-USE CHANGES RESULTING IN GREENHOUSE GAS FLUX
Forest Conversion
Forests in the U.S. cover about 731 million acres, about 5 percent of the world's forest area.
This is a decline of about 4 million acres between 1977 and 1987 (Wadeli, et al., as cited by Birdsey
1991b) Highways, urban and suburban developments and other rights-of-way accounted for most
of the loss. When forest lands are converted to these types of uses, the amount of carbon emitted
into the atmosphere will depend primarily on the fate of the woody biomass cleared from the site.
Some of it may be converted to forest products, in which case the carbon in the wood will continue
to be stored until the particular product (lumber, plywood, paper) is discarded or rots. If the timber
cannot be utilized commercially it may be burned (on site, or as firewood), chipped and used as
mulch, or placed in landfills.
If the forest is converted to cropland or pasture, there will be some uptake of CO2 from the
new vegetation. (The results of convening forests to tree plantations is covered below, under the
topic of plantations.)
Burning of Forest Areas
Prescribed burning. Prescribed burning is used in several forest regions of the U.S. It is often
used to reduce logging slash (residues) and control competitive weed growth following logging, as a
tool in wildlife habitat vegetation management, and to reduce fire hazards from accumulated forest
fuels. Because carbon is allowed to reaccumulate on the land after burning, no net CO2 emissions
occur over time, although emissions of CH4, CO, N2O, and NOX result from the biomass combustion.
However, prescribed burning, and its consequent emissions, are really just a man-made replacement
for what would have probably occurred naturally. Therefore, prescribed burning may not contribute
to net greenhouse gas emissions (above natural levels), and is not included in the methodology
outlined below. For the same reason, man-caused forest fires are not considered as producing
anthropogenic emissions, and are excluded from the methodology.
STATES WORKBOOK D10-3 November 1992
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Fuehvood
Emissions of CO2 from the burning of fuelwood are not included in the inventory
methodology, under the assumption that the wood came from an area that will, in time, revegetate.
Fuelwood derive-4 from permanent forest clearing would, however, make a net CO2 contribution, and
could be calculated if accurate data are available at the local level on the amount and type (softwood
or hardwood) of the cleared wood that ends up as fuelwood.
The burning of fuelwood does make a net contribution of methane (CH^, carbon monoxide
(CO), nitrous oxide (N2O) and oxides of nitrogen (NOJ. Since the use of fuehvood is expected to
increase substantially in the future (Trader, 1991), states should, if possible, determine the amount
and type of fuehvood being burned annually, from which these net emissions of CH4, CO, N2O and
NOX can be calculated.
Lopping
Logging generally is not considered a deforestation activity because over the long term logged
forests are allowed to regrow (if selectively logged) or are replanted (if clear-cut). Over the short
term, however, net release or uptake of carbon may occur, depending on the fate of the harvested
wood, the type of forest logged, and the intensity of the logging.4 Harvested wood releases its
carbon at rates dependent upon its end-use. Decay of biomass damaged or killed during logging
results in short-term release of CO2.
Air Pollution and Forest Decline
Both localized air pollution, (e.g., concentrated sulfur dioxide or hydrogen fluoride emissions
from smelters, powerplants, or other large industrial point sources), and regional air pollution, (e.g.,
ozone and acid rain,) are known to contribute to forest degradation and decline. Such damage has
been observed and documented in both the United States and Europe (e.g., MacKenzJe and D-
Ashry, 1989). Both forest decline (e.g., needle and leaf loss, abnormal growth) and forest death
eventually result in net CO2 emissions once the dead material decays. This effect is quite difficult
to quantify, however, since natural factors, such as disease, insects, competition, and weather
extremes, may contribute to a forest's decline and death. It is usually difficult, if not impossible, to
separate one contributing factor from another.
4 This is particularly the case if the forest being logged is high in carbon content compared to the forest
that replaces it. Recent simulations of carbon flux associated with the harvesting of old-growth Douglas fir-
hemlock stands in Oregon and Washington, carried out by forest scientists in the Pacific Northwest, led the
researchers to conclude that, '..conversion of old-growth forests to younger forests under current harvesting
and use conditions has added and will continue to add carbon to the atmosphere. This conclusion is likely
to hold in most forests in which the age of harvest is less than the age required to reach the old-growth stage
of success. The amount of conversion will vary among forests, depending on their maximum storage capacity
and the difference between the timber rotation age and the age of the old-growth state within the given
ecosystem.' (Harmon, et at, 1990). In a similar vein, Birdsey (1991b) reports that "in almost all cases, allowing
a mature forest to continue growing (even at a slower rate) would store more timber and carbon than cutting
and regeneration, even if faster growing plantation species were used.'
STATES WORKBOOK D10-4 November 1992
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Timber Stand Improvement
Increasing the growth of forest trees through various timber stand improvement practices can
increase the uptake of CO2, with the amount sequestered depending on the intensity of the
treatment Treatment include practices such as precommercial thinning or removal of poorly formed
or non-commercial stems (to increase growth of future crop trees); and planting of seedlings in
understocked stands. In a poorly stocked stand (ie., where the trees are not fully utilizing the
growing capacity of the site), these practices can increase total growth. If the stand is fully stocked,
improvement cuts may only redistribute the carbon to the healthier trees, with no net increase in
carbon uptake.
Plantation Establishment
Establishment of plantations and other tree planting activities result in absorption of CO2
from the atmosphere and storage of this carbon until the vegetation is burned or decays. Restocking
of managed forests, tree-planting in urban areas, and establishing plantations on unforested lands,
therefore, result in an uptake of carbon from the atmosphere (until the biomass is harvested).
Establishment of plantations for industrial forest products (e.g., pulpwood, plywood, lumber), in which
the trees are harvested sustainably (i.e., harvested so there is no net loss of biomass over time) would
result in zero net emissions of CO2 over the long term because emissions of CO2 due to subsequent
burning or decomposition of the paper products would be balanced by absorption of CO2 (or carbon
sequestration) due to regrowtb of the trees.5 The same would be true of biomass plantations for
fuel or for the production of ethanol and methanol, as long as the amount of biomass regrown
equaled that which was harvested. Nonsustainable use of plantations (or of forests) would result in
net C02 emissions because emissions would be greater than sequestration. Also, although plantation
establishment usually results in an accumulation of soil carbon, conversion of natural forests to
plantations may cause a net loss of carbon from the soil (Holt and Spain, 1986). And as mentioned
above, conversion of natural forests to plantations may result in a loss of biomass carbon due to a
reduction in standing biomass ( Birdsey 1991 b, Harmon, et al., 1990, Hougbton, etaL 1983).
Carbon yield tables for many of the common U.S. forest types have been developed by U.S.
Forest Service researchers. The tables provide basic estimates of carbon storage and carbon storage
over time, which can be used to analyze carbon dynamics over one or several cutting periods or to
analyze the conversion of one forest type or age class to another. The tables include carbon from four
forest components; trees, soils, understory vegetation and the litter, humus and woody debris on the
forest floor (Birdsey, 1991 a).
If it is determined that emissions of carbon have occurred from unsustainable logging or
conversion of old-growth or mature forests to plantations, the amount of gross carbon emissions will
have to be adjusted by subtracting the carbon that continues to be held in forest products.
5 Idso (1991) predicts that increased forest growth induced by higher levels of CO2 in the atmosphere will,
in the future, sequester enough carbon to keep CO2-induced global wanning to about the same level
experienced since the beginning of the Industrial Revolution, i.e., about 0.5 degrees C Idso based his
conclusion on Marland's (1988) calculation that anthropogenic emissions of CO2 at current emission rates can
be balanced by a doubling of global forest growth, coupled with his (Idso's) own research that indicates trees
grown in a CO2-enriched atmosphere increase their rate of carbon sequestration by 2.8 X the normal rate.
STATES WORKBOOK DIO-S November 1992
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Carbon Storage in Forest Products
In several of the above activities, (Le., forest clearing, logging, and plantation establishment),
it may be both desirable and possible to account for delayed carbon emissions due to storage of
carbon in forest products. Making the adjustment should reduce the overstatement of emissions that
will occur if no adjustment is made.
Carbon held in wood that is transformed into forest products will not be admitted into the
atmosphere until the product burns or decays. To calculate the amount and timing of these emissions
will require a determination of how much of the wood goes into each type of product From this an
estimate can be made of these delayed emissions based on the expected life of the product.
Wastewood is usually burned immediately or within a couple of years, paper usually decays in up to
5 years (although landfilling of paper can result in long-term storage of the carbon), and lumber
decays in up to 100 or more years. The average life of timber used in residential construction in the
U.S. is about 67 years. Most paper products, on the other hand, are burned or recycled within a year.
(Row and Pheips, 1991). Using a newly developed model called HARVCARB, researchers have
analyzed carbon flows and storage from various types of timber harvesting, through logging,
processing use and disposal. Figure D10-1 illustrates the average lives for the 12 final end-use
markets used in the model. Figure D10-2 illustrates the carbon remaining in four major wood-in-use
sinks based on their analysis. Table D10-1 lists average end-use lives, adjusted to reflect an estimated
recycle rate (Row and Pheips, 1991). The HARVCARB model is one component of a carbon budget
model for U.S. forests being developed by the U.S. Forest Service (Birdsey and Plantinga, 1991).
Flooding
Flooding of lands due to construction of hydroelectric dams, or other activities, results in
emissions of CH4 due to anaerobic decomposition of the vegetation and soil carbon that was present
when the land was flooded, as well as of organic material that grows in the floodwatcr, dies, and
accumulates on the bottom. As discussed above, CH4 emissions from this source are highly variable
and are dependent on the ecosystem "type" that is flooded (i.e., above- and below-ground carbon,'
plant types, whether any pre-flooding clearing occurred, etc.) and on the depth and length of flooding
(some regions may only be flooded for part of a year). Rates of methane emissions from freshwater
wetlands are also strongly dependent on temperature, and therefore vary seasonally, as well as daily.
Net emissions of N2O and CO also may be affected by this activity, although the direction and
magnitude of the effects on these gases are highly uncertain and therefore will not be included in the
methodology outlined below.
Wetland Drainage
Drainage of wetlands will result in a reduction in CH4 uptake and an increase in CO2
emissions as the soils change from an anaerobic to an aerobic state. Depending on the fate of the
drained wetlands, these soils may also become a net sink of CH4. Net emissions of N2O and CO may
be affected by this activity, although the direction and magnitude of the effects on these gases are
highly uncertain and therefore will not be included in the methodology outlined below.
STATES WORKBOOK Dio.6 November 1992
-------
FIGURE D10-1
AVERAGE LIFE IN USE
p
A
P1
E
R
NONRE3
3INQlE-fAM
MULTHFAM
UPKEEP/IMPROV
MOBILES
MANUFACTURE
SHIPPING
OTHER USES
PRINT/WRITE
NEWSPRINT
TISSUE
PACKAGING
10 20
SO 40 60
YEARS
70 80
Source: Row & Phelps, 1991.
FIGURE D10-2
CARBON REMAINING IN SELECTED
WOOD-IN-USE SINKS
100%
80%
60%
40%
20%
0%
STATES WORKBOOK
80 70 80 SO 100
D10-7
November 1992
-------
Table D10-1
Recycle Rates, and Single-Use and
Adjusted Average Product Lives in Years
Final End Use
1 -Family Houses
Multi-Family Houses
Mobile Homes
Residential Maintenance and Repair
Nonresidential Construction
Manufactures
Shipping
Other Solid Wood Use
Newsprint
Printing and Writing Paper
Tissue Paper
Packaging Paper and Board
Single-Use
Life
60
50
12
30
67
12
6
30
1
6
1
1
Recycle
Rate
0.030
0.030
0.107
0.107
0.030
0.107
0.107
0.107
0230
0.070
0.000
0.150
Adjusted
Life
in Use
61.9
51.5
13.4
33.6
69.1
13.4
6.7
33.6
13
6.5
1.0
U
Note: The estimated average life for a single use cycle, the recycle rate, and the adjusted average
use life for each of the 12 final end-use categories. The adjustment for recycling adds several
years to the effective half-life of building materials. It is more important for paper, but the
average life for most types of paper is quite short anyway.
Source: Row and PheJps, 199L
Conversion of Grasslands to Cultivated Lands
Conversion of natural grasslands to managed grasslands and to cultivated lands may affect net
CO2, CH4, N2O, and CO emissions. Conversion of natural grasslands to cultivated lands may result
in CO2 emissions due to a reduction in both biomass carbon and soil carbon. Such a land-use change
has been found (at least in the semi-arid temperate zone) to also decrease CH4 uptake by the soils
(Mosier et al. 1991). It is not clear what the effect on N2O would be, unless of course nitrogen
fertilization occurs. The effect of conversion of natural grasslands to managed grasslands on trace
gas emissions has not been evaluated in the field, except for the effect of associated nitrogen
fertilization on N2O emissions. Nitrogen fertilization on managed Gelds may increase carbon
accumulation on land, relative to the unfertilized system, and grazing by domestic animals may also
affect trace gas fluxes. CO fluxes may be affected due to changes in soil temperature and moisture.
These effects on N2O and CO Duxes, however, are highly speculative, and will not be included in the
methodology outlined below.
STATES WORKBOOK
D10-8
November 1992
-------
Estimates of Greenhouse Gas Emissions Due to Land-Use Change
Estimates of greenhouse gas emissions due to land-use change vary considerably. Estimates
of emissions resulting from changes in forest area vary due to uncertainties in annual forest clearing
rates, the fate of the land that is cleared, amounts of biomass (or carbon) contained in different
ecosystems, the tate of the biomass removed, and the amounts of CH4, CO, N2O, and NOZ released
when biomass is burned and soils are disturbed. The net release of CO2 due to land-use change in
the temperate and boreal regions in 1980 was approximately 0.4 billion tons CO2 (0.1 billion tons
CO2-C), since CO2 emissions from deforestation were almost balanced by CO2 uptake from regrowth
of forests (Houghton et at, 1987; Melillo et ai, 1988).
Prescribed burning of temperate forests annually emits approximately 1-4 million tons CH4
(1-3 million tons CH4-C), 33-77 million tons CO (14-33 million tons CO-C), 0.02-0.03 million tons
N2O (0.01-0.02 million tons N2O-N), and 0.4-1.0 million tons NO, (0.2-0.5 million tons NOX-N)
(Crutzen and Andreae, 1990), although, as discussed above, these emissions may have occurred
anyway due to natural Ores if the prescribed burning had not occurred.
Estimates of annual emissions of CH4 due to land flooding, of annual emissions of N2O from
soil disturbance in the temperate regions, of .changes in the annual uptake of CH4 due to forest and
grassland loss, and of reductions in CH4 emissions and increases in CO2 emissions due to wetland
drainage are not available, but are not likely to be significant relative to the rest of their respective
global budgets. However, these land-use changes and associated changes in net emissions may be
significant on a state or regional scale. Further research is needed so that these emissions can
eventually be accounted for where they are important
Natural dryland soils are a source of ^O, believed to emit 10-31 million'tons N2O (6-20
million tons N2O-N) annually as a result of nitrification and denitrification processes (Seiler and
Conrad, 1987). This emission estimate is highly uncertain, however, as emission measurements vary
both temporally and spatially by up to an order of magnitude, and are not consistently correlated with
what are believed to be controlling variables such as soil temperature, moisture, and composition, and
vegetation type. Dryland soils both produce and consume CO. Carbon monoxide production,
estimated at 2-35 million tons CO (1-15 million tons CO-C) per year, is an abiotic process due to
chemical oxidation of humus material (Seiler and Conrad, 1987). It is strongly dependent on soil
temperature, moisture, and pH. Destruction of CO is a biological process believed to be due to
microorganisms present in the soil. Carbon monoxide destruction (275-585 million tons CO/yr, or
118-250 million tons CO-C/yr) increases with increasing temperature, although it is independent of
soil surface temperature (indicating that the process is more active in deeper soil layers) and requires
a minimum soil moisture (Seiler and Conrad, 1987). Desert soils have always been found to be a net
source of CO, as have savanna soils, at least during the hottest parts of the day. CO destruction
outweighs production in humid temperate soils.
The effect of conversion of natural, semi-arid grasslands to cultivated lands (a wheat-fallow
system) was investigated in Colorado by Mosier et al. (1991). Results indicate that cultivation of
natural grassland significantly reduces CH4 uptake; the average reduction in CH4 uptake ranged from
30% on the fallow fields to 50% on the cropped fields. Conversion of grassland to unfertilized
cultivated land did not have a consistent effect on N2O emissions. However, nitrogen fertilization
STATES WORKBOOK D10-9 November 1992
-------
of grasslands resulted in a significant elevation of N2O emissions as well as a reduction in CH4
uptake, relative to the natural grasslands.6
DESCRIPTION OF WORKBOOK METHODOLOGY
As described above, gross emissions of methane (CH4), carbon monoxide(CO), nitrous oxide
(N2O), and nitrous oxides (NOX* due to biomass burning are also net emissions and are produced
immediately, while gross emissions of CO2 due to reductions in forest area or timber volume may or
may not be balanced by uptake of CO2 and may occur over immediate or delayed time frames.
Similarly, increases in forest area or in the biomass density of existing forests will result in CO2
uptake at varying rates and over delayed time frames. The simplest way to calculate annual net CO2
emissions due to changes in forest area would be to multiply the net forest area converted by the
average change in carbon stocks on that cleared land (including soils). However, this method would
not account for lags in the release and uptake of carbon due to decay of vegetation and soils,
oxidation of long-term wood products, regrowth of vegetation, and reaccumulation of carbon in soils.
Ideally, each state would "back-track" their land-use changes and associated trace gas emissions
and uptake over the past 40 to 50 years so that their estimates of current annual net emissions would
include delayed emissions and uptake due to activities that occurred in prior years. Since this is not
feasible for most states, the methodology described below instead accounts for only the emissions and
uptake that occur in the inventory year, plus, where adequate information is available, a
proportionate amount of future emissions and uptake resulting from actions taken in the inventory
year. Since some activities result in emissions or uptake that occur at varying rates over periods of
time greater than one year, it is recommended that time-averaged emission and uptake rates be used.
For example, the rate of accumulation of biomass carbon in a plantation will vary from (on average)
medium, to high, to low rates between initial planting and maturity. In this case, an average annual
carbon accumulation rate, over the life of the plantation, would be used to estimate annual uptake
of carbon in the year that the plantation is established. Carbon yield tables for major forest types
in the U.S. have been developed by the U.S. Forest Service would be useful in making these
calculations (Birdsey, 1991 a).
It is important to remember that because this methodology does not account for emissions
and uptake that occur in the inventory year due to activities that took place in years prior to the
inventory year, net emission estimates could be over- or underestimates. Similarly, this methodology
does not account for all of the future emissions and uptake resulting from activities that occur in the
inventory year. For example, if a plantation is established on previously unforested land during the
inventory year, the methodology will only account for one year of annual average uptake of CO2
resulting from this activity, rather than the total uptake of CO2 over the lifetime of the plantation.
6 The effect of nitrogen fertilizers on greenhouse gas emissions is addressed in Discussion 9.
STATES WORKBOOK D10-10 November 1992
-------
The steps in this methodology are as follows:
(1) Calculate emissions of CO^ CH4, CD, N2O, and NOZ due to permanent conversion
of forests to cropland, pasture, or other use.
(2) Calculate emissions of CO2 due to logging.
(3) Calculate emissions of CO2 due to forest degradation and decline as a result of air
pollution.
(4) Calculate uptake of CO2 due to establishment of plantations and other tree planting
and timber stand improvement activities.
(5) Calculate emissions of CH4 due to flooding of lands.
(6) Calculate CH4 emissions reduction and C02 emissions due to wetland drainage.
(7) Calculate CH4 uptake reduction and CO2 emissions due to conversion of grasslands
to cultivated lands.
Emissions will first be calculated in mass units of carbon (C) or nitrogen (N), rather than full
molecular weights, i.e., CO2, CH4, etc. In the final step, after emissions and uptake are summed for
each gas, emissions will be converted to full molecular weights.
Step (1): Emissions Due to Conversion of Forests to Permanent Cropland and Pasture or
Other Use
Emissions of CO2 due to permanent clearing are calculated as follows. Only mechanical
clearing is discussed, since the amount of forest land cleared by deliberate burning in the U.S. is
insignificant First, the amount of carbon cleared annually is calculated by multiplying the annual
forest area convened by the amount of carbon stored in the aboveground biomass on that forestland
prior to conversion. If the type and volume of timber removed is known, this can be converted to
carbon using the conversion factors contained in Table D10-2. If only the size of the area cleared
is known, an estimate of carbon can be obtained by using the appropriate state figure from Table
D10-3, of pounds per acre and multiplying by 0.41 to estimate the amount of carbon contained in
above-ground biomass. Next, the net amount of aboveground carbon released is calculated. The
net release is the gross amount of aboveground biomass carbon released minus the amount of carbon
contained in the biomass that regrows on the land. If the area is converted to cropland or pasture,
this new stock of carbon can be estimated by multiplying the area cleared by 22 tons CVacre
(Houghton et al., 1987).7 To be precise, time-average amounts of biomass placed in long-term use
or storage could also be subtracted from gross CO2 emissions if this information is available or can
be collected from land clearing operations (i.e., how much of the wood removed goes in to each
major end-use category).
7 This is an average figure. Research should be conducted to determine specific estimates of biomass
carbon in different types of croplands and pastures in different regions.
STATES WORKBOOK D10-11 November 1992
-------
Table DIM
Factors to Convert Tret Volnme (cubic feet) to Carbon (pounds)
Region
Southeast and
South Central
Northeast and
Mid Atlantic
North Central
and Central
Rocky
Mountain and
Pacific Coast
Forest Type
Fines
Oak-Hickory
Oak-Pine
Bottomland
Hardwoods .
Pines
Spruce-Fir
Oak-Hickory
Maple-Beech-
Bircfa
Bottomland
Hardwoods
Pines
Spruce-Fir
Oak-Hickory
Maple-Beech
Aspen-Birch
Bottomland
Hardwoods
Douglas Fir
Ponderosa Pine
Fir-Spruce
Hemlock-Silica
Spruce
Lodgepole Pine
Larch
Redwoods
Hardwoods
Specific Gravity1
Softwood
0510
0536
0523
0.460
0378
0369
0374
0384
0.460
0.421
0351
0.416
0372
0370
0.460
0.473
0.416
0349
0.434
0.423
0508
0.416
0.424
Hardwood
0.639
0.639
0.639
0580
0543
0525
0.636
0.600
0580
0530
0.480
0.632
0576
0.465
0580
0380
0380
0380
0.433
0380
0.433
0580
0384
Percent Carbon
Softwood
0531
0531
0531
0531
0521
0521
0521
0521
0521
0521
0521
0521
0521
0521
0521
0512
0512
0512
0512
0512
0512
0512
0512
Hardwood
0.497
0.479
0.497
0.497
0.498
0.498
0.498
0.498
0.498
0.498
0.498
0.498
0.498
0.498
0.498
0.496
0.496
0.496
0.496
0.496
0.496
0.496
0.496
Factor3
Softwood
16.90
17.76
1733
15.24
1229
12.00
12.16
12.48
14.96
13.69
11.41
1352
12.09
12.03
14.96
15.11
1329
9.80
42J7
11.86
1426
11.68
11.90
Hardwood
19.82
19.82
19.82
17.99
16.87
1631
19.76
18.65
17.99
16.47
14.92
19.64
17.90
14.45
17.99
11.76
11.76
10.67
12.16
10.67
12.16
1629
10.77
1 Weighted average specific gravity of the 3 most common (in terms of volume) softwood or hardwood species within the
forest type.
J From Koch (1989).
3 Factor = specific gravity times the weight of a cubic foot of water (62.4 IDS.) times percent carbon.
Source: Birdsey, 1991a.
STATES WORKBOOK
D10-12
November 1992
-------
Table 1)10-3
Average and Total Storage of Carbon in Live Trees in the United States by Region and State, 1987
...... nverait cerpon
Ion
and Alt foroit Unreserved
State Und tlaborland
eioraoe in tress -----
Reserved Other foreat
tlaberland land
...... |0
Alt foraat
land
Southeast:
Florida
Seorfla
north Carotin*
South Carolina
Virginia
TOTAL
South Central!
AlabaM
Arkenees
Louisiana
Mississippi
OklahoM
Tannaaaoa .
Teiae
TOTAL
Northaait and Nid
Connactleut
Dtlauart
Kantucky
Maine
Maryland
Naaaachuittta
NtM Hampshire
an Jersey
law fork
Ohio
Pennsylvania
Rhode la land
Vermont
Vaat Vlrelnla
TOTAL
Source: Birdsey, 1991 a.
32941
46837
56428
51IOS
58376
48930
42070
48020
55377
49229
24274
553«8
41523
4636*
Atlantic:
57119
61330
54990
42254
68662
54638
57799
38912
44503
47814
46416
46095
55378
54699
49232
34110
46858
56496
51105
58433
49377
42070
48216
53377
49230
26012
55393
43157
47279
57145
61330
55025
42290
68700
54902
57742
38972
44680
47906
46589
46127
55368
54629
49436
34110
46858
56496
51105
58433
49167
42070
48216
55377
49250
26012
55393
43157
50166
57145
61330
35025
42290
68700
54902
57742
38972
44680
47906
. 46589
46127
55368
54629
46978
14988
19852
26798
25233
43422
17023
22188
33244
32847
19966
20975
41943
23270
22465
34405
61330
49739
39856
63191
45514
59659
35001
36877
34052
35992
45548
56789
45584
41915
249845
507886
483548
284130
422814
1948222
414569
370001
348724
372777
80201
332971
257204
2176447
47024
11072
305701
339489
81973
76754
131636
35036
378995
158519
357831
8322
112508
295806
2340685
lai caroon ec
Unreserved
t liter I end
233764
496990
470468
282322
409128
1894673
413310
364648
348473
372468
56020
322589
243012
2120320
46061
10794
297210
329458
76689
74958
12S798
33835
320193
135174
342050
7700
111107
292373
2223398
Reaarvad Other foraat
t latter I and land
Ic tone)
7133
10733
12557
1808
12484
44715
1259
1990
251
201
271
9925
2349
16247
544
83
6664
3294
4768
0
1833
725
S1660
2608
11242
167
628
2874
89092
6948
162
523
0
1201
8834
0
3363
0
109
23909
457
11843
39680
420
195
1827
4737
31ft
1W
4003
476
7142
W
4339
435
m
358
28193
STATES WORKBOOK
D10-13
November 1992
-------
Table D10-3 (Continued)
Average and Total Storage of Carbon In Live Trees in the United States by Region and State, 1987
iaglon
and All foraet
tata land
ago e aro»n
Unraaarvad
t later I and
noraee in xrtaa -
laearvad Other foroat
tlafcerland land
All foraat
land
North Cantral and
III (note
Indiana
(OHO
Kanaaa
Michigan
Nlnnaaota
Nlaaourl
Nabraaka
North Oakota
South Dakata
Ulaconaln
TOTAL
Aocky Mountain!
Arltona
Colorado
Idaho
Montana
Nevada
ON Nan Ice
Utah
Uyoojlna
TOTAL
Pacific Coaett
Alaaka
California
Haiiall
Oregon
tfeahl niton
TOTAL
Cant rail
54243
57378
49258
37870
44462
36168
39379
39549
32586
39006
39929
41983
37910
37695
51749
57642
36212
26013
32648
39927
41013
36968
52670
7793
60877
78519
46720
54243
37378
49429
39369
44589
36883
39617
40966
34400
39305
40155
42504
47142
40344
55692
62413
42472
31491
36893
41262
49405
61891
65141
16756
66064
81060
67963
54243
57378
49429
39369
44589
36883
39617
40966
34400
39303
40155
42028
47142
40344
55692
62415
42472
31491
36893
41262
46556
61891
65141
16756
66064
81060
64925
38764
33503
39581
23473
34710
30418
29785
34686
27614
37018
29702
30936
34805
33176
35378
44316
36053
22996
31513
36320
32999
32109
40247
0
31382
36313
33434
104961
115532
34900
23327
367474
272031
223686
12952
6799
29901
277451
1469034
333322
364825
512138
372858
146632
218604
240394
180492
2569265
2163868
940835
6179
774750
778453
4664083
cai car con ai
Unraeervad
ttafcerland
laaarvad Other foroat
t later I and land
t ^ AAA ottAa> a> 1 * a> 4u» A % _. ,______.«
99154
111810
32711
21354
351189
227039
215567
9960
5258
25798
268238
1368279
81022
214818
367122
417189
4258
74005
31309
81078
1291001
442517
493794
5320
661771
619470
2222873
5807
3722
1704
411
12600
19708
4025
427
0
392
4754
53350
23308
31365
77072
39322
19
19983
3790
35081
252141
148563
86869
859
33230
101664
391203
0
0
483
1563
3684
25305
4094
2565
1541
3711
4459
47205
228992
118641
67944
116147
142355
124616
183095
44333
1026123
1372788
360172
0
59729
57319
2050008
Onltod ftataa Total 43720
Source: Birdsey, 1991 a.
30727
54063
33039 13167738 11120744
46949
3200043
STATES WORKBOOK
D10-14
November 1992
-------
Next, emissions of CO2 and N2O (in units of C and N, respectively) resulting from soil
disturbance are calculated. On average, approximately 50% is lost when temperate or boreal forests
are cleared (Houghton et at, 1983). Therefore, the annual forest area converted to pasture or
cropland is multiplied by the carbon content of the soil of that land (Table D10-4) and then by 0.50
if the land is in temperate or boreal regions, and then is divided by 25 years to estimate the average
annual release ol carbon from the soils. To calculate the emissions of N2O due to conversion of
forests to agricultural lands, the annual area of forests converted is multiplied by the factor 0.0017
tons N2O-N/acre/year, ±45% to represent the range of uncertainty in the estimates.9 This factor
is the difference in the measured annual flux of N2O and the average annual flux of N2O from 3-,
4-, S-, and 10-year-old pastures located nearby (Luizao et aL, 1989) J°
Table D10-4. Estimates of Organic Soil Carbon in Relatively Undisturbed,
Secondary Forests in the United States, by Region1
Region
Southeast
South Central
Northeast
Mid Atlantic
North Central
Central
Rocky Mountain
Pacific Coast
Soil Carbon
(kg/m2)
7.74
7.58
16.21
11.56
13.09
8.33
8.02
9.77
(Ibs/ac)
69,044
67,626
144,703
103,173
116,791 .
74,302
71,571
87,191
1 Data from Post et al. (1982).
Source: Birdsey, 1991 a.
8 These percentages of soil carbon lost are estimates of very uncertain numbers. As mentioned above.
some studies have found that conversion of forests to pasture may not result in a net loss of soil carbon. This
issue will need to be researched in the future to determine estimates that are specific to ecosystem types and/or
land disturbance activities.
9 This uncertainty range is based on the average of the standard errors of the mean flux rates measured
in pastures by Luizao et al. (1989).
10 Obviously, this estimate of N2O release is not what one would like to use to estimate N2O release from
soils due to temperate forest clearing or forest conversion to cropland. This issue will need to be researched
to determine more appropriate emission coefficients.
STATES WORKBOOK
D10-15
November 1992
-------
Step (2): Emissions Due to Logging
This next step is meant to capture CO2 emissions due to non-sustainable logging or
replacement of mature or old-growth forests by plantations. Given enough time, logged forests will
reaccumulate rr. Jt or all of the carbon lost due to logging (both the carbon converted to products
and the carbon lost due to damage during logging operations and due processing of the products).
However, if forests are logged too frequently (i.e^ logged non-sustainably), or if forest having high
carbon content (e.g., old-growth or mature forests) are replaced by forests planned for harvest at a
lower ultimate level of carbon storage, complete reaccumulation of carbon does not occur.
In this calculation, the area that is harvested is multiplied by the average aboveground biomass
carbon removed per unit of area, derived from carbon-yield tables for the timber type harvested,
applied to the volume of wood actually taken out Table D10-5 may be useful in this step. It gives
estimated net annual change in timber volume and forest carbon storage for major U.S. forest types
after harvesting and regenerating mature forests (Birdsey, 1991b). The amount of slash left on the
land is divided by the number of years that it takes the carbon to decay. This will vary, depending
on the climate.
This step will overestimate emissions since some of the cleared carbon was converted to long-
term use that may not decay for 100 years or more. To calculate this amount it would be necessary
to know how much of the harvested wood Gber went into each type of forest product; this
information may not be readily available. As mentioned above, however, research is underway
through the U.S. Forest Service and others to develop a U.S. carbon budget model for forestry
(Birdsey and Plantinga, 1991), and information from this and other ongoing research should be
included in the methodology as information becomes available.
Effects of logging on soil carbon are not included here because of both a lack of readily
available data as well as uncertainty surrounding the magnitude of these effects under various
silvicultural practices. This issue should be researched in the future.
Step (3): Emissions of CO2 Due to Forest Degradation and Decline from Air Pollution
The forest areas affected are first divided into two groups: 1) forest areas that have died in
the last year due to air pollution, and 2) forest areas that have degraded in the last year due to air
pollution. Estimating these areas will be quite difficult because, as explained above, it is often not
clear if it was the air pollution, or some other factor such as disease, that caused a forest's death or
decline. Estimates of the rate of release of CO2 due to forest degradation and decline and associated
soil degradation have not been made, so it is not possible to calculate this effect with any accuracy.
As a first approximation, it is suggested that the area that has died be multiplied by the average
aboveground biomass carbon per unit area, which assumes that all of the aboveground carbon is
released in the year of death and does not include any loss of soil carbon that occurs. The area that
has undergone degradation should be multiplied by an average aboveground biomass carbon loss per
unit area over the one year period. This is meant to approximate the needle and leaf loss that occurs
in forests affected by air pollution. The two estimates of carbon loss should then be summed.
STATES WORKBOOK D10-16 November 1992
-------
Table D10-S
Expected N*l Annul Changes In Timber Votane and Forest Carbon Slor*ge Alter
HomstlBf Matore Timber mad
Region and
Forest Type
Southeast
pine plantation
Natural pine
Oak-pine
Oak-hickory
Bottomland hardwood
South Central
Pine plantation
Natural pine
Oak-pine
Oak-hickory
Bottomland hardwood
Northeast
Whiie-red-jack pine
Spruce-fir
Maple-beech-bircb
Bottomland hardwood
Mid Atlantic
Oak-hickory
North Central
White-red-jack pine
Maple-beech
Aspen-birch
Central
Oak-hickory
Bottomland hardwood
Rocky Mountains
Douglas fir
Ponderosa pine
Fir-spruce
Larch
Lodf epole pine
Pacific Coast
Douglas fir
Ponderosa pine
Fir-spruce
Hemlock-sit ka spruce
Lodgepoie pine
Redwoods
Hardwoods
Average
Age of
Mature
Forest*
30
45
50
50
50
35
40
45
55
35
65
65
75
65
«5
65
85
55
75
75
95
95
85
80
85
80
85
80
80
80
80
45
Alternative Forest Type
Pine plantation
Pine plantation
Pine plantation
Pine plantation
Bottomland hardwood
Pine plantation
Pine plantation
Pine plantation
Pine plantation
Bottomland hardwood
White-red-jack pine
Spruce-Gr
Maple-beech-birch
Bottomland hardwood
White-red-jack pine
White-red-jack pine
Maple- beech
Aspen -birch
While-red-jack pine
Bottomland hardwood
Douglas fir
PonderoM pine
Fir-spruce
Larch
Lodgepoie pile
Douglas fir
Ponderosa pine
Fir-spruce
Hetnlock-siika spruce
Lodgepoie pine
Redwoods
Douglas fir
Cutting
Period*
(yean)
30
30
30
30
45
35
35
35
35
45
65
65
65
65
65
65
65
65
65
65
80
80
80
80
80
80
80
80
80
80
80
80
Timber
Volume*
(cuft/sc/yr)
-54.8
-74.7
-23.6
-IAS
-23.7
-343
-55.1
-13.0
23
-28.9
-23.0
-26*
-257
-193
-23.5
-34.4
-27.1
fi.7
-16.9
13J
-34.1
-31.1
-32.1
-26.4
-22J
-105.6
-49.9
-453
-1123
-16.6
-97.0
-91.0
Carbon'
(1b*/adyr)
-2177
-3030
-2080
-2750
-1249
-1577
-2323
-1840
1929
-1627
-1338
-1266
-1622
-1325
-1118
-1595
-1560
-603
-1551
'934
-1480
-1241
-1140
-1073
814
2967
-1448
-1238
-2589
-551
2404
2503
Maun tiaber mctudo
-------
Step (4): Uptake Due to Plantation Establishment and Other Tree-Planting Activities
The fourth step is to calculate the net uptake of carbon due to establishment of plantations,
restocking of managed forests, urban tree planting, and other tree planting activities. To calculate
the effect of plantation establishment, the initial biomass carbon density (before each plantation was
planted) is subtracted from the expected final biomass carbon density of the plantation (the biomass
carbon density at maturity) for each plantation type, based on appropriate carbon yield tables (Birdsey
1991a). This figure, the expected change in biomass carbon between initial plantation establishment
and maturity, is then multiplied by the area of land affected, and divided by the number of years
required for the plantation to reach maturity to calculate the average annual net uptake or release
of carbon due to establishment of that plantation type. This step is repeated for all plantation types.
Annual CO2 uptake due to restocking of managed forests and urban tree planting are
calculated by estimating the total biomass carbon added per unit area over the lifetime of the trees
planted, multiplied by the area of trees planted, divided by the estimated life of each set of trees.
Changes in soil carbon due to these activities are not included in the methodology at this time
because of uncertainties in both the magnitude and direction of these changes. This issue should be
researched in the future to determine if appropriate parameters for soil carbon loss and uptake can
be added to the methodology.
Step (5): Emissions of CH4 Due to Flooding of Lands
Anthropogenic methane emissions may result when lands are flooded due to changes in land
use (e.g., damming rivers for hydropower). While there has been some research on emissions from
natural wetlands, little data exists on which to develop emissions coefficients for methane generated
from lands that are newly flooded due to land-use change. Additionally, there is a large degree of
uncertainty associated with estimating emissions from flooded lands because methane generation
would vary significantly depending on temperature, season, characteristics of the submerged
vegetation, and numerous other factors. Accordingly, no methodology for estimating such emissions
is presented here. Though such emissions are not likely to be large in comparison with other
anthropogenic sources of methane, it is recommended that states estimate the number of acres that
have been flooded due to land use change in order to begin to assess the potential methane emissions
from this source.
Step (6): CH4 Emissions Reduction and CO2 Emissions Increase Due to Wetland Drainage
To calculate the reduction of CH4 emissions due to wetland drainage, the area drained is
multiplied by the difference in the average daily CH4 emission rate before and after draining, and is
multiplied by the number of days in a year that the wetland was emitting CH4 prior to drainage. The
number of days of CH4 emissions prior to drainage can be approximated by the number of days in
the year that the wetland was flooded. To calculate the increase in CO2 emissions due to this
activity, the area drained is multiplied by the difference in the average annual CO2 emission rate
before and after draining. This assumes that the elevation in CO2 emissions due to drainage continue
throughout the year. However, the length of time over which the elevated CO2 emissions continue
is uncertain - it could be less than or greater than a year.
STATES WORKBOOK D10-18 November 1992
-------
The difference in CH4 and CO2 emissions before and after drainage will vary depending on
factors such as soil temperature, extent of drainage, and wetland type. Very little data are available
on this subject A laboratory experiment with materials representing a fen, a bog, and a swamp found
that the reduction in CH4 emissions increased with increasing drainage, although the magnitude of
the reduction v?~ed between the three types of materials. CH4 emissions from the fen decreased
from about 0.19 Ibs CH4-C/acre/day (with the water level about 4 inches above the surface) to about
0.007 Ibs CH4-CVacre/day (with the water table about 27.5 inches below the surface); CH4 emissions
from the swamp decreased from about 0.08 to about 0.005 Ibs CH4-C/acre/day; and CH4 emissions
from the bog decreased only slightly, from about 0.006 to about 0.005 Ibs CH4-C/acre/day. CO2
emissions from all three materials were about 0.0007 Ibs CO2-C/acre/day (with the water level about
4 inches above the surface), and increased to about 0.0)8 Ibs CO2-C/acre/day (with the water table
about 27.5 inches below the surface.
Step (7): CH4 Uptake Reduction and Net CO2 Emissions Due to Conversion of Grasslands to
Cultivated Lands
To calculate the reduction of CH4 uptake due to conversion of grasslands to cultivated lands,
the grassland area converted is multiplied by the average annual CH4 uptake rate per unit area of
the grassland before clearing and by 0.40 (using the results of the work by Mosier at at. (1991) in
Colorado grasslands). Estimates of average annual CH4 uptake rates of natural grasslands will need
to be collected in the future.
To calculate the net release of CO2, the grassland area converted to cultivated land is
multiplied by the difference in aboveground biomass carbon and soil carbon before and after clearing.
Estimates of these carbon losses should be researched in the future.
Calculation of Net Emissions
The calculations involved in each of the previous seven steps are summarized in Table D10-6.
Annual net carbon dioxide emissions (i.e., emissions minus uptake) are calculated by adding the CO2-
C emissions (some of which may be negative) calculated in steps 1,2,3,6 and 7 and then subtracting
from that sum the sum of the CO2-C uptake calculated in step 4. To convert the net release of CO2-
C from units of C to full molecular weight, the net release is multiplied by 44/12 (the ratio of the
molecular weight of CO2 to the atomic weight of C). Net emissions of CH4-C due to biomass
burning, flooding of lands, wetland drainage, and conversion of grasslands to cultivated lands are
calculated by adding the net emissions calculated in steps 1, 5, and 6 to the CH4 uptake reduction
calculated in step 7, and then subtracting from that sum the CH4-C emissions reduction calculated
in step 6. Net emissions of N2O-N due to biomass burning are calculated by step 1. And net
emissions of CO-C and NOX-N due to biomass burning are calculated in step 1. The emissions CH4-
C, CO-C, N2O-N, and NO -N are multiplied by 16/12,28/12,44/28, and 30/14, respectively, to convert
to full molecular weights.
11 The numbers used to convert NOX emissions to full molecular weight are based on the assumption that
all of the NOX emissions are NO, rather than some combination of NO and NO2, since NO is the primary
form of NOX emitted during biomass combustion (Andreae, 1990).
STATES WORKBOOK D10-19 November 1992
-------
Table D10-6
Methodology Summary
Step (1): Net Emissions Due to Conversion of Forests to Perouoeot Cropland, Pasture and Other Uses
COr-C emissions « {[(annual forest area cleared mechanically) x (aboveground carbon removed per unit forest area)
(the amount of above ground carbon stored in forest products (if any) from the area cleared)
+ (carbon content of soil per unit forest area) x (fraction that is released.
Step (2): Emissions Dae to Logging
COj-C emissions * {[(annual forest area logged noo-sustainably) x (average aboveground biomass carbon removed
per unit area) + {((annual forest area togged) x [(average aboveground btomass carbon per unit
of mature forest area) - (average aboveground biomass carbon per unit of replacement
forest/plantation)]
Step (3): Emissions Due to Forest Degradation and Death from Air Pollution
COj-C emissions « [(annual forest area that has died from air pollution) x (average aboveground biomass carbon
per unit area)] + [(annual forest area that has degraded due to air pollution) x (average
annual loss of aboveground biomass carbon per unit area)]
Step (4): Uptake Due to Plantation Establishment and Other Tree Planting Activities
CO2-C uptake = {[(initial aboveground biomass carbon per unit area prior to establishment of plantation) -
(aboveground biomass carbon per unit area at plantation maturity)] x (annual area of plantations
established) x (I/number of years to reach maturity)} + [(area of restocking) x (average
aboveground biomass added per unit area over lifetime of trees) x (I/number of years to reach
maturity)] + [(area of non-plantation tree planting) x (average aboveground biomass added per
unit area over lifetime of trees) x (I/number of years to reach maturity)]
Step (5): Emissions Due to Flooding of Lands
CH,-C emissions = Specific emissions coefficients are not provided, however states should estimate the annual area
flooded due to land use change. Flooded areas should be grouped by type (e.g. lake, bog, etc.).
With additional research, appropriate emissions emission coefficients could be developed for each
type, and then the results summed over all areas.
Step (6): CH4 Emissions Reduction and CO, Emissions Due to Wetland Drainage
CH«-C emissions
reduction = (area drained) x [(average daily CH4 emissions per unit area before drainage) - (average daily
CH4 emissions per unit area after drainage)] x (number of days wetland was emitting CH4 prior
to drainage)
COj-C emissions = (area drained) x [(average annual COrC emissions per unit area before drainage) - (average
annual COj-C emissions per unit area after drainage)]
Step (7): CH4 Uptake Reduction and CO, Emissions Due to Conversion of Grasslands to Cultivated Land
CH«-C uptake
reduction = (area converted) x (average annual CH4 per unit area before conversion) x 0.40
COrC uptake - (area convened) x [(annual CO2-C emissions before conversion) - (annual CO2-C emissions after
conversion)]
STATES WORKBOOK D10-20 November 1992
-------
Some of the emissions and uptake are calculated above as ranges, and some are not States
should include ranges in their estimates of areas cleared, areas flooded, etc. used in the calculations
whenever possible. Research should also be undertaken in the future to obtain emission coefficient
rates for use in the methodology where appropriate.
AVAILABILITY OF ACTIVITY DATA
The data needed to calculate greenhouse gas emissions due to land-use change using the
methodology outlined above are forest and agriculture area statistics. Possible sources for some of
these data are outlined hi this section.
The area data needed are current annual estimates of:
Forest area cleared for permanent conversion to cropland, pasture, dams, roads,
developments, etc. by forest type;
Forest area logged; timber type, amount harvested;
Forest area lost due to air pollution;
Forest area degraded due to air pollution;
Plantation area established by type;
Forest area restocked;
Area of non-plantation tree planting;
Area flooded by type;
* Wetland area drained by wetland type; and
Grassland area (by type) converted to cultivated land.
Most states will have their own forest and agriculture statistics with which these areas can be
estimated. Satellite imagery, aerial photography, and land-based surveys are all possible sources of
this data.
Many states have colleges or universities engaged in research on forestry and other aspects
of land use. In addition, the U.S. Forest Service has a network of forest experiment stations located
throughout the country, some of which are engaged in studies relating to forest ecosystem biomass,
timber inventories, timber growth and yield, forest products, etc., which can provide information
relating to the amount, type and volume of forest biomass and forest land use changes at the state
level.
USDA Forest Service Experiment Stations
Intermountain: 324 25th St, Ogden, UT 84401
North Central: 1992 Folwell Ave., St. Paul; MN 55108
Northeastern: 100 Matsonford Rd, Radnor, PA 19087
Pacific Northwest: P.O. Box 3890, Portland, OR 97208
Pacific Southwest: 1960 Addison St., Berkeley, CA 94704
Southeastern: 200 Weaver Blvd., Asheville, NC 2802
STATES WORKBOOK D10-21 November 1992
-------
CONCLUSION
Uncertainties in the methodology described above are due to both uncertainties in the data
used in the calculations and to the omission of past land-use changes as a factor in the calculation
of uptake and r-'ease of carbon. The data uncertainties, essentially a reflection of limited scientific
understanding of the carbon and nitrogen cycles and a lack of accurate land-use statistics, cannot be
avoided at present The issue of time lags in carbon flows cannot fully be taken care of without a
complex accounting framework (typically a computer model, e.g., Houghton et at, 1983; Detwiler and
Hall, 1988) that tracks time-dependent changes in the carbon content of vegetation and soils
following disturbance over a 50-year period, or longer. Such models are under development
Research is clearly needed to determine current annual areas of land-use change by type.
More accurate, ecosystem-specific statistics on the amount of carbon contained in the aboveground
biomass and in the soils are needed. Research is also needed to determine the magnitude and
direction of the effects of the land-use changes described above on emissions of trace gases. The
development of accurate state and national emission inventories for land-use activities will not be
possible until these research needs are addressed
However, it is hoped that the methodology outlined above will provide a starting point by
which states can begin, if they have not already done so, to collect and assimilate land-use change
statistics. As the inventory methodologies become more sophisticated, the statistics needed to assess
greenhouse gas emissions from land-use change will then be available for use. In the meantime,
rough estimates of these emissions can be made based on the proposed methodology in this
document
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nitric oxide and nitrous oxide following surface biomass burning. Journal of Geophysical Research
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global climate. Paper presented at the Chapman Conference on Global Biomass Burning:
Atmospheric, Climatic, and Biospheric Implications, 19-23 March 1990, Williamsburg, Virginia.
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paddies, their primary productivity, seasonal!ty and possible methane emissions. Journal of
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Birdsey, R. A. 199la. Carbon Storage and Accumulation in United States Forest Ecosystems,
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Birdsey, R. A. 1991 b. Prospective changes in forest carbon storage from increasing forest area
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"Conference on Forests and Global Change," June 11-12,1991, Arlington, Va. American Forestry
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Brown, S., A.E Lugo, and J. Chapman. 1986. Biomass of tropical tree plantations and its
implications for the global carbon budget Canadian Journal of Forest Research 16390-394.
Cicerone, RJ., and R.S. Oremland. 1988. Biogeochemical aspects of atmospheric methane.
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Crutzeo, PJ., M.O. Andreae. 1990. Biomass burning in the Tropics: Impact on atmospheric
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Detwiler, R.P., and C.A.S. Hall. 1988. Tropical forests and the global carbon cycle. Science
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Harmon, M.E., W.K. Ferrell, and J.F. Franklin. 1990. Effects on Carbon Storage of Conversion
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soil following replacement of rainforest with Araucaria cunninghammii. (Coniferae:
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Houghton, R.A., R.D. Bonne, J.R. Fruci, J.E. Hobbie, J.M. Melillo, CA. Palm, BJ. Peterson,
G.R. Shaver, and G.M. Woodwell. 1987. The flux of carbon from terrestrial ecosystems to the
atmosphere in 1980 due to changes in land use: geographic distribution of global flux. Tellus
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Woodwell. 1983. Changes in the carbon content of terrestrial biota and soils between 1860 and
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STATES WORKBOOK D10-23 November 1992
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MacKenzie, JJ., and M.T. El-Ashry (eds.). 1989. Air Pollution's Toll on Forests and Crops. Yale
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Matson, P.A., and P.M. Viotousek. 1981. Nitrogen mineralization and nitrification potentials
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Mosier, A., D. Schrmel, D. Valentine, K. Bronson, and W. Parton. 1991. Methane and nitrous
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-------
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STATES WORKBOOK D10-25 November 1992
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-------
DISCUSSION 11
GREENHOUSE GAS EMISSIONS FROM BURNING OF AGRICULTURAL CROP WASTES
OVERVIEW
Large quantities of agricultural wastes are produced from fanning systems worldwide. These
wastes are in the form of crop residue and animal waste.1 Burning of crop residues is not thought
to be a net source of carbon dioxide (CO^ because the carbon released to the atmosphere during
burning is reabsorbed during the next growing season. However, crop residue burning is a significant
source of methane (GELJ, carbon monoxide (CO), nitrogen oxides (NOJ, and nitrous oxide (N2O).Z
DESCRIPTION OF WORKBOOK METHODOLOGY
The methodology for estimating greenhouse gas emissions from burning of agricultural wastes
is based on the amount of carbon burned and the emission ratios of CH4, CO, N2O, and NOX to CO2
measured in the smoke of biomass fires (Crutzen and Andreae, 1990).
1 Animal dung and some crop residues are often burned as a fuel or incorporated into the soil as a
fertilizer. Greenhouse gas emissions from burning animal dung and crop residues for energy production
should be estimated as pan of biomass material used as energy; this issue is discussed in the discussion section
on CO2 emissions from fossil and biomass fuels. Incorporation of organic wastes into the soil can enhance
greenhouse gas emissions (e.g-, CO2 emissions from soils, CH4 emissions from flooded rice fields), out the
magnitude of this effect Is highly uncertain. Because of this uncertainty, none of the methodologies outlined
in this document explicitly address the use of organic wastes as a fertilizer. However, the methodologies in
Discussion 8 implicitly account for the enhancement of CH4 emissions from flooded rice fields due to
application of organic fertilizers in that the recommended emission coefficients are based in part on
measurements in organically fertilized fields. Methane emissions also result if animal dung is allowed to
accumulate and decompose anaerobically. This 'activity* and the resultant CH4 emissions are examined in
Discussion 7.
2 As mentioned in the previous footnote, other methods of agricultural waste disposal (e.g., burning of
wastes for fuel, incorporation of wastes into the soil) may also result in greenhouse gas emissions. A
suggestion has been made that the emissions resulting from alternative waste disposal methods be estimated,
and only the net change in emissions due to waste burning (emissions due to burning the average of
emissions due to alternative disposal methods) be included in the inventory. However, the purpose of this
inventory is to estimate anthropogenic emissions and sinks, not the difference between emissions from one
anthropogenic activity and some alternate anthropogenic activity. The cultivation of crops is an anthropogenic
activity that results in elevated greenhouse gas emissions above natural levels (i.e., above the level of emissions
that would occur if the land were in its natural state). Ideally, one would calculate aU the annual emissions
and sinks of the soib and vegetation of the land area as it existed in its original state, and calculate the annual
emissions and sinks of the land in its current state, and then include only the difference in the inventory.
Since this is not possible, the emissions and sinks that occur as a result of agricultural waste burning (or that
would not have occurred had this activity not taken place) are included here.
STATES WORKBOOK Dll-1 November 1992
-------
Total Carbon Burned
Four types of data are required to calculate the amount of carbon burned in agricultural
wastes.
The amount of crops produced with residues that are commonly burned,
The ratio of residue to crop product,
The fraction of residue burned, and
. The carbon content of the residue.
The first step is to multiply annual production data (in pounds) for each of the pertinent
crops by the ratio of residue to crop product for each crop, to generate the amount of residue
available for combustion. Estimates of residue/crop product ratios for certain crops are presented
in Table DIM.
Next, to calculate the total tonnage of crop residue burned, the total amount of residue
produced, for each crop, is multiplied by the fraction of residue burned in the field. If no other data
are available, assume as a default factor that 50% of the crop residue is burned (Seller and Crutzen,
1987).
Once the amount of crop residue burned is estimated, it must be converted to dry matter mass
units. Dry matter refers to biomass in a dehydrated state. Therefore, with information about the
moisture content of a crop residue, the dry matter of that residue can be estimated. For example,
200 tons of crop residue with a moisture content of 10%, would have a dry matter content of 90%,
equal to 180 tons dry matter. At this time, however, limited information on the moisture content of
crop residue is available. According to Elgin (1991), the moisture content of crop residue varies
depending on the type of crop residue, climatic conditions (i.e., in a humid environment the residue
will retain more moisture than in an arid environment), and the length of time between harvesting
and burning of the residue.3 Average moisture contents for selected crops are presented in Table
Dll-1. To derive an average dry matter content, the average moisture content is subtracted from
1.
After the total mass of crop residue burned is converted to mass of dry matter burned, the
figure must be multiplied by the carbon content per unit of dry matter of the residue to convert to
units of carbon. Carbon contents for selected crop residues are presented in Table Dll-1; an average
value of 0.45 Ibs C/lb dm can be used in the cases where data are not available. The steps described
above can be combined into the following equation to calculate the total carbon burned:
(1) Total Carbon Bumed (Ibs C) = Amount of crop produced (Ibs) x residue/crop ratio x residue
burned (%) x dry matter content
x carbon content (Ibs C/lb dm)
3 For example, alfalfa has a moisture content of 75-85% when harvested (wet), 18-20% when baled, and
12% when cured and in equilibrium with the environment (Elgin, 1991).
STATES WORKBOOK Dn.2 November 1992
-------
Table DIM
Selected Crop Residue Statistics
Product
Cereals
Wheat
Barley
Maize
Oats
Rye
Rice
Millet
Sorghum
Legumes
Pea
Bean
Soya
Tuber and Root Crops
Potatoes
Feed beet
Sugarbeet
Jerusalem Artichoke
Peanut
Sugar Cane1
Residue/Crop Product
U
12.
1.0
13
1.6
1.4
1.4
1.4
2.1
XI
2.1
0.4
0.4
03
0.8
1.0
Moisture Content
(*)
-
12-22
12-22
50-70
05-15
05-15
12-22
12-22
12-22
05-15
05-15
05-15
40-70
80-90*
80-90*
40-70
40-70
Carbon Content
(%dm)
48.53
45.67
47.09
4&53
4&53
41.44
4&53
48.53
45.0
45.0
45.0
4226
40.72*
40.72*
42.46
4X46
1 Sugar cane data were available only for bagasse as the residue. Bagasse is the dry pulp remaining from sugar cane after
the juice has been extracted; Le., it is the residue after processing of cane, not the residue left in the field after harvesting
cane.
2 These statistics are for beet leaves.
Source: Strechler and StuzJe, 1987; USDA-ARS personal communication.
Emission Ratios
Once the total carbon burned is estimated, the emissions of CH4, CO, N2O, and NOX
can be calculated based on the methodology in Crutzen and Andreae (1990).
STATES WORKBOOK
Dll-3
November 1992
-------
To calculate emissions of CH4 and CO due to burning of crop residue, the amount of carbon
burned (Equation 1) is multiplied by 0.90 to account for the approximate 10% of the carbon that
remains on the ground (Seller and Crutzen, 1980; Crutzen and Andreae, 1990).4 The resulting
figure (the amount of carbon dioxide released instantaneously, in units of carbon) is then multiplied
by the ratios of ~n:ssions of CH4 and CO relative to CO2 (see Table Dll-2) to yield emissions of
CH4 and CO (each expressed in units of C). The emissions of CH4 and CO are then multiplied by
16/12 and 28/12, respectively, to convert to full molecular weights.
To calculate emissions of N2O and NOX due to burning of crop residue, the amount of carbon
burned (Equation 1) is multiplied by a range of 1-2% (the N/C ratio of the fuel by weight) to
calculate the total amount of nitrogen released (Crutzen and Andreae, 1990). The total N released
is multiplied by the ratios of emissions of N2O and NOX relative to the N content of the fuel (see
Table Dll-2) to yield emissions of N2O and NOX expressed in units of N. To convert to full
molecular weights, the emissions of N2O and NOX are multiplied by 44/28 and 30/14, respectively.5
Table Dll-2. Emission Ratios for Biomass Burning Calculations
Compound
CH4
CO
N2O
NO*
Ratios
0.007 - 0.013
0.075 - 0.125
0.005 - 0.009
0.094 - 0.148
Source: Crutzen and Andreae, 1990.
Note: Ratios for carbon compounds, Le., CH< and CO, are mass of carbon
compound released (in units of C) relative to mass of CO, released from
burning (in units of C); those for the nitrogen compounds are expressed
as the ratios of emission relative to tbe nitrogen content of the fuel
4 This estimated 10% of the carbon exposed to burning that remains on tbe ground is probably some
combination of charcoal and unburned material that gets reincorporated into the soil during field preparation
for the next crop. Tbe charcoal represents a long-term sink of carbon, and in a complete accounting of carbon
flows, would be treated as such. The unburned material represents carbon that may be reabsorbed by next
year's crops or emitted as CO2 or remain in the soil. The 10% estimate is highly uncertain; the fractions of
this 10% that are charcoal and unburned material have never been measured nor estimated. Because of the
uncertainty and lack of data surrounding these 'carbon flows,* no attempt will be made to incorporate them
in the methodology at this time.
The numbers used to convert NO, emissions to full molecular weight are based on the assumption that
all of the NO, emissions are NO, rather than some combination of NO and NO2, because NO is the primary
form of NOX emitted during biomass combustion (Andreae, 1990).
STATES WORKBOOK
Dll-4
November 1992
-------
AVAILABILITY OF ACTIVITY DATA
Annual crop production statistics are available from state agriculture departments and
agricultural experiment stations. Also, crop production by state can be found in the USDA's Crop
Production and the U.S. Department of Commerce's Census of Agriculture.
SUMMARY
The steps for calculating emissions of CH^ CO, N2O, and NOX from burning of agricultural
wastes may be broken into two parts. In the first part, the amount of carbon in crop residue that is
burned is estimated, as shown in Equation 1. In the second part the emissions are calculated based
on the amount of CO2 released (carbon burned - 10%) and on the emission ratios provided by
Crutzen and Andreae (1990). This second calculation is summarized below.
CH4-C emissions (low) = (carbon burned) x (0.90) x (0.007)
CH4-C emissions (high) = (carbon burned) x (0.90) x (0.013)
CH4 emissions (low, high) = CH4-C emissions (low, high) x 16/12
CO-C emissions (low) fe (carbon burned) x (0.90) x (0.075)
CO-C emissions (high) - (carbon burned) x (0.90) x (0.125)
CO emission (low, high) = CO-C emissions (low,high) x 28/12
N2O-N emissions (low) = (carbon burned) x (0.01) x (0.005)
N2O-N emissions (high) = (carbon burned) x (0.02) x (0.009)
N2O emissions (low, high) = N2O-N emissions (low, high) x 44/28
NOX-N emissions (low) = (carbon burned) x (0.01) x (0.094)
NOX-N emissions (high) = (carbon burned) x (0.02) x (0.148)
NOX emissions (low, high) = NO^-N emission (low, high) x 30/14
REFERENCES
Andreae, M.O. 1990. Biomass burning in the tropics: Impact on environmental quality and
global climate. Paper presented at the Chapman Conference on Global Biomass Burning:
Atmospheric, Climatic, and Biospheric Implication, 19-23 March 1990. Williamsburg, Virginia.
Crutzen, P J., and M.O. Andreae. 1990. Biomass burning in the tropics: Impact on atmospheric
chemistry and biogeochemical cycles. Science 250:1669-1678!
Elgin, J. 1991. National Program Leader for Forages and Pastures, U.S. Department of
Agriculture. Personal communication.
Seller, W., and P J. Crutzen. 1980. Estimates of gross and net fluxes of carbon between the
biosphere and the atmosphere from biomass burning. Climatic Change 2:207-247.
Strehler, A., and W. Stutzie. 1987. Biomass residues. In: Hall, D.O., and R.P. Overend (eds.).
Biomass: Regenerable Energy. John Wiley, Chichester. pp. 75-102.
USDA-ARS. 1992. Personal communication.
STATES WORKBOOK Dll-5 November 1992
-------
-------
DISCUSSION 12
OTHER GREENHOUSE GAS EMISSIONS FROM STATIONARY COMBUSTION
OVERVIEW
This section discusses greenhouse gas emissions (NOr N2O, CO, CH4, and NMVOCs) from
energy consumption in stationary sources. The reader should note before proceeding with this
section that these calculations can be time consuming and complex. Moreover, the amount of gases
emitted from these activities are not thought to be major contributors to climate change.
Additionally, data on gases such as CO, NOr and NMVOCs may already be collected by state
environmental or air quality agencies to determine state compliance with the Clean Air Act or other
regulations. For these reasons a methodology for this source category was not included in the
workbook.
Emissions of non-CO2 greenhouse gases across activities (sectors) will depend upon fuel,
technology type, and pollution control policies. Emissions will also vary more specifically with size
and vintage of the combustion technology, its maintenance, and its operation.
Electricity generation and industrial fuel combustion activities are similar in that they provide
combustion conditions conducive to NO, formation. NO, emissions depend in part on the nitrogen
contained in the fuel (this may be especially important for coal), but more importantly on the firing
configuration of the technology. Excess air and high temperatures contribute to high NOX emissions.
Such conditions are highly variable by type of boiler; for instance, for oil-fired plants, tangential
burner configurations generally have lower emission coefficients than horizontally opposed units.
Also, the size of the boiler will affect the NOX emission rate due to the lower temperatures of smaller
units.
Usage of the technology can also significantly alter the pattern of NOX emissions.
Measurements of emissions show a 0.5% to 1.0% decrease in NOX emission rates for every 1.0%
decrease in load from full load operation. That is, as the usage rate increases, so does the emission
rate associated with the facility.
Finally, control policies and related technological changes to meet emission limits directly
influence NOX emissions. Emissions from large facilities can be reduced by up to 60% by
straightforward adjustments to the burner technology.1 These adjustments are often standard in new
facilities, but may not exist in older facilities. NOX controls may also increase the rate of CO
emissions. Information on the stock of combustion facilities, their vintage, and level of control are
therefore necessary to accurately estimate emissions from large combustion facilities.
NOX emissions from small combustion facilities (small industry, commercial, and residential)
tend to be much less significant than for large facilities due to lower combustion temperatures.
Nevertheless, emissions will depend on the specific combustion conditions of the activity in question,
This can be done, for example, by limiting the excess air in combustion or by staging the combustion
process.
STATES WORKBOOK D12-1 November 1992
-------
and an effort should be made to carefully characterize the type of activity, on average, in order to
select appropriate emission factors.
By comparison to NOr combustion conditions in large facilities are less conducive to
formation and release of CO and VOC (methane included) emissions. VOCs and CO are unburnt
gaseous combustibles that are emitted in small quantities due to incomplete combustion. They have
also been the target of emission control policies and hence must be estimated with these controls in
mind. They are directly influenced by usage patterns, technology type and size, vintage, maintenance,
and operation of the technology. Emissions can vary by several orders of magnitude, for example,
for facilities that are improperly maintained or poorly operated, such as may be the case for many
older units. Similarly, during periods of start-up, combustion efficiency is lowest, and CO and VOC
emissions are higher than during periods of full operation.
Size of the unit may indicate that combustion is less controlled and, hence, the VOC and CO
emission coefficients for smaller units are likely to be higher than for large plants. Also, wood stoves,
due to their largely inefficient combustion of the fuel, have particularly high emission rates of CO and
VOCs. For these reasons, an understanding of commercial and residential activities are key to the
estimation of these greenhouse gases.
N2O is produced from combustion activities, although the importance of this source is
unclear. Early research indicated that N2O formation may be linked to the nitrogen content of the
fuel, although recent evidence indicates some of these results may have been the result of a sampling
artifact in a standard sampling procedure that incorrectly measured N2O levels. Preliminary data at
this time do indicate that technology type may affect the level of N2O emissions.
DESCRIPTION OF METHODOLOGY
General Method
Estimation of emissions from stationary sources can be described using the following basic
formula:
Emissions = £ (EFabc x Activityabc)
where:
EF - Emission Factor (lbs/106 Btu);
Activity = Energy Input (106 Btu);
a = Fuel type;
b = Sector-activity; and
c = Technology type.
Total emissions for a particular state is the sum across activities, technologies, and fuels of the
individual estimates.
Emission estimation is based on at least three distinct sets of assumptions or data: 1) emission
factors; 2) energy activities; and 3) relative share of technologies in each of the main energy activities.
STATES WORKBOOK DJ2.2 November 1992
-------
Sources of the emission factors and energy activities data that are relevant are described briefly below
and suggestions on appropriate use of such data are made.
Technology share or technology splits for each of the various energy activities are needed on
a state level for non-CO2 greenhouse gas estimation since emission levels are affected by the
technology type.
The main steps in the inventory method can be summarized as follows:
1) Determine source of, and the form of, the best available, verifiable, state energy
activity data;
2) Based on a survey of state energy activities, determine the main categories of emission
factors;
3) Compile best available emission factor data for the state, preferably from state sources
or national sources (e.g., U.S. EPA data). If no state source is available, select from
the options provided here. Selection among the options should be based on an
assessment of the similarity of the state to the source of original measurements for
types of technology and operating conditions across main energy activities. The
selection should also consider the extent to which control technologies may be in
place and the ability to clearly separate and understand control policy assumptions
that may be embedded in the emission factor data.
4) Based on the form of the selected emission factor data, develop assumptions regarding
the technology splits within the state;2
5) Using these assumptions on technology splits, develop estimates, main activity by main
activity, of each of the greenhouse gases.
6) Sum the individual activity estimates to arrive at the state inventory total for the
greenhouse gases.
Energy Activity Data
Industry, agriculture, commercial, residential, and electric utilities are final consumption
sectoral activities found in EIA data sources. Each of these sectors provides energy services through
a variety of fuel combustion modes. Similar energy services within a sector are often provided in
somewhat different ways. For example, there are a number of technology/fuel options for heating
a household, and emissions for a given amount of heat will vary according to these technology
options. The energy service sectors, therefore, provide a useful starting point for emission
inventories, but will need to be further specified by the share of key technologies represented in each.
This is addressed in more detail below.
2 This may also require assumptions about the control technologies in place.
STATES WORKBOOK D12-3 November 1992
-------
The basic sector/fuel categories for reporting purposes in Table D12-1 are based on EIA's
State Energy Data Report, States are encouraged to provide the most detailed information available.
DOE or EIA data could be used as a starting point, but states should use the energy data thought
to be the most reliable. If states use in-state sources rather than EIA data, they are strongly urged
to provide thorough documentation on the energy statistics, the reporting procedures, and definitions
of sectoral activities, and to aggregate their inventories to the categories in Table D12-1 for
comparison purposes. This would help to ensure consistency and comparability among all state
estimates.
Table D12-1. Basic Sector/Fuel Categories
OIL
Asphalt and Road Oil
Aviation Gas
Distillate Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Liquid Fuels
COAL AND OTHER SOLIDS
Bituminous Coal and Lignite
Anthracite
Other Solid Fuels
GAS
Natural Gas
SECTORS
ELECTRIC UTILITIES
INDUSTRY
Iron and Steel
Chemical
Paper, Pulp, and Print
Petroleum Refining
Food and Tobacco
Other Industry
COMMERCIAL
RESIDENTIAL
OTHER
Emission Factors: Basic Data Sources
Emission factors represent the average emission performance of a population of similar
technologies. Emission factors for all non-CO2 greenhouse gases from combustion activities vary to
lesser or greater degrees with:
fuel type;
technology;
operating conditions; and
maintenance and vintage of technology.
STATES WORKBOOK
D12-4
November 1992
-------
Good emission factors for gases other than CO2 are therefore usually technology specific, but may
still represent a wide distribution of possible values. In addition to technology type, the impacts of
equipment vintage, operating conditions, maintenance conditions, and pollution control also affect
emission factors. When available, the standard deviation of the emission factor should be used to
show the range of possible emissions factors, and hence emissions, for each particular energy
activity.3
Representative emission factors for NOX4, CO, CH4, N2O, and NMVOCs by main
technology and fuel types are outlined in Tables D12-2 to D12-6 for the major sectoral categories.5
These data are taken from Radian (1990) and show uncontrolled emission factors for each of the
technologies indicated. These emission factor data therefore do not include the level of control
technology that might be in place in some states. For instance, for use in places where control
policies have significantly influenced the emission profile, either the individual factors or the final
estimate will need to be adjusted.
Adjustments to emission estimates for control policies may be critical to estimation of
emissions from large stationary sources in states. Alternative control technologies, with representative
percentage reductions, are shown in Tables D12-7 to D12-10 (Radian, 1990) for the main control
technologies applicable to each sector. These data should be used in combination with the
uncontrolled emission factors to develop a "net" representative emission factor for each of the
technologies to be characterized in a state's emission profile; alternatively, the total emission estimate
could be adjusted downward according to the indicated percentage reduction. Table D12-11 provides
the fuel property assumptions upon which the Radian data are based.
For non-CO2 emissions the Radian data cited above reflect the performance range of main
combustion technologies in place in the U.S. with a few exceptions. Since most of the data are based
on measurement samples taken from the United States, they represent averages of operating
conditions, sizes and vintages of units found in the U.S.
NOX
For many years, NOX has been the target of environmental policies for its role in forming
ozone (O3), as well as for its direct acidification effects. As a result, NOX emission inventories and
related data such as emission factors are more widely available than those for the other non-CO2
greenhouse gases considered here.
3 Unfortunately, the standard deviation of emission factors is rarely reported with emission factor data.
One study shows that when considered, variation of the final estimates by energy activity vary widely, from 20%
to more than 50% (Eggleston and Mclnnes, 1987).
4 As a general rule, it is recommended that NOX emissions be converted to a full molecular basis by
assuming that all NOX emissions are emitted as NO2.
5 Little information on N2O and NMVOCs emission factors is included at this time for reasons discussed
below (some N2O factors thought to be reliable are included). These factors should be added as the data
become available.
STATES WORKBOOK D12-S November 1992
-------
CO
CO emissions from stationary sources are estimated in the same way as for NOX emissions.
Detailed energy Jata provide the basis for estimation, but there may be significant variation in the
precise size and type of combustion technologies in place. A main combustion source of CO is the
of manufacturers.
Table D12-2. Utility Boiler Soon* Performance
Emissions Factors (Ibs/10* Btu energy input)
Source
Natural Gas - Boilers
Gas Turbine Combined Cycle
Gas Turbine Simple Cyde
Residual Oil Boilers
Distillate Oil Boilers
Shale Oil Boilers
MSW . Mass Feed
Coal Spreader Stoker
Coal - Fluidized Bed Combined Cycle
Coal - Fluidized Bed
Coal - Pulverized Coal
Coal - Tangential)? Fired
Coal - Pulverized Coal WaU Fired
Wood-Fired Boilers
CO
0.040
0.067
0.067
0.033
0.033
0.033
0.217
0.267
N/A
N/A
OX3J
0.031
0.031
3.255
CH«
0.0002
0.0128
0.0124
0.0015
0.00007
0.0015
N/A
0.0015
0.0013
0.0013
0X013
0.0013
0.0013
0.0398
NO,
0.559
0.391
0394
' 0.444
0.150
0.444
0309
0.720
N/A
0.563
1.894
0.729
1.019
0.247
N,O
N/A
N/A
N/A
N/A
N/A
N/A
N/A
0.0018
N/A
N/A
0.0018
0.0018
0.0018
N/A
NMVOCs
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Source: Radian, 1990.
STATES WORKBOOK
D12-6
November 1992
-------
Table DIM. Industrial Boiler Pferformuc*
Emissions Factors (lbs/10* Btu
Source
Coal-Fired Boilers
Residual Oil-Fired Boilers
Natural Gas-Fired Boilers
Wood-Fired Boilers
Bagasse/Agric. Waste-Fired Boilers
MSW - Mass bum
MSW - Small Modular.
CO
0.206
0.033
0.036
332
3.77
0.212
0.042
CH,
0.0053
0.0064
0.0029
0.0331
N/A
N/A
N/A
NO,
0.73
036
0.14
0.25
0.19
031
0.3 1
energy input)
N2O
N/A
N/A
N/A
N/A
N/A
N/A
N/A
NMVOCs
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Source: Radian, 1990.
Table D12-4, Kilns, Ovens, and Dryers Source Performance
Emissions Factors (Ibs/104 Btu energy input)
Industry
Cement, Lime
Cement, Lime
Cement, Lime
Coking, Steel
Chemical Processes, Wood,
Asphalt Copper, Phosphate
Chemical Processes, Wood,
Asphalt Copper, Phosphate
Chemical Processes, Wood,
Asphalt Copper, Phosphate
Source
Kilns-
Natural Gas
Kilns -Oil
Kilos -Coal
Coke Oven
Dryer -
Natural Gas
Dryer - Oil
Dryer Coal
CO
0.174
0.175
0.175
0.466
0.023
0.035
0396
CH,
0.0023
0.0022
0.0022
0.0022
0.0023
0.0022
0.0022
NO,
233
1.16
1.16
N/A
0.13
037
0.50
N2O NMVOCs
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Source: Radian, 1990.
STATES WORKBOOK
D12-7
November 1992
-------
Table D12-5 Residential Sonrc* Perfonunce
EmiMion* Factors (lbs/10* Btu energy input)
Source
Wood Pits
Wood Fireplaces
Wood Stoves
Propane/Butane Furnaces
Coal Hot Water Heaters
Coal Furnaces
Coal Stoves
Distillate Oil Furnaces
Gas Heaters
CO
10.94
1376
40.95
0.022
0.040
1.070
7.911
0.029
0.021
CH«
0.442
N/A
0.164
0.0024
N/A
N/A
N/A
0.0110
0.0021
NO,
0325
0.256
0.442
0.104
0349
0.513
0396
0.113
0.098
N,O
N/A
N/A
N/A
N/A
N/A
N/A
, N/A
N/A
N/A
NMVOCs
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Source: Radian, 1990.
Table D12>6. Commercial Source Performance
Source
Wood Boilers
Gas Boilers
Residual Oil Boilers
Distillate Oil Boiters
MSW Boilers
Coal Boilers
Shale Oil Boilers
Open Burning - MSW1
Open Burning - Agriculture1
Incineration - high efficiency1
Incineration - low efficiency1
CO
0.440
0.020
0.038
0.035
0.042
0.431
0.038
918
128.2
11.0
22.1
Frnisiions
CH,
0.0331
0.0025
0.0035
0.0013
N/A
0.0221
0.0035
14.4
19.9
N/A
N/A
Factors (tts/10* Btu
NO,
0.073
0.100
0343
0.141
1.023
OJ22
0.411
6.6
N/A
33
22
input)
Np
0.0095
0.0050
0.103
0.035
N/A
0.131
0.103
N/A
N/A
N/A
N/A
NMVOCs
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
1 Emission factors are presented in Ibs/biUion Btu
Source: Radian, 1990,
STATES WORKBOOK
D12-8
November 1992
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Table D12>7. Utility Emission Controls Performance
Technology
Low Excess Air
Overfire Air Coal
Overfire Air - Gas
Overfire Air Oil
Low NO, Burner
Coal
Low NO, Burner - TF
Low NO, Burner - Oil
Low NO, Burner -
Gas
Cyclone Combustion
Modification
Ammonia Injection
SCR - Coal
SCR - Oil, AFBC
SCR -Gas
Water Injection Gas
Turbine Simple Cycle
SCR - Gas Turbine
COj Scrubbing - Coal
CO2 Scrubbing - Oil
CO2 Scrubbing - Gas
Retrofit LEA
Retrofit OFA - Coal
Retrofit OFA - Gas
Retrofit OFA - Oil
Retrofit LNB - Coal
Retrofit LNB - Oil
Retrofit LNB - Gas
Burners Out of
Service (BOOS)
Efficiency
Loss1
4)5
05
1.25
05
0.25
0.25
0.25
0.25
05
05
1
1
1
1
1
225
16.0
113
-05
05
1.25
05
0.25
0.25
0.25
05
CO CH4 NO,
Reduction Reduction Reduction
(%} (%) (%)
+ + 15
+ + 25
+ + 40
-l- + 30
+ + 35
+ + 35
+ + 35
+ + 50
N/A N/A 40
+ .+ 60
8 + 80
8 + 80
8 + 80
+ + 70
8 + 80
N/A N/A N/A
N/A N/A N/A
N/A N/A N/A
+ + 15
+ + 25
+ + 40
+ + 30
+ + 35
+ + 35
+ + 50
+ + 30
N2O
Reduction
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
60
N/A
60
WA
N/A
N/A
N/A
N/A
N/A
N/A
N/A'
N/A
N/A
N/A
NMVOCs
Reduction
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Date
Available2
1970
1970
1970
1970
1980
1980
1980
1980
1990
1985
1985
1985
1985
1975
1985
2000
2000
2000
1970
1970
1970
1970
1980
1980
1980
1975
1 Efficiency loss as a percent of end-user energy conversion efficiency.
2 Date technology is assumed to be commercially available.
Note: A "+" indicates negligible reduction.
Source: Radian, 1990.
Negative loss indicates an efficiency improvement
STATES WORKBOOK
D12-9
November 1992
-------
Table D12-8. Industrial Hotter Emission Cootnb Performance
Efficiency CO CFL.
Loss1 Reduction Reduction
Technology (%) (%) (%)
Low Excess Air -0.5 + *
Ovcrfire Air - Coal OS + *
Overfire Air - Gas 1.25 + +
Overfire Air - OU 0.5 + +
LowNO.Buraer- (US -I- +
Coal
Low NO, Burner - Oil 0.25 + + '
Low NO, Burner- 0.25 + +
Gas
Flue Gas 0.5 + +
Recirculatioo
Ammonia Injection 0.5 + +
SCR -Coal 1 8 +
SCR-OU.AFBC 1 8. +
SCR - Gas 1 8 +
Retrofit LEA -OS + , +
Retrofit OFA - Coal 0.5 + +
Retrofit OFA - Gas 1JZS -f +
Retrofit OFA - OU 0.5 + +
Retrofit LNB - Coal 0.25 + -f
Retrofit LNB - Oil 0.25 + -f
Retrofit LNB - Gas 0.25 + +
1Efficiency loss as a percent of end-user energy conversion efficiency.
'Date technology is assumed to be commeroaUy available.
Note: A *+" indicates negligible reduction.
Source: Radian, 1990.
NO,
Reduction
(*>
15
25
40
30
35
35
50
40
60
80
80
80
15
25
40
30
35
35
50
Negative 1ms
Table D12-9. Kiln, Ovens, and Dryers Emission Controls
Efficiency CO CH4
Loss1 Reduction Reduction
Technology (%) (%) (%)
LEA - Kilns, Dryers -6.4 + +
LNB - Kilns, Dryers 0 + +
SCR - Coke Oven 1.0 8 +
Nitrogen Injection N/A N/A N/A
Fuel Staging N/A N/A N/A
'Efficiency loss as a percent of end-user energy conversion efficiency.
NO.
Reduction
(%)
14
35
80
30
50
Negative loss
N2O
Reduction
(%)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
60
N/A
N/A
N/A
N/A
N/A
N/A
N/A
tiylirat^c An
NMVOCs
Reduction
(%)
N/A
N/A
N/A
N/A
N/A
' N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Date
Available1
1970
1970
1970
1970
1980
1980
1980
1975
1985
1985
1985
1985
1970
1970
1970
1970
1980
1980
1980
efficiency improvement.
Pcrfoniuiicc
N2O
Reduction
(%)
N/A
N/A
60
N/A
N/A
indicates an
NMVOCs
Reduction
(%)
N/A
N/A
N/A
N/A
N/A
efficiency imp
Date
Available1
1980
1985
1979
1990
1995
irovement.
*Date technology is assumed to be commercially available.
Note: A "+" indicates negligible reduction.
Source: Radian, 1990.
STATES WORKBOOK
D12-10
November 1992
-------
Table D12-10. Residential and Commercial Emission Controls Performance
Technology
Catalytic Woodstove
Non-Catalytic MCS
Flame Ret. Bum. Hi
Cootr. Mix. Bum. Hd.
Integr. Furn. Syst
Blueray Burn^Furn.
M.A.N. Burner
Radiant Screens
Secondary Air Baffle
Surface Comb. Burner
AmanaHTM
Modulating Furnace
Pulse Combuster
Catalytic Combuster
Replace Worn Units
Tuning, Seasonal
Maintenance
Red. Excess. Firing
Red fir with new ret b
Pos. Chimney
Dampers
Inc. thermal anticip.
Night therm, cutback
Low Excess Air
Flue Gas
Recirculation
Over-fire Air
Over-fire Air
Low NO, Burners
Low NO, Burners
Efficiency
I nut1
(%)
-44
-30
-9
7
12
-12
-13
-7
N/A
N/A
-21
-7
-36
-29
N/A
-2
-19
-40
-8
-1
-15
-0.8
0.6
1
1
0.6
0.6
CO
Reduction
(%)
90
15
28
43
13
74
N/A
62
16
55
-55
N/A
N/A
N/A
65
16
14
14
11
43
17
N/A
N/A
N/A
N/A
N/A
N/A
CH<
Reduction
(%)
90
50
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
NO,
Reduction
(%}
-27
-5
N/A
44
69
84
71
55
40
79
79
32
47
86
N/A
N/A
N/A
N/A
N/A
N/A
N/A
15
50
20
30
40
50
N20
Reduction
(%)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
NMVOCs
Reduction
(%)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Date
Available2
1985
1985
1980
1970
1975
1970
1970
1980
1980
'Efficiency loss as a percent of end-user energy conversion efficiency.
2Date technology is assumed to be commercially available.
Note: A "+" indicates negligible reduction.
Source: Radian, 1990.
Negative loss indicates an efficiency improvement.
STATES WORKBOOK
D12-11
November 1992
-------
Table D12-11
ruei rroperaes
Fuel
GAS
Butane/Propane
Coke Oven Gas
Methane (pure)
Natural Gas
Process Gas
LIQUID
Crude Shale Oil
Diesel/Distillate
Gasoline
Jet A
Methanol
Residual Oil
SOLID
Bagasse/Agriculture
Charcoal
Coal
MSW
Wood
Heating Value
(106 Btu/ton)1
43.7
35.1
43.0
43.9
46.4
37.0
38.8
lllxl^Btu/gal
37.1
53 x 103 Blu/gal
37.0
7.8
25.0
19.9
9.7
9.1
Carbon
(wt percent)
82.0
56.1
75.0
,70.6
70.6
84.5
872
85.7
86.1
37.5
85.6
22.6
87.0
65.0
26.7
27.0
1 Unless otherwise indicated.
Source: Radian, 1990.
STATES WORKBOOK
D12-12
November 1992
-------
CH4
At this time no emission data other than the Radian data have been identified in a consistent
and comprehensive form from which to reliably estimate CH4.
N2O
Nitrous oxide (N2O) is produced directly from the combustion of fossil fuels, although the
mechanisms that cause N2O formation are not fully understood. Hao et al. (1987) measured N2O
emissions from several stationary sources and found them to be directly .correlated to NOX emissions.
Hao et al. also reported that N2O emissions were lower in fuel-rich flames and during applications
of certain combustion modification techniques for NOX control Additionally, N2O formation seems
dependent on the nitrogen content of fuels.
In recent analyses, however, such as Linak et al. (1990), the earlier procedures used to
measure N2O emissions were found to suffer from a sampling artifact whereby N2O formed in the
sample containers as a result of reactions between water, sulfur dioxide, and NOr These reactions
could increase N2O concentrations by more than an order of magnitude unless the samples are
carefully dried or N2O is measured immediately (Muzio and Kramlich, 1988; Muzio et at, 1989;
Montgomery et al., 1989). These recent findings seriously question whether the relationships between
N2O and fuel nitrogen or N2O and KOX found by Hao et al. (1987) are valid. At this time the
evidence would seem to imply that direct N2O emissions from conventional fossil-fuel combustion
are low, although N2O formation may occur indirectly following combustion in the exhaust plume or
in the atmosphere through mechanisms involving N0r
i
Recent research suggests that emerging technologies such as fluidized bed combustion and
non-selective catalytic reduction methods for NOX control using ammonia, urea, and cyanuric acid may
promote N2O emissions. Additionally, mobile sources which are catalysts for pollution control may
emit N2O in concentrations higher than previously thought Further research is necessary to quantify
these sources.
Due to these major uncertainties concerning N2O formation during fossil-fuel combustion,
including the processes by which N2O may form and the extent to which N2O emissions may be
generated, it is difficult at this time to develop a comprehensive methodology for estimating N2O
emissions from fossil-fuel combustion for stationary or mobile sources. Some limited data on N2O
emission factors are presented if the estimates are believed to be unaffected by the sampling artifact
However, additional monitoring and measurement studies on N2O are needed to improve the
emission factor data.
NMVOCs
Non-methane volatile organic compounds should also be included in the emissions inventory.
At this time, however, no emission data are readily available in a consistent and comprehensive form
from which to reliably estimate NMVOCs. As with N2O, additional research is needed to improve
the emission factor data from which emission inventories can be developed.
STATES WORKBOOK D12-i3 November 1992
-------
REFERENCES
Cofer m, W.R., J.S. Levine, D.I. Sebacber, EL. Winstead, PJ. Riggan, BJ. Stocks, JA. Brass, V.G.
Ambrosia, and FJ. Boston. 1989. Trace gas emissions from chaparral and boreal forest fires.
Journal of Geophvtical Research 94(D2):2255-2259.
Cbfer HI, W.R^ J.S. Levine, PJ. Riggan, DJ. Segacher, EJ. Winstead, EJF. Shaw Jr., J.A. Brass, and
V.G. Ambrosia. 1988. Trace gas emissions from a mid-latitude prescribed chaparral fire. Journal
of Geophysical Research 93(D2):1653-1658.
EIA (Energy Information Administration). 1988. State Energy Data Report. DOE/EIA-02l4-(86).
Eggleston, H.S., and G. Mclnnes. 1987. Method for the Compilation of UK Air Pollutant Emission
Inventories. ISBN-0-85624-493-7. Warren Spring Laboratory, Stevenage, UK
Hao, W.M., S.C Wofsy, MB. McElroy, J.M. Beer, and M.A. Toqan. 1987. Sources of atmospheric
nitrous oxide from combustion. Journal of Geophysical Research 923098-3104.
Linak, WJ>., J.A. McSorley, RE. Hall, JiV. Ryan, R.K. Srivastava. J.O.L. Wendt, and J.B. Mereb.
1990. Nitrous oxide emissions from fossil fuel combustion. Journal of Geophysical Research 95:7533-
7541.
Montgomery, T.A., G.S. Samuelsen, and LJ. Muzio. 1989. Continuous infrared analysis of N2O in
combustion products. Journal of the American Chemical Society 39:721-726.
Muzio, LJ., and J.C Kramlich. 1988. An artifact in the measurement of N2O from combustion
sources. Geophysical Research Letters 15:1369-1372.
Muzio, LJ., MX. Teague, J.C Kramlich, Jj\. Cole, J.M. McCarthy, and RJCXyon. 1989. Errors in
grab sample measurements of N2O from combustion sources. Journal of the American Chemical
Society 39:287-293.
OECD/IEA. 1991. Greenhouse Gas Emissions: The Energy Dimension. OECD/IEA, Paris.
Forthcoming.
Radian Corporation. 1990. Emissions and Cost Estimates for Globally Significant Anthropogenic
Combustion Sources of NO^ N^Q, CH+ CO, and CO2. Prepared for the Office of Research and
Development, U.S. Environmental ProFection AgencyrWashington, D.C.
STATES WORKBOOK D12.i4 November 1992
-------
DISCUSSION 13
OTHER GREENHOUSE GAS EMISSIONS FROM MOBILE COMBUSTION
OVERVIEW
This section discusses emissions of greenhouse gases from mobile sources, including carbon
monoxide (CO), nitrogen oxides (NOX), methane (CH^, nitrous oxide (N2O), and non-methane
volatile organic compounds (NMVOCs). The reader should note before proceeding with this section
that these calculations can be time consuming and complex. Moreover, the amount of gases emitted
from these activities are not thought to be major contributors to climate change. Additionally, data
on gases such as CO, NOr and NMVOCs may already be collected by state environmental agencies
to determine state compliance with the Clean Air Act or other regulations. For these reasons a
methodology for this source category was not included in the workbook.
Emissions from mobile sources are most easily estimated by major transport activity, i.e., road,
air, rail, and ships. Several major fuel types need to be considered, including gasoline, diesel, jet fuel,
aviation fuel, natural gas, liquified petroleum gas, and residual fuel oil Road transport accounts for
the majority of mobile source fuel consumption (e.g., U.S. demand for oil accounts for 50% of the
worldwide total), followed by air transport This indicates that the primary emphasis in developing
emission factors should be placed on road vehicles, followed by aircraft.
If transport fuels (mostly composed of hydrocarbons [HC]) were completely combusted the
only products emitted would be CO2 and H2O. However, under actual conditions, not all the fuel
is combusted, resulting in the formation of other gases. As one example of combustion-related
emissions, motor vehicles emit a large portion of total anthropogenic NOX emissions. NOX emissions
are closely related to air-fuel mixes and combustion temperatures, as well as pollution control
equipment. For uncontrolled vehicles, NOX emissions from diesel-fueled vehicles are generally lower
than from gasoline-fueled vehicles. In terms of tons of emissions per ton-kilometer transported,
heavy duty vehicles (HDV) are more efficient than light duty vehicles (LDV), but HDV still
contribute a significant share of motor vehicle NOX emissions. Moreover, they are more difficult to
control than light duty vehicles and are generally subject to less stringent emission control regulations
than automobiles.
The majority of CO emissions from fuel combustion comes from motor vehicles. CO
emissions, even more so than CH4 emissions, are a function of the efficiency of combustion and post-
combustion emission controls. Like CH4 emissions, CO emissions are highest when air-fuel mixtures
are "rich," with less oxygen than required for complete combustion. This occurs especially in idle, low
speed, and cold start conditions in spark ignition engines.
Methane emissions and NMVOCs from motor vehicles are a function of the methane content
of the motor fuel, the amount of hydrocarbons passing unbumt through the engine, and any post-
combustion control of hydrocarbon emissions, such as use of catalytic converters. The emissions of
unburned HC, including CH4, are lowest in uncontrolled engines when the quantity of hydrogen,
carbon, and oxygen are present in exactly the right combination for complete combustion (the
"stoichiometric ratio"). Thus, CH4 and NMVOC emissions will be determined by the air-fuel ratio.
They are generally highest in low speed and engine idle conditions. Poorly tuned engines may have
STATES WORKBOOK D13-1 November 1992
-------
particularly high output of total HC including CH4. Emissions are also strongly influenced by the
engine type and the fuel combusted.
For more information on greenhouse gas emissions from mobile sources, the reader is referred
to Mark DeLurhi's Emissions of Greenhouse Cases from the Use of Transportation Fuels and
Electricity, published by the Argonne National Laboratory (November 1991).
DESCRIPTION OF METHODOLOGY
An estimation of mobil source emissions is .a very complex undertaking that requires
consideration of many parameters, including information on such factors as:
- transport class
- fuel consumed
operating characteristics
- emission controls
- maintenance procedures
- fleet age
other factors
The need for data on several parameters and the wide variety of conditions that can affect the
performance of each category of mobile sources makes it very difficult to generalize the emission
characteristics in this area.
Nevertheless, a minimum emission estimation methodology, as discussed below, is suitable as
a starting point for states to establish emissions estimations. In fact, the complexity of this issue
makes it difficult even for states with extensive experience to develop highly-precise emission
inventories. As such, it may be appropriate to avoid excessive complexity with any starting emission
estimation methodology.
In order to develop a minimum estimation method for greenhouse gas emissions from mobile
sources, basic information is required on the types of fuels consumed in the transport sector, the
combustion technologies that are used to consume the fuels, operating conditions during combustion,
and the extent of emission control technologies employed during and after combustion. The basic
approach for estimating these emissions is:
Emissions = £ (EFabc x Actrvityabc)
where: EF = emissions factor
Activity = amount of energy consumed for a given mobile source activity
a = transport mode (rail, road, air, water)
b = fuel type (diesel, gasoline, LPG1, bunker, etc.)
c = vehicle type (e.g., passenger, light duty or heavy duty for road vehicles)
1 LPG refers to liquid petroleum gas.
STATES WORKBOOK D13.2 November 1992
-------
Emission Factors
This section presents mobile source emission factors for gases contributing to global warming.
Emission factor estimates have been developed for CO, NOp N2O, methane, and non-methane
VOCs for several classes of highway vehicles, railway locomotives, ships and boats, farm and
construction equipment, and aircraft This discussion is taken from Weaver and Turner (1991).
Highway Vehicles - Conventional Fuels
Technical Approach. The emissions estimates developed for NOP CO, methane, and
NMVOC from highway vehicles were based on the U.S. EPA's MOBILE4 model (EPA, 1989). This
model, the most widely used emission factor model in the U.S., reflects more than a decade of devel-
opment, and incorporates the results of emissions tests on more than 10,000 vehicles in customer use
performed over the last 20 years. In addition to testing under standard conditions, many of these
tests have included emissions measurements at other temperatures, with different grades of fuel, and
under different driving cycles. Much less effort has been expended on testing and modeling of heavy-
duty vehicle emissions than those from light-duty vehicles, so that the emission factor estimates for
these vehicles are considered less reliable.
MOBILE4 calculates exhaust emission factors for U.S. vehicles using gasoline and diesel fuel,
based on the year in which they were manufactured For gasoline vehicles, it also calculates VOC
emissions due to evaporative, running, and refueling losses (VOC emissions from diesel vehicles due
to these causes are negligible). To develop emissions estimates for different emission control
technology types, calculations were carried out for specific model years during which U.S. vehicles
were equipped with the technology in question. To reflect normal in-use deterioration over the
vehicle's life, emissions were calculated for each vehicle type when they were five years old, or
approximately halfway through their useful lives. For example, estimates of uncontrolled passenger
vehicle emissions -were based on MO8ILE4 results for model year 1963 vehicles in calendar year
1968, when they would be five years old. Similarly, emissions estimates for advanced-technology
vehicles were based on 1990 U.S.model vehicles, calculated in 1995. Table D13-1 shows the corre-
spondence between technology types and the U.S. model years used to represent them in the model.
The emission factors calculated by MOBILE4 are affected by the assumptions regarding aver-
age speeds, ambient temperature, diurnal temperature range, altitude, and fuel volatility that are pro-
vided to the model They are also affected by the assumed presence or absence of
inspection/maintenance and anti-tampering programs. Since it would not'be possible to represent the
state diversity in these conditions in a single set of factors, the conditions chosen for the modeling
were "typical" values of 75 °F, with a diurnal range from 60 to 85 °F (24±8 °C), and Reid vapor
pressure of gasoline at 9.0 PSI (62 kPa). Average speed was taken as the MOBILE4 default of 19.5
mph, typical of uncongested urban driving.
Changes in these input assumptions would change the resulting emission factors. Exhaust
pollutant emission factors increase markedly at low temperatures, while evaporative VOC emissions
decrease with temperature. Exhaust emissions in Ibs/mile also tend to rise with decreasing average
speed, due mostly to the increase in fuel consumption/mile. Evaporative VOC emissions tend to in-
crease with increasing gasoline volatility and increasing diurnal temperature range.
STATES WORKBOOK D13-3 Novembw 1992
-------
them.
In order to reflect the emissions control Table D13-1: Emission control technology types
potential of the different technologies, we assumed and U.S. vehicle model years used to represent
an effective inspection/maintenance and anti- -L
tampering program, which would help to assure
that the vehicle emission controls were in place
and functioning as designed. This assumption may
result in some under-estimation of actual emissions
from emission-controlled vehicles, since not all
vehicles are subject to such effective standards.
The estimated vehicle fuel economies were
also used to calculate fuel-specific (Ibs/ton fuel)
and energy-specific (lbs/106 Btu) emission factors
for all of the pollutants. Since emissions and fuel
consumption tend to vary in parallel (vehicles and
operating modes causing high emission rates also
tend to result in high fuel consumption, and vice
versa), these energy-specific emission factors are
expected to be more generally applicable than the
factors in Ibs/mile, and use of these factors rather
than the Ibs/mile values is recommended.
Since MO8ILE4 does not estimate N2O
emissions, it was necessary to develop separate
estimates of these. N2O formation in internal
combustion engines is not yet well understood, and
data on these emissions are scarce. It is believed,
with substantial evidence, that N2O emissions
come from two distinct processes. The process
occurs during the actual combustion process in the
cylinder. It is believed that the major contributor
is the interaction of NO with combustion
intermediates such as NH and NCO. This N2O is
then very rapidly removed in the post-flame gas by
the reaction between N2O and hydrogen. While a
significant amount of N2O may be formed in the
flame, it can only survive if there is very rapid
quenching of the gases, which is not common.
Thus, only very small amounts of N2O are
produced as engine-out emissions.
The N2O forming process occurs during
catalytic aftertreatment of exhaust gases. Otto,
Shelef, and Kummer (1970) have shown that N2O
is produced during the reaction of NO and NH3
over the platinum in the catalytic converter. The order of magnitude for the maximum NO
conversion into N2O was about 5 to 10%. The output of N2O from the catalyst is highly
temperature dependent Prigent and Soete (1989) showed that as the catalyst warmed up after a
Technology
Model
Year
Gasoline Passenger Cars and Light
Tracks
Uncontrolled
Non-catalyst controls
Oxidation catalyst
Early three-way catalyst
Advanced three-way catalyst
1963
1972
1978
1983
1990
Heavy-Duty Gasoline Vehicles
Uncontrolled
Non-catalyst control
Three-way catalyst
1968
1983
1991
Diesel Passenger Cars and Light Trucks
Uncontrolled
Moderate control
Advanced control
1978
1983
1990
Heavy Duty Diesel Vehicles
Uncontrolled
Moderate Control
Advanced control
1968
1983
1991
Motorcycles
Uncontrolled
Non-catalyst controls
1972
1990
STATES WORKBOOK
D13-4
November 1992
-------
cold start, N2O levels increased heavily (4.5 times the inlet value) at around 360°C The emissions
then decreased to the inlet level at a catalyst temperature of 460*G Above this temperature there
is less N2O from the catalyst downstream than upstream. N2O emissions are thus formed primarily
during cold starts of catalyst-equipped vehicles. Comparison of N20 emissions data for the U.S.
Highway Fuel Economy Test (HFET) with that for the Federal Test Procedure (FTP) shows much
lower N2O emissions. The FTP contains a cold-start phase, while the HFET does not
.»-
Several methods were used to estimate N2O emission factors for this study. Prigent and
Soete (1989), Dascb (1991), Ford (1989-1991), and Wamer-Selph and Smith (1991) gave N2O
emissions for light-duty gasoline vehicles equipped with different catalyst technologies. The different
catalyst types were divided into four groups: uncontrolled, oxidation catalyst, early three-way catalyst,
and modern three-way catalyst technologies. The FTP emissions data from these studies were
combined and averaged to determine the mean N2O emissions from light-duty vehicles equipped with
each technology. -These numbers were used directly to estimate N2O emissions from gasoline
passenger cars. For light-duty gasoline trucks and motorcycles, fuel-specific N2O emissions were
assumed to be the same as for the corresponding passenger car technology. N2O emissions per
kilometer were then calculated from the fuel-specific emissions and the fuel consumption
characteristics for each class.
No data on N2O emissions from heavy-duty gasoline trucks were available. Therefore, since
the engines used in these vehicles are fairly similar to those in passenger cars, it was decided to
approximate the N2O emissions by assuming that emissions per unit of fuel burned would be similar
to those for passenger cars having similar technology. However, since these trucks undergo a heavier
duty cycle, and experience fewer cold-starts, it was considered more appropriate to use N2O emission
factors based on the U.S. highway fuel economy test (HFET) rather than the cold-start FTP
procedure. Fuel-specific emissions N2O emissions for passenger cars in the HFET procedure were
obtained from the same data sources listed above.
Dietzmann, Paraess, and Bradow (1980) reported N2O emissions data for four heavy-duty
diesel trucks, including a range of representative engines. The average of these data was used directly
as the N2O emission factor for heavy-duty diesel vehicles. No N2O emissions data were available for
light-duty diesel vehicles. N2O emissions for diesel passenger cars and light-duty trucks were
estimated by assuming the same fuel-specific emission rates as for heavy-duty diesels.
Light-duty gasoline passenger cars. The U.S. EPA considers a passenger car to be any vehicle
with rated gross vehicle weight less than 8400 Ib (3,855 kg) designed primarily to carry 12 or fewer
passengers, and not possessing special features such as four wheel drive for off-road operation. Table
D13-2 summarizes the estimated emission factors for gasoline passenger cars. Estimates for five levels
of gasoline-vehicle control technology are shown. These technology levels range from completely
uncontrolled (still typical of most vehicles around the world) through non-catalyst emission controls,
oxidation catalysts, and two levels of three-way catalyst control. Non-catalyst emission controls
include modifications to ignition timing and air-fuel ratio to reduce emissions, exhaust gas
recirculation (EGR), and air injection into the exhaust manifold. Oxidation catalyst systems normally
include many of the same techniques, plus a two-way catalytic converter to oxidize HC and CO. The
"early" three-way catalyst results are representative of those for vehicles sold in the U.S. in the early
to mid '80s, which were mostly equipped with carburetors having electronic "trim". The "advanced"
three-way catalyst values are based on current U.S. technology vehicles, using electronic fuel injection
under computer control.
STATES WORKBOOK D13-5 November 1992
-------
Table D13-2: Estimated emissions factors for gasoline passenger cars.
EMISSIONS
NO,
CH, NMVOC CO N2O
Advanced Three-Way Catalyst Cootrat
Total - Ibs/mile
Exhaust
Evaporative
Refueling
Running loss
Ibs/ton fuel
lbs/10* Btu
0.0018
0.0018
15.88
0398
0.00007
0.00007
0.64
0.016
Early Three-Way
Total - Ibs/mile
Exhaust
Evaporative
Refueling
Running loss
Ibs/ton fuel
lbs/10* Btu
0.0018
0.0018
12.98
0331
0.00014
0.00014
1.00
0.02S
0.0023
0.0009
0.0004
0.0005
0.0005
20.96
0.530
Catalyst
0.0024
0.0009
0.0004
0.0006
0.0005
16.72
0.420
0.0111
0.0111
99.74
2.497
0.0111
0.0111
77.86
1.945
0.00007
0.00007
0.60
. 0.015
0.00016
0.00016
1.14
0.029
Oxidation Catalyst
Total - Ibs/mile
Exhaust
Evaporative
Refueling
Running loss
Ibs/ton fuel
lbs/104 Btu
0.0056
0.0056
25.26
0.641
0.00032
0.00032
1.42
0.036
0.0062
0.0040
0.0007
0.0007
0.0008
27.80
0.707
0.0461
0.0461
206.14
5.171
0.00010
0.00010
0.42
0.011
Non-Catalyst Control
Total - Ibs/mile
Exhaust
Evaporative
Refueling
Running loss
IbsVton fuel
lbs/10* Btu
0.0070
0.0070
31.28
0.796
0.00062
0.00062
2.76
0.069
0.0112
0.0076
0.0016
0.0010
0.0010
50.02
1260
0.0844
0.0844
377.97
9.502
0.000018
0.000018
0.08
0.002
Uncontrolled
Total - Ibs/mile
Exhaust
Evaporative
Refueling
Running loss
Ibs/ton fuel
lbs/10' Btu
0.0076
0.0076
33.98
0.862
0.00062
0.00062
2.76
0.069
0.0225
0.0155
0.0049
0.0010
0.0011
100.54
2519
0.1441
0.1441
645.11
16.198
0.000018
0.000018
0.08
0.002
STATES WORKBOOK
D13-6
November 1992
-------
Table D13-3: Estimated emission factors for light-duty gasoline trucks.
EMISSIONS
NO. CH4
NMVOC CO N,O
Advanced Three-Way Catalyst Control
Total - Ibs/mile
Exhaust
Evaporative
Refueling
Running loss
Ibs/toD fuel
lbs/10*Btu
0.0024
0.0024
16.72
0.420
0.00014
0.00014
1.00
0.025
0.00266
0.00142
0.00035
0.00071
0.00014
18,12
0.464
0.0166
0.0166
116.80
2,939
0.000085
0.000085
0.60
0.015
Early Three-Way Catalyst;
Total Ibs/mile
Exbaust
Evaporative
Refueling
Running loss
Ibs/too fuel
lbs/10* Btn
0.0035
0.0035
18.16
0.464
0.00025
0.00025
1.28
0.032
0.00415
0.00277
0.00046
0.00075
0.00014
21.24
0.530
0.0327
0.0327
167.52
4.199
0.000224
0.000224
1.14
0.029
Oxidation Catalyst
Total - Ibs/mile
Exhaust
Evaporative
Refueling
Running loss
Ibs/too fuel
lbs/10* Btu
0.0057
0.0057
22.06
0.552
0.0003
0.0003
1.22
0.031
0.0069
0.0043
0.0008
0.0010
0.0008
26.54
0.663
0.0431
0.0431
165.40
4.154
0.00011
0.00011
0.42
0.011
Non-Catalyst
Total - Ibs/mile
Exhaust
Evaporative
Refueling
Running loss
Ibs/ton fuel
lbs/10* Btu
0.0100
0.0100
38.38
0.972
0.0006
0.0006
236
0.059
0.0161
0.0107
0.0032
0.0012
0.0010
61.94
1.547
0.1022
0.1022
392.17
9.856
0.00002
0.00002
0.08
0.002
Uncontrolled
Total - Ibs/mile
Exhaust
Evaporative
Refueling
Running loss
Ibs/ton fuel
lbs/10* Btu
0.0093
0.0093
35.80
0.906
0.0006
0.0006
236
0.059
0.0303
0.0177
0.0104
0.0011
0.0011
116.26
Z917
0.1581
0.1581
606.45
15.225
0.00002
0.00002
0.08
0.002
STATES WORKBOOK
D13-7
November 1992
-------
Light-duty gasoline trucks. Light-duty trucks are defined as vehicles having rated gross vehicle
weight less than 8^00 Ib (3,855 kg), and which are designed primarily for transportation of cargo of
more than 11 passengers at a time, or which are equipped with special features for off-road operation.
They include most pickup trucks, passenger and cargo vans, four-wheel drive vehicles, and derivatives
of these. The rngine and other technologies used in these vehicles are basically similar to those used
in passenger cars, but these vehicles usually have larger engines, poorer fuel economy, and somewhat
higher emissions. Table D13-3 summarizes the estimated pollutant emissions for this vehicle class.
The technology classifications used are the same as those for gasoline passenger vehicles.
Heavy-duty gasoline vehicles. A heavy-duty vehicle is defined as one having a manufacturer's
gross vehicle weight rating exceeding 8^00 Ib (3,855 .kg). In the U.S., this includes a number of
models of large pickups and vans, along with specialized trucks using pickup and van chassis, as well
as the larger "true" heavy-duty trucks, which typically have gross vehicle weight ratings of eight short
tons or more. In the U.S., the large pickups and vans in this category greatly outnumber the heavier
trucks, so that the emission factors calculated by MOBILE4 are more representative of these vehicles.
This is also reflected in the fuel economy estimate for these vehicles of 6.1 miles/gal. The resulting
emissions estimates are shown in Table D13-4.
Estimates were developed for three levels of emission control technology: uncontrolled, non-
catalyst emission controls, and three-way catalyst technology. Non-catalyst emission controls include
control of ignition timing and air-fuel ratio to minimize emissions, EGR, and air injection into the
exhaust manifold to reduce HC and CO emissions. Three-way catalyst technology is presently used
on heavy-duty gasoline vehicles in the U.S. It includes electronically-controlled fuel injection, EGR,
air injection, and electronic control of ignition timing, as well as the catalyst itself.
Light-duty dieselpassenger cars. The U.S. EPA defines a diesel passenger car similarly to its
gasoline counterpart, as a vehicle designed primarily to cany fewer than 12 passengers, and with
manufacturer's rated gross vehicle weight less than 8,500 Ib (3,855 kg), and not possessing special
features such as four wheel drive for off-road operation. Table 1313-5 summarizes the estimated
emission factors for diesel passenger cars. Estimates are shown for three levels of emission control
technology, ranging from uncontrolled, through moderate emissions control (achieved by changes in
injection timing and combustion system design), through advanced emissions control utilizing modern
electronic control of the fuel injection system, and exhaust gas recirculation.
Light-duty diesel trucks. Again, the U.S. EPA defines light-duty diesel trucks much like their
gasoline counterparts, including gross vehicle weight, utility, and off-road operation features. Table
D13-6 summarizes the estimated pollutant emissions for this vehicle class. The technology
classifications are the same as those for diesel passenger cars.
Heavy-duty diesel vehicles. Although the EPA classification for heavy-duty diesel vehicles is
the same as for gasoline vehicles, the characteristics of the vehicles themselves are rather different
Unlike heavy-duty gasoline vehicles, heavy-duty diesel vehicles are primarily large trucks, with gross
vehicle weight ratings of 10 to 40 tons. Therefore, the MOBILE4 emission factors are more
representative of large trucks (and buses) than the smaller pickup and van-type vehicles, and this is
reflected in the fuel economy estimates. The resulting emission factors are summarized in Table D13-
7. As with the other diesel categories, three levels of control are represented: uncontrolled, moderate
control (typical of 1983 U.S.) and advanced control (for engines meeting U.S. 1991 emissions stan-
dards).
STATES WORKBOOK D13-8 November 1992
-------
Table D13-4: Estimated emission factors for heavy-duty gasoline vehicles.
EMISSIONS
NO,
CH«
NMVOC
CO
N,0
Three-Way Catalyst Control
Total - Ibfi/mile
Exhaust
Evaporative
Refueling
Running toss
Ibtfon Atel
IbVHTBta
0.0094
0.0094
20.14
0.508
0.00035
0.00035
0.76
0.022
0.00557
0.00244
0.00115
0.00121
0.00078
1L98
0309
0.0299
0.0299
6432
1.613
0.00002
0.00002
0.04
0.001
Non-Catalyst Control
Total - lot/mile
Exhaust
Evaporative
Refueling
Running loss
Ibs/too fuel
IW10* Bto
0.0122
0.0122
2S.96
0.641
0.00062
0.00062
130
0.022
0.0179
0.0077
0.0078
0.0013
0.0011
16.24
0398
0.1427
0.1427
302.64
7.602
0.00002
0.00002
0.04
0.001
UBControlled
Total Ibs/mUe
Exhaust
Evaporative
Refueling
Running loss
Ihs/ton fuel
lbs/10* Btn
0.0203
0.0203
31.08
0.773
0.0013
0.0013
2.04
0.044
0.0644
0.0409
0.0204
0.0020
0.0011
98.88
2.475
03078
05078
77935
19579
0.00003
0.00003
0.04
0.001
Motorcycles. Estimated emission factors for motorcycles are shown in Table D13-8. The
MOBILE4 emission factors for these vehicles are based on the U.S. motorcycle population. The
factors for uncontrolled motorcycles include a mixture of two-stroke and four-stroke engines, with
the VOC emissions due primarily to the two-strokes, and the NOX to the four-stroke engines. The
factors for motorcycles with non-catalyst emission controls reflect four-stroke engines only, as U.S.
emission control regulations have essentially eliminated two-stroke engines from the market.
Highway Vehicles - Alternative Fuels
\
Alternative motor vehicle fuels such as natural gas, LP gas, methanol, and ethanol are
presently being used in a limited way, and are the subjects of a great deal of research and
development effort aimed at increasing their usage in the future. These fuels are considered
attractive for a number of reasons, including potentially lower pollutant emissions, reductions in
emissions of gases contributing to global warming, and increased diversity of fuel supply. Since the
number of vehicles using these fuels is relatively small, and they are, in many cases, still under
STATES WORKBOOK
D13-9
November 1992
-------
Table D13-5: Estimated emission factors for diesel passenger cars.
EMISSIONS
NO,
CH,
NMVOC
CO
NjO
Advanced Control
Total - Ibs/mile
Ibs/toa fuel
lbs/10* Btu
0.0023
16.08
0.420
0.000035
O24
0.007
0.0010
7.18
0.186
0.0031
2L28
0.552
0.000025
0.16
0.004
Moderate Cootrol
Total - las/mile
Ibs/ton fuel
tbs/10*Btn
0.0033
14.72
0376
0.000035
ai6
0.004
0.0010
4.60
0.119
0.0031
13.62
0354
0.000035
0.16
0.004
Uncontrolled
Total - Ibs/mile
Ibs/ton fuel
lbs/10* Btu
0.0036
12.10
0309
0.000035
0.12
0.002
0.0018
6.18
0.161
0.0038
1Z58
0331
0.00005
0.16
0.004
Table D13-6: Estimated emission factors for light-duty diesel trucks.
EMISSIONS
NO,
CH<
NMVOC
CO
N2O
Advanced Control
Total - Ibs/mUe
Ibs/ton fuel
lbs/104 Btu
0.0027
1334
0354
0.000035
0.18
0.005
0.0015
7.48
0.199
0.0035
17.46
0.464
0.000032
0.16
0.004
Moderate Control
Total - Ibs/mile
Ibs/ton fuel
lbs/10* Btu
0.0037
1234
0331
Total - Ibs/mile
Ibs/ton fuel
lbs/10' Btu
0.0051
1434
0376
0.000035
0.12
0.003
0.0015
4.98
0.133
0.0035 .
11.64
0309
040005
0.16
0.004
Uncontrolled
0.00007
0.20
0.000
0.0029
8.22
0.221
0.0057
15.92
0.420
0.00006
0.16
0.004
development, little information is available on typical pollutant emission levels in service. MOBHJE4
and other emissions models do not yet address alternative-fuel vehicles. This section presents some
rough estimates of the emissions to be expected from vehicles using these fuels, based on fuel
propenies and the limited emissions data available. The reader is cautioned, however, that actual
emission levels from these vehicles may be very different, and further testing would be needed to
confirm these estimates.
Natural gas. Because natural gas is mostly methane, natural gas vehicles (NGVs) have lower
exhaust NMVOC emissions than gasoline vehicles, but higher emissions of methane. Since the fuel
system is sealed, there are no evaporative or running-loss emissions, and refueling emissions are
negligible. Cold-start emissions from NGVs are also low, since cold-start enrichment is not required,
and this reduces both NMVOC and CO emissions. NGVs are normally calibrated with somewhat
STATES WORKBOOK
D13-10
November 1992
-------
Table D13-7: Estimated emission factors for heavy-duty diesel vehicles.
EMISSIONS
NO,
CH,
NMVOC | CO
hUO
Advanced Control
Total - no/mat
Ins/ton fad
lbs/10* Btn
0.0178
3254
0.840
0.0002
038
0.011
0.0045
8.18
0.221
0.0241
44.18
1.149
0.00009
0.16
0.004
Moderate Control
Total - Ibs/mile
Ibs/tonfbd
lbs/10' Bto
0.0424
76.82
1.989
0.00025
0.46
0.022
0.0060
10.94
0.287
0.0294
53.28
1392
0.00009
0.16
0.004
Uncontrolled
Total - Ihs/mile
Ibs/ton fuel
lbs/10* Btu
0.0596
85.72
Z232
0.00035
0.52
0.022
0.0106
15.26
0398
0.0303
43.60
1.127
0.00011
0.16
0.004
Table D13-8: Estimated emission factors for motorcycles.
EMISSIONS
NO,
CH<
NMVOC | CO
N2O
Non-Catalytic Control
Total Ibs/mile
Ibs/ton fuel
lbs/10* Btu
0.0019
21.04
0.530
0.0005
5.%
0.155
0.0078
85.80
2.143
0.0468
521.99
13.038
0.000007
0.08
0.002
Uncontrolled
Total - Ibs/mile
Ibs/ton fuel
lbs/10* Btu
0.0007
6.46
0.155
0.0012
11.20
0.287
0.0231
22100
5.524
0.0844
809.99
20330
0.000007
0.08
0.002
leaner fuel-air ratios than gasoline vehicles, which also reduces CO emissions. Given equal energy
efficiency, CO2 emissions from NGVs will be lower than for gasoline vehicles, since natural gas has
a lower carbon content per unit of energy. In addition, the high octane value for natural gas
(Research Octane Number [RON] of 120 or more) makes it possible to attain increased efficiency
by increasing the compression ratio. Optimized heavy-duty NGV engines can approach diesel
efficiency levels. NOX emissions from uncontrolled NGVs may be higher or lower than comparable
gasoline vehicles, depending on the engine technology. Due to the low reactivity of the exhaust,
NGV NOX emissions are more difficult to control using three-way catalysts. Data on N2O emissions
from NGVs are not available, but probably resemble those for gasoline vehicles.
Table D13-9 shows very rough emissions estimates for three types of NGVs: passenger cars,
gasoline-type heavy-duty vehicles, and diesel-type heavy-duty vehicles. Two sets of emission factors
are shown for each: uncontrolled (typical of a simple natural gas conversion, without catalytic
converter or optimization for emissions) and advanced control (reflecting an engine and catalytic
converter factory-produced and optimized for natural gas). The estimates for the passenger car and
gasoline-type heavy duty vehicle are based on a gasoline-type engine, converted to use natural gas.
STATES WORKBOOK
D13-11
November 1992
-------
Table D13-9: Estimated emission factors for light- and heavy-duty natural gas vehicles.
EMISSIONS
NO,
CH«
NMVOC
CO
Np
Passenger Car
Advanced Control
Ibs/mile
Ibs/ton fuel
lbs/10* Btu
0.0018
20.60
0.44
0.0025
29.00
0.61
0.00018
2.00
0.042
0.0011
12.40
025
N/A
N/A
N/A
Uncontrolled
Ibs/mile
Iba/ton fuel
lbs/10* Btu
0.0075
38.00
0.80
0.0124
6320
132
0.0018
9.00
0.19
0.0142
7220
1.51
N/A
N/A
N/A
Heavy-Duty Vehicles: Stoichiometric
Advanced Control
ibs/miie
lb$/too fuel
lbs/10* Btu
0.0092
26.00
034
0.0106
30.00
0.63
0.0007
ZOO
0.042
0.0035
10.00
021
N/A
N/A
N/A
Uncontrolled
Ibs/mile
Ibs/ton fuel
lbs/10* Btu
0.0202
34.80
0.73
0.0355
6120
1.28
0.0050
8.60
0.19
0.0426
73.40
1.53
N/A
N/A
N/A
Heavy-Duty Vehicles: Lean Bum Engine
Advanced Control
Ibs/mile
Ibs/ton fuel
lbs/10' Btu
Ibs/mile
Ibs/ton fuel
lbs/10* Btu
0.0142
26.60
OSJ
0.0142
26.60
0.57
0.0014
2.60
0.063
0.0053
10.00
021
Uncontrolled
0.0816
127.80
2.68
0.0355
55.60
1.17
0.0071
1120
023
0.0284
44.40
0.92
N/A
N/A
N/A
N/A
N/A
N/A
For the uncontrolled vehicles, no changes in the engine are assumed beyond the fitting of a natural
gas mixer and modified spark timing such that the efficiency would be the same. For the vehicles
with advanced control, a higher compression ratio is assumed to give 15% better fuel efficiency.
For the diesel-rype heavy-duty vehicles, the engine assumed is a diesel-type engine, converted
to lean, Otto-cycle operation using natural gas. The uncontrolled case reflects no further
optimization beyond the conversion, while the controlled case includes extensive combustion
optimization for NOX control and an oxidation catalytic converter.
In each case, the emissions considered are only those of the vehicle itselfadditional emissions
due to, e.g., compression or liquefaction of gas for storage on the vehicle, leakage from pipelines, etc.
are not included, nor are the potential emissions credits due to, e.g., production of methane from
biomass.
STATES WORKBOOK
D13-12
November 1992
-------
LP gas. LPG has many of the same emissions characteristics as natural gas. The fact that
it is primarily propane (or a propane/butane mixture) rather than methane affects the composition
of exhaust VOC emissions, but otherwise the two fuels are similar. Evaporative and refueling
emissions are nil, and CO and exhaust NMVOC emissions are usually lower than gasoline vehicles.
The CO2 emissions should be somewhat lower than gasoline, due to the lower carbon-energy ratio,
and the higher octane allows some increase in efficiency, although less than for natural gas. NOX
emissions from LPG vehicles tend to be higher than for gasoline, but can also be controlled using
three-way catalysts. N2O emissions data are not available, but should be similar to those for gasoline
vehicles. Table D13-10 shows rough emissions estimates for four categories of LPG vehicles. The
engines and technologies considered are the same as those for natural gas, except that the lean,
diesel-derived natural gas engine with propane is not considered.
Methanol and ethanol. The two alcohols have similar properties, and wfll be discussed
together. Pure alcohols are handicapped as fuels for Otto-cycle engines by their low vapor pressure,
which makes cold starting difficult For this reason, development efforts have focused primarily on
mixtures of alcohols with gasoline. Flexible fuel vehicles, capable of running on any combination of
gasoline and up to 85% methanol or ethanol have been developed, and a number of fleets of these
vehicles are being demonstrated. The engines and emission control systems on these vehicles are
similar to those for advanced-technology gasoline vehicles, and the overall energy efficiency and
emissions properties are similar. Table D13-11 shows estimated emissions for a vehicle of this type
using M85 (85% methanol/15% gasoline) fuel.
Heavy-duty engines can also be operated on methanol or ethanol, using a variety of technical
approaches. Emissions data for heavy-duty engines on ethanol are not available, but a number of
heavy-duty methanol engines have been developed. The most promising approach is to inject the
methanol in liquid form, as in a diesel engine, so that engines using this approach can attain diesel-
like efficiencies. Table D13-11 also shows some rough emissions estimates for heavy-duty vehicles
equipped with such engines.
In each of the cases in Table D13-11, the estimates include only the emissions produced by
the vehicle itself. The additional methane emissions associated with producing the methanol from
natural gas are not included.
Electric Vehicles. No methodology is provided at this time for emissions from electric vehicles.
Electric vehicles have received much attention because they do not generate emission while in use;
as a result, they may offer substantial benefits in urban areas with local air quality problems. They
are not, however, "pollution free". Electric vehicles rely on electricity when the vehicles are not in
use to recharge the batteries for continued operation. The electricity required for this activity could
be generated from a number of fuels, including renewables, nuclear, natural gas, or coal. An in-depth
examination of total fuel cycle emissions from the use of electric vehicles is beyond the scope of the
current analysis. The reader is cautioned, however, from viewing electric vehicles as zero-emitters
of greenhouse gases since, arguably, the emissions produced to provide the electricity for recharging
are a direct result of fuel consumption by electric vehicles.
STATES WORKBOOK D13.13 November 1992
-------
Table D13-10: Estimated emission factors for light* and heavy-duty LP gas vehicles.
EMISSIONS
NO,
CH4
NMVOC
CO
Passenger Car
N,O
Advanced Control
Ibs/mile
Ibs/tOD fuel
lbs/10*Btu
0.0018
17.60
0.420
0.00007
0.80
0.022
0.0009
8JO
0221
0.0011
10.60
0.243
N/A
N/A
N/A
Uncontrolled
Ibs/mile
Ibs/ton fad
|bV10*Btu
0.0075
35.40
0.840
0.00064
3.00
0.066
0.0124
59.00
1.414
0.0284
135.00
3.204
N/A
N/A
N/A
Heavy-Duty Vehicles: Stoicbiometric
Advanced Control
Ibs/mile
tin/ton fuel
lbs/10* Btu
0.0092
22.40
0330
0.00035
0.80
0.022
0.0025
6.00
0.155
0.0035
8.60
0.199
N/A
N/A
N/A
Uncontrolled
Ibs/mile
Ibs/ton fuel
lbs/10* Btu
0.0202
33.60
0.796
0.0014
2.40
0.066
0.0284
47.00
1.127
0.0851
141.20
3359
N/A
N/A
N/A
Non-Road Mobile Sources
Although mobile sources other than road vehicles account for a significant fraction of total
mobile source emissions, they have received relatively little study compared to passenger cars and
heavy-duty trucks. Major sources of pollutant emissions among non-road vehicles include farm and
construction equipment, railway locomotives, boats, and ships (all primarily equipped with diesel
engines), jet aircraft, and gasoline-fueled piston aircraft
A recent study by Weaver (1988) for the U.S. EPA compiled the available (extremely scarce)
data on emissions from diesel engines used in railway locomotives, farm equipment such as tractors
and harvesters, construction equipment such as bulldozers and cranes, and diesel boats, and developed
emission factors for each category. Fuel-specific emission factors calculated from Weaver (1988) are
shown in Table D13-12. Since Weaver (1988) did not estimate N20 emissions, and no other data
for off-road diesels were available, we assumed that fuel-specific N2O emissions would be similar to
those for heavy-duty on-highway diesel engines.
Weaver (1988) did not estimate emission levels for large ocean-going ships, as opposed to
boats used in coastal and inland traffic. Commercial cargo ships are driven primarily by large, slow-
speed and medium-speed diesel engines. Other power sources that are occasionally found include
steam turbines and gas turbines (the latter in high power-weight ratio vessels such as fast ferries and
warships). The number of vessels equipped with steam or gas-turbine propulsion is small, however,
since these vessels are unable to compete with the more efficient diesels in most applications.
STATES WORKBOOK
D13-14
November 1992
-------
Table D13-11: Estimated emission factors for light- and heavy-duty methanol vehicles.
EMISSIONS
NO,
CH4
NMVOC
CO
N,O
Passenger Car
Adranced Control
Jos/mile
Ibs/ton fuel
lbs/10* Btu
0.0018
9.00
0.42
0.00007
0.40
0.022
0.0023
11.80
0.55
0.0111
56.00
2.63
N/A
N/A
N/A
Heavy-Duty Vehicles: Methanol Diesel Engine
Advanced Control
Ibs/mile
Ibs/ton fuel
lbs/10*Btii
0.0142
1120
0.66
0.00035
0.40
0.022
0.0053
4.60
0.24
0.0142
1230
0.66
N/A
N/A
N/A
A number of emissions measurements have recently become available for large marine diesel
engines. Hadler (1990) reports data from which it can be calculated that a 9800 kW engine in a
containership, operating at 85% power, produced 174 Ibs of NOZ per ton of fuel burned. Melhus
(1990), studying engines used in Norwegian coastal vessels, found NOX emissions ranging from 86 to
150 Ibs/ton fuel in four-stroke medium-speed engines, and from 100 to 166 Ibs/ton fuel in (presumably
slow-speed) two-stroke engines. Bremnes (1990) used an average value of 140 Ibs/ton fuel, based on
earlier measurements by Marintek. Alexandersson (1990) used NOX emission factors of 0.04 Ibs/kWH
(188 Ibs/ton fuel) for two-stroke and 0.03 Ibs/kWH (144 Ibs/ton fuel) for four-stroke marine diesel
engines, both at 80% load. Although these measurements vary considerably among themselves, it is
apparent that brake-specific and fuel-specific NOZ emission from marine diesel engines are compa-
rable to those from other uncontrolled diesel engines. For the results shown in Table D13-12, we
used the data of Hadler (1990). NOX emissions reported by this source appear representative of the
range of emission values reported, and this was also the only data source reporting CO as well as NOX
data. None of the data sources available reported VOC or N2O data. For lack of better data, fuel-
specific N2O emissions for these engines were assumed to be the same as those for other heavy-duty
diesels. VOC emissions from these large diesels are probably negligible.
Pollutant emissions from aircraft are another area that has received relatively little attention.
While emission factors have been developed for most commercial aircraft types (EPA, 1985), these
are expressed in terms of emissions per landing and take-off cycle (LTO)-an inconvenient unit for
cross comparison. Another problem with these factors is that they include only emissions in the
immediate vicinity of the airport, i.e., emissions under cruise conditions are not included. Since most
aircraft operate primarily in cruise, this is a serious concern.
Data on cruise emissions from aircraft are being developed by the U.S. Federal Aviation
Administration, but are not available at this time. The factors for jet (turbine) aircraft shown in
Table D13-12 were developed by Radian (1990) based on emissions from a Pratt and Whitney JT-17
engine, one of the most commonly used types. These factors are repeated here for lack of better
data. The emission factors for small gasoline-fueled piston aircraft were also developed by Radian
(1990) based on a Cessna engine. These are ako considered very approximate. Additional research
will be necessary to resolve the problems associated with limited data on cruise emissions.
STATES WORKBOOK
D13-15
November 1992
-------
Table D13-12: Estimated emission factors for non-highway mobile sources.
UNCONTROLLED EMISSIONS
NO,
CH4
NMVOC
CO
N,0
, ,.
OCEAN-GOING SHIPS
Ibs/ton Aid
lbs/10* Btu
Ibs/ton fuel
lbs/10* Btu
174.0
4.64
13S.O
3-54
n/a
at*
BOATS
0.46
0.011
Dfc
ola
9.80
0.24
3.80
0.10
42.60
1.10
0.16
0.0044
0.16
0.0044
LOCOMOTIVES
Ibs/too fuel
lbV10*Btti
148.6
3.98
050
0.013
11.00
0.29
5120
135
0.16
0.0044
FARM EQUIPMENT
Ibs/ton fuel
lbs/10* Btu
127.0
331
0.90
0.024
19.20
031
50.80
133
0.16
0.0044
CONSTRUCTION & INDUSTRIAL EQUIPMENT
Ibs/ton fuel
lbs/10* Btu
100.4
2.65
036
0.009
7.80
0.20
32.60
0.84
a 16
0.0044
JET & TURBOPROP AIRCRAFT
Ibs/ton fuel
lbs/10* Btu
25.0
0.64
0.17
0.0044
136
0.04
10.40
027
GASOLINE (PISTON) AIRCRAFT
Ibs/ton fuel
lbs/1 0* Btu
7.04
0.18
5.28
0.133
48.00
1.19
2067.97
53.03
n/a
n/a
0.08
0.002
No data on TOD emissions from aircraft turbine engines were available. For the gasoline
piston engines, fuel-specific N2O emissions were assumed to be similar to those for uncontrolled
passenger cars.
Basic Methodology
Using these sources, the following basic steps are required to estimate mobile source
emissions:
Determine the amount of energy consumed by energy type for all mobile sources
using data from state energy or transportation agencies, the U.S. EPA or DOE, or
other data sources (all values should be reported in million Btu).
For each energy type, determine the amount of energy that is consumed by each
technology type, e.g., light-duty gasoline vehicles, etc. (all units are in million Btu).
Determine the percentage of each technology type that has some form of emission
control technology. If only a portion of the energy consumed by a particular
technology type has the emission control technology, then only the energy attributable
STATES WORKBOOK
D13-16
November 1992
-------
to this portion of the vehicle stock should be identified as subject to emission controls
in order to determine net emissions.
Multiply the amount of energy consumed by each technology type by the appropriate
emission factor from Tables D13-2 through D13-12. If some or all of the technology
type has some form of emission control (as determined in the previous step), the
emission factor should reflect the appropriate level of emission control
Emissions can be summed across all fuel and technology type categories, including for
all levels of emission control, to determine total emissions from mobile source-related
activities.
Regardless of the specific methodology that is used to determine emissions, it is important
to remember that there is a substantial amount of uncertainty surrounding the estimation of emissions
from mobile sources. Some means should be developed to rank the differences in data quality and
the uncertainties affecting emission estimates. This may involve the use of standard deviations, ranges
of uncertainty, or some other means of indicating to the data consumer the relative reliability of the
data.
DATA SOURCES
The emission factors in Tables D13-2 through D13-12 can only be used if energy consumption
can be adequately characterized by the fuel and technology types contained in these tables. For
example, for transportatidn needs, information is required on the percentage of light-duty versus
heavy-duty vehicles by fuel type (gasoline- versus diesel-fueled) and the extent of emission controls
for each category. There is no single data source that comprehensively provides all relevant
information. There are several sources, however, that can help to determine this information.
For example, activity data on vehicle fleet characteristics will be needed. The main sources
of data available on transport are the U.S. EPA, U.S. Department of Transportation, the U.S. DOE,
the U.S. Federal Aviation Administration, state Department of Motor Vehicles, or other state
agencies may also be useful data sources.
Information on energy consumption in the transport sector is also needed to determine
emissions. As discussed earlier, the most reliable source for energy statistics is probably the U.S.
Department of Energy (DOE), where data on transport activities are detailed by fuel type and basic
transport mode. These data are available for most mobile source energy consumption in the U.S..
STATES WORKBOOK D13.17 November 199Z
-------
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- --"-.-rfrfc'^fts.-?:-'-!-:.-,.-_- =- -^ffl-i;**1---*---.fr^*-- ".;;=, .-= -..--zi-
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-------
REFERENCES
Alexandersson, A. (1990). The Swedish investigation - Exhaust emissions from ships. Proc. EMEP
Workshop on Emissions from Ships. Oslo, Norway, June 7-8,1990. State Pollution Control Authority,
Oslo.
Bremmes, P.K. 1990. Calculations of exhaust gas emissions from sea transport: Methodology and
results. Proc. EMEP Workshop on Emissions from Ships. Oslo, Norway, June 7-8, 1990. State
Pollution Contro Authority, Oslo.
Dasch, J-M. 1990. Nitrous oxide emissions from vehicles. General Motors Research Publication No.
GMR-7236, Warren Michigan.
Dietzinann, HJL, M.A. Parness, and R.L. Bradow, 1980. Emissions from Trucks by Chassis Version
of 1983 Transient Procedure. SAE Paper No. 801371. SAE International, Warrcndale, PA.
Ford Motor Company. 1989. Annual report to EPA on non-regulated pollutants for calendar year
1988. Dearborn, MI.
Ford Motor Company. 1990. Annual report to EPA on non-regulated pollutants for calendar year
1989. Dearborn, MI.
Ford Motor Company. 1991. Annual report to EPA on non-regulated pollutants for calendar year
1990. Dearborn, MI.
Hadler, C. 1990. Investigation of exhaust gas emission from heavy fuel operated diesel engines on
board ships. Proc. EMEP Workshop on Emissions From Ships. Oslo, Norway, June 7-8,1990. State
Pollution Control Authority, Oslo.
Machiele, P.A. 1988. Heavy-Duty Vehicle Emission Conversion Factors II: 1962-2000. EPA Report
No. EPA-aa-sdsb-89-1. Ann Arbor, MI.
MacKenzie, JJ. and M.P. Walsh. 1990. Driving Forces. World Resources Institute (WRI), 1990.
Melhus, O. 1990. NOX emission factors from marine diesel engines. Proc. EMEP Workshop on
Emissions from Ships. Oslo, Norway, June 7-8, 1990. State Pollution Control Authority, Oslo.
OECD/IEA. 1991. Greenhouse Gas Emissions: The Energy Dimension. Draft Report by the OECD,
Paris. Forthcoming.
Otto, K., M. Shelef. and J.T. Kummer. 1970. Studies of surface reactions of nitric oxide by nitrogen-
15 isotope labeling; I. The reaction between nitric oxide and ammonia over supported platinum at
200-250. Journal of Physical Chemistry 74, 2690-2698.
Piccot, S.D., J.A. Buzun, and H.C Frey. 1990. Emissions and Cost Estimates for Globally Significant
Anthropogenic Combustion Sources of NOf N2O, CH+ CO, and CO? EPA-600/7-90-010. Report
STATES WORKBOOK D13-19 November 1992
-------
under EPA Contract No. 68-02-4288, Work Assignment 38. Radian Corporation, Research Triangle
Park, NC
Prigent, M. and G. De Soete. 1989. Nitrous Oxide N2O in Engines Exhaust Gases-A First Appraisal
of Catalyst Impnct. SAE Paper No. 890492. SAE International, Warrendale, PA.
Radian Corporation. 1990. Emissions and Cost Estimates for Globally Significant Anthropogenic
Combustion Sources of NOV N^O, CH+ CO and CO^ Prepared for the Office of Research and
Development, U.S. EPA, Washington, D.C
U.S. EPA (U.S. Environmental Protection Agency). 1985. Compilation of Air Pollution Emission
Factors: Highway Mobile Sources. AP-42, Fourth Edition, Ann Arbor, Michigan. September.
U.S. EPA (U.S. Environmental Protection Agency). 1989. User's Guide to Mobile4 (Mobile Source
Emission Model). Emission Control Technology Division, Ann Arbor, MI.
U.S. National Technical Information Service. 1991. MOBHJE4 Model and User's Guide. U.S. NTIS,
U.S. Department of Commerce, Springfield, Virginia.
Warner-Selpb, M.A. and L.R. Smith. 1991. Assessment of Unregulated Emissions from Gasoline
Oxygenated Blends. EPA 460/3-91-002. Report under EPA Contract No. 68-C9-0004. Southwest
Research Institute, San Antonio, TX.
Weaver, C.S. 1988. Feasibility and Cost-Effectiveness of Controlling Emissions from Diesel Engines
Used in Rail, Marine, Construction, Farm, and Other Mobile Off-Highway Equipment. Report under
EPA Contract No. 68-01-7288, Radian Corporation, Sacramento, CA,
Weaver, C.S and S.H. Turner. 1991. Memorandum to Jane Leggett U.S. EPA and Craig Ebert, ICF,
Incorporated, June 3,1991.
STATES WORKBOOK D13.20 November 1992
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APPENDICES
STATES WORKBOOK November 1992
-------
-------
GLOSSARY1
Aerosol: Paniculate material, other than water or ice, in the atmosphere. Aerosols are important
in the atmosphere as nuclei for the condensation of water droplets .and ice crystals, as
participants in various chemical cycles, and as absorbers and scatterers of solar radiation,
thereby influencing the radiation budget of the earth-atmosphere system, which in turn
influences the climate on the surface of the Earth.
Afforestation: The process of establishing a forest, especially on land not previously forested.
Anaerobic Fermentation: Fermentation that occurs under conditions where oxygen is not present
For example, methane emissions from landfills result from anaerobic fermentation of the
land filled waste.
Anthropogenic: Of, relating to, or resulting from the influence of human beings on nature. .
Atmosphere: The envelope of air surrounding the Earth and bound to it by the Earth's gravitational
attraction.
Biomass: The total dry organic matter or stored energy content of living organisms that is present
at a specific time in a defined unit (ecosystem, crop, etc.) of the Earth's surface.
Biosphere: The portion of Earth and its atmosphere that can support life.
Carbon Sink: A poo) (reservoir) that absorbs or takes up released carbon from another part of the
carbon cycle. For example, if the net exchange between the biosphere and the atmosphere
is toward the atmosphere, the biosphere is the source, and the atmosphere is the sink.
Carbon Dioxide (CO2): Carbon dioxide is an abundant greenhouse gas, accounting for about 66
percent of the total contribution in 1990 of all greenhouse gases to radiative forcing.
Atmospheric concentrations have risen 25% since the beginning of the Industrial Revolution.
Anthropogenic source of carbon dioxide emissions include combustion of solid, liquid, and
gases fuels, (e.g., coal, oil, and natural gas, respectively), deforestation, and non-energy
production processes such as cement-production.
Carbon Monoxide (CO): Carbon monoxide is an odorless, invisible gas created when carbon-
containing fuels are burned incompletely. Participating in various chemical reactions in the
atmosphere, CO contributes to smog formation, acid rain, and the buildup of methane (CH4).
CO elevates concentrations of CH4 and tropospheric ozone (O3) by chemical reactions with
the atmospheric constituents (i.e., the hydroxyl radical) that would otherwise assist in
destroying CH4 and O3.
1 Some of the definitions shown here are taken from the Carbon Dioxide and Climate Glossary produced
by the Carbon Dioxide Information Analysis Center of Oak Ridge National Laboratory.
STATES WORKBOOK Al-1 November 1992
-------
Chlorofluorocarbons (CFCs): A family of inert non-toxic and easily liquified chemicals used in
refrigeration, air conditioning, packaging, and insulation or as solvents or aerosol propellants.
Because they are not destroyed in the lower atmosphere, they drift into the upper atmosphere
where their chlorine components destroy ozone.
Climate Change: The long-term fluctuations in temperature, precipitation, wind, and all other
aspects of the Earth's climate.
Deforestation: The removal of forest stands by cutting and burning to provide land for agricultural
purposes, residential or industrial building sites, roads, etc. or by harvesting trees for building
materials or fuel.
Enteric Fermentation: Fermentation that occurs in the intestines. For example, methane emissions
produced as part of the normal digestive processes of ruminant animals is referred to as
"enteric fermentation."
Flux: Rate of substance flowing into the atmosphere (e.g. kg/m2/second).
Global Warming Potential (GWP): Gases can exert a radiative forcing both directly and indirectly:
direct forcing occurs when the gas itself is a greenhouse gas; indirect forcing occurs when
chemical transformation of the original gas produces a gas or gases which themselves are
greenhouse gases. The concept of the Global Wanning Potential has been developed for
policymakers as a measure of the possible warming effect on the surface-troposphere system
arising from the emissions of each gas relative to CO2-
Greenhouse Effect: A popular term used to describe the roles of water vapor, carbon dioxide, and
other trace gases in keeping the Earth's surface warmer than it would be otherwise.
Greenhouse Gases: Those gases, such as water vapor, carbon dioxide, tropospheric n/onc, nitrous
oxide, and methane that are transparent to solar radiation but opaque to infrared or longwave
radiation. Their action is similar to that of glass in a greenhouse.
Methane (CH4): Following carbon dioxide, methane is the most important greenhouse gas in terms
of global contribution to radiative forcing (18 percent). Anthropogenic sources of methane
include wetland rice cultivation, enteric fermentation by domestic livestock, anaerobic
fermentation of organic wastes, coal mining, biomass burning, and the production,
transportation, and distribution of natural gas.
Nitrous Oxide (N2O): Nitrous oxide is responsible for about 5 percent of the total contribution in
1990 of all greenhouse gases to radiative forcing. Nitrous oxide is produced from a wide
variety of biological and anthropogenic sources. Activities as diverse as the applications of
nitrogen fertilizers and the consumption of fuel emit N2O.
Nitrogen Oxides (NOX): One form of odd-nitrogen, denoted as NOX is defined as the sum of two
species, NO and NO2. NO, is created in lighting, in natural fires, in fossil-fuel combustion,
and in the stratosphere from N2O. It plays an important role in the global wanning process
due to its contribution to the formation of ozone (O3).
STATES WORKBOOK Al-2 November 1992
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Non-Methane Volatile Organic Compounds (NMVOCs): VOCs are frequently divided into methane
and non-methane compounds. Non-methane VOCs include compounds such as propane,
butane, and ethane (see also discussion on Volatile Organic Compounds).
Ozone (O3): A molecule made up of three atoms of oxygen. In the stratosphere, it occurs naturally
and it provides a protective layer shielding the Earth from ultraviolet radiation and
subsequent harmful health effects on humans and the environment In the troposphere, it
is a chemical oxidant and major component of photochemical smog.
Radiative Forcing: The measure used to determine the extent to which the atmosphere is trapping
heat due to emissions of greenhouse gases.
Radiatively Active Gases: Gases that absorb incoming solar radiation or outgoing infrared radiation,
thus affecting the vertical temperature profile of the atmosphere. Most frequently cited as
being radiatively active gases are water vapor, carbon dioxide, nitrous oxide,
chlorofluorocarbons, and ozone.
Stratosphere: Region of the upper atmosphere extending from the tropopause (about 5 to 9 miles
altitude) to about 30 miles.
Trace Gas: A minor constituent of the atmosphere. The most important trace gases contributing
to the greenhouse effect include water vapor, carbon dioxide, ozone, methane, ammonia,
nitric acid, nitrous oxide, and sulfur dioxide.
Troposphere: The inner layer of the atmosphere below about 15 km, within which there is normally
a steady decrease of temperature with increasing altitude. Nearly all clouds form and weather
conditions manifest themselves within this region, and its thermal structure is caused primarily
by the beating of the Earth's surface by solar radiation, followed by beat transfer by turbulent
mixing and convection.
Volatile Organic Compounds (VOCs): Volatile organic compounds along with nitrogen oxides are
participants in atmospheric chemical and physical processes that result in the formation of
ozone and other photochemical oxidants. The largest sources of reactive VOC emissions are
transportation sources and industrial processes. Miscellaneous sources, primarily forest
wildfires and non-industrial consumption of organic solvents, also contribute significantly to
total VOC emissions.
STATES WORKBOOK Al-3 November 1992
-------
CHEMICAL SYMBOLS AND CONVERSION FACTORS
1. The columns below list symbols and their associated chemical compounds or meanings:
Symbol Compound
CH4 Methane
N2O Nitrous Oxide
CO Carbon Monoxide
NOX Nitrogen Oxides
CO2 Carbon Dioxide
CO2-C Carbon Dioxide in units
of Carbon
-C in units of Carbon
N2O-N Nitrous Oxide in units
of Nitrogen
-N in units of Nitrogen
The columns below show compounds and conversions by molecular weight equivalents to
other compounds.
To Convert To: Multiply by:
tons CO2-C
tons CH4-C
tons CO-C
tons N2O-N
tons NOX-N
(biomass burning)
tons CO2
tons CH4
tons CO
tons N2O
tons NOX
44/12
16/12
28/12
44/28
30/14
46/14
tons NO, tons NOX
(all other applications)
STATES WORKBOOK AM November 1992
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STATE AGENCY CONTACTS FOR CLIMATE CHANGE
ENERGY OFFICES
ALABAMA Alabama Department of Economic and Community Affairs, Science, Technology, and
Energy Division, P.O. Box 5690, Montgomery, AL 36103-5690. (205)242-5291 Fax (205)
242-5515. Dialcom DOE459
ALASKA - Alaska Energy Office, Rural Development Division, Department of Community and Regional
Affairs, 333 West 4th Avenue, Suite 220, Anchorage, AK 99501-2341. (907) 269-4630. Fax (907)
269-4520
ARIZONA - Arizona Energy Office, 3800 North Central, 12th Floor, Phoenix, AZ 85012. (602)
280-1402, Fax (602) 280-1305, Dialcom DOE472
ARKANSAS - Arkansas Energy Office, One State Capital Mall, Suite 4B-215, Little Rock, AR 72201.
(501) 682-1370, Fax (501) 682-7341
CALIFORNIA - California Energy Commission-(SECP), 1516 9th Street, Sacramento, CA 95814. (916)
654-5000, Fax (916) 654-4420
CALIFORNIA - California Energy Extension Service-(EES), 1400 10th Street, Sacramento, CA 95814.
(916) 323-4388, Fax (916) 324-4523, Dialcom
COLORADO - Colorado Office of Energy Conservation, 1675 Broadway, Suite 1300, Denver, CO 80202-
4613, (303) 894-2144, Fax (303) 620-4288, Dialcom DOE412,1-800-OEC-6662
CONNECTICUT - Connecticut Office of Policy and Management, Energy Division, 80 Washington
Street, Hartford, CT 06016. (203) 566-2800, Tax (203) 566-8463, Dialcom DOE455
DELAWARE - Delaware Division of Facilities Management. P.O. Box 1401. Dover, DE 19903. (302)
736-5644, Fax (302) 739-6148,. Dialcom DOE458
DISTRICT OF COLUMBIA - District of Columbia Energy Office, 613 G Street, NW, 5th Floor,
Washington, D.C 20001 (202) 727-1800, Fax (202) 727-9582, Dialcom DOE486
FLORIDA - Florida Energy Office, The Capitol, Tallahassee, FL 32399-0001. (904) 488-6764, Fax (904)
488-7688, Dialcom DOE470
GEORGIA - Governor's Office of Energy Resources, 254 Washington Street, SW, Suite 401, Atlanta, GA
30334. (404) 656-5176, Fax (404) 656-7970
HAWAII - Hawaii State Energy Division, Department of Business, Economic Development & Tourism,
335 Merchant Street, Room 110, Honolulu, HI 96813. (808) 587-3800, Fax (808) 587-3820
IDAHO - Idaho Department of Water Resources, Statehouse Mail, Boise, ID 83720-9000. (208)
327-7900, Fax (208) 327-7866, Dialcom DOE474
STATES WORKBOOK A2-1 November 1992
-------
ILLINOIS - Illinois Department of Energy &. Natural Resources, 325 West Adams Street, Room 300,
Springfield, IL 62704. (217) 785-2800, Fax (217) 785-2618, Dialcom DOE438
INDIANA - Indiana Division of Energy Policy, 1 North Capitol, Suite 700, Indianapolis, IN 46204-2243,
(317) 232-8940, Fax (317) 232-8995, Dialcom DOE487
IOWA - Iowa Department of Natural Resources Energy Bureau, Wallace Building, DCS Moines, 1A
50319. (515) 281-8681, Fax (515) 281-8895, Dialcom DOE449
KANSAS - Kansas Corporation Commission, 1500 SW Arrowhead Road, Topeka, KS 66604, (913)
271-3170, Fax (913) 271-3354
KENTUCKY - Kentucky Division of Energy, 691 Teton Trail, 2nd Floor, Frankfort, KY 40601, (502)
564-7192, Fax (502) 564-7484, Dialcom DOE439
LOUISIANA - Louisiana Department of Natural Resources, Energy Division, P.O. Box 44156, Baton
Rouge, LA 70804. (504) 342-1399 or (504) 342-2707, Dialcom DOE461
MAINE - State House Station No. 53, Augusta, ME 04333, (207) 624-6000, Fax (207) 624-6023, Energy
Conservation Division Dialcom DOE413
MARYLAND - Maryland Energy Administration, 45 Catvert Street, Annapolis, MD 21401-1907, (301)
974-3751, Fax (301) 974-2250, Dialcom DOE440
MASSACHUSETTS - Massachusetts Division of Energy Resources, Saltonstall Building, 100 Cambridge
Street, Room 1500, Boston, MA 02202, (617) 727-4732, Fax (617) 727-0030
MICHIGAN - Michigan Office of Energy Programs, Public Service Commission, P.O. Box 30221, Lansing,
MI 48909, (517) 334-6272, Fax (517) 882-5170, Dialcom 414
MINNESOTA - Minnesota Department ot Public Service, 790 American Center Building, 150 East
Kellogg Boulevard, St. Paul, MN 55101, (612) 296-5120, Fax (612) 297-1959, Dialcom DOE452
MISSISSIPPI - Mississippi Department of Economic and Community Development, Energy and
Transportation Division, 510 George Street, Suite 101 , Jackson, MS 39202-3096, (601) 359-6600,
Fax (601) 359-6642, Dialcom DOE467
MISSOURI - Missouri Department of Natural Resources, Division of Energy, P.O. Box 176, Jefferson
City, MO 65102, (314) 751-4000, Fax (314) 751-6860, Dialcom DOE421
MONTANA - Montana Department of Natural Resources and Conservation, 1520 East Sixth Avenue,
Helena, MT 59620, (406) 444-6696, Fax (406) 444-6721, Dialcom DOE441
NEBRASKA - Nebraska Energy and Policy Research Office, P.O. Box 95085, State Capitol Building,
Lincoln, NE 68509-5085, (402) 471-2867, Fax (402) 471-3064, Dialcom DOE
NEVADA - Nevada Governor's Office of Community Services, 400 West King Street, Carson City, NV
89710, (702) 687-4990, Fax (702) 687^914
NEW HAMPSHIRE - New Hampshire Governor's Office of Energy & Community Service, 2 1J2 Beacon
Street. Concord, NH 03301-8519, (603) 271-2611, Fax (603) 271-2615, Dialcom DOE451
STATES WORKBOOK A2-2 November 1992
-------
NEW JERSEY - New Jersey Board of Regulatory Commissioners, 2 Gateway Center, Newark, NJ 07102,
(201) 648-3621, Fax (201) 648-4298, Dialoom DOE415
NEW MEXICO - New Mexico Energy, Minerals and Natural Resources Department, 2040 South Pacheco
Street, Santa Fe, NM 87505, (505) 827-5900, Fax (505) 827-5912, Dialcom DOE484
NEW YORK - New York State Energy Office, 2 Rockefeller Plaza, Albany, NY 12223, (518) 474-7183,
Fax (518) 473-2017, Dialcom DOE417
NORTH CAROLINA North Carolina Department of Economic and Community Development Energy
Division, 430 North Salisbury Street, Raleigh, NC 27611, (919) 733-2230, Fax (919) 733-2953,
Dialcom DOE 482
NORTH DAKOTA - North Dakota Office of Intergovernmental Assistance, State Capitol, 14th Floor,
600 East Boulevard Ave.. Bismarck, ND 58505-0170. (701) 224-2094, Fax (701) 224-3000, Dialcom
DOE465
OHIO - Ohio Department of Development, Office of Energy Efficiency, 77 South High Street, 24th
Floor* Columbus, OH 43266^)413, (614) 466-6797, Fax (614) 46*4708, Dialcom DOE556
OKLAHOMA - Oklahoma Department of Commerce, P.O. Box 26980, Oklahoma City, OK 73126-0980,
(405) 843-9770, Fax (405) 841-5199
OREGON - Oregon Department of Energy, 625 Marion Street, ME, Salem, OR 97310, (503) 378-4040,
Fax (503) 229-5173, Dialcom DOE464
PENNSYLVANIA - Pennsylvania Energy Office, 116 Pine Street, Harrisburg, PA 17101-1227, (717)
783-9981, Fax (717) 783-2703, Dialcom DOE419
RHODE ISLAND « Rhode Island Governor's Office of Housing, Energy and Intergovernmental
Relations, State House, Room 111, Providence, RI 02903, (401) 277-2850, Fax (401) 273-5301,
Dialcom DOE444
SOUTH CAROLINA - South Carolina Governor's Division of Energy, Agriculture and Natural
Resources, 1205 Pendleton Street, Suite 333, Columbia, SC 29201, (803) 734-1740, Fax (803)
734-0356
SOUTH DAKOTA - South Dakota Governor's Office of Energy Policy, 217 West Missouri, Suite 200,
Pierre, SD 5750M516, (605) 773-3603, Fax (605) 773-4802, Dialcom DOE
TENNESSEE - Tennessee Department of Economic and Community Development Energy Division, 320
6th Avenue North, 6th Floor, Nashville, TN 37243-0405, (615) 741-2994, Fax (615) 741-5829,
Dialcom DOE420
TEXAS - Texas Governor's Energy Office, P. O. Box 12428, Capitol Station, Austin, TX 78711, (512)
463-1931, Fax (512) 475-2569, Dialcom DOE439
UTAH - Utah Division of Energy, 3 Triad Center, Suite 450, Salt Lake City, UT 84180-1204, (801)
538-5428, Fax (801) 521-0657, Dialcom DOE433
VERMONT - Vermont Department of Public Services, Energy Efficiency Division, 120 Statew Street,
Montpelier, VT 05620, (802) 828-2393. Fax (802) 828-2342
STATES WORKBOOK A2-3 November 1992
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VIRGINIA - Virginia Department of Mines, Minerals, and Energy, Division of Energy, 2201 West Broad
Street, Richmond, VA 23220, (804) 367-0979, Fax (804) 3674211, Dialcom DOE466
WASHINGTON - Washington State Energy Office, 809 Legion Way SE, FA-11, Orympia, WA 98504-1211
or P.O. Box 43615, Otympia, WA 98504-3165, (206) 956-2000, Fax (206) 753-2397
WEST VIRGINIA - West Virginia Fuel and Energy Office, Building 6, Room 553, State Capitol,
Charleston, WV 25305, (304) 348-1010, Fax (304) 348-3248, Dialcom DOE489
WISCONSIN - Wisconsin Division of Energy and Intergovernmental Relations, P.O. Box 7868, Madison,
WI53707, (608) 2664234, Fax (608) 267-0200
WYOMING - Wyoming Energy Conservation Office, Dept of Commerce, Div. of Economic
Development, Energy Section, Herschler Building, Second Floor West, Cheyenne, WY 82002,
(307) 777-7284, Fax (307) 777-5840, Dialcom DOE434
ENVIRONMENTAL OFFICES
ALABAMA Environmental Management Department, 1751 Congressman W.L., Dickinson Drive,
Montgomery, AL 36130
ALASKA - Environmental Conservation Department, P.O. Box O, Juneau, AK 99811-180
ARIZONA - Environmental Quality Department, 2005 N. Central Avenue, Phoenix, AZ 85004
ARKANSAS - Department of Pollution Control and Ecology, P.O. Box 9583, Little Rock, AR 72219
CALIFORNIA - California Air Resources Board, P.O. Box 2815, Sacramento, CA 95812
CALIFORNIA - California Environmental Protection Agency, 555 Capitol Mall, Suite 235, Sacramento,
CA 95814
COLORADO - Office of Environment, Colorado Department of Health, 4210 East llth Avenue, Denver,
CO 80220
CONNECTICUT - Department of Environmental Protection, 165 Capitol Avenue, Hartford, CT 06101
DELAWARE - Natural Resources and Environmental Control, P.O. Box 1401, 89 Kings Highway, Dover,
DE 19901
DISTRICT OF COLUMBIA - Department of Consumer and Regulatory Affairs, 614 H Street, N.W.,
Washington, DC 20001
FLORIDA - Department of Environmental Regulation, 2600 Blair Stone Road, Tallahassee, Florida
323399-2400
GEORGIA - Department of Natural Resources, 205 Butler Street, S.W., Suite 1252, Atlanta, GA 30334
HAWAII - Hawaii Department of Health, 1250 Punchbowl, Honolulu, HI 96801
STATES WORKBOOK A2-4 November 1992
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IDAHO - Division of Environmental Quality, 1410 N. Hilton Street, Boise, ID 83720
ILLINOIS - Illinois Environmental Protection Agency, 2200 Churchill Road, P.O. Box 19276, Springfield,
IL 02794
INDIANA - Indira Department of Environmental Management, 105 S. Meridian Street, Indianapolis, IN
4622S
IOWA - Iowa Natural Resources Department, 900 E. Grand Avenue, Des Moines, IA 50319
KANSAS - Department of Health and Environment, 900 S.W. Jackson, Suite 901, Topeka, KS 66612-1290
KENTUCKY - Department of Environmental Protection, Ft Boone Plaza, 18 Reilry Road, Frankfort, KY
40601
LOUISIANA - Department of Environmental Quality, P.O. Box 44066, Baton Rouge, LA 70804
MAINE - Department of Environmental Protection, State House Station 17, Augusta, ME 04333
MARYLAND - Department of Environment, 2500 Broening Highway, Baltimore, MD 21224
MASSACHUSETTS - Department of Environmental Protection, One Winter Street, Boston, MA 02108
MICHIGAN - Department of Environmental Protection, Box 30028, Steven T. Mason Building, Lansing,
MI 48909
MINNESOTA - Minnesota Pollution Control Agency, 520 Lafayette Road, 6th Floor, St. Paul, MN
55155-3898
MISSISSIPPI - Department of Natural Resources, Box 10385, Jackson, MS 39289-0383
MISSOURI - Department of Natural Resources, 2010 Missouri Boulevard, P.O. Box 176, Jefferson City,
MO 65102
MONTANA -- Environmental Sciences Division, Health and Environmental Science Department,
Cogswell Building, Helena, MT 59620
NEBRASKA -- Environmental Control, P.O. Box 98922, Lincoln, NE 68509
NEVADA - Environmental Protection Division, Nevada Department of Conservation and Natural
Resources. 201 South Fall Street, Carson City, NV 89701
NEW HAMPSHIRE - New Hampshire Department of Environmental Services, Hazen Drive, P.O. Box
95, Concord. NH 03302
NEW JERSEY - Department of Environmental Protection, 401 East State Street - CN 402, Trenton, NJ
08625
NEW MEXICO - Department of Environment, P.O. Box 26110, Same Fe, NM 875034)968
NEW YORK - Environmental Conservation Department, 50 Wolf Road, Albany, NY 12233
STATES WORKBOOK A2-5 November 1992
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NORTH CAROLINA - Division of Environmental Management, Natural Resources and Community .
Development, P.O. Box 27687, Raleigh, NC 27611
NORTH DAKOTA - Environmental Health Section, 1200 Missouri Avenue, Bismarck. ND 58505
OHIO - Ohio Environmental Protection Agency, P.O. Box 1049,1800 Watermark Drive, Columbia, OH
432664)149
OKLAHOMA - Environmental Health Services, Health Department, 1000 N.E. 10th Street, P.O. Box
S3SS1, Oklahoma City, OK 731S2
OREGON - Department of Environmental Quality, 811 S.W. Sixth Avenue, Portland, OR 97204-1334
PENNSYLVANIA - Department of Environmental Resources, Fulton Building, 9th Floor, Box 2063.
Harrisburg, PA 17120
PUERTO RICO - Environment and Quality Board, P.O. Box 11488, Santurce, Puerto Rico 00910
RHODE ISLAND - Director, Department of Environmental Management, 9 Hayes Street, Providence, RI
02908
SOUTH CAROLINA - Deputy Commissioner, Environmental Quality Control, Department of Health
and Environmental Control, 2600 Bull Street, Columbia, SC 29201
SOUTH DAKOTA - Environment and Natural Resources, 523 E. Capitol Avenue, Pierre, SD 57501
TENNESSEE ~ Commissioner, Department of Health and Environment, 344 Cordell Hull Building,
Nashville, TN 37219
TEXAS - Texas Water Commission, P.O. Box 13087, Capitol Station, Austin, TX 78711
UTAH - Executive Director, Department of Environmental Quality, 288 N. 1460 West, Salt Lake City,
UT 84116-0700
VERMONT - Secretary, Natural Resources Agency, 103 S. Main Street, Building 1-South, Waterbury, VT
05676
VIRGINIA - Secretary, Department of Natural Resources, 9th Street Office Building, Richmond < VA
23219
WASHINGTON - Director, Department of Ecology, St. Martin's College Campus, Abbott-Raphael Hall -
- PV-11, Olympia, WA 98504-8711
WEST VIRGINIA - Director, Natural Resources Department, 1800 Washington Street, Charleston, WV
25305
WISCONSIN - Secretary, Wisconsin Department of Natural Resources, P.O. Box 7921, Madison, WI
53707
WYOMING - Director, Environmental Quality Department, Herschler Building, 122 West 25th Street,
Cheyenne, WY 82002
STATES WORKBOOK A2-6 November 1992!
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BIBLIOGRAPHY OF KEY REPORTS
Benioff, R. 1990. Potential State Responses to Climate Change. OfEce of Policy, Planning and
Evaluation, U.S. Environmental Protection Agency. 1990.
DeLuchi, NLA. 1991. Emissions of Greenhouse Cases from the Use of Transportation Fuels and
Electricity. Center for Transportation Research, Energy Systems Division, Argonne National
Laboratory. Argonne, Illinois. November 1991.
Fisher, D.C 1991. Reducing Greenhouse Gas Emissions with Alternative Transporation Fuels,
_k Environmental Defense Fund. 1991.
IPCC (Intergovernmental Panel on Climate Change). 1990. Climate Change: The IPCC Scientific
Assessment. Report Prepared for Intergovernmental Panel on Climate Change by Working Group
1.
IPCC (Intergovernmental Panel on Climate Change). 1992. Climate Change 2992: The Supplementary
Report to the IPCC Scientific Assessment. Report prepared for Intergovernmental Panel on Climate
Change by Working Group 1.
Lashof, D. A., and D.A. Tirpak. 1990. Policy Options for Stabilizing Global Climate Change. Office
of Policy, Planning and Evaluation, U.S. Environmental Protection, Washington, D.C. December.
Lashof, D.A., and E.L. Washborn. 1990. The Statehouse Effect: State Policies to Cool the
Greenhouse. NRDC, Washington, D.C. July.
National Academy of Sciences. 1991. Policy Implications of Greenhouse Warming. National
, Academy Press, Washington, D.C 127 pp.
National Governors' Association. 1991. A World of Difference: Report of the Task Force on Global
Climate Change. NGA, Washington, D.C. 36 pp.
National Wildlife Federation. 1991. Conservation Directory. Washington, D.C.
Oregon Department of Energy. 1991. Fourth Biennial Energy. Plan. Oregon Department of Energy,
Salem, Oregon.
Radian Corporation. 1990. Emissions and Cost Estimates for Globally Significant Anthropogenic
Combustion Sources ofNOv N2O, CH^ CO, and CO% Prepared for the Office of Research and
Development, U.S. Environmental Protection Agency,~Washington, D.C.
Stlberg, A. and N. Gonring. 1991. Selected Summary of Current State Responses to Climate Change.
Prepared for Climate Change Division, Office of Policy, Planning and Evaluation. United States
Environmental Protection Agency. 1991.
United States Environmental Protection Agency (U.S. EPA). 1991. Adapting to Climate Change:
STATES WORKBOOK A3-1 November 1992
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What Governments Can Do. U.S. EPA, Office of Policy, Planning and Evaluation. Climate Change
Adaptation Branch. 1991.
Walls, M. et al. 1991. Greenhouse Gas Emissions from Alternative Fuels, Prepared for Office of
Policy Analysis, US. EPA. 1991.
STATES WORKBOOK A3-2 November 1992
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