Research and Development of Ammonia Emission Inventories for the 

Central States Regional Air Planning Association 

Stephen B. Reid, Dana Coe Sullivan, Lyle R. Chinkin 

Sonoma Technology, Inc., 1360 Redwood Way, Suite C, Petaluma, CA 94954 

sreid@sonomatech.com 

ABSTRACT 

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

regional haze plan, Sonoma Technology, Inc. (STI) developed a 2002 ammonia emission inventory for 

the nine-state CENRAP region, which includes Texas, Oklahoma, Louisiana, Arkansas, Kansas, 

Missouri, Nebraska, Iowa, and Minnesota. The inventory was developed by applying the Carnegie 

Mellon University (CMU) ammonia emissions model, version 3.0, and by supplementing the model with 

updated activity data, emission factors, temporal profiles, and inventories of additional source 

categories. The most important source categories were estimated to be livestock and poultry, fertilizers, 

and biogenics. These three sources combined accounted for 87% of the annual ammonia emissions in 

the CENRAP region (see Figure 1). Emissions peaked in spring and fall (especially during the months 

of April and October)—times when manure and fertilizer are typically applied to croplands (see 

Figure 2). 

Figure 1. 2002 NH3 emissions by source category. 

Fertilizer 27%

Livestock &

Poultry 53%

Misc. 7%

Point Sources

3%

Mobile

Sources 3%

Biogenics 7%

Figure 2. Monthly variation in total emissions (biogenic sources excluded). 

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INTRODUCTION 

The Central Regional Air Planning Association (CENRAP) is responding to the U.S. 

Environmental Protection Agency’s (EPA) mandate to protect visibility in Class I areas by researching 

visibility-related issues and developing a regional haze plan for the CENRAP region, which includes the 

states of Texas, Oklahoma, Louisiana, Arkansas, Kansas, Missouri, Nebraska, Iowa, and Minnesota. An 

understanding of ammonia emissions is vital to the CENRAP because ammonia is a significant 

precursor to secondary particulate matter, which contributes significantly to episodes of low visibility. 

Visibility impairment is largely caused by particulate matter of aerodynamic diameter less than 

2.5 microns (PM2.5). PM2.5 may be introduced directly into the atmosphere from sources such as 

fugitive dust and combustion soot (primary PM2.5), or it may be the result of atmospheric chemical 

reactions (secondary PM2. 5). Atmospheric ammonia participates in secondary PM2.5 formation by 

combining with nitrogen oxides (NOx) or sulfur oxides (SOx) to form ammonium nitrate and ammonium 

sulfate—a chemical reaction that tends to favor the formation of very tiny particles. Analysis of 

speciated PM2.5 data in the CENRAP region indicates that at sites where sulfates and nitrates were 

measured, these secondary compounds together comprised approximately 30% to 60% of PM2.5. 

In support of its needs to understand ammonia emissions patterns, the CENRAP funded the 

development of a model-ready inventory of ammonia emissions to represent the year 2002. The project 

objectives were to identify and evaluate information resources that could be immediately applied to 

improve emissions estimates produced by version 3.0 of the Carnegie Mellon University (CMU) 

model—an ammonia emissions modeling tool and database system. The majority of these efforts 

focused on improving emission estimates for two types of emissions sources—livestock production and 

fertilizer use—while the remainder of the work was directed toward improving or incorporating 

emission estimates for other types of emissions sources (such as biogenics, off-road mobile sources, 

landfills, and industrial point sources). 

TECHNICAL APPROACH 

Ammonia emissions were calculated for 13 source categories using three basic approaches: 

1) Use version 3.0 of the CMU ammonia model “as is” for relatively small source categories, 

such as publicly owned treatment works, human perspiration/respiration, domestic and wild 

animals, wildfires, and on-road mobile sources. 

2) Update the CMU model’s emission factors, activity data, and/or temporal profiles for 

significant source categories, such as livestock production, fertilizer application, and 

biogenic sources. 

3) When necessary, prepare emission estimates for source categories not included in the CMU 

model, such as industrial point sources, landfills, ammonia refrigeration, and non-road 

mobile sources. 

The following sections describe the techniques and data sources that were used to generate 

emission estimates for the source categories listed under approaches 2 and 3 above. 

Livestock Production 

Emission calculations for livestock and poultry production were based on county-level animal 

population estimates. Where possible, 2002 National Agricultural Statistics Service (NASS) data were 

substituted for the older population data from the 1997 USDA Agricultural Census used by the CMU 

model. Also, data on animal populations at confined animal feeding operations (CAFOs) were gathered 

from state agricultural and environmental agencies and subtracted from NASS estimates to prevent 
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double counting. These methods and data sources are reasonably similar to those recently applied by the 

EPA1

 to estimate livestock and poultry populations. However, the EPA’s population estimates differ 

significantly from those estimated for the CENRAP. The reasons for the differences are subjects of 

areas of ongoing discussion and investigation. 

Updated population estimates were used in conjunction with the CMU model’s existing emission 

factors to generate emission estimates for all livestock types except dairy cattle. For dairy cattle, 

ammonia emission estimates produced by a recently developed dairy farm model 2

 were incorporated 

into the inventory. 

Emissions were spatially allocated using two methods. Individual CAFO facilities were treated 

as point sources, with emissions from those facilities allocated according to specific location 

coordinates. Other emissions from livestock and poultry were treated as area sources and spatially 

allocated according to the distribution of rangeland in each county (as defined by the EPA’s Biogenic 

Emissions Landcover Database [BELD]). 

Emissions were temporally distributed according to county-specific seasonal allocation factors 

derived from the Pinder dairy farm model, which takes local climate and husbandry practices into 

account. (Emission estimates for livestock production produced by the CMU model are unvarying from 

month to month.) Diurnal allocation factors for CAFOs were derived from a European study of 

ammonia emissions from swine houses.3

 For “free range” animals, manure and urine depositions are 

spread over a much larger area than would be the case with animals housed at feedlots. Therefore, a 

diurnal profile based on nitric oxide fluxes from soil4

 was used. 

Fertilizer Application 

Emissions from fertilizer application were calculated by applying appropriate emission factors to 

county-level estimates of the amounts of fertilizers consumed in 2002. 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 county-level for over 100 types of fertilizers, including those 

that emit ammonia. A fertilizer sales database prepared from 2002 AAPFCO data was substituted for the 

1995 AAPFCO data that were included in version 3.0 of the CMU model. 

Version 3.0 of the CMU model makes use of European emission factors5

 that vary by fertilizer 

type, soil type, and climate. These 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. 

The CMU model assigns whole states to one of the groupings listed above; five of the nine 

CENRAP states are classified as Group II. However, because emission factors for some fertilizer types 

vary by as much as 200% among the three classifications, greater refinement of emission factors by 

geography was preferable. Therefore, the European approach was more fully implemented through the 

use of soil type databases and geographic information systems (GIS). The NRCS State Soil Geographic 

database (STATSGO) was used to identify the dominant soil type (calcareous or acidic) in each county 

so that emission factors could be selected at a more local level. 
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Emissions from fertilizer application were spatially allocated to cropland areas in the EPA’s 

BELD database. Emissions were seasonally allocated by using county-specific crop acreages published 

by NASS and the crop calendars and fertilizer timing rates employed by the CMU model. Because 

nitric oxide fluxes arise from analogous biological processes but are better quantified than ammonia 

fluxes, a diurnal profile based on nitric oxide fluxes from soil4

 was used for emissions from fertilizer. 

Biogenic Sources 

Biogenic (or “soil”) emission 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.6,7 Preliminary CMU model runs for the CENRAP region indicated that emissions from 

soil accounted for 50% of the total annual ammonia inventory. This result seemed unlikely; therefore, a 

literature search was performed to seek improved emission factors. STI chose to apply emission factors 

selected for use by Battye et al.,8

 which were based on factors reviewed or published by Schlesinger and 

Hartley,9

 Buowman et al.,10 Kinnee et al,11 and Van Der Hoek.12

The result of altering the CMU model’s emission factors was a 93% reduction in biogenic 

emissions across the CENRAP domain. Thus, biogenic emissions were estimated to account for 7% of 

the total CENRAP ammonia inventory. Battye et al.8

 calculated similar percent contributions—about 

6.6% and 6.3%—for emission inventories in North Carolina and California’s San Joaquin Valley. 

Other Sources 

In addition to the three source categories described above (and the six sources for which the 

CMU model was run without alteration), emission estimates were prepared independently of the model 

for four other sources: landfills, ammonia refrigeration, non-road mobile sources, and industrial point 

sources. 

For landfills, emission estimates were based on waste-in-place (WIP) data obtained from state 

agencies and the EPA’s Landfill Methane Outreach Program (LMOP) database. Emissions from 

ammonia refrigeration systems used at food processing facilities were based on county-level 

employment estimates for industries that commonly employ such systems. Emissions from non-road 

mobile and industrial point sources were taken from existing inventories. County-level ammonia 

emission estimates from the 1999 National Emission Inventory (NEI) were used for non-road mobile 

sources, and 1999 NEI point source data were used in conjunction with 2001 Toxic Release Inventory 

(TRI) data for industrial point sources. Point source emissions were spatially allocated according to the 

location coordinates of individual facilities. (The CMU model contains only county-level point source 

emission estimates derived from the 1995 TRI). 

Sources considered but omitted from the final inventory included biomass burning, composting, 

geothermal emissions, ammonia injection for NOx control, and biosolids (sewage sludges). Ammonia 

emissions from planned burning activities in the CENRAP region are the subject of another study, and 

the remaining categories were judged to be insignificant sources of ammonia (NH3) or lacked the data 

needed to generate an inventory. 

RESULTS 

Year 2002 NH3 emissions for the CENRAP region were estimated to be 1.7 million tons, almost 

50% less than the 3.2 million tons predicted by the CMU model without alteration. This significant 

difference is mostly the result of different approaches to biogenic sources (in fact, when biogenic 

sources are excluded from both inventories, the remaining emissions are actually 5% higher in the STI 

inventory than those estimated with the CMU model). It was determined that emissions from livestock 
and poultry production contribute 53% to this total, ranging state to state from 23% (Louisiana) to 63% 

(Iowa). Fertilizer application was the second largest source, accounting for 27% of the total NH3

emissions in the region and ranging state to state from 20% (Oklahoma) to 37% (Kansas). Other source 

categories contributed significantly to the NH3 inventory in individual states. For example, industrial 

point sources were responsible for only about 3% of the NH3 emissions in the CENRAP region as a 

whole, but made up over 13% of the inventory for Louisiana. (See Figure 3 for a breakdown of 

emissions by state and source category and Figure 4 for a map of emissions densities for the CENRAP 

region). 

Figure 3. 2002 NH3 emissions by source category for each state of the CENRAP region. 

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H3 (tons/year)

Misc. Sources

Mobile Sources

Point Sources

Biogenics

Fertilizer

Livestock

Figure 4. Geographic map of emissions densities for the CENRAP region, July 10, 2002. 

The seasonal variability of NH3 emissions follows a bimodal pattern, with a large spring peak in 

April and a smaller fall peak in October (see Figure 2). These peaks correspond to times when manure 

and fertilizer are typically applied to croplands. Emissions are lowest during the winter months of 

December, January, and February. 

CONCLUSIONS 

As anticipated, livestock production and fertilizer application were the most significant sources 

of NH3 emissions in the CENRAP region, accounting for 80% of the total emissions in 2002. For the 

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region as a whole, estimates for these two source categories were within 5% of the totals previously 

predicted by version 3.0 of the CMU model. However, differences from state to state and county to 

county are much more pronounced. For example, CENRAP’s emissions from livestock and poultry 

production in Kansas and from fertilizer application in Nebraska are 14% and 27% higher, respectively, 

than predicted by the CMU model. 

Similarly, temporal emission distributions are significantly different when the CENRAP and 

CMU model inventories are compared. As Figure 2 shows, CENRAP’s emissions estimates are 

consistently higher than those from the CMU model inventory for spring, summer, and fall, but the 

reverse is true for winter. These significant changes in the geographic and temporal distribution of 

emissions are critical for modeling efforts, through which the impact of emission sources on Class I 

areas will be analyzed throughout the region. 

REFERENCES 

1. National emission inventory—Ammonia emissions from animal husbandry operations. U.S. 

Environmental Protection Agency, 2004. Available at: 

<http://www.epa.gov/ttn/chief/net/2002inventory.html>; last accessed April 22, 2004. 

2. Pinder, R.; Anderson, N.; Strader, R.; Davidson, C.; Adams, P. "Ammonia emissions from dairy 

farms: development of a farm model and estimation of emissions from the United States". In 

Proceedings, EPA’s 12th International Emission Inventory Conference, April 29 - May 1, 2003. 

3. Aarnink, A. A. J. Ammonia emission from houses for growing pigs as affected by pen design, 

indoor climate, and behavior; Kok-Lyra Publishers; Kampen, The Netherlands, 1997. 

4. Anderson, I. C.; Levine, J. S. "Simultaneous field measurements of biogenic emissions of nitric 

oxide and nitrous oxide", J. Geophys. Res. 1987, 92, 965-976. 

5. European Environment Agency. Joint EMEP/CORINAIR Atmospheric Emission Inventory 

Guidebook; Third Edition ed.; European Environment Agency; Copenhagen., 2001. 

6. Strader, R.; Anderson, N.; Davidson, C. "User's Guide--CMU Ammonia Inventory Version 2.0"; 

2001. 

7. 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.; Riedo, M. "Biosphere-atmosphere 

interactions of ammonia with European Grasslands"; Contracts: ENV4-CT98-0722 and IN20-

CT98-0118, Final synthesis report prepared for Grassland Ammonia Interactions Across Europe 

(GRAMINAE) 2002. 

8. Battye, W.; Aneja, V. P.; Roelle, P. A. "Evaluation and improvement of ammonia emissions 

inventories", Atmos. Environ. 2003, 37, 3873-3883. 

9. Schlesinger, W. H.; Hartley, A. E. "A global budget for atmospheric NH3", Biogeochemistry

1992, 15, 191-211. 
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10. Buowman, A. F.; Lee, D. S.; Asman, W. A. H.; Dentener, F. J.; Van der Hoek, K. W.; Olivier, J. 

G. F. "A global high-resolution emission inventory for ammonia", Global Biogeochemical 

Cycles 1997, 11(4), 561-587. 

11. Kinnee, E.; Geron, C.; Pierce, T. "United States land use inventory for estimating biogenic ozone 

precursor emissions", Ecological Applications 1997, 7, 46-58. 

12. Van der Hoek, K. W. "Estimating ammonia emission factors in Europe: summary of the work of 

the UNECE ammonia expert panel", Atmos. Environ. 1998, 32, 315-316. 

13. Coe, D. L.; Reid, S. B. "Research and development of ammonia emission inventories for the 

Central States Regional Air Planning Association"; STI-902501-2241-FR, Final report prepared 

for The Central States Air Resource Agencies and The Central Regional Air Planning 

Association, Oklahoma City, OK. Prepared by Sonoma Technology, Inc., Petaluma, CA. 2003. 

ACKNOWLEDGMENTS 

The authors wish to thank the co-chairs of the CENRAP’s Emission Inventory Work Group 

(EIWG), Kathy Pendleton (Texas Commission on Environmental Quality) and Elizabeth Byers 

(Louisiana Department of Environmental Quality), for their guidance, technical feedback, and leadership 

of the EIWG. In addition, the authors wish to thank the members of the EIWG, as well as many other 

individuals and stakeholders, for providing their valuable ideas and inputs. Further information about 

this study can be found in the project final report: “Research and Development of Ammonia Emission 

Inventories for the Central States Regional Air Planning Association.” 13
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KEY WORDS 

Ammonia 

Emissions Inventories 

Emission Inventory 

Livestock 

Fertilizer 

CENRAP 

Arkansas 

Iowa 

Kansas 

Louisiana 

Minnesota 

Missouri 

Nebraska 

Oklahoma 

Texas 

CMU model