1360 Redwood Way, Suite C

Petaluma, CA 94954-1169

707/665-9900

FAX 707/665-9800

www.sonomatech.com

RESEARCH AND DEVELOPMENT OF 

AMMONIA EMISSION INVENTORIES 

FOR THE CENTRAL STATES REGIONAL 

AIR PLANNING ASSOCIATION

FINAL REPORT

STI-902501-2241-FR

By:

Dana L. Coe

Stephen B. Reid

Sonoma Technology, Inc.

1360 Redwood Way, Suite C

Petaluma, CA 94954-1169

Prepared for:

The Central States Air Resource Agencies and

The Central Regional Air Planning Association 

10005 South Pennsylvania Avenue, Suite C

Oklahoma City, Oklahoma 73159

October 30, 2003
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iii

QUALITY ASSURANCE STATEMENT

This report was reviewed and approved by the project Quality Assurance (QA) Officer or 

his delegated representatives, as provided in the project QA Plan (Coe, 2003).

Lyle R. Chinkin

Project QA Officer
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v

TABLE OF CONTENTS

Section Page

QUALITY ASSURANCE STATEMENT .................................................................................... iii

LIST OF FIGURES ...................................................................................................................... vii

LIST OF TABLES......................................................................................................................... ix

EXECUTIVE SUMMARY .......................................................................................................ES-1

1. INTRODUCTION.............................................................................................................. 1-1

1.1 Background and Key Issues...................................................................................... 1-1

1.1.1 Secondary Formation of Fine Particulate Matter in the CENRAP 

Region .......................................................................................................... 1-1

1.1.2 Prior Statuses of Ammonia Emission Inventories........................................ 1-6

1.1.3 Project Priorities........................................................................................... 1-7

1.2 Current Status of the CENRAP Emission Inventory................................................ 1-7

2. SUMMARY AND ASSESSMENT OF THE INVENTORY............................................ 2-1

2.1 Emissions From Livestock and Poultry.................................................................... 2-1

2.1.1 Summary of Emissions from Livestock and Poultry.................................... 2-1

2.1.2 Assessment of Emissions from Livestock and Poultry ................................ 2-3

2.2 Emissions from Fertilizer Application...................................................................... 2-6

2.2.1 Summary of Emissions from Fertilizer Application .................................... 2-6

2.2.2 Assessment of Emissions from Fertilizer Application................................. 2-7

2.3 Biogenic Emissions .................................................................................................. 2-8

2.3.1 Summary of Biogenic Emissions................................................................. 2-8

2.3.2 Assessment of Biogenic Emissions.............................................................. 2-9

2.4 Emissions from Other Source Categories............................................................... 2-10

2.4.1 Summary of Emissions from Other Source Categories.............................. 2-10

2.4.2 Assessment of Emissions from Other Source Categories.......................... 2-10

2.5 Source Categories Omitted from the Inventory...................................................... 2-11

3. RECOMMENDATIONS FOR FURTHER RESEARCH ................................................. 3-1

3.1 Recommendations for Biogenic Sources.................................................................. 3-1

3.2 Recommendations for Livestock .............................................................................. 3-2

3.2.1 Address Next-Generation Emissions Models............................................... 3-2

3.2.2 Refine Animal Population Estimates ........................................................... 3-2

3.3 Recommendations for Fertilizers.............................................................................. 3-3

3.4 Recommendations Other Source Categories............................................................ 3-3

4. REFERENCES................................................................................................................... 4-1

APPENDIX A: AMMONIA EMISSION ESTIMATION METHODS FOR THE CENRAP 

AMMONIA EMISSION INVENTORY ......................................................... A-1

APPENDIX B: TABULATION OF AMMONIA EMISSIONS ESTIMATES FOR THE 

CENRAP REGION...........................................................................................B-1

APPENDIX C: TABULATION OF DIFFERENCES BETWEEN THE STI INVENTORY 

AND CMU MODEL OUTPUTS .....................................................................C-1

APPENDIX D: EMISSION INVENTORY FILE DOCUMENTATION AND 

ELECTRONIC FILES............................................................................... ON CD
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vii

LIST OF FIGURES

Figure Page

ES-1. CENRAP annual ammonia emissions by source category ............................................ES-1

ES-2. Monthly variability in total emissions for the CENRAP region....................................ES-2

1-1. Compositions of annual average concentrations of PM2.5 observations in urban

locations........................................................................................................................... 1-2

1-2. Contributions of chemical components to observed PM2.5 concentrations in 

the CENRAP region......................................................................................................... 1-3

1-3. Concentrations of particulate sulfates and nitrates observed in the CENRAP 

region ............................................................................................................................... 1-3

1-4. Estimated seasonal average concentrations of PM2.5....................................................... 1-4

1-5. Uncertainty in estimated seasonal average PM2.5 concentrations.................................... 1-4

1-6. Comparison of IMPROVE and CDN measurements of sulfate and nitrate at 

Big Bend National Park, Texas........................................................................................ 1-5

1-7. Annual SO2 emissions in 1998 ........................................................................................ 1-5

1-8. Population density in the United States in 2000 .............................................................. 1-6

1-9. Total annual ammonia emissions by source category for each state of the 

CENRAP region............................................................................................................... 1-8

1-10. Geographic map of emissions densities for the CENRAP region, July 10, 2002............ 1-8

2-1. Livestock and poultry emissions by state and animal type.............................................. 2-2

2-2. Seasonal variabilities in emissions from livestock and poultry by state.......................... 2-3

2-3. Comparison of livestock emission totals......................................................................... 2-4

2-4. Point source emissions for July 10, 2002......................................................................... 2-5

2-5. Animal population density map for confined livestock, 1997......................................... 2-5

2-6. Emissions by fertilizer type for each state of the CENRAP region................................. 2-6

2-7. Seasonal variabilities in emissions from fertilizer application for each state of the 

CENRAP region............................................................................................................... 2-7

2-8. Comparison of emission totals from fertilizer application .............................................. 2-8
viii

LIST OF FIGURES (Concluded)

Figure Page

2-9. Biogenic emissions by land cover type for each state of the CENRAP region ............... 2-9

2-10. Comparison of biogenic emission totals........................................................................ 2-10
ix

LIST OF TABLES

Table Page

2-1. Livestock and poultry emissions by state ........................................................................ 2-1

B-1. Annual ammonia emissions by state and source category...............................................B-3

C-1. Comparison between the STI inventory and CMU model outputs for key sources ........C-3
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ES-1

EXECUTIVE SUMMARY

The Central States Regional Air Planning Association (CENRAP) is researching 

visibility-related issues for its region and is developing a regional haze plan in response to the 

U.S. Environmental Protection Agency’s (EPA) mandate to protect visibility in Class I areas. In 

support of the CENRAP’s need to develop a regional haze plan, Sonoma Technology, Inc. (STI)

developed a 2002 ammonia emission inventory for the region in keeping with the emissions 

estimation techniques presented in Appendix A—Ammonia Emission Estimation Methods for 

the CENRAP Ammonia Emission Inventory (Methods Document).

Consistent with the Methods Document, ammonia emissions were estimated for 

13 source categories using the Carnegie Mellon University (CMU) model and supplemental 

technical work; 80% of technical work was dedicated to improving emissions estimates for two 

source categories—livestock production and fertilizer use. For these two categories, as well as 

biogenic sources, improvements were made to the activity data and/or emission factors used by 

the CMU model. For four other source categories (industrial point sources, landfills, ammonia 

refrigeration, and non-road mobile sources), emissions estimates were prepared independently of 

the CMU model, and for the remaining six source categories (publicly owned treatment works, 

wildfires, domestic animals, wild animals, human respiration, and on-road mobile sources), 

emissions estimates were derived by running the CMU model with no alterations.

In the resulting inventory, the most important source categories are estimated to be 

livestock and poultry, fertilizers, and biogenics. When combined, these three sources account for 

87% of the annual ammonia emissions in the CENRAP region (see Figure ES-1). Seasonally, 

peaks in emission levels occur in spring and fall (especially during the months of April and 

October), times when manure and fertilizer are typically applied to croplands (see Figure ES-2).

Livestock & 

Poultry 53%

Fertilizer 27%

Biogenics 7%

Mobile 

Sources 3%

Point Sources

3%

Misc. 7%

Figure ES-1. CENRAP 2002 ammonia emissions by source category.
ES-2

0

50,000

100,000

150,000

200,000

250,000

300,000

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Month

NH3 (tons/month)

Figure ES-2. Monthly variability in total 2002 emissions for the CENRAP region.

As anticipated, emissions from livestock and poultry made the largest contribution to 

total estimated emissions for the CENRAP region and for each individual state (with the 

exception of Louisiana). This source category was especially significant in the states of Iowa 

and Oklahoma, where emissions from livestock and poultry accounted for over 60% of the total 

inventory (see Table 2-1). Fertilizer application was the second most significant source of 

ammonia emissions in the region, and this source category was especially important in Kansas 

and Nebraska, accounting for about 36% of the total inventory in those states. (Actual emissions 

estimates by source category and state are tabulated in Appendix B.)

The source with the greatest uncertainty in the inventory is biogenic emissions, because 

the emission rates and character of the natural environment as a source or a sink of ammonia are 

not studied as extensively as they are for other source categories. Significant uncertainties also 

exist in the available ammonia emission factors for agricultural activities, as they fail to 

adequately consider some important governing principles (such as climate, manure management, 

and animal diet). In addition, two source categories that may be significant at local geographic 

scales were necessarily omitted from the inventory due to lack of information: ammonia 

injection for NOx controls and biosolids (or sewage sludges).

In order to improve the CENRAP’s emission inventory in the future, we recommend 

research efforts, such as studies of activity data, which will allow the CENRAP to take 

advantage of next-generation emissions models that are currently under development for 

biogenics, livestock, and fertilizers. In addition, we recommend a survey of power plants in the 

CENRAP states to assess the emissions from ammonia-injection control technologies. Lastly, 

recognizing that a viable emissions model for sewage sludges may not be available for a long 

time, we recommend that an emissions inventory be developed for this source category through 

the initiation of emissions measurement programs and systems to gather or track pertinent 

activity data. 
1-1

1. INTRODUCTION

The Central States Regional Air Planning Association (CENRAP) is researching 

visibility-related issues for its region, which includes the states of Texas, Oklahoma, Louisiana, 

Arkansas, Kansas, Missouri, Nebraska, Iowa, and Minnesota, and is developing a regional haze 

plan in response to the U.S. Environmental Protection Agency’s (EPA) mandate to protect 

visibility in Class I areas. To develop an effective regional haze plan, the CENRAP must 

develop a conceptual model of the phenomena that lead to episodes of low visibility in the 

CENRAP region. It is widely recognized that the formation of secondary particulate matter—

which is generated from chemical transformations in the atmosphere of gaseous precursor 

species such as ammonia, nitrogen oxides, sulfur oxides, and volatile organic compounds—

contributes significantly to regional haze issues in the CENRAP region. Therefore, development 

of accurate and comprehensive emission inventories of these precursor species is important.

In support of the CENRAP’s need to develop a regional haze plan, Sonoma Technology, 

Inc. (STI) responded to the CENRAP Work Assignment Number 02-0214-RP-003-001,

“Research and Development of Ammonia Emission Inventories for the Central States Regional 

Air Planning Association.” The project objectives were to identify and evaluate information 

resources that may be immediately applied to mitigate known weaknesses in the Carnegie 

Mellon University (CMU) model—an ammonia emissions modeling tool and database system 

that was developed by CMU and recently evaluated by STI—and to apply the findings to 

improve the CMU ammonia emission inventories for the CENRAP region. As directed by the 

CENRAP Emissions Work Group, STI dedicated at least 80% of its technical effort to improving 

emission estimates for two types of emissions sources—livestock production and fertilizer use—

while the remainder of the work was directed toward improving emission estimates for other 

types of emissions sources (such as wastewater treatment plants, biogenics, or on- and off-road 

mobile sources).

1.1 BACKGROUND AND KEY ISSUES

1.1.1 Secondary Formation of Fine Particulate Matter in the CENRAP Region

Visibility impairment is primarily caused by particulate matter of aerodynamic diameter 

less than 2.5 microns (PM2.5). PM2.5 may be directly emitted from sources such as fugitive dust 

and combustion soot, which are termed sources of “primary PM2.5”. Condensable organic 

aerosols form from air emissions of semi-volatile and heavy organic compounds. In addition, 

PM2.5 forms from photochemical reactions of gaseous precursors, including sulfur oxides (SOx), 

nitrogen oxides (NOx), volatile organic compounds (VOC), and ammonia (NH3). This 

mechanism of PM2.5 formation is termed “secondary formation”.

The chemical composition of ambient PM2.5 provides an understanding of the types of 

emissions sources that contribute to regional haze issues in different regions (see Figure 1-1). 

Ammonium sulfate, ammonium bisulfate, and ammonium nitrate are important secondary 

PM2.5 constituents. In urban and ammonia-depleted areas of the eastern United States, sulfate is

relatively a more significant contributor to PM2.5 than it is in the western United States. 
1-2

Conversely, nitrate is more important in urban and ammonia-rich areas of the western United 

States than it is in eastern areas. In both the eastern and western United States, the carbonaceous 

fraction of PM2.5 is significant in urban areas. However, in more pristine areas, the contribution 

of geologic dust to ambient PM2.5 becomes more important and may contribute up to 50% of 

observed PM2.5 concentrations. Areas with abundant gaseous NH3 experience rapid 

transformation of the atmospheric oxidation products of SOx and NOx emissions into fine 

aerosols of ammonium sulfate and ammonium nitrate.

C arbonaceo us Nitrate Crusta l Sulfate Not Chemica ll y 

Cha racterized b

South Coast Basin

(4 Site Avg - 28 ug/m3)

W. Phoenix

(13.5 ug/m3)

E. Tennessee (3 Cities)

(Avg - 16.7 ug/m3)

Spokane

(11.0 ug/m3)

Rochester

(14.9 ug/m3)

San Joaquin Valley

(4 Site Avg - 37 ug/m3)

Boston

(16.2 ug/m3)

Urban

Figure 1-1. Compositions of annual average concentrations of PM2.5 observations in urban

locations (U.S. Environmental Protection Agency, 1998). 

In order to develop a preliminary1 understanding of the components that contribute to 

ambient levels of PM2.5 in the CENRAP region, STI analyzed a data set of speciated PM2.5 data 

for the region that was compiled by researchers at the Center for Air Pollution Impact and Trend 

Analysis at Washington University (Schichtel et al., 1999). The data were collected during 

various time periods and using various analytical sampling techniques; thus, the results should be 

considered uncertain until a more rigorous evaluation can be completed. Nevertheless, the data

indicated several important characteristics. The preliminary data summaries shown in 

Figures 1-2 and 1-3 indicate that at sites where sulfates and nitrates were measured, these 

secondary compounds together comprised approximately 30% to 60% of PM2.5. In addition, 

sulfate concentrations are 2 to 10 times larger than nitrate concentrations. Furthermore, the 

• 

1

 It is recognized that more-recent monitoring data have become available since this preliminary analysis was done. 

While the data sources cited provide the level of understanding needed for this background discussion, it is 

recommended that future efforts include analysis of the most recent monitoring data.

Washington D.C., 

19.2ug/m3
1-3

highest ratios of sulfates to nitrates and the highest average concentrations of PM2.5 were 

observed in the southeastern portions of the CENRAP region. This is consistent with 

observations presented by Falke (1999) shown in Figure 1-4. However, it is important to note 

that Falke’s analyses also indicated relatively high levels of uncertainty in the western portion of 

the CENRAP region (Figure 1-5). Other potential uncertainties in the monitoring data also can 

affect analyses of PM2.5 data. For example, Malm et al. (2000) reported differences as large as a 

factor of two between IMPROVE and CastNet (CDN) observations at Big Bend National Park, 

Texas, (the only CENRAP Class I site studied) (see Figure 1-6). Examination of estimated SO2

emission rates and population densities across the United States (Figures 1-7 and 1-8) helps 

explain why PM2.5 concentrations are somewhat higher and sulfates are so important in the 

southeast portion of the CENRAP region.

Figure 1-2. Contributions of chemical

components to observed 

PM2.5 concentrations in 

the CENRAP region.

Figure 1-3. Concentrations of particulate

sulfates and nitrates 

observed in the CENRAP 

region.
1-4

Figure 1-4. Estimated seasonal average concentrations of PM2.5 (1994-1996) (Falke, 1999).

Figure 1-5. Uncertainty in estimated seasonal average PM2.5 concentrations (Falke, 1999).
1-5

Figure 1-6. Comparison of IMPROVE and CDN measurements of sulfate (right)

and nitrate (left) at Big Bend National Park, Texas (Malm et al., 2000).

Emission Density

tons/sq.mile

>5

>1

>0.4

>0.2

>0

Emission Density

tons/sq.mile

>5

>1

>0.4

>0.2

>0

Emission Density

tons/sq.mile

>5

>1

>0.4

>0.2

>0

Figure 1-7. Annual SO2 emissions in 1998 (U.S. Environmental Protection Agency, 2000).
1-6

Figure 1-8. Population density in the United States in 2000 (U.S. Census Bureau, 2001).

1.1.2 Prior Statuses of Ammonia Emission Inventories

As a whole, few areas of the CENRAP region historically have experienced significant 

air quality problems and, therefore, monitoring and emissions estimates were relatively scarce. 

The most comprehensive sources of emissions estimates were the EPA’s National Emissions 

Inventory (NEI), which is used as the basis of the EPA’s National Emission Trends (NET) 

document series and analyses, and the CMU ammonia modeling tool and database system. 

Through previous studies, STI identified the following weaknesses and areas needing 

improvement in the NEI and CMU databases.

• Potentially important anthropogenic sources of ammonia include agricultural sources 

(animal husbandry and fertilizer application), mobile sources, natural (or “biogenic”)

sources, ammonia injection for NOx control at power plants, and wastewater treatment 

plants. In many cases, the associated emission factors, activity parameters, seasonal 

profiles, and spatial patterns are highly uncertain and in need of improvement. The 

CMU model provides a framework for the development of county-level ammonia 

inventories, but it required significant improvements in its emission factors and activity 

data in order to represent the most up-to-date and geographically specific information 

possible.

• The NEI is estimated on an annual average basis. As Figure 1-4 illustrates, regional haze 

has a seasonal character and is partly driven by photochemical processes. Adjustments 

were necessary to develop seasonal, diurnal, and, possibly, day-of-week emission 

estimates.
1-7

• For mobile sources, improved activity inputs would be helpful, such as region-specific or 

state-specific fleet characteristics and improved vehicle miles traveled (VMT) estimates 

for rural areas. (Note that this improvement will be completed through a separate work 

assignment with the CENRAP.)

• For smaller point sources, STI found that the NET inventory can be highly inaccurate. 

For example, in the region surrounding Memphis, Tennessee, STI found that the 1996 

NET inventory underestimated emissions of VOCs and NOx by factors of 10 or more.

• To support modeling sensitivity runs, measures of uncertainty for all emission estimates

are highly valuable for policy decisions and prioritization of future research efforts. 

Because the CENRAP ammonia inventory was compiled entirely from pre-existing 

emission factors and data, which lacked associated quantitative uncertainties, we are 

limited to providing only qualitative assessments of the emissions estimates. These are 

discussed in the Executive Summary and in Section 3, Recommendations for Further 

Research.

1.1.3 Project Priorities

To meet the CENRAP’s primary goals, STI balanced the immediate need for a practical 

and cost-effective ammonia emission inventory with the need to incorporate the latest research 

and best available information. Thus, STI dedicated the majority of its resources to areas that the 

CENRAP has indicated are the highest priority: emissions from livestock management and 

fertilizer application. In addition, STI provided technology transfer services and documentation

so that the work products of this project may be easily modified or applied by third parties, such 

as the CENRAP’s Modeling Work Group or the CENRAP States’ emissions and air quality 

specialists.

1.2 CURRENT STATUS OF THE CENRAP EMISSION INVENTORY

The resultant emission inventory produced through this work assignment is illustrated in 

Figures 1-9, and 1-10 and tabulated in Appendix B. In all cases, we have applied generally 

accepted emission factors and the most complete and up-to-date activity data sets that could be 

identified and acquired. However, we also understand that available ammonia emission factors 

are uncertain, that they fail to adequately consider some important governing principles (such as 

climate, manure management, and animal diet), and that they continue to be the subject of 

research. These considerations are especially important for those areas of the inventory that we 

qualitatively consider to contribute the greatest degrees of uncertainty to the total estimated 

emissions: biogenic emissions (often called “natural soils”), livestock emissions, and fertilizer 

emissions. To help mitigate the effects of these uncertainties in the future, we have provided the 

CENRAP with an inventory and system of data files that can be updated with revised emission 

factors and activity data as new information become available (see Appendix D).
1-8

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

AR IA KS LA MN MO NE OK TX

State

NH3 (tons/year)

Misc. Sources

Mobile Sources

Point Sources

Biogenics

Fertilizer

Livestock

Figure 1-9. Total annual ammonia emissions by source category for each state of the CENRAP 

region.

Figure 1-10. Geographic map of emissions densities for the CENRAP region, July 10, 2002.
2-1

2. SUMMARY AND ASSESSMENT OF THE INVENTORY

STI calculated emissions as detailed in Appendix A—Ammonia Emission Estimation 

Methods for the CENRAP Ammonia Emission Inventory. (Results are tabulated in 

Appendix B—Tabulation of Ammonia Emissions Estimates for the CENRAP Region.) In 

addition, STI carried out quality assurance procedures as provided in the Quality Assurance Plan 

and as detailed in this section. In summary, the most important source categories are estimated 

to be livestock and poultry, fertilizers, and biogenics, all three of which are also considered to 

contribute the greatest sources of uncertainty in the overall inventory. Total emissions vary 

seasonally by a factor of 3 to 8, with peaks occurring in the spring or fall. Total emissions vary

geographically across the CENRAP region from <0.0.003 to >2 metric tons per day per square 

kilometer.

2.1 EMISSIONS FROM LIVESTOCK AND POULTRY

2.1.1 Summary of Emissions from Livestock and Poultry

Emissions estimates were generated for several types of livestock and poultry, including 

beef cattle, milk cows, hogs/pigs, sheep, goats, horses, broilers, layers, pullets, turkeys, geese, 

and ducks. The population of each of these animal types housed in concentrated animal feeding 

operations (CAFOs) was determined so that emissions from these facilities could be treated as 

point sources, with emissions from the remaining “free range” animals being treated as area 

sources. It was determined that emissions from livestock and poultry contribute 53% to total 

estimated emissions for the CENRAP region, ranging from 23% to 63% of total emissions from 

state to state (see Table 2-1). Emissions associated with concentrated animal feeding operations 

(CAFOs) were especially high for Iowa and Kansas, exceeding emissions associated with “free 

range” livestock for those two states. Figure 2-1 shows the relative importance of each animal 

type in each state. The most important animal types are beef cattle (especially in the states of 

Texas, Kansas, and Nebraska), hogs and pigs (especially in the states of Iowa and Minnesota), 

and poultry (especially in the state of Arkansas).

The seasonal variability of livestock emissions follows a bimodal pattern, with peaks in 

spring and fall when manure is typically applied on croplands as fertilizer. Figure 2-2 illustrates 

the seasonal variability in livestock emissions for each state.
2-2

Table 2-1. Livestock and poultry emissions by state.

1

Includes emissions from all animal types housed at CAFOs in each state.

2

Includes emissions from all non-CAFO animals in each state.

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

AR IA KS LA MN MO NE OK TX

State

NH3 (tons/year)

Other

Turkeys

Poultry

Hogs & Pigs

Beef Cattle

Milk Cows

Figure 2-1. Livestock and poultry emissions by state and animal type.

NH3 Emissions (tons/year)

State CAFOs1Free range2Total

% of 

State

Inventory

Arkansas 4,096.0 81,978.6 86,074.6 56.5%

Iowa 88,722.5 69,713.6 158,436.1 63.3%

Kansas 57,611.1 33,455.9 91,067.0 52.1%

Louisiana 82.4 15,837.5 15,919.9 23.1%

Minnesota 25,518.0 72,562.1 98,080.1 49.8%

Missouri 24,685.5 68,925.6 93,611.1 56.6%

Nebraska 30,240.0 66,743.0 96,983.0 53.9%

Oklahoma 19,864.6 60,016.8 79,881.4 60.1%

Texas 45,650.0 143,115.0 188,765.0 49.2%
2-3

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Month

Percent Contribution

AR

IA

KS

LA

MN

MO

NE

OK

TX

Figure 2-2. Seasonal variabilities in emissions from livestock and poultry by state.

2.1.2 Assessment of Emissions from Livestock and Poultry

This category (livestock and poultry) was the largest source of ammonia emissions in the 

inventory, which is to be expected for large Midwestern states recognized for their livestock 

production. (Louisiana is an exception.) To check the reasonableness of the emissions presented 

above, state totals were compared with results obtained by running the CMU model with no 

alteration in livestock population estimates (see Figure 2-3 and Appendix C). Each state’s total 

was within 5% of the CMU result with the exception of Kansas, where our emissions estimate 

was 14% higher. This difference is due to the fact that we estimate a greater population of beef 

cattle in Kansas based on National Agricultural Statistics Service (NASS) and CAFO data than is 

reported in the 1997 Census of Agriculture.
2-4

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

AR IA KS LA MN MO NE OK TX

State

NH3 (tons/year)

STI Inventory CMU Model

Figure 2-3. Comparison of livestock emission totals.

A second quality assurance step taken was the production of an emissions density plot 

showing only the point source portion of the inventory, which largely consists of CAFOs

(14,000 point sources were included in the inventory, of which 80% were CAFOs). Figure 2-4

shows a band of sources across northern Texas, western Kansas, central Nebraska, all of Iowa, 

and southern Minnesota, a distribution that can also be seen in an animal population density map 

for confined livestock produced by the U.S. Department of Agriculture for 1997 (see 

Figure 2-5).
2-5

Figure 2-4. Point source emissions for July 10, 2002.

Figure 2-5. Animal population density map for confined livestock, 1997 (Kellogg et al., 2000).
2-6

2.2 EMISSIONS FROM FERTILIZER APPLICATION

2.2.1 Summary of Emissions from Fertilizer Application

Emissions from fertilizer application contribute 27% to total estimated emissions for the 

CENRAP region, ranging from 20% to 37% of total emissions from state to state. The most 

important fertilizer types are urea (especially in the states of Minnesota, Texas, and Arkansas), 

nitrogen solutions (especially in the states of Texas, Nebraska, and Iowa), and anhydrous 

ammonia (especially in the states of Iowa, Nebraska, and Kansas). Figure 2-6 illustrates the 

relative importance of each fertilizer type in each state. 

Similar to emissions from livestock and poultry, emissions from fertilizer application 

follow a bimodal pattern of seasonal variability, with peaks in the spring and fall (see 

Figure 2-7). Some states exhibit particularly pronounced emission spikes in certain months due 

to the types of crops that dominate in the state. Iowa, for example, is dominated by corn growers 

and does not produce as wide a variety of crops as other CENRAP states. Thus, in Iowa 40% of 

all emissions from fertilizer application occur in the month of April. Oklahoma and Kansas, on 

the other hand, produce a great deal of winter wheat and, therefore, have unusually high emission 

rates in August and September. 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

AR IA KS LA MN MO NE OK TX

State

NH3 (tons/year)

Ammonium nitrate

Miscellaneous

Ammonium sulfate

Liquid ammonium

Mix

Monoammonium phos.

Diammonium phos.

Anhydrous ammonia

Nitrogen solutions

Urea

Figure 2-6. Emissions by fertilizer type for each state of the CENRAP region.
2-7

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Month

Percent Contribution

AR

IA

KS

LA

MN

MO

NE

OK

TX

Figure 2-7. Seasonal variabilities in emissions from fertilizer application for each state of the

CENRAP region.

2.2.2 Assessment of Emissions from Fertilizer Application

As expected, this category was the second largest source of ammonia emissions in the 

inventory. To check the reasonableness of the emissions presented above, state totals were again 

compared with results obtained by running the CMU model with no alteration in fertilizer 

activity data or emission factors (see Figure 2-8 and Appendix C). The emissions totals for five 

states were within 5% of the CMU result, while the emissions for three states (Nebraska, 

Missouri and Louisiana) were 10% - 27% higher than the totals produced by the CMU model. 

Only one state, Kansas, proved to have significantly lower emissions (-8%) than those predicted 

by the CMU model.

These differences are largely due to changes in activity data and emission factors. First, 

we replaced the 1995 Association of American Plant Food Control Officials (AAPFCO) fertilizer 

usage data used in the CMU model with 2002 AAPFCO data, resulting in significant changes in 

activity data for some states. For example, total fertilizer usage in Kansas dropped 25% from 

1995 to 2002, resulting in a significant emission reduction. On the other hand, fertilizer usage in 

Nebraska increased by 15% over the same time period, resulting in a 27% increase in emissions.

Additionally, we updated the emission factors used by the model. These emission factors 

were developed by the European Environment Agency (2001) and are dependent on soil type and 

climate. The European factors can be grouped according to the following classification system:
2-8

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

AR IA KS LA MN MO NE OK TX

State

NH3 (tons/year)

STI Inventory CMU Model

Figure 2-8. Comparison of emission totals from fertilizer application.

• Group I – Warm, temperate areas with a large proportion of calcareous soils.

• Group II – Temperate and warm-temperate areas with some calcareous soils (or managed 

with soil pH>7), but with large areas of acidic soils.

• Group III – Temperate and cool-temperate areas with largely acidic soils.

While the CMU model assigns whole states to one of the groupings listed above, we 

made these assignments at the county level based on the average soil pH in a given county (as 

reported by the National Resources Conservation Service (1994). Thus, for example, while the 

CMU model assigns all Nebraska counties to Group III, we assigned the majority of the state’s 

counties to Group II, a classification with higher emission factors than Group III.

2.3 BIOGENIC EMISSIONS

2.3.1 Summary of Biogenic Emissions

Biogenic emissions (often called “natural soil” emissions) are especially uncertain 

because emission rates and the character of the natural environment as a source or a sink of 

ammonia are not studied as extensively as they are for other source categories. We estimated 

that biogenic emissions contribute 7% to total estimated emissions for the CENRAP region, 

ranging from 2% to 15% of total emissions from state to state. The most important land cover
2-9

types are croplands and pasture (especially in the states of Texas, Iowa, and Kansas), deciduous 

forests (especially in the states of Missouri, Oklahoma, and Texas), and mixed forests (especially 

in the states of Texas, Minnesota, and Arkansas). Figure 2-9 illustrates the relative importance 

of each land cover type in each state. No information about seasonal variabilities was available; 

therefore, no monthly temporal profiles have been assigned.

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

AR IA KS LA MN MO NE OK TX

State

NH3 (tons/year)

Forested Wetlands

Nonforested Wetlands

Mixed Rangeland

Shrub/Brush Rangeland

Herbaceous Rangeland

Mixed Forest

Evergreen Forest

Deciduous Forest

Croplands and Pasture

Figure 2-9. Biogenic emissions by land cover type for each state of the CENRAP region.

2.3.2 Assessment of Biogenic Emissions

Emissions estimates for this source category are highly uncertain. Initial estimates

calculated using the CMU model’s activity data and emission factors resulted in biogenic 

emission totals that accounted for half the total ammonia inventory in the CENRAP region. 

After a literature search, we chose to apply emission factors that were selected for use by 

Battye et al. (2003), which were based on factors reviewed or published by Schlesinger and 

Hartley (1992), Buowman et al. (1997), Kinnee et al (1997), and Van Der Hoek (1998). Use of 

these emission factors reduced biogenic emissions by 93% overall (see Figure 2-10), with the 

result that biogenic emissions accounted for 7% of the total CENRAP ammonia inventory. 

Battye et al. (2003) calculated similar percent contributions—about 6.6% and 6.3%—for 

emission inventories in North Carolina and California’s San Joaquin Valley. When the 

CENRAP inventory is used for modeling sensitivity runs, it will be important to consider a wide 

range of uncertainty in the estimates of biogenic emissions.
2-10

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

AR IA KS LA MN MO NE OK TX

State

NH3 (tons/year)

STI Inventory CMU Model

Figure 2-10. Comparison of biogenic emission totals.

2.4 EMISSIONS FROM OTHER SOURCE CATEGORIES

2.4.1 Summary of Emissions from Other Source Categories

All other source categories contributed 13% to total estimated emissions for the 

CENRAP region, ranging from 5% to 35% of total emissions from state to state. These included 

publicly owned treatment works (POTWs), wildfires, on-road mobile sources, non-road mobile 

sources, industrial point sources, landfills, ammonia refrigeration, and miscellaneous sources 

(domestic animals, human respiration, and wild animals). The most important of these source

types are wild animals (especially in the states of Texas, Louisiana, and Arkansas), domestic 

animals (especially in the states of Texas, Missouri, and Oklahoma), and on-road mobile sources

(especially in the states of Texas, Minnesota, and Missouri). 

2.4.2 Assessment of Emissions from Other Source Categories

Because these source categories were relatively unimportant in comparison with livestock 

and fertilizer application, simple methods were employed to estimate the ammonia emissions 

associated with them. Emissions from six of these source categories (POTWs, wildfires, on-road 

mobile sources, domestic animals, wild animals, and human respiration) were taken directly 

from the CMU model with no updated activity data or emission factors.

Three other source categories (non-road mobile sources, landfills, and ammonia 

refrigeration) were omitted from the CMU model, so we independently prepared emissions 

estimates for these categories. Emissions from non-road mobile sources were taken directly 
2-11

from the 1999 NEI, emissions from landfills were calculated from facility-specific, waste-inplace estimates, and emissions from ammonia refrigeration were estimated on an employmentbased emission factor of 187 kg NH3/employee reported by Battye et al. (1994). As the latter 

estimate is the most uncertain, we verified the scale of the emission factor by determining that 

annual production of ammonia for refrigeration uses in the United States is between 270,000 Mg 

and 350,000 Mg (Battye et al., 1994; International Institute of Ammonia Refrigeration, 2003). 

For the food-production industries that commonly use ammonia refrigeration, total United States

employment equals approximately 1 million employees (U.S. Census Bureau, 2003). Thus, 

these figures yield a factor of 270 to 350 kg NH3/employee-yr, which is on the same order of 

magnitude as the factor estimated by Battye et al. (1994), although it is 44% to 87% larger.

Finally, for industrial point sources, county-level emissions data from the EPA’s 1995 

Toxics Release Inventory (TRI), which was loaded into the CMU model, were replaced with data 

from the 2001 TRI and 1999 NEI point source inventory. Emissions reported in the more recent 

TRI data were selected for facilities with emissions records in both data sets, and emissions for 

several “supersized” ammonia sources (those with emissions greater than 5 tons/summer day) in 

the 1999 NEI were altered or eliminated based on guidance received from individual states 

(Sabo, 2003).

2.5 SOURCE CATEGORIES OMITTED FROM THE INVENTORY

We considered, but omitted, several source categories from the final inventory, including 

biomass burning, composting, geothermal emissions, ammonia injection for NOx control, and 

biosolids (sewage sludges). These categories were excluded for the following reasons:

• Biomass Burning. The CMU model estimates ammonia emissions from wildfires, but not 

planned burning. However, because a planned burning emissions inventory, which will 

include ammonia, is being developed by STI under a different work assignment, this 

source category will be addressed through that separate project.

• Composting. Ammonia is released during the degradation of organic waste at 

composting operations. A 2000 inventory of ammonia emissions prepared by AVES for 

California’s South Coast Air Basin (Botsford et al., 1999) utilized an emission factor of 

2.755 pounds of ammonia per ton of material processed to estimate emissions from this 

source. In that inventory, composting operations accounted for 5.25% of the total 

inventory. However, this estimate was based on an annual throughput of 2,445,600 tons 

of waste in the South Coast Air Basin alone. By comparison, the only CENRAP states 

for which composting activity data were readily available—Iowa and Minnesota—report 

statewide annual throughputs of only 628,000 tons and 462,000 tons, respectively. Based 

on this indication that composting efforts are not likely to be widespread in the CENRAP 

region and a lack of easily accessible data for the seven other states, we excluded this 

source category.

• Geothermal Emissions. Geothermal power-generation facilities release significant 

ammonia emissions from cooling towers. However, the Renewable Energy Annual 1996

(Energy Information Administration, 1997) indicates that “known geothermal resource 
2-12

areas in the United States with resource conditions sufficient to generate electricity are 

rare, occurring domestically only in the Western United States and Hawaii.”

• Ammonia Injection for NOx Control. One technology for controlling NOx emissions from 

stack gases is the injection of ammonia into the exhaust of boilers or gas turbines—an 

approach that is primarily used at power generation facilities. Excess ammonia that does 

not react with NOx is emitted to the atmosphere and is referred to as “ammonia slip.”

In an attempt to determine whether any facilities in the CENRAP states use this 

technology, we searched control codes contained in the 1999 NEI database. No facilities 

reported the use of ammonia injection in the NEI. A telephone survey of power 

generation facilities would be necessary to confirm the absence of these controls. 

However, such a survey was beyond the scope of the current project; therefore, this 

source category was omitted.

• Biosolids. Our recent review of the CMU Ammonia Model identified biosolids (or 

sewage sludges) as a source of potentially significant emissions on local scales, but also a 

source for which emission factors and activity data were insufficient to generate an 

emission inventory (Chinkin et al., 2003). The effort required to develop the necessary 

information through measurement programs and facility surveys is beyond the scope of 

the current project; therefore, this source category was omitted.
3-1

3. RECOMMENDATIONS FOR FURTHER RESEARCH

In this report, we have identified the following significant sources of uncertainty (roughly 

in order of importance): emission factors and temporal profiles for biogenic emissions, livestock 

emissions, and fertilizer emissions. Research is currently progressing into improved emissions 

models in each of these three areas. In general, we recommend keeping current with the latest 

published findings as they are released; from these, identifying the governing parameters that 

influence emission rates; and setting goals and planning research efforts to gather and track the 

activity data that will be needed as inputs for the next generations of emissions models. In 

addition, we have identified two source categories that lack sufficient information to generate 

emissions estimates for the CENRAP region: ammonia injection for NOx control and biosolids.

3.1 RECOMMENDATIONS FOR BIOGENIC SOURCES

The largest degree of uncertainty in total emissions is associated with the biogenic source 

category. Depending on the choice of currently available emission factors, biogenics may be 

estimated to contribute more than 50% of total annual emissions in the CENRAP region (10 to 

15 times more than we estimated), or plant-soil systems may be considered to behave as a net 

sink of ambient ammonia. In addition, ammonia fluxes for plant-soil systems have been shown 

to change direction—from net source to net sink—at different times of the year and at different 

times of the day (Sutton et al., 2002). However, we were unable to identify information that 

would readily translate into seasonal or diurnal temporal profiles.

Additionally, a lack of clarity currently exists regarding assignment of ammonia 

emissions to the biogenics category versus the livestock or fertilizer categories. Application of 

fertilizers or manures to grasslands and cutting of grass have been shown to greatly increase the 

release of ammonia from living plants (Sutton et al., 2002). However, an argument can be made 

that because the excess emissions are driven by anthropogenic processes, they are more 

appropriately assigned to an anthropogenic source category (such as fertilizer use, livestock, or 

land management). 

At this time, the best prospect for improving biogenic emissions estimates for the 

CENRAP region is the application of research findings from recent and ongoing projects that 

focus on measurements and modeling of ammonia fluxes from European grasslands: the 

GRassland-AMmonia INteractions Across Europe (GRAMINAE) and the Emissions and 

Deposition programs, which have been conducted in partnership with the Coordinated Ammonia 

Research Activities (CARA) program of the Centre for Ecology and Hydrology (CEH), 

Edinburgh Research Station, United Kingdom. (Information about CARA, GRAMINAE, and 

the Emissions and Deposition research programs may be found at 

http://www.nbu.ac.uk/cara/nh3home.htm.) Recent publications have presented models of 

ammonia exchange with grassland ecosystems (Flechard et al., 1999; Nemitz et al., 2001; 

Spindler et al., 2001). In order to apply these models and generate improved estimates of 

biogenic emissions for the CENRAP region, research should be undertaken to validate their 

applicability to the CENRAP region, to acquire activity data that the models use as inputs (such 

as land use information, climatological data, and levels of agricultural nitrogen inputs), and to 
3-2

modify the models for application to the CENRAP region. Such a research effort would be 

significant in scope; however, the payoff is likely to be worth the expense.

3.2 RECOMMENDATIONS FOR LIVESTOCK 

3.2.1 Address Next-Generation Emissions Models

The degree of uncertainty in livestock emissions is also large. Battye et al. (2003), who 

applied methods similar to ours, recently estimated uncertainties of roughly ±35% for annual 

emission inventories that were prepared for regions of North Carolina and California. Emission 

factors and models for livestock and poultry are an area of active research. Researchers are 

developing emission factors and models that take into account weather and soil conditions, 

manure management practices, animal diets, and animal housing configurations. Recent peerreviewed publications include Huijsmans et al. (2003), Battye et al. (2003), Mathur and Dennis

(2003), Gilliland et al. (2003), and Riedo et al. (2002). Journals in which research is often 

published include Environmental Pollution; Atmospheric Environment; Water, Air, and Soil 

Pollution; Journal of Agricultural Engineering Research; Nutrient Cycling in Agroecosystems;

Agriculture, Ecosystems and Environment; and Plant and Soil.

In order to take advantage of the research results that have been recently published and 

will be published over the next few years, productive research efforts could be directed toward

the study and acquisition of the activity parameters that are likely to be needed for the emission 

models under development, such as thorough surveys of manure management practices, manure 

disposal methods, land application methods, and typical weather conditions experienced at 

various stages of the manure handling cycle.

3.2.2 Refine Animal Population Estimates

Though the 1997 Census of Agriculture livestock population data used by the 

CMU model was updated with more current NASS data and state-provided CAFO population 

data, some uncertainties still exist, particularly in reference to the CAFO data. During this 

process of updating livestock populations, it was noticed that for several counties the CAFOreported animal populations greatly exceeded the county totals found in the NASS and/or Census 

data. For example, the 1997 Census of Agriculture reports 8.3 million broilers (a type of 

poultry) in Barry County, Missouri, whereas the CAFO data received from the Missouri 

Department of Natural Resources shows 12 million broilers in the county.

Investigation revealed that for CAFO permitting purposes, facilities report maximum 

capacities rather than actual animal populations. While it can be assumed that most CAFOs 

attempt to operate near their capacity, there are situations where this does not appear to be the 

case, or where facilities are reporting an annual throughput rather than a one-time population 

figure (we discovered the latter to be the case with a large livestock auction yard in Canadian 

County, Oklahoma, for example). Because of such anomalies, CAFO data for some counties 

were scaled down to ensure that total animal population counts remained comparable with data 

reported in NASS estimates and the 1997 Agricultural Census. For future efforts, uncertainties 
3-3

could be reduced by conducting a survey of large CAFOs in selected counties to determine how 

closely they operate to their reported maximum capacities.

3.3 RECOMMENDATIONS FOR FERTILIZERS

Uncertainties in the emissions estimates for fertilizers carry similar significance for the 

CENRAP inventory as the uncertainties for livestock emissions—roughly 20% or so of the total 

inventory. As is the case for livestock, emission factors and models for fertilizers are an area of 

active research. Researchers are developing emissions models that take into account weather and 

soil conditions, land application methods, and fertilizer quality. Research is frequently published 

in the same journals listed for livestock: Environmental Pollution; Atmospheric Environment;

Water, Air, and Soil Pollution; Journal of Agricultural Engineering Research; Nutrient Cycling 

in Agroecosystems; Agriculture, Ecosystems and Environment; and Plant and Soil. In order to 

apply the next-generation models that are likely to become available over the next several years, 

it would be useful to develop research strategies to acquire the activity data that will be needed to 

support emissions modeling, such as thorough surveys of application practices, typical weather 

conditions, and fertilizer quality.

3.4 RECOMMENDATIONS OTHER SOURCE CATEGORIES

Information was insufficient to develop emissions estimates for ammonia injection for 

NOx control and biosolids, sources which may be significant on local geographic scales. We 

recommend a survey of power plants in the CENRAP region in order to identify facilities that 

use the ammonia-injection control technology and to assess the potential importance of this 

source category for the CENRAP inventory.

Development of an inventory for biosolids is more complicated. Suitable emission 

factors and models are currently unavailable for use, and we have not identified any ongoing 

research projects related to this area. Should an emissions model be developed at a future date, it 

will likely be necessary to gather activity data that are not currently tracked: facility-specific 

estimates of sludge quantities produced, sludge management and disposal practices, ammoniacal 

contents of produced sludges, and weather conditions.
4-1

4. REFERENCES

Battye R., Battye W., Overcash C., and Fudge S. (1994) Development and selection of ammonia 

emissions factors. Final report prepared for U.S. Environmental Protection Agency, 

Office of Research and Development, Washington, DC by Atmospheric Research and 

Exposure Assessment Laboratory, U.S. Environmental Protection Agency, Research 

Triangle Park, NC, EPA Contract No. 68-D3-0034, Work Assignment 0-3, August.

Battye W., Viney P.A., and Roelle P.A. (2003) Evaluation and improvement of ammonia 

emissions inventories. Atmos. Environ. 37, pp. 3873-3883. 

Botsford C.W., Chitjian M., Koizumi J., Wang Y., Gardner L., and Winegar E. (1999) 1997 

gridded ammonia emission inventory update for the South Coast Air Basin. Draft report 

prepared for the South Coast Air Quality Management District, Diamond Bar, CA by 

AVES, Arcadia, CA, Contract #99025, October.

Buowman A.F., Lee D.S., Asman W.A.H., Dentener F.J., Van der Hoek K.W., and Olivier J.G.F. 

(1997) A global high-resolution emission inventory for ammonia. Global Biogeochemical 

Cycles 11 (4), pp. 561-587. 

Chinkin L.R., Ryan P.A., and Coe D.L. (2003) Recommended improvements to the CMU 

Ammonia Emission Inventory Model for use by LADCO. Revised final report prepared 

for Lake Michigan Air Directors Consortium (LADCO), Des Plaines, IL, by Sonoma 

Technology, Inc., Petaluma, CA, STI-902350-2249-FR2, March.

Coe D.L. (2003) Research and development of ammonia emission inventories for the Central 

States Regional Air Planning Association. Quality Assurance Plan prepared for the 

Central States Air Resources Agencies and the Central States Regional Air Planning 

Association, Oklahoma City, OK, by Sonoma Technology, Inc., Petaluma, CA, STI902504-2331-QAP2, April.

Energy Information Administration (1997) Renewable Energy Annual 1996. DOE/EIA0603(96), April. Available on the Internet at 

http://www.eia.doe.gov/cneaf/solar.renewables/renewable.energy.annual/chap11.html.

European Environment Agency (2001) Joint EMEP/CORINAIR Atmospheric Emission Inventory 

Guidebook, Third Edition, European Environment Agency, Copenhagen Available on the 

Internet at http://reports.eea.eu.int/technical_report_2001_3/en.

Falke S. (1999) PM2.5 topic summaries. Available on the Internet at 

<http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/UrbanSpatialPattern/sld001.

htm> (last accessed 03/11/02) 

<http://capita.wustl.edu/PMFine/Workbook/PMTopics_PPT/NationalSpatialPattern/sld00

1.htm> (last accessed 03/11/02).
4-2

Flechard C.R., Fowler D., Sutton M.A., and Cape J.N. (1999) A dynamic chemical model of bidirectional ammonia exchange between semi-natural vegetation and the atmosphere. 

Quart. J. Roy. Meteorol. Soc. 125 (559), pp. 2611-2641. 

Gilliland A.B., Dennis R.L., Roselle S.J., and Pierce T.E. (2003) Seasonal NH3 emission 

estimates for the eastern United States based on ammonium wet concentrations and an 

inverse modeling method. J. Geophys. Res. 108 (D15), p 4477. 

Huijsmans J.F.M., Hol J.M.G., and Vermeulen G.D. (2003) Effect of application method, 

manure characteristics, weather and field conditions on ammonia volatilization from 

manure applied to arable land. Atmos. Environ. 37 (6), pp. 3669-3680. 

International Institute of Ammonia Refrigeration (2003) Aboutammoniarefridgeration.com. 

Available on the Internet at 

http://www.aboutammoniarefrigeration.com/aaranswers_history.cfm; last accessed 

August 4, 2003.

Kellogg R., Lander C., Moffitt D., and Gollehon N. (2000) Manure nutrients relative to the 

capacity of cropland and pastureland to assimilate nutrients: spatial and temporal trends 

for the United States. NPS00-0579, December. Available on the Internet at 

www.nrcs.usda.gov/technical/land/pubs/manntr.pdf.

Kinnee E., Geron C., and Pierce T. (1997) US land use inventory for estimating biogenic ozone 

precursor emissions. Ecological Applications 7, pp. 46-58. 

Malm W.C., Pitchford M.L., Scruggs M., Sisler J.F., Ames R., Copeland S., Gebhard K., and 

Day D.E. (2000) Spatial and seasonal patterns and temporal variability of haze and its 

constituents in the United States: Report III. May. Available on the Internet at 

<http://vista.circa.colostate.edu/improve/publications/reports/2000/2000.htm>; last 

accessed March 13, 2002.

Mathur R. and Dennis R.L. (2003) Seasonal and annual modeling of reduced nitrogen 

compounds over the eastern United States: Emissions, ambient levels, and deposition 

amounts. J. Geophys. Res. 108 (D15), p 4481. 

National Resources Conservation Service (1994) State Soil Geographic (STATSGO) database. 

Available on the Internet at Available on the Internet at 

http://www.ftw.nrcs.usda.gov/stat_data.html.

Nemitz E., Milford C., and Sutton M.A. (2001) A two-layer canopy compensation point model 

for describing bi-directional biosphere-atmosphere exchange of ammonia. Quart. J. Roy. 

Meteorol. Soc. 127 (573), pp. 815-833. 

Pacific Environmental Services (2001) Assessment of emission inventory needs for regional 

haze plans. Prepared for the Ozone Transport Commission and the Southeast States Air 

Resources Managers by Pacific Environmental Services under contract to the MidAtlantic Regional Air Management Association, March.
4-3

Riedo M., Milford C., Schmid M., and Sutton M.A. (2002) Coupling soil-plant-atmosphere 

exchange of ammonia with ecosystem functioning in grasslands. Ecological Modelling

158 (1-2), pp. 83-110. 

Sabo E. (2003) Supersized ammonia sources in the 1999 NEI. E-mail, July 11.

Schichtel B., Falke S.R., and Husar R.B. (1999) North American integrated fine particle data set. 

Air & Waste Management Association's 92nd Annual Meeting and Exhibition, St. Louis, 

MO, June 20-24. 

Schlesinger W.H. and Hartley A.E. (1992) A global budget for atmospheric NH3. 

Biogeochemistry 15, pp. 191-211. 

Spindler G., Teichmann U., and Sutton M. (2001) Ammonia dry deposition over grasslandmicrometeorological flux-gradient measurements and bidirectional flux calculations 

using an inferential model. Quart. J. Roy. Meteorol. Soc. 127 (573), pp. 795-814. 

Sutton M.A., Milford C., Nemitz E., Theobald M.R., Hargreaves K.J., Fowler D., Schjoerring 

J.K., Mattsson M.E., Husted S., Erisman J.W., Hensen A., Mosquera J., Otjes R., Jonejon 

P., Cellier P., Loubet B., David M., Neftel A., Blatter A., Herrmann B., Jones S.K., 

Horvath L., Weidinger T., Raso J., Meszaros R., Fuhrer E., Mantzanas K., Koukoura Z., 

Papanastasis V., Gallagher M., Dorsey J.R., Flynn M., and Riedo M. (2002) Biosphereatmosphere interactions of ammonia with European Grasslands. Final synthesis report 

prepared for Grassland Ammonia Interactions Across Europe (GRAMINAE), Contracts: 

ENV4-CT98-0722 and IN20-CT98-0118, February.

U.S. Census Bureau (2001) Census 2000 redistricting data (Public Law 94-171): Summary File.

U.S. Census Bureau (2003) CenStats databases - county business patterns data. Database 

maintained by the U.S. Census Bureau, Washington, D.C. Available on the Internet at 

http://censtats.census.gov/. last accessed August 4, 2003.

U.S. Environmental Protection Agency (1998) National air quality and emissions trends report, 

1997. Report prepared by the U.S. Environmental Protection Agency, Office of Air 

Quality Planning and Standards, Emissions Monitoring and Analysis Division, Air 

Quality Trends Analysis Group, Research Triangle Park, NC, 454/R-98-016, December.

U.S. Environmental Protection Agency (2000) National air pollutant emission trends: 1900-

1998. Web page (EPA 454/R-00-002). Available on the Internet at 

<http://www.epa.gov/ttn/chief/trends/trends98>, March (last accessed 03/11/02).

Van der Hoek K.W. (1998) Estimating ammonia emission factors in Europe: summary of the 

work of the UNECE ammonia expert panel. Atmos. Environ. 32, pp. 315-316. 
A-1

APPENDIX A

AMMONIA EMISSION ESTIMATION METHODS FOR THE CENRAP 

AMMONIA EMISSION INVENTORY
1360 Redwood Way, Suite C

Petaluma, CA 94954-1169

707/665-9900

FAX 707/665-9800

www.sonomatech.com

AMMONIA EMISSION ESTIMATION 

METHODS FOR THE CENRAP

AMMONIA EMISSION INVENTORY

Methods Document

STI-902502-2386-MD2

By:

Stephen B. Reid

Dana L. Coe

Sonoma Technology, Inc.

1360 Redwood Way, Suite C

Petaluma, CA 94954-1169

Prepared for:

The Central States Air Resource Agencies and

The Central Regional Air Planning Association

10005 South Pennsylvania Avenue, Suite C

Oklahoma City, OK 73159

October 30, 2003
QUALITY ASSURANCE STATEMENT

This report was reviewed and approved by the project Quality Assurance (QA) Officer or his 

delegated representative, as provided in the project QA plan.

Lyle R. Chinkin

Project QA Officer
iii

TABLE OF CONTENTS

Section Page

LIST OF FIGURES .........................................................................................................................v

LIST OF TABLES...........................................................................................................................v

1. INTRODUCTION.............................................................................................................. 1-1

1.1 Summary of Recommended Methods ...................................................................... 1-1

1.2 Important Premises and Assumptions ...................................................................... 1-6

2. LIVESTOCK PRODUCTION........................................................................................... 2-1

2.1 Overview................................................................................................................... 2-1

2.2 Livestock and Poultry Emission Factors .................................................................. 2-1

2.3 Livestock and Poultry Activity Data ........................................................................ 2-2

2.4 Livestock and Poultry Temporal Allocation............................................................. 2-3

2.4.1 Seasonal Allocation...................................................................................... 2-3

2.4.2 Diurnal Allocation........................................................................................ 2-5

2.5 Livestock and Poultry Spatial Allocation................................................................. 2-5

3. FERTILIZER APPLICATION .......................................................................................... 3-1

3.1 Overview................................................................................................................... 3-1

3.2 Fertilizer Emission Factors....................................................................................... 3-1

3.3 Fertilizer Activity Data ............................................................................................. 3-2

3.4 Fertilizer Temporal Allocation ................................................................................. 3-2

3.4.1 Seasonal Allocation...................................................................................... 3-2

3.4.2 Diurnal Allocation........................................................................................ 3-3

3.5 Fertilizer Spatial Allocation...................................................................................... 3-3

4. OTHER SOURCE CATEGORIES.................................................................................... 4-1

4.1 Overview................................................................................................................... 4-1

4.2 Landfills.................................................................................................................... 4-2

4.3 Ammonia Refrigeration............................................................................................ 4-3

4.4 Non-road Mobile Sources......................................................................................... 4-4

4.5 Point Sources............................................................................................................ 4-4

4.6 Biogenic Sources...................................................................................................... 4-5

5. PREPARATION OF DIGITAL FILE SYSTEMS............................................................. 5-1

5.1 National Emissions Inventory Input Format File Conversion.................................. 5-1

5.2 SMOKE-Compatible File Systems........................................................................... 5-1

5.3 Deliverable Files....................................................................................................... 5-1

6. REFERENCES................................................................................................................... 6-1
This page is intentionally blank.
v

LIST OF FIGURES

Figure Page

2-1. Graphical comparison of monthly seasonal allocation factors........................................ 2-4

3-1. Diurnal ammonia emissions profile for fertilizer application.......................................... 3-4

LIST OF TABLES

Table Page

1-1. Summary of approaches to estimate ammonia emissions................................................ 1-2

1-2. Summary of emission factors and activity data sources.................................................. 1-3

1-3. Summary of seasonal profiles and spatial surrogates...................................................... 1-4

1-4. Summary of diurnal profiles............................................................................................ 1-5

2.1. Vintages of most current NASS estimates by state ......................................................... 2-2

2-2. Side-by-side comparison of monthly seasonal allocation factors.................................... 2-4

3-1. Fertilizer emission factors used in CMU model version 3.0 ........................................... 3-2

4-1. List of industries that use ammonia refrigeration ............................................................ 4-3

4-2. Comparison of soil emission factors................................................................................ 4-6
This page is intentionally blank.
1-1

1. INTRODUCTION

In support of the Central States Regional Air Planning Association’s (CENRAP) need to 

develop a regional haze plan, Sonoma Technology, Inc. (STI) developed an ammonia emission 

inventory for the region. This Methods Document presents the techniques applied to develop the 

inventory. In summary, we used the Carnegie Mellon University (CMU) model—an ammonia 

emissions modeling tool—and supplemental emissions estimation techniques for miscellaneous 

source categories that were omitted from the CMU model. 

Inventory development began with the identification and evaluation of information 

resources to enhance version 3.0 of the CMU model. STI previously evaluated an earlier 

version (2.0) of the CMU model as part of a study for the Lake Michigan Air Directors 

Consortium (LADCO) (Chinkin et al., 2003), and CMU incorporated many of STI’s 

recommendations in the most recent version of the model (version 3.0), which was released in 

April 2003. This Methods Document identifies further revisions that were made to improve the 

CMU model outputs for the CENRAP region, including revisions to the emission factors and 

updates of the activity data.

Consistent with the project goals presented in the Work Plan (Coe, 2003), this Methods 

Document primarily discusses emission estimation techniques for two source categories: 

livestock production and fertilizer use. In addition, it provides a cursory treatment of emissions 

sources that are omitted from version 3.0 of the CMU model, such as landfills, non-road mobile 

sources, and ammonia refrigeration.

1.1 SUMMARY OF RECOMMENDED METHODS

We estimated ammonia emissions for thirteen source categories listed in Table 1-1. For 

livestock production, fertilizer application, and biogenic sources (soil), we used the CMU model 

to estimate emissions, but only after the model’s emission factors and activity data were revised 

and/or updated as summarized in Table 1-2. We also used the CMU model to estimate 

emissions for six other categories, including publicly owned treatment works (POTWs) and

on-road mobile sources. For these categories, we simply used the version 3.0 model outputs 

without modification. In addition, we adopted existing emission inventories for point sources. 

Lastly, we estimated emissions for three source categories omitted from the CMU model: 

landfills, non-road mobile sources, and ammonia refrigeration.

Table 1-2 briefly identifies recommended sources of emission factors and activity data. 

Tables 1-3 and 1-4 list recommended temporal profiles and spatial allocation surrogates by 

source category. The diurnal profile for livestock house at feedlots was derived from a study of 

ammonia emissions from swine operations (Aarnink, 1997). The diurnal profile for fertilizer 

application and free-range livestock was based on a study of nitric oxide fluxes from soil 

(Anderson and Levine, 1987). The diurnal profile for emissions from soil was derived from a 

recent European research project conducted on grasslands (Sutton et al., 2002). Diurnal 

allocations for other categories were based on default profiles assigned by the Sparse Matrix 

Operator Kernal Emissions Modeling System (SMOKE). The subsequent chapters of this 

Methods Document provide many more details about the information summarized in Tables 1-2 

through 1-4.
1-2

Table 1-1. Summary of approaches to estimate ammonia emissions.

Emissions Estimation Approach

Use CMU Model

Source Category Without Revisions With Revisions

Generate or Adopt Estimates Independently

from CMU Model

Included in CMU model:

Livestock  ̧

Fertilizer  ̧

POTWs  ̧

Human perspiration and respiration  ̧

Domestic animals  ̧

Wild animals  ̧

Wildfires  ̧

On-road mobile sources  ̧

Industrial point sources  ̧

Biogenic sources (“soil”)  ̧

Not included in CMU model:

Landfills  ̧

Ammonia refrigeration  ̧

Non-road mobile sources  ̧
1-3

Table 1-2. Summary of emission factors and activity data sources.

Source 

Category Emission Factor(s) Activity Data Comments

Livestock 

production

Retain factors from CMU 

Model 3.0 (Region-specific 

characteristics will be applied 

through temporal and spatial 

allocations).

Existing 1997 USDA Agricultural Census 

data will be improved with 2002 NASS 

population data and data for confined 

animal feeding operations (CAFO), which 

will be obtained from individual states.

Emissions from CAFOs will be 

treated as point sources.

Fertilizer 

application

Refine factors from CMU 

model to make them more 

specific to climate and soil 

types in the CENRAP.

2002 fertilizer usage data from the 

Association of American Plant Food 

Control Officials (AAPFCO) will be used 

with additional data from state agencies.

The CMU model currently uses 1995 

AAPFCO data.

Landfills

Use equations published in the 

Emission Inventory 

Improvement Plan (EIIP)

Volume VIII and a published 

ratio of methane to ammonia 

emissions.

Obtain waste in place (WIP) data from 

EPA’s Landfill Methane Outreach Program 

(LMOP) database and use with additional 

data from state agencies.

The EIIP methodology generates 

methane emissions, which are then 

converted to ammonia.

Ammonia 

refrigeration

Apply an employment-based 

emission factor.

Use county-level employment data 

published by the U.S. Census Bureau.

Emissions estimates are based on 

employment in specific food 

processing industries.

Non-road

mobile 

sources

N/A N/A

Emissions will be taken directly from 

the 1999 National Emission Inventory 

(NEI) non-road inventory.

Point sources N/A N/A

Emissions will be taken directly from 

the 2001 Toxics Release Inventory 

(TRI) and 1999 NEI inventories.

Biogenic 

sources (soil)

Replace factors used in the 

CMU model with those 

recently published by Battye 

et al. (2003).

Use soil-type data from the CMU model 

with no revisions.

Emissions estimates from this source 

category are highly uncertain.
1-4

Table 1-3. Summary of seasonal profiles and spatial surrogates.

Source Category Source of Information for 

Seasonal Profiles

Source of Information for Spatial 

Surrogates

Livestock Production Use monthly allocation factors 

published by Pinder et al (2003).

Rangeland landuse category from the 

EPA’s Biogenic Emissions 

Landcover Database (BELD).

Fertilizer Application Develop new seasonal profiles. Cropland landuse category from the 

EPA’s BELD data.

Landfills

Point Sources

Use SMOKE default temporal 

profiles assigned by Source 

Category Code (SCC).

Facility-reported coordinates, 

addresses, or centroid of zip codes.

Ammonia Refrigeration

Non-road Mobile Sources

Use SMOKE default temporal 

profiles assigned by Source 

Category Code (SCC).

County area.

Biogenics (soil)

Use diurnal profile from recent 

European study (Sutton et al., 

2002).

County area.
1-5

Table 1-4. Summary of diurnal profiles.

Proportion of Total Daily Emissions (%)

Hour of Day Livestock Housed 

at Feedlots

Fertilizer/Free-range 

Livestock

Soil

Midnight-1 a.m. 3.9% 2.0% 3.9%

1-2 a.m. 4.0% 2.0% 3.1%

2-3 a.m. 4.0% 2.0% 2.3%

3-4 a.m. 4.1% 2.0% 1.6%

4-5 a.m. 4.1% 2.0% 1.1%

5-6 a.m. 4.2% 2.1% 0.8%

6-7 a.m. 4.2% 2.8% 0.7%

7-8 a.m. 4.2% 4.1% 0.9%

8-9 a.m. 4.2% 7.0% 1.5%

9-10 a.m. 4.3% 7.4% 2.3%

10-11 a.m. 4.3% 8.2% 3.4%

11 a.m.-Noon 4.3% 8.2% 4.5%

Noon-1 p.m. 4.3% 8.1% 5.5%

1-2 p.m. 4.3% 7.8% 6.4%

2-3 p.m. 4.3% 6.5% 6.9%

3-4 p.m. 4.3% 4.1% 7.1%

4-5 p.m. 4.2% 4.1% 7.1%

5-6 p.m. 4.2% 3.1% 6.9%

6-7 p.m. 4.2% 2.9% 6.7%

7-8 p.m. 4.2% 2.9% 6.4%

8-9 p.m. 4.1% 2.9% 6.0%

9-10 p.m. 4.1% 2.9% 5.5%

10-11 p.m. 4.0% 2.9% 5.0%

11 p.m.-Midnight 4.0% 2.0% 4.5%
1-6

1.2 IMPORTANT PREMISES AND ASSUMPTIONS

The methods that we selected for use were based on several important premises or 

assumptions: 

• For livestock populations, the grounds for selecting 2002 county-level National 

Agricultural Statistics Service (NASS) data—which are based on local surveys—is that 

we gauged them to be an improvement over the older population data reported in the 

1997 USDA Agricultural Census (which is the latest version of the Agricultural Census 

available). 

• We assumed that temporal allocation factors for dairy cows (seasonal) and swine 

(diurnal) are reasonably similar to those of other types of livestock.

• For fertilizer application, emission factors developed by the European Environment 

Agency (2001) for similar climate zones and similar types of fertilizers were presumed to 

be applicable to fertilizers used in the United States.

• Natural Resources Conservation Service (NRCS) classifications of soil types were

presumed to be representative and indicative of the fertilizer emission factors that are 

most applicable to each county in the CENRAP region. 

• We assumed that diurnal patterns of nitric oxide flux from soil emissions observed by 

Anderson and Levine (1987) can be used to approximate diurnal patterns of ammonia 

emissions from fertilizer application (Chinkin et al., 2003).

• For landfills, the U.S. Environmental Protection Agency’s (EPA) Landfill Methane 

Outreach Program (LMOP) is assumed to be sufficiently complete for those states where 

other data is unavailable.

• For point sources, a combination of ammonia emissions data contained in the 2001

Toxics Release Inventory (TRI) and 1999 National Emissions Inventory (NEI) are 

expected to be sufficiently complete for the purposes of this inventory.

• We expected that the emission factors and activity data contained in version 3.0 of the 

CMU model were sufficient to characterize ammonia emissions from POTWs, human 

beings, domestic animals, wild animals, wildfires, and on-road mobile sources. In 

addition, we assumed that the hidden algorithms in version 3.0 of the CMU model

function correctly and as reported in the model documentation for these sources.
2-1

2. LIVESTOCK PRODUCTION

2.1 OVERVIEW

For other inventories, livestock and poultry operations have been estimated to be the most 

significant sources of ammonia emissions nationwide (U.S. Environmental Protection Agency, 

2000). Nationally, the EPA estimated that ammonia emissions from commercial animal 

husbandry in the United States were dominated by calves and cattle (78%), followed by hogs and 

pigs (19%). The other 3% of emissions came from chickens (2%) and sheep (1%). 

In order to estimate the magnitude of livestock and poultry emissions for a given state or 

county, local animal populations are needed, as well as emission factors that quantify pounds of 

ammonia emissions per head of livestock. Ideally, emission factors also should vary with 

weather conditions, animal management practices, and manure management practices. However, 

emission factors with this level of detail generally are unavailable. To compensate, we used a 

new study recently published by Pinder et al. (2003) and discussed in Section 2.4 to generate an 

inventory for livestock that reasonably represents region-specific temporal patterns.

2.2 LIVESTOCK AND POULTRY EMISSION FACTORS

A wide variation in livestock emissions is reported in different studies in the United 

States and Europe. Version 2.0 of the CMU model—previously evaluated by STI for the 

LADCO project—made use of composite emission factors compiled by Battye et al. (1994) for 

each U.S. Department of Agriculture (USDA) category. The Battye report recommended 

European animal waste ammonia factors, which were developed by Asman (1992) on the basis 

of measurements collected in the 1980s in Europe, for use in the United States. However, STI 

concluded that these factors might not be well-suited for estimating emissions in the United 

States because of differences in both animal waste management practices and animal husbandry 

practices between Europe and the United States. For example, animal waste in the United States 

is commonly stored in lagoons, and ranches are generally larger in size and enable wider cattle 

grazing activity. Confined cattle with diets high in nitrogen, as are more common in Europe, 

tend to emit more ammonia so that European emission factors may over-represent cattle 

emissions in the United States.

Version 3.0 of the CMU model utilizes emission factors recommended by the EPA’s 

Office of Research and Development (ORD) (U.S. Environmental Protection Agency, 2002b). 

The original source of the ORD factors were Bouwman and Van der Hoek (1997). Although 

these, too, are European emission factors, the ORD considered them to be more representative of 

U.S. agricultural practices on the basis of a detailed mass balance. In addition, these emission 

factors equate to roughly 25% of excreted nitrogen, which is more in line with current thinking. 

Therefore, we used the ORD-recommended emission factors as they exist in version 3.0 of the 

CMU ammonia model.
2-2

2.3 LIVESTOCK AND POULTRY ACTIVITY DATA

Version 3.0 of the CMU model, like its predecessor, relies exclusively on the 1997 

USDA Agricultural Census as its source of livestock and poultry activity data. This census is 

conducted every five years, and data for the 2002 study will not be released until spring of 2004. 

Therefore, STI will supplement the 1997 data with estimates from the NASS, which are 

generated annually. Each January and July, the NASS conducts surveys of a sample group of 

livestock producers, taking steps to ensure statistically representative coverage of all livestock 

operations in each state. These surveys are then used to produce an estimated livestock 

inventory by county. The vintages of the NASS agricultural surveys vary somewhat by state, so 

we will use the most current estimates available for each. Table 2-1 shows the vintages of all 

available NASS estimates more current than 1997.

Table 2-1. Vintages of most current NASS estimates by state.

NASS Livestock Types

State Beef 

Cattle

Milk 

Cows

Hogs & 

Pigs

Sheep/

Goats

Arkansas 2002 2000 2000 N/A

Iowa 2002 2002 N/A N/A

Kansas 2002 2002 N/A 2001

Louisiana 2002 2002 N/A N/A

Minnesota 2002 2002 N/A 2002

Missouri 2002 2002 N/A 2002

Nebraska 2002 2001 N/A 2002

Oklahoma 2002 2002 1999 2002

Texas 2002 2002 N/A 2000

Note: N/A indicates none available from NASS or that data pre-date the 1997 Agricultural 

Census. For these cases, 1997 Agricultural Census data will be used.

Some limitations are associated with the 1997 USDA Agricultural Census and the NASS 

data. First, these estimates do not reflect some factors that affect actual state cattle populations, 

such as seasonal import and export of animals to other states. Also, these data sources do not 

contain any information on the locations of concentrated animal feeding operations (CAFOs). 

Feedlots service thousands of animals in fairly confined spaces, such that emissions from the 

facilities are better treated as point sources. To address these limitations, we contacted 

agricultural and environmental agencies or departments in each state to request information 

about livestock populations and seasonal movements of herds, as well as data on CAFO animal 

populations which might be available from National Pollutant Discharge Elimination System 

(NPDES) records or other sources. Though no useful data was available on herd movements, we 

were able to obtain current permit data on CAFO locations and animal populations from all nine 

CENRAP states. This allowed us to subtract livestock populations at the various CAFOs from 

the CMU model’s county-wide totals so that emissions from those facilities could be treated as 

point sources and allocated to specific CAFO locations.
2-3

During this process of updating livestock populations, it was noticed that for several 

counties the reported CAFO animal populations greatly exceeded the county totals found in the 

NASS and/or census data. Investigation revealed that for CAFO permitting purposes, facilities 

report maximum capacities rather than actual animal populations. While it can be assumed that 

most CAFOs attempt to operate near their capacity, there are situations where this does not 

appear to be the case, or where facilities are reporting an annual throughput rather than a onetime population figure. For example, an auction yard named OKC West Livestock Market in 

Canadian County, Oklahoma, reports a beef cattle population of 198,797 head, which turned out 

to be the total number of animals moved through the facility in one year (for comparison, the 

1997 Agricultural Census identifies only 25,700 head of beef cattle in the entire county). 

Because of such anomalies, CAFO data for some counties were scaled down to ensure that total 

animal population counts remained comparable with data reported in NASS estimates and the 

1997 Agricultural Census.

Finally, we utilized recent estimates of ammonia emissions from dairy farms published

by Pinder et al. (2003) instead of estimates produced by the CMU model for this category. The 

estimates calculated by Pinder et al. (2003) were derived using a model that considers manure 

management practices and climatic conditions and, from these, calculates month-specific and 

region-specific emission factors for dairy cows. Pinder et al. (2003) applied these emission 

factors to dairy cow populations from the 1997 USDA Agricultural Census to produce a countylevel emissions inventory for the entire United States. 

2.4 LIVESTOCK AND POULTRY TEMPORAL ALLOCATION

2.4.1 Seasonal Allocation

The CMU model assumes a flat, unvarying rate for livestock emissions. Earlier, STI 

recommended (Chinkin et al., 2003) the use of a seasonal distribution proposed by Gilliland et al. 

(2002), which is based on modeled results. While there were concerns about the use of modeled 

outputs to adjust emission rates, a non-varying seasonal distribution seemed even less likely to 

reflect real-world conditions.

However, the recent dairy farm model developed by Pinder et al. (2003) is a new source 

of county-specific seasonal allocation factors that seem more representative of emissions from 

livestock. Side-by-side comparison shows that the seasonal variability in Pinder et al.’s 

aggregate national-scale inventory matches up somewhat comparably with the monthly 

allocation factors proposed by Gilliland et al. (see Table 2-2 and Figure 2-1), with some 

differences.
2-4

Table 2-2. Side-by-side comparison of monthly seasonal allocation factors (Proportions are 

relative to annual average emission rates).

Month Gilliland Seasonal

Allocation Factors

Pinder Seasonal

Allocation Factors

January 67% 33%

February 75% 36%

March 75% 78%

April 82% 161%

May 126% 139%

June 164% 122%

July 183% 116%

August 154% 119%

September 115% 120%

October 73% 150%

November 51% 78%

December 51% 50%

0%

20%

40%

60%

80%

100%

120%

140%

160%

180%

200%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Seasonal Allocation Factor

Gilliland

Pinder

Figure 2-1. Graphical comparison of monthly seasonal allocation factors.

Though the Pinder et al. study was specifically related to dairy cattle, we expect that the 

allocation factors derived from this study are more representative of real-world conditions than 

Gilliland’s reverse-modeling approach. Therefore, we will use Pinder et al.’s profiles to 

seasonally allocate emissions from all livestock. Because the Pinder et al. model takes local 

conditions into account (emission factors are generated for each county in the United States 

based on climate, husbandry practices, etc.), this approach will result in the development of 
2-5

county-specific seasonal allocation factors for this source category. (It should be noted that the 

national-level seasonal allocation shown in Table 2-2 is almost certainly weighted toward the 

colder northern states, which have the most dairies and would not be suitable for the entire 

CENRAP domain).

2.4.2 Diurnal Allocation

Aarnink (1997) reported that ammonia emissions from houses with rearing and fattening 

pigs had higher emissions during the day than during the night: +10% for rearing pigs and +7% 

for fattening pigs. For rearing pigs, emissions peaked in the morning, but for fattening pigs, they 

peaked in the afternoon. We used this information to develop the diurnal profile for ammonia 

emissions from pigs shown in Table 1-3 (Chinkin et al., 2003). Because information is currently 

unavailable to determine a diurnal profile of emissions from other types of livestock, we applied

the swine diurnal profile to feedlot cattle, poultry, and other livestock that tend to be housed at 

close quarters.

For rangeland cattle, manure and urine depositions are spread out over a much larger area 

than would be the case with cattle housed at feedlots. It is our judgment that diurnal emissions 

patterns for such cattle are more likely to be consistent with emissions from fertilizer application 

than with emissions from housed swine. Therefore, we used a diurnal profile developed from 

nitric oxide fluxes from soil (Anderson and Levine, 1987) that is also being applied to emissions 

from fertilizer application (see Section 3.4.2).

2.5 LIVESTOCK AND POULTRY SPATIAL ALLOCATION

CAFOs were treated as point sources, with emissions from those facilities allocated 

according to specific reported coordinates. Other emissions from livestock and poultry will be 

spatially allocated according to the “rangeland” land type category, an aggregate of the “pasture” 

and “grassland” landuse categories available from the EPA’s Biogenic Emissions Landcover 

Database (BELD).
3-1

3. FERTILIZER APPLICATION

3.1 OVERVIEW

In other inventories, fertilizer application has been estimated as the second most abundant 

source of ammonia emissions nationwide (U.S. Environmental Protection Agency, 2000). 

Emissions from this source can be calculated by applying an appropriate emission factor to the 

amount of fertilizer applied. Emission factors vary by fertilizer type, application method, soil 

type, and climate.

3.2 FERTILIZER EMISSION FACTORS

Historically, the EPA recommended the use of Battye et al. (1994) emission factors—in 

part, because they were accompanied by supporting data and an explanation of factor 

development. These emission factors range from 24 lb to 264 lb of NH3 emitted per ton of 

fertilizer nitrogen applied (or equivalently, 1% to 5% of NH3 as nitrogen emitted per ton of 

fertilizer nitrogen applied). The emission factors in version 2.0 of the CMU model were similar 

to those recommended by EPA and reported by Battye et al. (1994)

Because ammonia emissions from fertilizer are a function not only of fertilizer type but 

also soil type and climate, STI recommended (Chinkin et al., 2003) the use of emission factors 

from the European Environment Agency (2001) which are fertilizer type-, soil type-, and 

climate-dependent. The European factors were developed according to the following 

classification system:

• Group I – Warm, temperate areas with a large proportion of calcareous soils.

• Group II – Temperate and warm-temperate areas with some calcareous soils (or managed 

with soil pH>7), but with large areas of acidic soils.

• Group III – Temperate and cool-temperate areas with largely acidic soils.

Version 3.0 of the CMU model makes use of these European emission factors, assigning 

whole states to one of the groupings listed above:

• Group I: Texas

• Group II: Arkansas, Kansas, Louisiana, and Missouri

• Group III: Iowa, Minnesota, and Nebraska

While this approach is an improvement over that used in the previous version of the 

CMU model, greater refinement of emission factors by geography is possible and preferable. As 

Table 3-1 shows, emission factors for some fertilizer types vary widely among classifications. 

For ammonium sulfate-based fertilizers, for example, the Group I emission factor is 200% higher 

than the Group III factor and 50% higher than the Group II factor.
3-2

Table 3-1. Fertilizer emission factors used in CMU model version 3.0 (%N volatilized as NH3).

State

EEA

Group

Mix/

Ammonium

Nitrate

Ammonium

Sulfate

Calcium

Ammonium

Nitrate Urea Misc.

Texas I 3 15 3 20 8

Arkansas II 2 10 2 15 6

Kansas II 2 10 2 15 6

Louisiana II 2 10 2 15 6

Missouri II 2 10 2 15 6

Oklahoma II 2 10 2 15 6

Iowa III 1 5 1 15 7

Minnesota III 1 5 1 15 7

Nebraska III 1 5 1 15 7

Because of these large differences, we more fully implemented the European approach 

through the use of an integrated geographic information system (GIS). The NRCS State Soil 

Geographic database (STATSGO) was used to identify the dominant soil type (calcareous or 

acidic) in each county so that the best possible emission factors could be applied. The emission 

factors in the CMU model were updated as necessary.

3.3 FERTILIZER ACTIVITY DATA

National fertilizer use data are available from the Association of American Plant Food 

Control Officials (AAPFCO). These data contain semi-annual sales distributions at a countylevel for over 100 different types of fertilizers, including those that emit ammonia. Version 3.0 

of the CMU model uses AAPFCO data from 1995; therefore, we prepared an updated fertilizer 

sales database from the 2002 AAPFCO data. State agricultural experts were contacted for 

further information on fertilization consumption, but no improvements to the AAPFCO data 

proved to be available. 

3.4 FERTILIZER TEMPORAL ALLOCATION

3.4.1 Seasonal Allocation

In version 3.0 of the CMU model, six-month sales data from AAPFCO was broken down 

into a monthly resolution via an algorithm that incorporated fertilizer timing based on crop 

calendars and application rates. This algorithm operates on separate six-month intervals—first 

distributing spring sales data over the first six months of the year, then fall data over the second 

half of the year. Such a method assumes, of course, that all fertilizer purchased in a given semiannual period is completely consumed during that time frame.
3-3

However, a survey of the AAPFCO data showed that a significant number of records 

report zero or negative values for fertilizer shipped during the fall cycle (second half of the year). 

This anomaly is due to returns of unused fertilizer in the fall to fertilizer sellers. While these 

anomalies tend to occur in counties with relatively small fertilizer sales, they do indicate that 

fertilizer is not always consumed in the season during which it was purchased. Therefore, they 

may be indicative of a larger scale, systematic bias throughout the inventory.

Furthermore, the CMU model calculates a single temporal distribution for each state 

based on the total acreage of various crop types within the state. This approach does not take 

into account local variations in crop types, which can be considerable across the large states 

found within the CENRAP region.

To address these concerns, we generated revised temporal algorithms that summed the 

semi-annual sales data for each county and redistributed the annual total over the twelve months 

of the year by using county-specific crop acreages published by NASS and the crop calendars 

and fertilizer timing rates currently employed by the CMU model.

3.4.2 Diurnal Allocation

Midwest Research Institute (1998) found that hourly emission rates of ammonia from 

fertilizer applications exhibit diurnal patterns that follow temperature fluctuations. Anderson and 

Levine (1987) found a similar pattern in diurnal nitric oxide fluxes from soil. Because nitric 

oxide flux profiles are better quantified than ammonia flux profiles, we used them to create the 

diurnal profile shown in Figure 3-1. Although a profile that is directly based on good-quality

ammonia emissions measurements would be preferable, we believe that, at present, the better 

quality of the nitrogen oxides data is a good rationale for its use (Chinkin et al., 2003). 

3.5 FERTILIZER SPATIAL ALLOCATION

Emissions from fertilizer application will be spatially allocated to cropland areas

available from EPA’s BELD.
3-4

Figure 3-1. Diurnal ammonia emissions profile for fertilizer application (% of total daily 

emissions).

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

1 3 5 7 9 11 13 15 17 19 21 23

Hour of Day

% Contribution
4-1

4. OTHER SOURCE CATEGORIES

4.1 OVERVIEW

Emissions from other source categories omitted from the CMU model were estimated in 

order to compile a complete inventory. However, because these sources were assigned a lesser 

priority than livestock and fertilizer application, we derived emissions estimates by using simple 

methods.

We considered several source categories for possible inclusion in the final inventory, 

including landfills, biomass burning, composting, ammonia refrigeration, non-road mobile 

sources, geothermal emissions, and ammonia injection for NOx control. We concluded that 

emissions should be estimated for all of these except biomass burning, composting, geothermal 

sources, and ammonia injection for NOx control. These categories were excluded for the 

following reasons:

• Biomass Burning. The CMU model estimates ammonia emissions from wildfires, but not 

planned burning. However, because a planned burning emissions inventory, which will 

include ammonia, is being developed by STI under a different work assignment, this 

source category will be addressed through that separate project.

• Composting. Ammonia is released during the degradation of organic waste at 

composting operations. A 2000 inventory of ammonia emissions prepared by AVES for 

California’s South Coast Air Basin (Botsford et al., 1999) utilized an emission factor of 

2.755 pounds of ammonia per ton of material processed to estimate emissions from this 

source. In that inventory, composting operations accounted for 9.7 tons per day of 

ammonia emissions, or 5.25% of the total inventory. However, this estimate was based 

on an annual throughput of 2,445,600 tons of waste in the South Coast Air Basin alone. 

By comparison, the only CENRAP states for which composting activity data were readily 

available—Iowa and Minnesota—report statewide annual throughputs of 628,000 tons 

and 462,000 tons, respectively. Based on this indication that composting efforts are not 

likely to be widespread in the CENRAP region and a lack of easily accessible data for 

seven states, it is recommended that this source category be excluded from the current 

inventory effort.

• Geothermal Emissions. Geothermal power-generation facilities release significant 

ammonia emissions from cooling towers. However, the Renewable Energy Annual 1996

(Energy Information Administration, 1997) indicates that “known geothermal resource 

areas in the United States with resource conditions sufficient to generate electricity are 

rare, occurring domestically only in the Western United States and Hawaii.”

• Ammonia Injection for NOx Control. One technology for controlling NOx emissions 

from stack gases is the injection of ammonia into the exhaust of boilers or gas turbines—

an approach that is primarily used at power generation facilities. Excess ammonia that 

does not react with NOx is emitted to the atmosphere and is referred to as “ammonia 

slip.”
4-2

In an attempt to determine if any facilities in the CENRAP states use this technology, we

searched control codes contained in the 1999 NEI database. No facilities reported the use 

of ammonia injection in the NEI. A telephone survey of power generation facilities 

would be necessary to confirm the absence of these controls. However, such a survey is 

beyond the scope of the current project.

The remainder of this chapter highlights emissions estimations methodologies for the 

new categories that were included in the final inventory, as well as two other source categories 

(point and biogenic sources) for which improvements were made to the CMU approach.

4.2 LANDFILLS

According to EIIP Volume VIII: Estimating Greenhouse Gas Emissions (Emission 

Inventory Improvement Program, 1999), methane emissions from landfills depend on three key 

factors: (1) total “waste in place” (WIP), which is defined as the sum of waste disposal over a 

30-year period; (2) landfill size; and (3) location in an arid or non-arid climate. When WIP data 

is available for specific landfills within a state, the following equations can be used to estimate 

methane emissions:

Small Landfills (less than 1.1 millions tons of waste in place)

• Arid climate (less than 25 inches of rainfall per year):

CH4 (tons/year) = WIPtons x 

ton

0.27 ft /day 3

x 

ft /day

0.0077 tons CH /yr

3

4

(4-1)

• Non-arid climate (25 inches or more of rainfall per year):

CH4 (tons/year) = WIPtons x 

ton

0.35 ft /day 3

x 

ft /day

0.0077 tons CH /yr

3

4

(4-2)

Large Landfills (over 1.1 millions tons of waste in place)

• Arid climate (less than 25 inches of rainfall per year):

CH4 (tons/year) = [417,957 ft3/day + 

ton

0.16 ft /day 3

 x (WIPtons)] x 

ft /day

0.0077 tons CH /yr

3

4

(4-3)

• Non-arid climate (25 inches or more of rainfall per year):

CH4 (tons/year) = [417,957 ft3/day + 

ton

0.26 ft /day 3

x (WIPtons)] x 

ft /day

0.0077 tons CH /yr

3

4

(4-4)

We have contacted state agencies to obtain landfill WIP estimates, and data has been 

obtained from Iowa, Kansas, Texas, and Minnesota. For the remaining CENRAP states, we 

utilized data available through EPA’s LMOP database. This program is voluntary, and the 
4-3

LMOP database only includes data for landfills that use gas recovery systems or have the 

potential to employ such systems in the future. Though the database is not likely to be 

exhaustive for any of the CENRAP states, it does contain records for 372 landfills within the 

CENRAP region, and only Arkansas appears to have data that are severely incomplete (with only 

three data records for that state).

We used the equations above to estimate methane emissions for each landfill for which 

we have WIP data, then calculated ammonia emissions by using a published ratio of 0.7% 

ammonia to methane (Eggleston, 1992). We apportioned landfill emissions to the best spatial 

data available—either the facility latitude/longitude (for state-specific data) or the centroid of the 

postal code area (ZIP) where each landfill is located (for facilities in the LMOP database).

For Arkansas, landfill emissions were treated as an area source, and a statewide estimate 

of ammonia emissions was calculated based on an alternative population-based method 

described in the EIIP documentation. This statewide emissions total was then disaggregated to 

the county level based on population.

4.3 AMMONIA REFRIGERATION

Ammonia is commonly used as a refrigerant at food processing facilities. The 

Occupational Safety & Healthy Administration (OSHA) of the U.S. Department of Labor has 

identified common industries that use ammonia refrigeration systems (Occupational Safety & 

Health Administration, 2002). These industries and their corresponding NAICS (North 

American Industry Classification System) number appear in Table 4-1.

Table 4-1. List of industries that use ammonia refrigeration.

Industry NAICs

Meat, poultry, and fish processing facilities 31161, 31171

Dairy and ice cream plants 31151, 31152

Wineries and breweries 31212, 31213

Juice and soft drink processing facilities 31211

Cold storage warehouses 49312

To estimate emissions from this source, we applied an emission factor that was reported 

by Battye et al. (1994): 187 kg NH3/employee. This factor is based on the assumption that 

annual production levels of ammonia refrigerants are roughly in material balance with fugitive 

losses from refrigeration systems. If a significant fraction of ammonia refrigerants is recovered 

for disposal, stabilization, or re-use in non-refrigeration applications, or if the demand for new 

refrigeration systems outstrips the demand for system recharges, then this emission factor should 

represent a conservatively high estimate. 

We contacted the International Institute of Ammonia Refrigeration to determine the 

relative market demands for ammonia refrigerants (manufactures of new systems versus 
4-4

recharges of old systems) and to determine the likely environmental fates of manufactured 

ammonia refrigerants (fugitive losses, recovery, etc.). However, we were unable to acquire 

definitive information from this resource. Additionally, we are currently locating the latest issue 

of the serial, Chemical Economics Handbook by Stanford Research Institute, which includes a 

chapter of statistics and discussions about the ammonia manufacturing industry. Given the 

scheduled delivery of the ammonia emission inventory files, we were unable to review this 

resource in time for delivery. However, if the Chemical Economics Handbook contains relevant 

and useful information, then we will (at a later time) issue a revision memorandum to the 

CENRAP with simple instructions for updating the emissions estimates for ammonia 

refrigeration.

In order to verify the scale of the emission factor, we determined that annual production 

of ammonia for refrigeration uses in the United States is between 270,000 Mg and 350,000 Mg 

(Battye et al., 1994; International Institute of Ammonia Refrigeration, 2003). To prevent the 

possibility of significant double-counting, we confirmed that this figure is much larger than total 

air releases of ammonia reported by United States facilities in the food products industry 

(SIC 20) for the 2001 TRI (approximately 7400 Mg). For the industries listed in Table 4-1, total 

United States employment equals approximately 1 million employees (U.S. Census Bureau, 

2003). Thus, these figures yield a factor of 270 to 350 kg NH3/employee-yr, which is on the 

same order of magnitude as the factor estimated by Battye et al. (1994), although it is 44% to 

87% larger.

4.4 NON-ROAD MOBILE SOURCES

NEI contains ammonia emissions from non-road mobile sources. These data will be 

extracted and incorporated into the final inventory. It should be noted that the most current NEI 

data are for 1999, or three years out of date. Because this is a minor category, we will consider 

1999 emissions to be reasonably representative of 2002 emissions.

4.5 POINT SOURCES

To quantify ammonia emissions from industrial/point sources, the CMU model takes

county-level emissions from the EPA’s 1995 TRI and distributes these totals evenly over 

12 months of the year for each county. It should be noted that the CMU model does not contain 

any location information on these sources, meaning that they cannot be treated as true “point 

sources”. 

However, we have evaluated the TRI to be incomplete for several types of point sources, 

such as power generation facilities. Reporting requirements for the TRI limit the inventory to 

specific industrial sectors such as manufacturing, mining, and petroleum processing (U.S. 

Environmental Protection Agency, 2002c). Our preliminary investigation showed that a number 

of ammonia sources included in the 1999 NEI do not appear in the TRI data, and there appears to 

be little overlap between these two inventories. For example, the 2001 TRI contains ammonia 

emissions data for 211 facilities in the state of Texas, which emit 2,603 tons per year of 

ammonia. The NEI contains emissions data for 87 facilities—mostly power generation plants, 

none of which are listed in the TRI—that emit 1,834 tons per year of ammonia.
4-5

In order to improve upon the CMU model output for point sources, we downloaded the 

most current TRI data available (year 2001), and we located the emissions as point sources 

according to the reported coordinates of each facility. In addition, we augmented the TRI point 

source inventory with point source emissions estimates and facility locations reported in the 

1999 NEI. 

To check this approach, we verified that emissions from an important source of ammonia 

emissions—fertilizer manufacturing—are adequately represented in the point source inventory. 

Kansas was examined as a sample state, and ammonia emissions in the NEI and TRI inventories 

from fertilizer manufacturing amounted to 12,400 tons. Using emission factors from AP-42

(U.S. Environmental Protection Agency, 2002a) and fertilizer sales data for Kansas contained in 

the CMU model, an emissions estimate of 14,650 tons of ammonia from fertilizer manufacturing

was calculated. Though this calculation was crude, it does indicate that the data contained in the 

NEI and TRI inventories will provide a reasonable representation of emissions from this source.

4.6 BIOGENIC SOURCES

As the documentation provided with the CMU emissions model states, biogenic (or 

“soil”) emissions estimates are highly uncertain. Literature sources indicate that the soil-plant

canopy system can be a source of ammonia emissions under some conditions and a sink under 

other conditions (Strader et al., 2001; Sutton et al., 2002). The emission factors used in 

Version 3.0 of the CMU model were derived from a variety of sources, and activity data on soil 

types was obtained from the EPA’s GIRAS land-use data set.

Preliminary CMU model runs indicated that emissions from soil accounted for 50% of 

the total annual ammonia inventory. This result seemed unlikely; therefore, we performed a 

literature search to seek improved emission factors. We chose to apply emission factors that 

were selected for use by Battye et al. (2003), which were based on factors reviewed or published 

by Schlesinger and Hartley (1992), Buowman et al. (1997), Kinnee et al (1997), and Van Der 

Hoek (1998). Table 4-2 shows a comparison between the CMU model’s emission factors and 

those selected for use.

The result of altering the CMU model’s emission factors was a 93% reduction in biogenic 

emissions across the CENRAP domain. Thus, we estimated that biogenic emissions account for 

7% of the total CENRAP ammonia inventory. Battye et al. (2003) calculated similar percent 

contributions—about 6.6% and 6.3%—for emission inventories in North Carolina and 

California’s San Joaquin Valley. 

However, it is important to note that these results are highly uncertain. Based on very 

recent research projects conducted on grasslands in Europe (Sutton et al., 2002), results continue 

to demonstrate that relatively undisturbed environments with low nitrogen inputs tend to absorb 

ammonia from the atmosphere, while lands with high nitrogen inputs—from fertilizer, 

decomposing plant matter, manure, large herds or flocks of wild animals, acid rain deposition, or 

other sources—tend to emit ammonia. Reported emission rates range from −590 kg/km2/yr to 

+2600 kg/km2/yr and areas may act as net sinks or net sources of ammonia at different times of 

the year and at different times of the day (Sutton et al., 2002). It will be very important to 
4-6

consider the uncertainties in the biogenic emissions estimates when evaluating air quality 

modeling sensitivities and uncertainties.

Table 4-2. Comparison of soil emission factors.

Emission Factor (kg/km2/yr) Soil Type CMU Model STI Update

Residential, Commercial, Industrial, Urban 160 10

Cropland, Pasture, Other Agricultural Land 1200 30

Orchards, Groves, Vineyards 1296 30

Rangeland 370 40

Forests 140 120

Wetlands 370 120

Dry Salt Flats 6.7 10

Transitional Areas 370 10

Mixed Barren Land 60 10

Unknown 370 10
5-1

5. PREPARATION OF DIGITAL FILE SYSTEMS

5.1 NATIONAL EMISSIONS INVENTORY INPUT FORMAT FILE CONVERSION

The CMU model creates output files in version 2.0 of the National Emissions Inventory 

Input Format (NIF), with the NIF 2.0 records containing ammonia emissions by county, source 

category code (SCC), and month. These output files were updated to the latest version of NIF

(version 3.0).

5.2 SMOKE-COMPATIBLE FILE SYSTEMS

In order to process these emissions data through SMOKE, the NIF files were converted to 

Inventory Data Analyzer (IDA) format, which contains data fields for annual or average ozone 

season emissions data. A simple utility was written in FORTRAN to perform this conversion. 

Also, seasonal and diurnal temporal profiles were created as necessary in order to apply 

the allocation factors recommended in earlier sections of this document. SMOKE crossreference files were updated to access these new profiles for the appropriate source categories.

STI created all input files and scripts so that they are compatible with the most recent 

version of SMOKE (version 1.5), as we have been advised that CENRAP plans to use version 

1.5 for its January and July 2002 modeling efforts.

5.3 DELIVERABLE FILES

The following files were delivered by STI upon completion of the ammonia emission 

inventory with accompanying documentation:

• Activity data files used as inputs to the CMU model

• Emission factor files used as inputs to the CMU model

• CMU output emission data files in latest NIF format

• Emission data files converted to IDA format and ready for input to SMOKE 1.5

• Temporal profile and cross-reference files for use by SMOKE

• Spatial surrogate and cross-reference files for use by SMOKE
6-1

6. REFERENCES

Aarnink A.A.J. (1997) Ammonia emission from houses for growing pigs as affected by pen 

design, indoor climate, and behavior, Kok-Lyra Publishers, Kampen, The Netherlands.

Anderson I.C. and Levine J.S. (1987) Simultaneous field measurements of biogenic emissions of 

nitric oxide and nitrous oxide. J. Geophys. Res. 92, pp. 965-976. 

Asman W.A.H. (1992) Ammonia emission in Europe: updated emission and emission variations. 

Prepared by National Institute of Public Health and Environmental Protection, Bilthoven, 

May.

Battye R., Battye W., Overcash C., and Fudge S. (1994) Development and selection of ammonia 

emissions factors. Final report prepared for U.S. Environmental Protection Agency, 

Office of Research and Development, Washington, DC by Atmospheric Research and 

Exposure Assessment Laboratory, U.S. Environmental Protection Agency, Research 

Triangle Park, NC, EPA Contract No. 68-D3-0034, Work Assignment 0-3, August.

Battye W., Viney P.A., and Roelle P.A. (2003) Evaluation and improvement of ammonia 

emissions inventories. Atmos. Environ. 37, pp. 3873-3883. 

Botsford C.W., Chitjian M., Koizumi J., Wang Y., Gardner L., and Winegar E. (1999) 1997 

gridded ammonia emission inventory update for the South Coast Air Basin. Draft report 

prepared for the South Coast Air Quality Management District, Diamond Bar, CA by 

AVES, Arcadia, CA, Contract #99025, October.

Bouwman A.F. and Van der Hoek K.W. (1997) Scenarios of animal waste production and 

fertilizer use and associated ammonia emission for the developing countries. Atmos. 

Environ. 31 (24), pp. 4095-4102. 

Buowman A.F., Lee D.S., Asman W.A.H., Dentener F.J., Van der Hoek K.W., and Olivier J.G.F. 

(1997) A global high-resolution emission inventory for ammonia. Global Biogeochemical 

Cycles 11 (4), pp. 561-587. 

Chinkin L.R., Ryan P.A., and Coe D.L. (2003) Recommended improvements to the CMU 

Ammonia Emission Inventory Model for use by LADCO. Revised final report prepared 

for Lake Michigan Air Directors Consortium (LADCO), Des Plaines, IL, by Sonoma 

Technology, Inc., Petaluma, CA, STI-902350-2249-FR2, March.

Coe D.L. (2003) Research and development of ammonia emission inventories for the Central 

States Regional Air Planning Association. Quality Assurance Plan prepared for the 

Central States Air Resources Agencies and the Central States Regional Air Planning 

Association, Oklahoma City, OK, by Sonoma Technology, Inc., Petaluma, CA, STI902504-2331-QAP2, April.

Eggleston H.S. (1992) An improved UK ammonia emission inventory. In Proceedings of a 

Workshop: Ammonia Emissions in Europe: Emission Coefficients and Abatement Costs, 
6-2

Laxenburg, Austria, February 4-6, 1991, pp. 95-107, G. Klaassen, ed., International 

Institute for Applied Systems Analysis, Laxenburg, Austria. 

Emission Inventory Improvement Program (1999) EIIP guidance document series: volume III, 

area sources-methods for estimating greenhouse gas emissions from municipal waste 

disposal. Report by ICF Consulting, October.

Energy Information Administration (1997) Renewable Energy Annual 1996. DOE/EIA0603(96), April. Available on the Internet at 

http://www.eia.doe.gov/cneaf/solar.renewables/renewable.energy.annual/chap11.html.

European Environment Agency (2001) Joint EMEP/CORINAIR Atmospheric Emission Inventory 

Guidebook, Third Edition, European Environment Agency, Copenhagen Available on the 

Internet at http://reports.eea.eu.int/technical_report_2001_3/en.

Gilliland A.B., Dennis R.L., Roselle S.J., and Pierce T.E. (2002) Seasonal NH3 emission 

estimates for the eastern United States. J. Geophys. Res. (in review). 

International Institute of Ammonia Refrigeration (2003) Aboutammoniarefridgeration.com. 

Available on the Internet at 

http://www.aboutammoniarefrigeration.com/aaranswers_history.cfm; last accessed 

August 4, 2003.

Kinnee E., Geron C., and Pierce T. (1997) US land use inventory for estimating biogenic ozone 

precursor emissions. Ecological Applications 7, pp. 46-58. 

Midwest Research Institute (1998) Emission factor documentation for AP-52, Section 9.2.1. 

Fertilizer application. Draft, MRI Project Number 4945, June.

Occupational Safety & Health Administration (2002) Safety and health topics: ammonia 

refrigeration. Available on the Internet at 

http://www.osha.gov/SLTC/ammoniarefrigeration/.

Pinder R., Anderson N., Strader R., Davidson C., and Adams P. (2003) Ammonia emissions 

from dairy farms: Development of a farm model and estimation of emissions from the 

United States. EPA's 12 International Emission Inventory Conference, April 29 - May 1, 

2003. 

Schlesinger W.H. and Hartley A.E. (1992) A global budget for atmospheric NH3. 

Biogeochemistry 15, pp. 191-211. 

Strader R., Anderson N., and Davidson C. (2001) User's Guide--CMU Ammonia Inventory 

Version 2.0. July.

Sutton M.A., Milford C., Nemitz E., Theobald M.R., Hargreaves K.J., Fowler D., Schjoerring 

J.K., Mattsson M.E., Husted S., Erisman J.W., Hensen A., Mosquera J., Otjes R., Jonejon 

P., Cellier P., Loubet B., David M., Neftel A., Blatter A., Herrmann B., Jones S.K., 

Horvath L., Weidinger T., Raso J., Meszaros R., Fuhrer E., Mantzanas K., Koukoura Z., 
6-3

Papanastasis V., Gallagher M., Dorsey J.R., Flynn M., and Riedo M. (2002) Biosphereatmosphere interactions of ammonia with European Grasslands. Final synthesis report 

prepared for Grassland Ammonia Interactions Across Europe (GRAMINAE), Contracts: 

ENV4-CT98-0722 and IN20-CT98-0118, February.

U.S. Census Bureau (2003) CenStats databases - county business patterns data. Database 

maintained by the U.S. Census Bureau, Washington, D.C. Available on the Internet at 

http://censtats.census.gov/. last accessed August 4, 2003.

U.S. Environmental Protection Agency (2000) National air pollutant emission trends: 1900-

1998. Web page (EPA 454/R-00-002). Available on the Internet at 

<http://www.epa.gov/ttn/chief/trends/trends98>, March (last accessed 03/11/02).

U.S. Environmental Protection Agency (2002a) AP-42, Fifth Edition, Volume I, Chapter 13: 

Miscellaneous Sources. Section 13.2.1. October. Available on the Internet at 

<http://www.epa.gov/ttn/chief/ap42/ch13/>; last accessed January 2, 2003.

U.S. Environmental Protection Agency (2002b) Review of emission factors and methodologies 

to estimate ammonia emissions from animal waste handling. National Risk Management 

Research Library, Research Triangle Park, NC 27711, EPA-600/R-02-017, April.

U.S. Environmental Protection Agency (2002c) Toxics Release Inventory (TRI): Factors to 

consider when using TRI data. EPA 260-F-02-017, November. Available on the Internet 

at http://www.epa.gov/tri/2002_tri_brochure.pdf.

Van der Hoek K.W. (1998) Estimating ammonia emission factors in Europe: summary of the 

work of the UNECE ammonia expert panel. Atmos. Environ. 32, pp. 315-316. 
B-1

APPENDIX B

TABULATION OF AMMONIA EMISSIONS ESTIMATES 

FOR THE CENRAP REGION
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B-3

Table B-1. Annual ammonia emissions by state and source category.

Ammonia Emissions by State (tons/year)

Source Category AR IA KS LA MN MO NE OK TX Total

Livestock

 CAFOs 4096.0 88722.5 57611.1 82.4 25518.0 24685.5 30240.0 19864.6 45650.0 296470.0

 Free-Range 81978.6 69713.6 33455.9 15837.5 72562.1 68925.6 66743.0 60016.8 143115.0 612348.0

 Total Livestock: 86074.6 158436.1 91067.0 15919.9 98080.1 93611.1 96983.0 79881.4 188765.0 908818.0

Fertilizer Application 38547.1 72258.7 64577.0 18113.5 65407.0 36742.9 64442.5 26209.4 78665.2 464963.4

Biogenics (Soil) 11971.1 5574.4 7802.6 10645.6 14977.6 11886.7 7829.2 9643.0 37511.2 117841.3

Point Sources

 Industrial 2845.2 3700.7 1882.7 7727.9 1362.2 4237.7 421.9 4599.9 4429.7 31207.8

 Landfills 140.6 848.4 812.3 1165.2 959.7 2096.1 378.2 1181.2 7959.0 15540.8

 Total Point Sources: 2985.8 4549.1 2694.9 8893.1 2321.9 6333.8 800.1 5781.1 12388.7 46748.6

Mobile Sources

 On-road 2557.5 2715.7 2407.3 3633.8 4898.4 4414.3 1520.8 3628.7 14104.9 39881.3

 Off-road 389.9 578.1 577.1 1256.8 810.8 755.3 320.1 773.7 4330.7 9792.4

 Total Mobile Sources: 2947.3 3293.8 2984.4 4890.6 5709.2 5169.6 1840.9 4402.4 18435.6 49673.7

Misc. Sources

 Ammonia 

Refrigeration 1799.7 1274.1 300.8 288.2 721.1 440.3 2440.0 90.7 4022.7 11377.5

 POTWs 5.3 6.8 5.2 9.3 11.0 15.0 4.3 5.2 60.9 122.9

 Domestic Animals 2520.1 2014.1 1998.2 2853.5 2599.6 4336.6 1296.7 3235.2 15940.5 36794.3

 Wild Animals 4022.9 1499.9 747.8 5000.0 4587.1 3820.2 1255.1 1651.2 18014.3 40598.7

 Humans 1229.3 1388.4 1253.9 2120.2 2277.1 2614.2 806.2 1611.5 9409.2 22710.0

 Wildfire 260.5 25.0 1347.2 203.6 182.2 366.8 2109.5 428.8 176.7 5100.2

 Total Misc. Sources: 9837.8 6208.3 5653.0 10474.7 10378.0 11593.0 7911.8 7022.5 47624.4 116703.6

Total Emissions: 152363.8 250320.4 174778.8 68937.4 196873.8 165337.1 179807.5 132939.8 383390.1 1704748.7
C-1

APPENDIX C

TABULATION OF DIFFERENCES BETWEEN 

THE STI INVENTORY AND CMU MODEL OUTPUTS
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C-3

Table C-1. Comparison between the STI inventory and CMU model outputs for key sources.

Ammonia Emissions (tons/year)

Livestock & Poultry Fertilizer Biogenics

State STI CMU % Difference STI CMU % Difference STI CMU % Difference

AR 86,074.6 82,657.0 4.1% 38,547.1 38,877.8 -0.9% 11,971.1 86,009.7 -86.1%

IA 158,436.1 163,651.4 -3.2% 72,258.7 68,756.6 5.1% 5,574.4 178,752.5 -96.9%

KS 91,067.0 80,178.7 13.6% 64,577.0 70,575.3 -8.5% 7,802.6 227,338.2 -96.6%

LA 15,919.9 15,918.9 0.0% 18,113.5 16,402.3 10.4% 10,645.6 66,879.7 -84.1%

MN 98,080.1 99,245.3 -1.2% 65,407.0 64,604.6 1.2% 14,977.6 179,269.6 -91.6%

MO 93,611.1 91,717.7 2.1% 36,742.9 32,264.0 13.9% 11,886.7 160,992.1 -92.6%

NE 96,983.0 97,833.8 -0.9% 64,442.5 50,822.8 26.8% 7,829.2 184,292.2 -95.8%

OK 79,881.4 77,359.0 3.3% 26,209.4 27,506.2 -4.7% 9,643.0 160,003.9 -94.0%

TX 188,765.0 186,762.5 1.1% 78,665.2 75,327.6 4.4% 37,511.2 450,664.0 -91.7%

Total: 908,818.0 895,324.2 1.5% 464,963.4 445,137.2 4.5% 117,841.3 1,694,201.8 -93.0%