### Resear ch and Development of Ammoni a Emission Inventories for th e Central S t ates Regional Air Planning Association

Stephen B. Reid, Dana Coe Sulliva n, Lyle R. Chinkin Sonom aTechnology, Inc., 1360 Redwood W a y, Suite C, Petalum a , CA 94954 sreid@sonom a tech.com

ABST RACT
In support of the Central Regional Air Planning A ssociation’s (CENRAP) need to develop a regional haze plan, Sonom aTechnol ogy, Inc. (S T I) developed a 2002 ammonia e m ission inventory for the nine-state CENRAP region, which includes Te xas, Oklahom a , Louisiana, Arkansas, Kansas, Missouri, Nebraska, Iowa, and Minnesota. The inventory was developed by applying the Carnegie Mellon University (CMU) ammonia em issions model, version 3.0, and by supplem enting the m o del with updated activity data, em ission factors, temporal profiles, an d invento r ies of addition a l source catego r ies. The m o st i m portan tsource categories were estimated to be livestoc k and poultry, fertilizers, and biogenics. These three sources com b ined account ed for 87% of the annua l amm o nia em issions in the CENRAP region (see Figure 1). Em issions peaked in spring and fall (especially during the m o nths of April and October)—tim e s when m a nure and fertilize rar e typica lly ap plied to crop lands (s ee Figure 2).
Figure 1. 2002 NH 3 e m issions by source category.

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Figure 2. Monthly variation in total em issi ons (biogenic sources excluded).

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J a n F e b M a r A pr M ay J u n e J ul y A ug S e p O c t Nov D e c Mo n t hNH3(tons/month)

S T I I nve nto r y C M U I nve n to r

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12 INTRODUCTION
The Central Regional Air Planning Associati on (CENRAP) is responding to the U.S. Environmental Protection Agency’s (E PA) mandate to protect visibility in Class I areas by researching visibility-related issues and developing a regional haze plan for the CENRAP region, which includes the states of Texas, Oklahoma, Louisiana, Arkansas, Ka nsas, Missouri, Nebraska, Iowa, and Minnesota. An understanding of ammonia emissions is vital to the CENRAP because ammonia is a significant precursor to secondary particulate matter, which contributes significantly to epis odes of low visibility. Visibility impairment is largely caused by part iculate matter of aerodynamic diameter less than 2.5 microns (PM 2.5 ). PM 2.5 may be introduced directly into th e atmosphere from sources such as fugitive dust and combustion soot (primary PM 2.5 ), or it may be the result of atmospheric chemical reactions (secondary PM 2. 5 ). Atmospheric ammonia pa rticipates in secondary PM 2.5 formation by combining with nitrogen oxides (NO x ) or sulfur oxides (SO x ) to form ammonium nitrate and ammonium sulfate—a chemical reaction that tends to favor the formation of very tiny pa rticles. Analysis of speciated PM 2.5 data in the CENRAP region indicates that at sites where sulfates and nitrates were measured, these secondary compounds together comprised approximately 30% to 60% of PM 2.5 . In support of its needs to understand ammonia emissions patterns, the CENRAP funded the development of a model-ready inventory of ammonia emissions to represent the year 2002. The project objectives were to identify and evaluate informati on resources that could be immediately applied to improve emissions estimates produced by version 3.0 of the Carnegie Mellon University (CMU) model—an ammonia emissions modeling tool and data base system. The majority of these efforts focused on improving emission estimates for two types of emissions sources—livestock production and fertilizer use—while the remainder of the work was directed toward im proving or incorporating emission estimates for other types of emissions sour ces (such as biogenics, off-road mobile sources, landfills, and industr ial point sources).

TECHNICAL APPROACH

Ammonia emissions were calculated for 13 sour ce categories using th ree basic approaches: 1) Use version 3.0 of the CMU ammonia model “as is” for relatively small source categories, such as publicly owned treatment works, human perspiration/respirati on, domestic and wild animals, wildfires, and on-road mobile sources. 2) Update the CMU model’s emission factors, activity data, and/or temporal profiles for significant source categories, such as livestock production, fertilizer application, and biogenic sources. 3) When necessary, prepare emission estimates for source categories not included in the CMU model, such as industrial point sources, landfills, ammonia refr igeration, and non-road mobile sources.
The following sections describe the techniques and data sources that were used to generate emission estimates for the source categorie s listed under approaches 2 and 3 above.

Livestock Production

Emission calculations for lives tock and poultry production were based on county-level animal population estimates. Where possible, 2002 National Agri cultural Statistics Service (NASS) data were substituted for the older population data from th e 1997 USDA Agricultural Census used by the CMU model. Also, data on animal populations at confined animal feeding operations (CAFOs) were gathered from state agricultural and environmental agencies and subtracted from NASS estimates to prevent3 double counting. These methods and data sources are r easonably similar to thos e recently applied by the EPA 1 to estimate livestock and poultry populations. However, the EPA’s population estimates differ significantly from those estimated for the CENRAP. The reasons for the differences are subjects of areas of ongoing discussion and investigation.
Updated population estimates were used in conj unction with the CMU m odel’s existing emission factors to generate emission estimates for all livesto ck types except dairy cattle. For dairy cattle, ammonia emission estimates produced by a recently developed dairy farm model 2 were incorporated into the inventory.
Emissions were spatially allocate d using two methods. Individual CAFO facilities were treated as point sources, with emissions from those f acilities allocated accordi ng to specific location coordinates. Other emissions from livestock and poultry were treated as area sources and spatially allocated according to the distribution of rangela nd in each county (as defined by the EPA’s Biogenic Emissions Landcover Database [BELD]).
Emissions were temporally distributed according to county-specific seas onal allocation factors derived from the Pinder dairy farm model, which takes local climate and husbandry practices into account. (Emission estimates for livestock produc tion produced by the CMU model are unvarying from month to month.) Diurnal allocation factors fo r CAFOs were derived from a European study of ammonia emissions from swine houses. 3 For “free range” animals, ma nure and urine depositions are spread over a much larger area than would be the case with animals hous ed at feedlots. Therefore, a diurnal profile based on nitr ic oxide fluxes from soil 4 was used.
Fertilizer Application
Emissions from fertilizer application were calculated by applying appropriate emission factors to county-level estimates of the amounts of fertilizers c onsumed in 2002. National fertilizer use data are available from the Association of American Plan t Food Control Officials (AAPFCO). These data contain semi-annual sales distributions at a county-lev el for over 100 types of fertilizers, including those that emit ammonia. A fertilizer sales database pr epared from 2002 AAPFCO data was substituted for the 1995 AAPFCO data that were included in version 3.0 of the CMU model.
Version 3.0 of the CMU model makes use of European emission factors 5 that vary by fertilizer type, soil type, and climate. These European factors were developed according to the following classification system:

• Group I – Warm-temperate areas with a large proportion of ca lcareous soils.

• Group II – Temperate and warm-temperate area s with some calcareous soils (or managed with soil pH>7), but with large areas of acidic soils.

• Group III – Temperate and cool-temperate areas with largely acidic soils.
The CMU model assigns whole states to one of the groupings listed above; five of the nine CENRAP states are classified as Group II. Howeve r, because emission factors for some fertilizer types vary by as much as 200% among the three classificat ions, greater refinement of emission factors by geography was preferable. Therefore, the European approach was more fully implemented through the use of soil type databases and geographic information systems (GIS). The NRCS State Soil Geographic database (STATSGO) was used to identify the domina nt soil type (calcareous or acidic) in each county so that emission factors could be selected at a more local level.4 Emissions from fertilizer application were spat ially allocated to cropland areas in the EPA’s BELD database. Emissions were seasonally allocated by using count y-specific crop acreages published by NASS and the crop calendars and fertilizer timing rates employe d by the CMU model. Because nitric oxide fluxes arise from an alogous biological processes but are better quantified than ammonia fluxes, a diurnal profile based on nitric oxide fluxes from soil 4 was used for emissions from fertilizer.
Biogenic Sources
Biogenic (or “soil”) emission esti mates are highly uncertain; litera ture sources indicate that the soil-plant canopy system can be a source of ammonia emissions under some conditions and a sink under other conditions. 6,7 Preliminary CMU model runs for the CENR AP region indicated that emissions from soil accounted for 50% of the total annual ammonia inventory. This resu lt seemed unlikely; therefore, a literature search was performed to seek improved emi ssion factors. STI chose to apply emission factors selected for use by Battye et al., 8 which were based on factors review ed or published by Schlesinger and Hartley, 9 Buowman et al., 10 Kinnee et al, 11 and Van Der Hoek. 12

The result of altering the CMU model’s emi ssion factors was a 93% reduction in biogenic emissions across the CENRAP domain. Thus, biogenic emissions were estimated to account for 7% of the total CENRAP ammonia inventory. Battye et al. 8 calculated similar per cent contributions—about 6.6% and 6.3%—for emission invent ories in North Carolina and Calif ornia’s San Joaquin Valley.

Other Sources

In addition to the three source categories described above (and the six sources for which the CMU model was run without alteration), emission estim ates were prepared inde pendently of the model for four other sources: landfills, ammonia refrigera tion, non-road mobile sources, and industrial point

sources.
For landfills, emission estimates were based on waste-in-place (WIP) data obtained from state agencies and the EPA’s Landfill Methane Outreach Program (LMOP) database. Emissions from ammonia refrigeration systems used at food processing facilities were based on county-level employment estimates for industries that commonly employ such systems. Emissions from non-road mobile and industrial point sources were taken fr om existing inventories. County-level ammonia emission estimates from the 1999 National Emission I nventory (NEI) were used for non-road mobile sources, and 1999 NEI point source data were used in conjunction with 2001 Toxic Release Inventory (TRI) data for industrial point sources. Point sour ce emissions were spatially allocated according to the location coordinates of individual f acilities. (The CMU model contai ns only county-level point source emission estimates derived from the 1995 TRI).

Sources considered but omitted from the fina l inventory included bi omass burning, composting, geothermal emissions, ammonia injection for NO x control, and biosolids (s ewage sludges). Ammonia emissions from planned burning activities in the CENRAP region are the subj ect of another study, and the remaining categories were judged to be insignificant sources of ammonia (NH 3 ) or lacked the data needed to generate an inventory.

RESULTS

Year 2002 NH 3 emissions for the CENRAP region were estimated to be 1.7 million tons, almost 50% less than the 3.2 million tons predicted by the CMU model without alteration. This significant difference is mostly the result of different approa ches to biogenic sources (in fact, when biogenic sources are excluded from both inve ntories, the remaining emissions are actually 5% higher in the STI inventory than those estimated with the CMU model). It was determined that emissions from livestockand poultry production contribute 53% to this total, ranging state to state from 23% (Louisiana) to 63% (Iowa). Fertilizer app l ication was th e second la rgest source, accounting for 27% of th e total NH 3
em issions in the region and ranging st ate to state from 20% (Oklahom a) to 37% (Kansas). Other source categories contributed significantly to the NH 3 inventory in individual stat es. For example, industrial point sources were responsible for only about 3% of the NH 3 em issions in the CENRAP region as a whole, but m a de up ove r 13% of the inventory for Louisiana. (See Figure 3 for a breakdown of

em issions by state and source catego ry and Figure 4 for a m a p of e m i ssions densities for the CENRAP region).
Figure 3. 20 02 NH3 e m i ssions by so urce category for each state of the C E NRAP region.

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A R IA K S L A MN MO N E O K T X St a t eNH3 (tons/year) Mi s c . So u r ce s

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Figure 4. Geographic map of e m issions densitie s for the CENRAP region, July 10, 2002.

The seasona l var i ability of NH 3 e m is sions follow s a bim odal patte rn, with a large spring peak in April and a sm aller fall peak in October (see Figu re 2). These peaks corr espond to tim es when manure and fertilizer are typically applie d to croplands. Em issions are lowe st during the winter m onths of Decem ber, January, and February.

CONCL US IONS
As anticipated, livestock production and fertilizer application were the m o st significant sources of NH 3 e m is sions in the CENRAP region, accoun ting for 80% of the total em issions in 2002. For the

56 region as a whole, estimates for these two source cat egories were within 5% of the totals previously predicted by version 3.0 of the CMU model. Howeve r, differences from stat e to state and county to county are much more pronounced. For example, CENRAP’s emissions from livestock and poultry production in Kansas and from fertil izer application in Nebraska ar e 14% and 27% higher, respectively, than predicted by the CMU model.
Similarly, temporal emission distributions are significantly different when the CENRAP and CMU model inventories are compared. As Figur e 2 shows, CENRAP’s emissions estimates are consistently higher than those from the CMU mode l inventory for spring, su mmer, and fall, but the reverse is true for winter. These significant cha nges in the geographic and temporal distribution of emissions are critical for modeling efforts, throug h which the impact of emission sources on Class I areas will be analyzed throughout the region.

REFERENCES

1. National emission inventory—Ammonia em issions from animal husbandry operations . U.S. Environmental Protection Agency, 2004. Available at: <http://www.epa.gov/ttn/chief/net/2002inventory.html>; last accessed April 22, 2004.

2. Pinder, R.; Anderson, N.; Strader, R.; Davidson, C.; Adams, P. "Ammonia emissions from dairy farms: development of a farm model and estima tion of emissions from the United States". In Proceedings, EPA’s 12th International Emission Inventory Conference, April 29 - May 1, 2003

. 3. Aarnink, A. A. J. Ammonia emission from houses for grow ing pigs as affected by pen design, indoor climate, and behavior ; Kok-Lyra Publishers; Kampen, The Netherlands, 1997.

4. Anderson, I. C.; Levine, J. S. "Simultaneous fiel d measurements of biogenic emissions of nitric oxide and nitrous oxide", J. Geophys. Res. 1987, 92, 965-976.
5. European Environment Agency. Joint EMEP/CORINAIR Atmospheric Emission Inventory Guidebook ; Third Edition ed.; European Environment Agency; Copenhagen., 2001.

6. Strader, R.; Anderson, N.; Davidson, C. "User' s Guide--CMU Ammonia Inventory Version 2.0"; 2001.

7. Sutton, M. A.; Milford, C.; Nemitz, E.; Theoba ld, M. R.; Hargreaves, K. J.; Fowler, D.; Schjoerring, J. K.; Mattsson, M. E.; Husted, S.; Erisman, J. W.; Hensen, A.; Mosquera, J.; Otjes, R.; Jonejon, P.; Cellier, P.; Loubet, B.; David, M. ; Neftel, A.; Blatter, A.; Herrmann, B.; Jones, S. K.; Horvath, L.; Weidinger, T.; Raso, J.; Me szaros, R.; Fuhrer, E.; Mantzanas, K.; Koukoura, Z.; Papanastasis, V.; Gallagher, M.; Dorsey, J. R.; Flynn, M.; Riedo, M. "Biosphere-atmosphere interactions of ammonia with European Grasslands"; Cont racts: ENV4-CT98-0722 and IN20- CT98-0118, Final synthesis report prepared for Gr assland Ammonia Interactions Across Europe (GRAMINAE) 2002.
8. Battye, W.; Aneja, V. P.; Roelle, P. A. "E valuation and improvement of ammonia emissions inventories", Atmos. Environ. 2003, 37, 3873-3883.
9. Schlesinger, W. H.; Hartley, A. E. "A global budget for atmospheric NH 3 ", Biogeochemistry 1992, 15, 191-211.7 10. Buowman, A. F.; Lee, D. S.; Asman, W. A. H.; De ntener, F. J.; Van der Hoek, K. W.; Olivier, J. G. F. "A global high-resolution emission inventory for ammonia", Global Biogeochemical Cycles 1997, 11(4), 561-587.

11. Kinnee, E.; Geron, C.; Pierce, T. "United States land use inventory for estimating biogenic ozone precursor emissions", Ecological Applications 1997, 7, 46-58.
12. Van der Hoek, K. W. "Estimating ammonia emissi on factors in Europe: summary of the work of the UNECE ammonia expert panel", Atmos. Environ. 1998, 32, 315-316.

13. Coe, D. L.; Reid, S. B. "Research and devel opment of ammonia emissi on inventories for the Central States Regional Air Planning Associat ion"; STI-902501-2241-FR, Final report prepared for The Central States Air Resource Agenci es and The Central Regional Air Planning Association, Oklahoma City, OK. Prepared by Sonoma Technology, Inc., Petaluma, CA. 2003.

ACKNOWLEDGMENTS

The authors wish to thank the co-chairs of the CENRAP’s Emission Inventory Work Group (EIWG), Kathy Pendleton (Texas Commission on E nvironmental Quality) and Elizabeth Byers (Louisiana Department of Environmen tal Quality), for their guidance, te chnical feedback, and leadership of the EIWG. In addition, the authors wish to th ank the members of the EIWG, as well as many other individuals and stakeholders, for pr oviding their valuable ideas and i nputs. Further information about this study can be found in the project final report : “Research and Development of Ammonia Emission Inventories for the Central States Regional Air Planning Association.” 138 KEY WORDS

Ammonia Emissions Inventories Emission Inventory Livestock Fertilizer CENRAP Arkansas Iowa Kansas Louisiana Minnesota Missouri Nebraska Oklahoma Texas CMU model