## A p p e n d i x H
. 8

S o n o m a T e c h n o l o g y , R e s e a r c h a n d D e v e l

o p m e n t o f A m m o n i

a E m i s s i o n I n v e n t o r i e s f o r t h e C e n t r a l S t a t e s R e g i o n a l A i r P l a n n i n g A s s o c i a t i o n ( O c t o b e r 3 0 , 2 0 0 3 )1360 Redwood Way, Suite C Petaluma, CA 94954

- 1169

707/665

- 9900 FAX 707/665

- 9800 www.sonomatech.com 

## RESEARCH AND DEVELOP MENT OF AMMONIA EMISSION INV ENTORIES FOR THE CENTRAL STAT ES REGIONAL AIR PLANNING ASSOCIA TION

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 , 200 3

## This page is intentionally blank.iii QUALITY ASSURANCE STATEMENT

This report was reviewed and approved by the project Q uality A ssurance (QA) O fficer or his delegated representative s , as provided in the project QA P lan (Coe, 2003)
.

Lyle R. Chinkin Project QA Officer

## This page is intentionally blank.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 Curr ent Status of the CENRAP Emission Inventory

................................ ................ 1

- 7 2. SUMMARY AND ASSESSME NT OF THE INVENTORY

................................ ............ 2

- 1 2.1 Emissions From Livestock an d Poultry

................................ ................................ .... 2

- 1 2.1.1 Summary of Emissions from Livestock and Poultry

................................ .... 2

- 1 2.1.2 Assessment of Emissions from Livestock and Poult ry
................................ 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 Sour ce Categories

................................ ............................... 2

- 10 2.4.1 Summary of Emissions from Other Source Categories

.............................. 2

- 10 2.4.2 Assessment of Emissions from Other Sou rce 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 APPEN DIX A: AMMONIA EMIS SION ESTIMATION METH ODS FOR THE CENRAP

AMMONIA EMISSION INV ENTORY

................................ ......................... A

- 1 APPENDIX B: TABULATION OF AMMONI A EMISSIONS ESTIMATE S FOR THE

CENRAP REGION

................................ ................................ ........................... B

- 1 APPENDIX C: TABULATION OF DIFFER ENCES BETWEEN THE ST I INVENTORY

AND CMU MODEL OUTPUT S

................................ ................................ ..... C

- 1 APPENDIX D : EMISSION INVENTORY F ILE DOCUMENTATION AN D

ELECTRONIC FILES

................................ ................................ ............... ON CD

## This page is intentionally blank.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 PM 2.5 observations in urban locations

................................ ................................ ................................ ........................... 1

- 2
1

- 2. Contributions of chemical components to observed PM 2.5 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 PM 2.5 ................................ ....................... 1

- 4
1

- 5. Uncertainty in estimated seasonal average PM 2.5 concentrations

................................ .... 1

- 4
1

- 6. Comparison of IMPROVE and CDN measurements of sulfate and nitrate at Big Bend Natio nal Park, Texas

................................ ................................ ........................ 1

- 5
1

- 7. Annual SO 2 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 f or 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 s tate of the CENRAP region

................................ ................................ ................................ ............... 2

- 7
2

- 8. Comparison of emission totals from fertilizer application

................................ .............. 2

- 8viii 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 emi ssion totals

................................ ................................ ........ 2

- 10ix 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

## This page is intentionally blank.ES

- 1 EXECUTIVE SUMMARY

The Central St ates 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 E stimation 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 th e 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 alteratio ns. 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 ).

Li vestoc k & Pou l try 53%
Fertilizer 27% Bi ogen ic s 7% Mob il e

Source s 3% Po int Sources 3% M i sc . 7%

Figure ES

- 1. CENRAP 2002 a mmonia 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 Ju ly Aug Sep Oct Nov Dec Mo nthNH3 (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 a nd 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 sou rce 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 a re 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 im portant 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 NO x 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 nex t

- 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. La stly, 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

. E nvironmen tal P rotection A gency ’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 CENR AP r egion

. It is widely recognized that the formation of secondary parti culate matter

— which is generated from chemical transformations in the atmosphere of gaseous precursor species such as ammonia, nitrogen oxides, sulfur oxides, and volatile organic com pounds

— contributes significantly to regional haze issues in the CENRAP r egion. Therefore, develop ment 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 ) respond ed 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 proj ect objectives were to i dentify 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 a nd recently evaluated by STI

— and to a pply the findings to improve the CMU ammonia emission inve ntories for the CENRAP region. As directed by the CE NRAP Emissions Work Group, STI dedicate d at least 80% of its technical effort to improving emiss ion es timates for two types of emissions sources

— livestock production and fertilizer use

— while the remainder of the work was directed toward improving emission es timates for other types of emissions sources (such as wastewater treatmen t 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 aer odynamic diameter less than 2.5 microns (PM 2.5 ). PM 2.5 may be directly emitted from sources such as fugitive dust and combustion soot, which are termed sources of “ primary PM 2.5 ”. Condensable organic aerosols form from air emissions of semi

- volatile and heavy org anic compounds. In addition, PM 2.5 forms from photochemical reactions of gaseous precursors, including sulfur oxides ( SO x ) , nitrogen oxides (NO x ) , volatile organic compounds (VOC) , and ammonia ( NH 3 ). This mechanism of PM 2.5 formation i s termed “secondary formation”. The chemical composition of ambient PM 2.5 provide s 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 PM 2.5 constituents. In urban and ammonia

- depleted areas of the eastern United States , sulfate is relatively a more significant contributor to PM 2.5 than it is in the western United States

.1

- 2

Conversely, nitrat e 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 PM 2.5 is significant in urban areas. However, in more pristine areas , the contribution of geologic dust to ambient PM 2.5 becomes more important and may contribute up to 50 % of observed PM 2.5 concentrations. Areas with abundant gaseous NH 3 experience rapid transformation of the atmospheric oxidation products of SO x and NO x emissions into fi ne aerosols of ammonium sulfate and ammonium nitrate

.

C ar b o n ace o us S ul f a te Cr us t a l Ni t ra te No t C h em ic a l l y C h a rac t erize d b

South Co a

s t B a s in ( 4 Sit e A v g - 2 8 ug/ m

3)

W

. Phoe n ix (13. 5 ug/ m

3) E. T e nne s se e (3 C iti e

s) (A vg - 16. 7 ug/ m

3) Spo k a ne (11. 0 ug/ m

3) Roch e s ter (14. 9 ug/ m

3)

S a n J o aqui n V a ll ey ( 4 Sit e A v g - 3 7 ug/ m

3) Bo s ton (16. 2 ug/ m

3) 

## U rban

Figure 1

- 1

. Compositions of annual average concentrations of PM 2.5 observations in urban

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

.
In order to develop a preliminary 1 understanding of the components that contribute to ambient levels of PM 2.5 in the CENRAP region , STI analyzed a data set of speciated PM 2.5 data for the region that was compiled by researchers at the Center f or Air Pollution Impact and Trend Analysis at Washington Un iversity (Schichtel et al., 1999) . The data were collected during various t ime periods and using various analytical sampling techniques ; thus , the results should be considered uncertain until a more rigorous evaluation can be completed. Nevertheless, the dat a indicated several important characteristics. The preliminary data su mmaries 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 PM 2.5 . In addition,
s ulfate concentrations are 2 to 10 t imes larger than nitrate concentrations. Furthermore, the

• 1 It is recognized that more

- recent monitoring data have become available since this preliminary ana lysis 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/m 31

- 3

highest ratios of sulfates to nitrates and the highest average concentrations of PM 2.5 were observed in the southeastern portions of the CENRAP region . This is consistent with observations present ed 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 ). O ther potential uncertainties in the monitoring data also can affect analyses of PM 2.5 data. For example, Malm et al. (2000) report ed 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 SO 2 emis sion rates and population densities across the U nited States ( Figures 1

- 7 and 1

- 8 ) helps explain why PM 2.5 concentrations are somewhat higher and sulfates are so important in the southeast portion of the CENRAP region

.

Figure 1

- 2. Contributio ns of chemical

components to observed PM 2.5 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 PM 2.5 (1994

- 1 996) (Falke, 1999)

.

Figure 1

- 5. Uncertainty in estimated seasonal average PM 2.5 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 SO 2 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 Status es of Ammonia Emission Inventories
As a whole, few areas of the CENRAP r egion historically have experienced significant air quality problems and , therefore , monitoring and emissions estimates were rela tively scarce. T he most comprehensive source s 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 fe rtilizer application), mobile sources, natural (or “biogenic”) sources, ammonia injection for NO x control at power plants, and wastewater treatment plants. In many cases, the associated e mission 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 represen t 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. Adj ustments were necessary to develop seasonal, diurnal, and, possibly, day

- of

- week emission
es timates.1

- 7

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

- specific or state

- specific fleet characteristics and improved vehicle miles t raveled (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 regi on surrounding Memphis, Tennessee, STI found that the 1996 NET inventory underestimated emissions of VOCs and NO x by factors of 10 or more.

• To support modeling sensitivity runs, measures of uncertainty for all emission es timates are highly valuable for pol icy 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 quali tative 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 balance d 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 dedicate d 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 provide d 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 S tates’ emissions and air quality specialists.

1.2 CURRENT STATUS OF TH E CENRAP EMISSION IN VENTORY The resultant emission inventory produced through this work assignment is illustrated in Figures 1

- 9 , and 1

- 10 and tab ulated 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 mit igate 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 StateNH3 (tons/year) Misc. Sources Mobil e Sources Po int Sources B iog en i cs Fert iliz er L iv estock

Fi gure 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 e missions 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 C EN RAP 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 al so 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 s quare kilometer

.

2.1 EMISSIONS FROM LIVES TOCK 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 tr eated 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% t o 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 state s. 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 p oultry (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 variabi lity 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 

### StateNH3 (tons/year) 

### Ot her Turk eys Poultry Hogs & P igs Beef Catt le Milk Cows

Figure 2

- 1. Livestock and poultry emissions by state and animal type. NH 3 Emissions (tons/year)
State CAFOs 1 Free range 2 Total % 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 Ju ly Aug Sep Oct Nov Dec Mo nthPercent Contribution AR IA KS LA MN MO NE OK TX

Figure 2

- 2

. Seasonal variabilities in emissio ns 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 fo r 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 F igure 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% highe r. This difference is due to the fact that we estimate a greater population of beef cattle in Kansas base d on National Agricultural Sta tistics 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 

### StateNH3(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 densi ty plot showing only the point source portion of the inventory, which largely consists of CAFO s (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, ce ntral 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 FERTI LIZER 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 % t o 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 (es pecially 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 gro wers 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, the refore, 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 

### StateNH3 (tons/year) Ammonium ni trate M iscellaneous Ammonium sulfa te Liquid ammonium M ix M onoammonium phos. Diammonium phos. Anh ydrous ammonia Nitro ge n solu t ions 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 Ju ly Aug Sep Oct Nov Dec Mo nthPercent 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 compa red 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 state s were within 5% of the CMU result , while the emissions for three sta tes (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 d ue to changes in activity data and emission factors. First,
w e 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 

### StateNH3 (tons/year)

STI Inventory CM U Model
Figure 2

- 8. Comparison of emission totals from f ertilizer 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

–Temp erate 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 Nation al Resources Conservation Service (1994) . T hus, 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 t he 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 cover2

- 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 mo nthly 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 StateNH3 (tons/year) Fore s ted Wet lands Nonforested Wet lands M ix ed Rangeland Shrub/ Br ush Rangeland Herbaceou s Range la

nd M ix ed Fore st E ve rgreen Fore st Dec iduous Forest Cr op lands 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
Em issions 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 the se emission factors reduced biogenic emissions by 93% over all (see Figure 2

- 10 ) , with the result that b iogenic 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 StateNH3 (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
Al l 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, L ouisiana , 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 t hese 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 ammo nia refrigeration) were omitted from the CMU model, so we independent ly prepared emissions estimates for these categories . Emissions from non

- road mobile sources were taken directly2

- 11

from the 1999 NEI, emissions from landfills were calculated from facility

- specific , waste

- in

place estimates, and emissions from ammonia refrigeration were estimated on an employment

based emission factor of 187 kg NH 3 /employee reported by Battye et al. (1994) . As the latter estimate is the most uncertain, we verified t he 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

- prod uction industries that commonly use ammonia refrigeration , total United States employment equals approximately 1 million employees (U.S. Census Bureau, 2003) . Thus, these figur es yield a factor of 270 to 350 kg NH 3 /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 OM ITTED FROM THE INVEN TORY
We considered , but omitted , several source categories from the final inventory, including biomass burning, composting, geothermal emissions, ammonia injection for NO x control , and biosolids (sewage sludge s) . 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 bein g 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 emis sions prepared by AVES for California’s South Coast Air Basin (Botsford et al., 1999) utilized an em ission 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 o f 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, res pectively. 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

- gen eration facilities release significant
a mmonia emissions from cooling towers. However, the Renewable Energy Annual 1996 (Energy Information Administration, 1997) indicates that “known geothermal resource2

- 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 NO x Control. One technology for controlling NO x emissions from stack gases is the injection of ammonia into the exhaust of boilers or gas turbines

— an approach that is prima

rily used at power generation facilities. Excess ammonia that does not react with NO x 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 sear ch ed control codes contained in the 1999 NEI data base . 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 t he 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 t he sco pe 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 fact ors 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 la test 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 NO x control and biosolids.

3.1 RECOMMENDATIONS FOR BIOGENIC S OURCES 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 ambie nt 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 season al 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 cu tting 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 C ENRAP 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 Dep osition 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 th e 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 us e as inputs (such as land use information, climatological data, and levels of agricultural nitrogen inputs), and to3

- 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 Nort h 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, anima l diets, and animal housing configurations. Recent peer

reviewed publications include Huijsmans et al

. (2003) , Battye et al. (2003) , Mathur and Dennis (2003) , Gillila nd et al. (2003) , and Rie d o et al. (2002) . J ournals 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 effort s c ould be directed to ward the study and acquisition of the activity parameters that are likely to be needed for the emission models under development, such as th o rough surveys of manure management practices, manure disposal methods, land application method s , and typ ical 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 CAFO

reported 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 fro m 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 mos t 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 e xample ). 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, uncertainties3

- 3

could be reduced by conducting a survey of large CAFOs in selected counties to determine how close ly they operate to their reported maximum capacities.

3.3 RECOMMENDATIONS FOR FERTILIZERS
Uncertaint ies 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 fertili zers 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 th o rough surveys of a pplication practices, typical weather conditions , and fertilizer quality.

3.4 RECOMMENDATIONS OTHE R SOURCE CATEGORIES Information was insufficient to develop emissions estimates for ammonia injection for NO x control and biosolids , sources which may be signific ant 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 inv entory. 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 d eveloped 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, an d weather conditions.4

- 1 4. REFERENCES

Battye R., Battye W., Overcash C., and Fudge S. (1994) Development and selection of ammonia
e missions 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, N C, 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., Gar dner 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 i mprovements 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. (200 3) 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, Ok lahoma City, OK, by Sonoma Technology, Inc., Petaluma, CA, STI

902504

- 2331

- QAP2, April. Energy Information Administration (1997) Renewable Energy Annual 1996. DOE/EIA

0603(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, Europ ean Environment Agency, Copenhagen Available on the Internet at http://reports.eea.eu.int/technical_report_2001_3/en

. Falke S. (1999) PM 2.5 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 mo del of bi

directional 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 NH 3 emission estimates for the ea stern 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 co nditions 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 crop land 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/pub s/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., a nd 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. Mete orol. 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 S ervices under contract to the Mid

Atlantic 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 92 nd Annual Meeting and Exhibition, St. Louis, MO, June 20

- 24

. Schlesinger W.H. and Hartley A.E. (1992) A global budget for atmospheric NH 3 . Biogeochemistry 15 , pp. 191

- 211. Spindler G., Teichmann U., and Sutton M. (2001) Ammonia dry deposition over grassland

micrometeorolo gical 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., Mattsso n 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., Papanastas is V., Gallagher M., Dorsey J.R., Flynn M., and Riedo M. (2002) Biosphere

atmosphere interactions of ammonia with European Grasslands. Final synthesis report prepared for Grassland Ammonia Interactions Across Europe (GRAMINAE), Contracts: ENV4

- CT98

- 0722 a nd 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 Agen cy (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 EM ISSION INVENTORY1360 Redwood Way, Suite C Petaluma, CA 94954

- 1169

707/665

- 9900 FAX 707/665

- 9800 www.sonomatech.com 

## AMMONIA EMISSION EST IMATION ME THODS FOR THE CENRAP AMMONIA EMISSION I NVENTORY

Methods Document STI

- 902502

- 2386

- MD 2

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, Sui te C Oklahoma City , OK 73159

October 30, 200 3QUALITY ASSURANCE STATEMENT

This report was reviewed and approved by the project Q ualit y A ssurance (QA) O fficer or his delegated representative, as provided in the project QA plan.

Lyle R. Chinkin Project QA Officeriii 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 Overvi ew
................................ ................................ ................................ ................... 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 APPLICATI ON

................................ ................................ .......................... 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 CATEGOR IES

................................ ................................ .................... 4

- 1 4.1 O verview

................................ ................................ ................................ ................... 4

- 1 4.2 Landfills

................................ ................................ ................................ .................... 4

- 2 4.3 Ammonia Refrigeration

................................ ................................ ............................ 4

- 3 4.4 Non

- ro ad Mobile Sources

................................ ................................ ......................... 4

- 4 4.5 Point Sources

................................ ................................ ................................ ............ 4

- 4 4.6 Biogenic Sources

................................ ................................ ................................ ...... 4

- 5
5

. PREPARATION OF DIGIT AL FILE SYSTEMS

................................ ............................. 5

- 1 5.1 National Emissions Inventory Input Format File Conversion

................................ .. 5

- 1 5.2 SMOKE

- Compatible Fil e 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. Graphic al 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 an d 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 use d the Carnegie Mellon U niversity (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 identif ication and evaluat ion of

info rmation 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 an d updates of the activity data. Consistent with the project goals presented in the Work Plan (Coe, 2003) , this Methods Document primarily discusses emission estimat ion techniques for two source categories : livestock production and fertilizer use. In addi tion, 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 RECOMMEND ED METHODS
We estimate d ammonia emissions for thirteen source categories listed in Table 1

- 1 . For livestock production , fertilizer application , and biogenic sources (soil), we use d the CMU model to estimate emissions, but only after the model’s emission factors and activity data were revised and/or updated a s summarized in Table 1

- 2

. We also use d 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 use d the version 3.0 model outputs without modification

. In addition, w e adopt ed existing emission inventories for point sources. Lastly , we estimate d 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 stud y 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 wa s 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 t he Sparse Matrix Operator Kernal Emissions Modeling System (SMOKE)

. The subsequent chapters of this Methods Document provide many more details about the inf ormation summarized in Tables 1

- 2 through 1

- 4.1-2 Table 1

- 1

. Summary of approaches to estimate amm onia 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 per spiration and respiration  ̧ Domestic animals  ̧ Wild animals  ̧ Wildfires  ̧ On

- road mobile sources  ̧ Industrial point sources  ̧ Biogenic sources (“s oil ”)  ̧

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 p roduction 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

. Em issions from CAFOs will be treated as point

sources
.

Fertilizer
a pplication 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 O fficials ( AAPFCO ) will be used with additional data from state agencies

. The CMU model currently uses 1995 AAPFCO data.

Landfills Use e quations published in the

Emission Inventory Improvement Plan (EIIP) Volume VIII and a published ratio of methane to amm onia emissions. Obtain w aste in pla ce (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
r efrigeration Apply an employment

- based

emission factor

. Use c ounty

- level employment data published by the U.S. Census Bureau

. Emissions estimates are based on employment in specific food processing industries

. Non

- road mobile
s ources N/A N/A Emissions wi ll be taken directly from the 1999 National Emission Inventory ( NEI ) non
- road inventory

. Point s ources 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 Info rmation for Seasonal Profiles Source of Information for Spatial Surrogates

Livestock Production Use monthly allocation factors published by Pinder et al (2003)

. Rangeland land use category from the EPA’s Biogenic Emissions Landcover Database (BELD). Fertilizer Application Develop new seasonal profiles. Cropland landuse category fro m 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) Us e 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 Li vestock 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 A ND ASSUMPTIONS
The methods that we selected for use w ere based on several im portant 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 gauge d them to be an improveme nt over the older population data reported in the 1997 USDA Agricultural Census (which is the latest version of the Agricultural Census available)

.

• We assume d that temporal allocation factors for dairy cows (seasonal) and swine (diurnal) are reasonably s imilar 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 applicab le to fertilizer s 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 CEN RAP 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 fertili zer application (Chinki n 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 emis sions 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 expect ed that the emission factors and activity data contained i n 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 assume d 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 O VERVIEW For other inventories, l ivestock and poultry operations have been estimated to be the most significant sources of ammonia emissions natio nwide (U.S. Environme ntal 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 chi ckens (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 use d a new study recentl y 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 co mpiled 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 i n the United States.
V ersion 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 o f the CMU ammonia model.2

- 2

2.3 L I VESTOCK AND POULTRY ACTIVITY DATA Version 3.0 of the CMU model, like its predecessor, relies exclusively on the 1997 US DA Agricultural Census as its source of livestock and poultry activity data. This census is conducted e very 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 fro m 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 statistical ly 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 vintage s of all available NASS estim ates more current than 1997.

Table 2

- 1

. Vintage s 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 Louisia na 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 mo vements 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 curr ent 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 a s 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 censu s 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 one

time 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 f or 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 utilize d 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 t hese emission factors to dairy cow populations from the 1997 USDA Agricultural Census to produce a county

level emissions inventory for the entire United States.

2.4 LI VESTOCK 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 r epresentative of emissions from livestock. Side

- by

- side comparison show s that the seasonal variability in Pinder et al.’s aggregate national

- scale inventory matches up somewhat comparably with the monthly allocation factors propos ed 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 mont hly seasonal allocation factors (Proportions are

relative t o annual average emission rates )

.

Month Gilliland Seasonal

Allocation Fact ors 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 M ar Apr May Jun Jul A ug Sep Oct Nov Dec MonthSeasonal 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 count y in the United States based on climate, husbandry practices, etc.), this approach will result in the development of2

- 5

county

- specific seasonal allocation factors for this source category. (It should be noted that the national

- level seasonal allocation show n 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 pro file 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 li vestock 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 use d a diurnal profile developed from nitric oxide fluxes from soil (Anderson and Le vine, 1987) that is also being applied to emissions from fertilizer application (se e 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 reporte d 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 D atabase (BELD).3

- 1 3. FERTILIZER

APPLICATION

3.1 OVERVIEW In other inventories, f

ertilizer 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 . E mission 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 NH 3 emitted per ton of fertil izer nitrogen applied (or equivalently, 1% to 5% of NH 3 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 b y 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 possi ble and preferable. As Table 3

- 1 shows, emission factors for some fertilizer types vary widely among classifications. For ammonium sulfate

- based fertilizers, f or 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 f actors used in CMU m odel version 3.0 (%N volatilized as NH 3 )

.

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 d atabase (STATS G O ) was used to identify the dominant s oil 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 Assoc iation of American Plant Food Control Officials (AAPFCO). These data contain semi

- annual sales distributions at a county

level for over 100 different types of fertilizers, including those that emit ammonia. Version 3.0 of the CMU m odel uses AAPFCO data f rom 1995; therefore, we prepare d an updated f ertilizer sales data base from the 2002 AAPFCO data

. State agricultural experts were contact ed for further information o n 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 r ates. 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 semi

annual 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 wit hin 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 generate d revised temporal algorithms that sum med the
s emi

- annual sales data for each county and redistribute d 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 Diu rnal 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 oxi de 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 measurem ents 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 avai lable 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 D ay% Contribution4

- 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 derive d emissions estimates by using simple methods.
We considered s everal source categories for possible inclus ion in the final inventory, including landfills, biomass burning, composting, ammonia refrigeration, non
- road mobile sources, geothermal emissions, and ammonia injection for NO x control

. We concluded that emissions should be estimated for all of these exc ept biomass burning, composting, geothermal
sources , and ammonia injection for NO x control . These categories were excluded for the following reasons:

• Biomass Burning

. The CMU model estimates ammonia emissions from wildfires , but not planned burning. How ever, 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 acti vity 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 eas ily 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
a mmonia emissions from cooling towers. Howev er, 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 N O x Control

. One technology for controlling NO x 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 N O x 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 search ed control codes contained in the 1999 NEI data base . No facilities reported the use of am monia 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 emissio ns 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) locat ion 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 tha n 25 inches of rainfall per year):

CH 4 (tons/year) = WIP tons x ton /day ft 0.27 3 x /day

ft /yr CH tons 0077

. 0
3 4 (4

- 1)

• Non

- arid climate (25 inches or more of rainfall per year):

CH 4 (tons/year) = WIP tons x ton /day ft 0.35 3 x /day

ft /yr CH tons 0077

. 0
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):

CH 4 (tons/year) = [417,957 ft 3 /day + ton /day ft 0.16 3 x (WIP tons )] x /day ft /yr CH tons 0077

. 0
3 4 (4

- 3)

• Non

- arid climate (25 inches or more of rainfall per year):

CH 4 (tons/year) = [417,957 ft 3 /day + ton /day ft 0.26 3 x (WIP tons )] x /day ft /yr CH tons 0077

. 0
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 the4

- 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 thr ee 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 t reated 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 REFRIGERATIO N 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 refrigerat ion systems (Occupational Safety & Health Administration, 2002) . These industries and their c orresponding NAICS ( North American Industry Classification S ystem) number appear in Table 4

- 1

.

Table 4

- 1 . List of i ndustries that us e ammonia refrigeration

.

Industry NAI Cs Meat, poultry, and fish processing facilities 31161, 31171 Dairy and ice cream p lants 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 NH 3 /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 signi ficant 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 repres ent 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 versus4

- 4

recharges of old systems) and to determine the lik ely 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
resour ce 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 ammon ia 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 signific ant 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 figur es yield a factor of 270 to 350 kg NH 3 /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

- ROA D MOBILE SOURCES
NEI contain s 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. B ecause thi s 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 dis tributes 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 eval uated 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) . O ur prelimina ry 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 contain s ammonia emissions data fo r 211 facilities in the state of Texas, which emit 2, 603 tons per year of ammonia. The NEI contains em issions 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 download ed the most curre nt TRI data available (year 2001 ) , and we locate d the emissions as point sources according to the reported coordinates of each facility. In addition, we augment ed the TRI poi nt 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 importa nt source of ammonia emissions

— fertilizer manufacturing

— are adequately represente d 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, 4 00 tons. Using emission factors from AP

- 42 (U.S. Environmental Protection Agency, 2002a) and fertilizer sales d ata 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 provid e 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 (Strade r 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 indica ted 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 us e by Battye et al. (2003) , which were based o n 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 matt er, 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/km 2 /yr to +2600 kg/km 2 /yr and areas may act as net sinks or net sources of ammonia at different time s of the year and at different times of the day (Sutton et al., 2002) . It will be very important to4

- 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/km 2 /yr) Soil Type CMU Model STI Update Residential, Commercial, Industrial, Urban 160 10 Cropland, Pasture, Other Agricultural La nd 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 370 105

- 1 5. PREPARATION OF DIGIT AL FILE SYSTEMS

5.1 NATIONAL EMISSIONS I NVENTO RY I N PUT 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

- C O MPATIBLE FILE SYSTEM S 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 cross

reference files were updated to access these new profiles for the appropriate source categories.
STI create d all input files and scripts so that they are compatible with the most recent version of SMOKE (version 1.5), as we h ave 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 da ta 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

- refer ence files for use by SMOKE

• Spatial surrogate and cross

- reference files for use by SMOKE6

- 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 em ission 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 prepar ed 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 g ridded 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) Sce narios 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 repo rt 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, STI

902504

- 2331

- QAP2, April. Eggl eston 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 Consultin g, October. Energy Information Administration (1997) Renewable Energy Annual 1996. DOE/EIA

0603(96), April. Available on the Internet at http://www.eia.doe.go v/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 NH 3 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/a aranswers_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 facto r 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 t he 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 NH 3 . 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., Loube t 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) Biosphere

atmosphere interaction s 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 patt erns 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) Nati onal 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. Env ironmental 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/ch1 3/>; 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 2 7711, 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

## This page is intentionally blank.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 22 710.0 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 19687 3.8 165337.1 179807.5 132939.8 383390.1 1704748.7C

- 1 

## APPENDIX C

TABULATION OF DIFFERENCES BETWEEN THE STI INVENTORY AND CMU MODEL OUTPUTS

## This page is intentionally blank.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%