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2020 National Emissions Inventory Technical
Support Document: Agriculture - Livestock
Waste

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EPA-454/R-23-001j
March 2023

2020 National Emissions Inventory Technical Support Document: Agriculture - Livestock

Waste

U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC

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Contents

List of Tables	i

10	Agriculture - Livestock Waste	10-1

10.1	Sector Descriptions and Overview	10-1

10.2	Sources of data	10-2

10.3	EPA-developed emissions	10-3

10.3.1	Activity data	10-3

10.3.2	Methodology overview	10-4

10.3.3	Emissions factor development	10-5

10.3.4	Process for estimating emissions	10-7

10.4	Emissions Summaries	10-12

10.4.1 Improvements/Changes in the 2020 NEI	10-14

10.5	References	10-17

List of Tables

Table 10-1: Livestock Waste SCCs that are estimated by EPA methods for 2020 NEI	10-1

Table 10- 2: Agencies that submitted Ag Livestock Waste emissions to the 2020 NEI	10-2

Table 10-3: Animal-specific VOC fractions used to estimate HAPs for this sector	10-6

Table 10-4: Description and sources of model inputs and parameters	10-10

Table 10-5: Model Input parameters related to manure characteristics	10-10

Table 10-6: Tuned model parameters for beef, swine, and poultry	10-12

Table 10-7: Tuned Parameter Values by practice and animal type for the 2020 NEI	10-12

Table 10-8: Animal population and national NH3 total emissions from: 2014, 2017, and 2020 NEIs ...10-12

Table 10-9: FEM farm manure management practice configuration probability table	10-15

Table 10-10: Description of farms in NAEMS including managment practices by animal type	10-16

List of Figures

Figure 10-1: Nitrogen flow in the FEM, used to estimate livestock waste NH3 emissions in 2020 NEI .10-5
Figure 10-2: Total NH3 emissions from livestock waste sector, 2020 NEI	10-13

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10 Agriculture - Livestock Waste

10.1 Sector Descriptions and Overview

The emissions from this category are primarily from domesticated animals intentionally reared for the
production of food, fiber, or other goods or for the use of their labor. The livestock included in the EPA-
estimated emissions include beef cattle, dairy cattle, goats, ponies, horses, poultry (layers and broilers),
sheep, turkeys and swine. We use the Farm Emissions Model (FEM) developed by Carnegie Mellon
University (CMU) to estimate the EFs from swine, layers, broilers, beef cattle and dairy cattle. For the
other animals estimated by EPA methods, we employ a nationwide average EF multiplied by appropriate
activity data. A few S/L/T agencies report data from a few other categories in this sector such as
domestic and wild animal waste, though these emissions are very small compared to the livestock listed
above. The domestic and wild animal waste emissions are not included for every state and not
estimated by the EPA. The pollutants that EPA reports using its methods for this sector are NH3, VOC,
and some VOC-HAPs by animal type as described further below.

The SCCs shown in Table 10-1 represent those for which EPA provides nationwide estimates, and in grey
highlight are the SCCs for which we use the FEM model; SCC level 1 are "Miscellaneous Area Sources"
and SCC level 2 are "Agricultural Production - Livestock" for all SCCs.

Table 10-1: Livestock Waste SCCs that are estimated by EPA methods for 2020 NEI

SCC

SCC Level 3

SCC Level 4

2805002000

Beef cattle production composite

Not Elsewhere Classified

2805018000

Dairy cattle composite

Not Elsewhere Classified

2805025000

Swine production composite

Not Elsewhere Classified

2805007100

Poultry production - layers with dry
manure management systems

Confinement

2805009100

Poultry production - broilers

Confinement

2805010100

Poultry production - turkeys

Confinement

2805045000

Goats Waste Emissions

Not Elsewhere Classified

2805035000

Horses/ Ponies Waste Emissions

Not Elsewhere Classified

2805040000

Sheep and Lambs Waste Emissions

Total

10-1

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It should be noted that there are other SCCs that make up this sector in the NEI, and SLTs can report to
them. However, they will be minor contributors to the overall emission levels from this sector and many
of those data are removed via our "tagging" process and are reviewed carefully via our QA process so
there is no double counting with emissions the EPA estimates and reports to the NEI.

10.2 Sources of data

The agencies listed in submitted emissions for this sector; agencies not listed used EPA estimates for
the entire sector. Some agencies submitted emissions for the entire sector (100%), while others
submitted only a portion of the sector (totals less than 100%). In cases where a full submittal was not
made, EPA data was used to backfill according to the information provided in the nonpoint survey for
this sector. Some states submitted to SCCs that EPA does not estimate via the CMU model (more details
provided later), but those emissions will all be small, and care was taken in assembling the final data for
this sector not to double count emissions across state submitted emissions and EPA developed
emissions.

Table 10- 2: Agencies that submitted Ag Livestock Waste emissions to the 2020 NEI

Region

Agency

S/L/T

3

Delaware Department of Natural Resources and Environmental Control

State

9

Arizona Division of Air Quality

State

8

Utah Division of Air Quality

State

9

California Air Resources Board

State

10

Coeur d'Alene Tribe

Tribe

10

Idaho Department of Environmental Quality

State

10

Kootenai Tribe of Idaho

Tribe

10
10

Nez Perce Tribe

Shoshone-Bannock Tribes of the Fort Hall Reservation of Idaho

Tribe
Tribe

Through the rigorous 2020 NEI nonpoint QA Process, we tagged out all of the emission submitted by
California, Delaware, and Idaho (with agreement from each of those states) and used EPA's estimates
instead across the board. The only SLT emissions remaining in this sector are from Utah, AZ, and the
tribes.

It should be noted that there are a couple of "Industrial Processes" point source SCCs for this sector. CA
is the only state in the 2020 NEI to submit to point source ammonia for this sector, and only a very
negligible amount of emissions. Some other states have submitted small amounts of PM, which is not
an expected EPA pollutant for this sector. EPA thus "tags out" all PM from this sector. In general, point
source emissions from this sector are negligible, particularly for NH3, compared to the nonpoint
emissions (many orders of magnitude lower). Generally, these emissions are ignored in the Nonpoint
NH3 emissions accounting process. All point source emission totals and will be ignored in all subsequent
discussions here and will not be included in the totals in other parts of this document for this sector. No
point source subtraction is deemed necessary for this sector.

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10.3 EPA-developed emissions

The general approach to calculating NH3 emissions due to livestock is to multiply the emission factor (in
kg per year per animal) by the number of animals in the county. The county-level NH3 emissions factors
are estimated using the FEM and county-level daily average meteorology (ambient temperature, wind
speed, and precipitation) [ref 1, 2], Once the FEM estimates NH3 emission factors by animal type, the
county-level NH3 emission factors (EFca) will be multiplied with the latest NEI animal population (Ac,a) to
compute the county-level NH3 emissions (Eca) for all animal types.

Where:

Ec,a, = NH3 emissions for animal type a and county c (short ton)

EFc,a, = NH3 emissions factor from the FEM model for animal type a and county c (kg/head)
Ac,a = animal count for animal type a and county c (head)

2.2/2000 = conversion factor from kg to short tons

VOC emissions were estimated by multiplying a constant national VOC/NH3 emissions ratio of 0.08 to
county-level NH3 emissions. Hazardous air pollutants (HAP) emissions were estimated by multiplying the
county-level VOC emissions by HAP/VOC ratios, which are obtained from the literature and can vary by
animal type. The VOC emissions (EV0Cca) are calculated using the ratio of VOC to NH3 emissions from
livestock. That ratio is 0.08 kg of VOC for every kg of NH3. HAP emissions were estimated by multiplying
the county-level VOC emissions by HAP/VOC ratios.

Where:

VOC/NHs	= 0.08 (Ratio of VOC/NH3)

Evoc,c,a =	VOC emissions for animal type a and county c (ton)

Ec,a =	NH3 emissions for animal type a and county c (ton)

10.3.1 Activity data

The activity data for this source category is based on livestock counts (average annual number of
standing heads) and population information by state and county used to develop U.S. EPA's Greenhouse
Gas (GHG) Inventory [ref 3], This data set is derived from multiple data sets from the United States
Department of Agriculture (USDA), particularly the National Agricultural Statistics Service (NASS) survey
and census [ref 4], The USDA NASS survey dataset, which represents the latest available, 2020 national
livestock data, is used to obtain the livestock counts for as many counties as possible across the United
States. For a full description of the GHG livestock population estimation methodology, the reader
should refer to the referenced citation for the EPA's GHG inventory document [ref 3],

Generally, counties not specifically included in the NASS survey data set (e.g., due to business
confidentially reasons) are known as "D counties". They were gap-filled based on the difference in the
reported state total animal counts, and the sum of all county-level reported animal counts. State-level
data on animal counts from the GHG inventory were distributed to counties based on the proportion of
animal counts in those counties from the 2020 NASS census. The general methods to allocate animal
populations from state to county, based on lack of data at the county level, can be found in the EPA's

ECia = EFca X ACia X 2.2/2000

(1)

Evoc,c,a ~ VOC/NH3 X Eca

(2)

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GHG Inventory document [ref 3], Equation (1) is used to allocate animal population to county, as
needed:

Pa,c ~ Pa,s * ra,c	(1)

Where:

Pa, c, 2020
Pa, s,2020

ra,c,2020

type a in county c

= Estimated population of animal type a in county c

= NASS survey reported state-level population of animal type a in state s

= Ratio of animal county- to state-level animal counts from the NASS census for animal

When we come across any "D counties", the county-level methodology relies on evenly distributing the
'available' population (the difference between the state population and the sum of the "non-D
counties") to each D county in the state. So, for example, if Broward, Orange, and Polk counties in
Florida are "D" and the sum of the non-D counties is 6,000 compared to a reported 9,000 population for
a given animal in FL, each of those counties each get 1,000 head. The point of determining the county
population is to get a ratio for each county/year/animal. That ratio is multiplied by the NASS population
(the goal is to always match the NASS data). That resulting value is then the estimated county-level
population. This procedure is very similar to how we handled these data in the 2017 NEI.

Please note that as with other sectors that rely on animal counts for activity, we allow SLTs to submit
activity information. Those SLT-submitted activity data are quality assured and used over EPA estimates
as appropriate. Please consult other parts of this document for the SLT data that were used over EPA's
for animal count activity. The final animal count data used in the 2020 NEI data for the CMU FEM model
animals are shown in Table 10-8. Only dairy cattle showed a bit of a growth in going from 2017 to 2020.

10.3.2 Methodology overview

Many of the methods and data described for this sector mirror exactly what was done in the 2017 NEI,
expect that 2020 information was used and the CMU FEM model was actually run for 2020. Thus,
throughout this section the reader should refer to the 2017 NEI TSD (section 4.5) for any further details
above and beyond what's provided in this document.

Before discussing the process used for the 2020 NEI, a brief discussion of EPA methods used in 2014 and
2017 NEI is beneficial. In 2004, Carnegie Mellon University (CMU) developed the FEM (Farm Emissions
Model) to first estimate NH3 emissions from only dairy farms [ref 5, 6, 7], Over time, this model was
modified to include all major animal types, such as swine, dairy cattle, beef cattle, poultry layers, and
poultry broilers [ref 1, 2], In the 2014 NEI, EPA implemented the FEM which is a semi-empirical process-
based emissions model, as the model is based on a nitrogen mass balance with inputs of meteorological
parameters and management practices to obtain the desired output of ammonia emissions as a function
of time but also be constrained through the use of tuned parameters to ensure agreement with
previously reported ammonia emission factors (see general diagram Figure 10-1 below and references
[ref 1, 2] for more details). The semi-empirical process-based emission modeling approach allows us to
evaluate the model for consistency with measured emission factors, maintain consistency by tracking
the actual nitrogen available for emission (and also estimate uncertainty in our model's estimates of
ammonia emissions, producing daily (and seasonally) variable EFs by animal type. Note that for the NEI,
we aggregate emissions to the county level on annual basis as required by the nonpoint sector.

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Figure 10-1: Nitrogen flow in the FEM, used to estimate livestock waste NH3 emissions in 2020 NEI

In the 2014 NEI, our estimates were developed by a graduate student working CMU. While she passed
on the code and input files to EPA, when we attempted to use these in the 2017 NEI, we were not
successful in reproducing some of her estimates; thus, we went to a simple ratioing technique (using
meteorology changes from 2014 to 2017) to estimate emissions for this sector in the 2017 NEI. For the
2020 NEI, we were able to better reproduce the 2014 results and used the original FEM code provided
by CMU with some improvements to estimate NH3 emissions for this sector. In this TSD, we summarize
the 2020 NEI Process, leaving out a lot of the details which can be found in the 2017 NEI TSD, since they
are unchanged. The 2020 NEI improvements section (Section 10.4.1) details new items that were added
in the 2020 cycle.

In the 2020 NEI, the EPA methodology for ammonia emissions that results from the use of the CMU
model, includes all processes from the housing/grazing, storage and application of manure from beef
cattle, dairy cattle, swine, broiler chicken, and layer chicken production, and these are assigned to the
"EPA" SCCs listed in Table 10-1. It is assumed the EFs used also account for, on average, all the
management practices that are used in waste treatment for each of those animals.

10.3.3 Emissions factor development

CMU developed a model to estimate NH3 emissions from livestock [ref 1-6], This model produces daily-
resolved, climate level emissions factors for a particular distribution of management practices for each
county and animal type (for dairy cows, beef cattle, swine, poultry layers, and poultry broilers only), as
expressed as emissions/animal. These county level emissions factors are then combined together to
create a state level emissions factor for each animal type. Thus, the CMU model provides a state specific
emission factor for each animal type (NH3 emissions/head). For the non-CMU model animals that EPA
estimates emissions for, we are reliant on use of population counts that come from the same source as
described above combined with one national EF for each animal type (horses, goats, turkeys, and sheep)
[ref 8], VOC emissions are always a constant 8% of NH3 emissions.

To develop emissions factors for the 2020 NEI for the CMU-based animals, the CMU model was modified
to use hourly meteorological data. HAP emissions were estimated by multiplying county-specific VOC
emissions by speciation factors that are animal-specific as shown in Table 10-3 below. The HAP
emissions are animal-specific and come from the SPECIATE database, as described in the 2017 NEI TSD

10-5

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for this sector. The HAP fractions found in SPECIATE are multiplied by the VOC estimates, record-by-
record, to estimate HAPs for this sector.

Table 10-3: Animal-specific VOC fractions used to estimate HAPs for this sector

see

Animal Type

HAP

Fraction of VOC

SPECIATE Profile
Number

2805002000

Beef Cattle

1,184-Dichlorobenzene

0.0013



2805002000

Beef Cattle

Methyl isobutyl Ketone

0.0008



2805002000

Beef Cattle

Toluene

0.0110

95240

2805002000

Beef Cattle

Chlorobenzene

0.0001

2805002000

Beef Cattle

Phenol

0.0006



2805002000

Beef Cattle

Benzene

0.0001



2805007100

Poultry—Layers

Methyl isobutyl ketone

0.0169



2805007100

Poultry—Layers

Toluene

0.0018



2805007100

Poultry—Layers

Phenol

0.0024



2805007100

Poultry—Layers

N-hexane

0.0111



2805007100

Poultry—Layers

Chloroform

0.0025



2805007100

Poultry—Layers

Cresol/Cresylic Acid (mixed isomers)

0.0048



2805007100

Poultry—Layers

Acetamide

0.0075

95223

2805007100

Poultry—Layers

Methanol

0.0608



2805007100

Poultry—Layers

Benzene

0.0052



2805007100

Poultry—Layers

Ethyl Chloride

0.0031



2805007100

Poultry—Layers

Acetonitrile

0.0088



2805007100

Poultry—Layers

Dichloromethane

0.0002



2805007100

Poultry—Layers

Carbon Disulfide

0.0034



2805007100

Poultry—Layers

2-Methyl Napthalene

0.0006



2805009100

Poultry-Broilers

Methyl isobutyl ketone

0.0169



2805009100

Poultry-Broilers

Toluene

0.0018



2805009100

Poultry-Broilers

Phenol

0.0024



2805009100

Poultry-Broilers

N-hexane

0.0111



2805009100

Poultry-Broilers

Chloroform

0.0025



2805009100

Poultry-Broilers

Cresol/Cresylic Acid (mixed isomers)

0.0048



2805009100

Poultry-Broilers

Acetamide

0.0075

95223

2805009100

Poultry-Broilers

Methanol

0.0608



2805009100

Poultry-Broilers

Benzene

0.0052



2805009100

Poultry-Broilers

Ethyl Chloride

0.0031



2805009100

Poultry-Broilers

Acetonitrile

0.0088



2805009100

Poultry-Broilers

Dichloromethane

0.0002



2805009100

Poultry-Broilers

Carbon Disulfide

0.0034



2805009100

Poultry-Broilers

2-Methyl Napthalene

0.0006



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2805018000

Dairy Cattle

Toluene

0.0018

8897

2805018000

Dairy Cattle

Cresol/Cresylic Acid (mixed isomers)

0.0276

2805018000

Dairy Cattle

Xylenes (mixed isomers)

0.0046

2805018000

Dairy Cattle

Methanol

0.3542

2805018000

Dairy Cattle

Acetaldehyde

0.0141

2805025000

Swine

Toluene

0.0047

95241

2805025000

Swine

Phenol (Carbolic Acid)

0.0179

2805025000

Swine

Benzene

0.0035

2805025000

Swine

Acetaldehyde

0.0155

For the non-FEM animals (goats, sheep, horses/ponies, and turkeys), animal-specific HAP speciation
profiles were not available in the literature, so the following assignments were made:

Sheep and Goats

Same HAP fractions as Dairy Cattle

Turkeys

Same HAP fractions as Chicken-Broilers

Horses/Ponies

Same HAP fractions as Beef Cattle

10.3.4 Process for estimating emissions

From a modeling perspective, the 2020 NEI process shadows what was done in the 2014 NEI, as
described in the 2017 NEI TSD, with some built in improvements to the 2020 NEI as discussed in the next
section.

However, unlike the 2017 NEI process, 2020 NEI for livestock waste emissions were estimated using
actual FEM simulations with the latest USDA animal population representing the year 2020, enhanced
county-level daily 2020 meteorology, after first calibrating the model with 2014 estimates developed
earlier by CMU researchers.

The remainder of this section details high-level procedures used to arrive at the 2020 NEI estimates as
well as presenting a summary of the model parameters derived for the 2020 process.

The basic steps in developing the 2020 inventory involved these basic steps:

•	Develop county-specific daily meteorology inputs based on the MCIP meteorology over the US
domain

•	Run FEM to produce daily NH3 emission factors with county-specific meteorology and farm
management practices, and animal-specific model parameters

o Repeat for all farm processes (housing, storage, application, and/or grazing)

o Compute a county composite process-specific EF as a weighted average across all
manure management practices in that county.

o Repeat for all animal types

•	County-based Emissions = (Emissions Factor from CMU model) x (Animal Population)

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o Resulting data has structure of emissions = f(county, day, livestock type, "practice")
where "practice" " is shorthand for the different housing/storage/application
configurations that prevail in a county.

• Result is ammonia emissions with:
o Daily temporal resolution
o County spatial resolution
o By livestock type and management practice

In the overall process described above, note that the FEM gets seasonal/daily variability due to the
resistance parameters in each sub model (see 2017 NEI TSD) being dependent on meteorology. The
model gets variability due to management practices because there is a separate resistance sub-model
for each livestock type, by manure management stage (housing, storage, etc.), and by major practice
(how often there are cleanouts). Regional variation comes from both meteorology effects and from
differences in practices across the country. It should be noted that 3 meteorological variables that
matter the most include: temperature, wind speed and precipitation.

Note that the FEM model does not cover Alaska, Hawaii, Virgin Islands, or Puerto Rico (only the lower 48
states) due to the lack of meteorology, we would thus be reliant on SLT submissions to cover this sector
for those states.

10.3.4.1	Meteorology

The source code provided to EPA for FEM model contained weather data for 2014. It did not use
standard identifiers (WBAN ID) and was limited to a small number of observations with an unknown
source. The FEM weather data used a single monthly value for wind, temperature, and precipitation.
FEM interpolated this data to hourly using different techniques. For temperature, a standard deviation
was used to raise and lower the mean temperature in the month. For wind speed, the average monthly
value was used for all hours. For precipitation, monthly amounts were divided into days (an hours)
based upon a parameter defining the frequency of rain in a month. These were all upgraded in the 2020
modeling process as described in the next section.

10.3.4.2	Animal practice documentation

The animal practice documentation used here is a summary of the information provided in A.
McQulling's dissertation entitled, "Ammonia emissions from livestock in the United States: from farm-
level models to a new national inventory." The reader should consult those references [ref 1, 2] for
further information.

Ammonia emissions from livestock depend on two major factors—the management practices employed
by the producers (i.e. what housing, storage and application methods are used) and the environmental
conditions of location where the farm is situated (i.e. temperatures, wind speeds, precipitation). All of
these factors have significant impacts on the conditions of the manure and waste (e.g. water content,
total ammoniacal nitrogen concentration) and as a result can enhance or reduce the emissions of
ammonia from these sources. The CMU model requires farm-type inputs which describe the type of
animal housing, manure storage and application methods used for a particular location. Each location is
expected to have some combination of practices; for example, in a single county, some of the swine

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farms may use deep-pit housing, lagoon storage, and irrigation application while other farms use
shallow-pit housing with lagoon storage and injection application.

In order to understand the differences in regional preferences for particular manure management
strategies, information was extracted from the most recent National Animal Health Monitoring Surveys
done by the USDA [ref 1, 2, 9-29], The beef cattle NAHMS was completed in 2007 and feedlot beef in
2011; dairy cattle data was from 2002 and 2007; swine data were collected for 2006 and 2012, and the
most recent poultry NAHMS was completed for 2010. The most recent data available had limited spatial
resolution and so the model is only able to resolve large-scale regional differences in practices. For beef
cow calf systems, the United States was divided into four regions, but only two regions for beef housed
on feedlots. For swine, the country was divided into three regions—Midwest, East, and South, and for
layers, there were four regions—Northeast, Southeast, Central and West. An additional limitation in the
data available for the characterization of the farm practices was that for some of the questions asked by
the study, results were only reported in terms of percent of operations which used a particular practice.
This may give too much weight to the practices used on smaller farms which have a relatively small
contribution to the overall level of ammonia emissions from a particular livestock type or practice. Thus,
some uncertainty is expected as a result of the limited quantity of data available regarding manure
management practices throughout the country. As was previously discussed by Pinder et al. [ref 5-7],
one of the main factors most limiting to the FEM's skill is the lack of information about manure
managment practices throughout the country. It is unclear whether these uncertainties result in the
overprediction or underprediction of total ammonia emissions from livestock in the United States. For
more detail on the NAHMS by animal type, the reader is referred to the 2017 NEI TSD, as that
information has not changed in going from 2017 to 2020 NEIs.

10.3.4.3	Model parameters

The FEM is a tuned model that applies adjustments to approximate observed data. However, the model
evaluation does not reflect the ability of the FEM to predict completely independent measurements but
the ability of a relatively simple process-based model, with a single set of mass transfer parameters for
each manure management practice, to describe the full range of observed variability.

The National Air Emissions Monitoring Study (NAEMS) data [ref 30] and literature data are used to both
tune the mass balances for different types of animal management practices as well as help set the
parameters the model needs to conduct the mass balance and estimate ammonia. The NAEMS
information is clearly outlined in the 2017 NEI TSD, the reader is referred to that document. It should be
noted that literature data beyond the NAEMS data is required, because the NAEMS dataset does not
cover emissions measurements for beef cattle operations nor does it cover several specific animal
manure management practices for some animals. Please refer to the 2017 NEI TSD for more details and
for references on this part of the process.

10.3.4.4	Manure Characteristics

Manure characteristics are important input parameters to the model because they govern the amount
of nitrogen available for emission, whether or not the nitrogen present is likely to be volatilized, and
how well the waste can infiltrate into the soil during manure application. These parameters have been
selected based on information extracted from published literature as well as reports from the NAEMS
study. Table 10-4 describes the types of parameters and inputs critical to the model and Table 10-5

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presents information about manure volume, nitrogen concentration and pH levels in the waste from
each type of animal included in the model. Please consult Table 4-38 in the 2017 NEI TSD for references
for the values shown in Table 10-5 below. The differences between Table 10-5 and what's shown in
Table 4-38 of the 2017 NEI TSD result from tuning of the model under 2020 conditions, as described
later in this document.

Table 10-4: Description and sources of model inputs and parameters

Data Type

Description

Source of input or parameter

Input or Tuned
Parameter?

Meteorology

Temperature (°C)
Wind speed (m/s)
Precipitation

From National Climate Data Center, based
on farm location

Input value (monthly
average for seasonal
emissions, daily values
for daily model run)

Manure
Management
Practice

Type of housing,
storage, or
application

Unique to each farm type; farm types have
a unique set of inputs

Input value

Resistance
Parameters

Surface mass
transfer resistance
from manure to
atmosphere

Tuned based on literature and NAEMS
observations to agree with previous work;
constant for a particular management
practice (for a particular animal type)

Tuned Parameters

Table 10-5: Model Input parameters related to manure characteristics



Animal Type

Value Used in

Units

Parameter Name

Model

Manure Volume

Beef Cattle

8.0

animal1 day1



Dairy Cattle

6.0

animal1 day1



Swine

6.0

animal1 day1



Poultry-Layer

0.07

animal1 day1



Poultry-Broiler

0.6

finished animal1

Manure Urea

Beef Cattle

10.0

kg N animal1 year1

Concentration

Dairy Cattle

14.0

kg N animal1 year1



Swine

19.0

kg N animal1 year1



Poultry-Layer

0.5

kg N animal1 year1



Poultry-Broiler

0.05

kg N finished animal1

Housing pH

Beef Cattle

7.0

Dimensionless



Dairy Cattle

7.7

Dimensionless



Swine

7.0

Dimensionless



Poultry-Layer

7.3

Dimensionless



Poultry-Broiler

7.3

Dimensionless

Storage pH

Dairy Cattle

7.3

Dimensionless



Swine

7.7

Dimensionless

Application pH

Beef Cattle

7.8

Dimensionless



Dairy Cattle

7.5

Dimensionless



Swine

7.8

Dimensionless



Poultry-Layer

7.2

Dimensionless



Poultry-Broiler

7.3

Dimensionless

Storage pH

Beef Cattle

7.7

Dimensionless



Dairy Cattle

7.7

Dimensionless

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There are only a very limited number of studies which describe the manure nitrogen and manure pH for
each animal type. As a result, there is considerable uncertainty in these input values which can result in
significant uncertainty in predicted emissions from the model.

10.3.4.5 Tunable Parameters

The FEM is a balance between an empirical approach and first-principles process-based model. A
nitrogen mass balance and a process description of ammonia losses are used, but the FEM model
parameters are tuned to reproduce measured emissions factors. Model complexity is limited to the
most important emissions processes and to inputs that are typically available. The strategy pursued for
developing process-based models is guided by the need to build emissions inventories, and the
requirements and data limitations associated with this application. Previous measurement campaigns
also often sampled emissions from a single part of the production process. This means that information
about the emissions process from the start to end of production might be lacking, making nitrogen mass
balance in the system difficult. The lack of whole-farm measurements is one gap in much of the
literature available and a benefit of the estimates of ammonia emissions produced by the FEM.

There are 2-3 tunable parameters associated with each sub-model in the farm emissions model. These
tunable parameters allow adjustment of model-predicted emissions and to correct for the unknowns
and uncertainties of the input parameters and to ensure that the model-predicted values are consistent
with those that have been reported in the literature and in the National Air Emissions monitoring study;
they are constant for a particular farm type—tuning is not done for a particular farm—and as a result,
there can be significant disagreement between model predictions and the measured emissions for a
single farm. The goal of the FEM is not necessarily to capture the emissions of single farms perfectly, but
rather to capture the effects of various parameters on emissions on a farm typical of a certain set of
practices.

In the FEM, as previously described [ref 1], ammonia emissions are estimated as a function of the
nitrogen present in the waste and the mass transfer resistance. This resistance is made up of the
following three parts: the aerodynamic (ra), quasi-laminar (r/,), and surface resistances (rs) [ref 33],
Aerodynamic and quasi-laminar resistances are used to describe the resistance to transport in the
gaseous layer above the animal wastes [ref 31, 34, 35], These parameters are based on widely used
theoretical formulas and are not tuned. The third part of the resistance is the surface resistance from
diffusion closest to the gas-liquid (manure) interface. Here, the surface resistance is a function of tuned
parameters as well as temperature which ensures the modeled ammonia emission factors are consistent
with observations; Table 10-6 lists which tunable parameters are used for each animal and each sub-
model.

These values are specific to a particular practice for a particular animal type. This means that a free stall
dairy with lagoon storage and injection application would employ the same tuned parameters whether
it was located in New York or California. Conversely, two farms in the same location but utilizing
different manure management practices would have different tuned parameters in their sub-models.
The values that have been used for each of these parameters can be found in Table 10-7 [ref 1, 2], The
2017 NEI TSD provides further references for the values discussed in Table 10-6 and shown in Table 10-
7.

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Table 10-6: Tuned model parameters for beef, swine, and poultry

Sub-model

Animal Type

Description

Housing

Cattle: Beef & Dairy
Swine

Poultry: Broiler & Layer

Resistance parameters Hi, H2

Storage

Dairy Cattle
Swine

Resistance parameters Si, S2

Application

Cattle: Beef & Dairy
Swine

Poultry: Broiler & Layer

Resistance parameters Ai, A2, A3

Grazing

Cattle: Dairy & Beef

Resistance parameters Gi, G2

Table 10-7: Tuned Parameter Values by practice and animal type for the 2020 NEI

Sub-model

Description

Animal Type

Tuning/Evaluation Sources



Resistance
parameters

Hi, H2

Dairy Cattle

H,=0.1 (s*m ^"C-1), H2=-0.015 (s2m 2)

Housing

Swine
Poultry-Broiler

H,=0.1 (s*m-^"C-1), H2=-0.08 (s2m 2)
H,=0.15 (s*m ^"C-1), H2=-0.0035 (s2m"2)



Poultry-Layer

H,=0.1 (s*m ^"C-1), H2=-0.001 (s2m"2)

Storage

Resistance
parameters

Si,S2

Dairy Cattle
Swine

Si=0.1(s*m"1), S2=1.00(s*m^'"C
Si=0.2(s*m-1), S2=4.00(s*m-^"C1)



Resistance

Dairy Cattle

A,=0.0004(s*nr1), A2 =8.8, As=1.4

Application

parameters

Swine

A,=0.001(s*m-1), A2 =-10, As=20



Ai, A2, A3

Poultry

A,=0.001(s*m-1), A2 =-0.01, As=0.2

Grazing

Resistance
parameters

Gi, G2

Dairy Cattle
Beef Cattle

G,= 0.12(s*m-1), G2=5.4

There are no controls assumed for this source category. Example calculations based on the sequence of
steps listed in the "2020 Process for estimating emissions" section shown above can be very involved,
but the 2017 NEI TSD section 4.5 shows an example of how these calculations are made. The program
that contains the FEM code will be made available to the public once we have finished all of the
documentation and some specific QA steps associated with the code.

10.4 Emissions Summaries

Table 10-8 below shows the comparison of animal population and national NH3 emissions total between
NEI 2014, 2017, and 2020. The average national ammonia emissions changes (tons/year) between 2017
and 2020 NEIs range from 1% (Swine), +10% (Beef Cattle), +15% (Broiler), +17% (Layer) to +22% (Dairy).
These increases in emissions result from a combination of differences in meteorology, increased animal
counts, and some updated manure management practice information. Please note that these numbers
represent only EPA estimates, but a noted in the earlier section, there were only a few emission
submissions made the SLTs to this sector and most states accepted our estimates. So, the 2020
numbers shown here should be reflective of actual 2020 NEI emissions at the national level.

Table 10-8: Animal population and national NH3 total emissions from: 2014, 2017, and 2020 NEIs

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Animal

Animal Population
(Number of animals*1000)

Total Emissions
(tons/year)

NEI2014

NEI2017

NEI2020

NEI2014

NEI2017

NEI2020

Beef

79,367

81,414

80,658

590,424

634,695

698,170

Dairy

9,035

18,888

18,802

225,919

475,573

580,858

Swine

67,766

72,145

77,255

722,622

834,314

845,306

Layer

362,319

497,254

509,914

73,492

109,404

127,548

Broiler

1,506,271

1,621,047

1,676,730

228,723

260,764

299,691

In Figure 10-2 below, actual 2020 NEI results are shown as total NH3 emissions by county. The hotspots
are seen to be in the San Joaquin Valley in CA, parts of the Midwest and eastern NC. Beef and dairy
cattle emissions drive the hotspots in California mostly. Poultry emissions dominate the southeastern
US hotspots, and swine emissions are very prevalent in NC. Turkeys (which are not estimated outside of
the FEM model) are important in both NC and in MN areas of the country.

In general, there is seen to be about a 5% increase in ammonia emissions in going from 2017 to 2020
NEIs which manifest as increases or decreases by different animal types across the states.

Figure 10-2: Total NH3 emissions from livestock waste sector, 2020 NEI

Legend

I I State Boundaries

Sum NH3
Emissions (TON)

~ 0 or No Value

Min: 0.195406 - 150
150 - 1,000
r 1 1,000 - 5,000

IZZ! 5,ooo -10,000

10,000 - 20,000
20,000 - Max:
42,263.994586

Alaska

Hawaii

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10.4.1 Improvements/Changes in the 2020 NEI

A few improvements were made for this sector in going from the 2017 to 2020 NEI. These are
highlighted below in summary fashion:

10.4.1.1A variability of working computer code to estimate NH3 emissions for2020

In the 2017 NEI, we could not develop a code to run the actual FEM model, because we were not
confident in how the model reproduced 2014 estimates, so we used a simple ratio approach to estimate
emissions based on meteorology changes. In the 2020 NEI cycle, we worked extensively with the code
supplied during the 2014 NEI process to EPA and talked to several experts and were able to reproduce
2014 estimates to a certain degree of confidence, where we could use that as a calibration step and
move forward to running the actual FEM code for 2020 conditions. We therefore now have a working
program at EPA that can generate 2020 NEI emissions for this category using all of the model
parameters and processes detailed in this document. We expect to make the code publicly available
after we are done thoroughly quality assuming it for the emissions it produces at all temporal
resolutions (day specific EFs, for example, which is not needed for the NEI).

10.4.1.2	Improvements to Meteorological Modeling

One of the primary enhancements made to the FEM for the 2020 NEI is a re-design of the modeling
system to accept spatially and temporally enhanced local meteorology. Earlier in this document, it was
detailed that a limited number of meteorological observations without proper indexing and
identification were used in previous FEM NEI simulations.

To improve this aspect in the 2020 NEI, one of the SMOKE-based (Spare Matrix Operator Kerner
Emission) utility programs, called GenTPro (Generating Temporal Profiles) was updated to generate
county-level daily average meteorological inputs for the FEM based on the gridded hourly meteorology
data from Meteorology-Chemistry Interface Processor (MCIP) model simulations over the U.S. [ref 36],
The MCIP modeling process relies on hourly meteorological measurements across the US as well as
other information to obtain meteorological parameters. The reader should consult the reference above
for how these data are formatted and available for download and access. Utilizing the MCIP hourly
meteorology for FEM simulations allows us to greatly enhance the spatial and temporal representations
of meteorology on NH3 emissions from the agricultural livestock sector. GenTPro can generate the
spatially and temporally resolved county-level daily average meteorology inputs (e.g., temperatures,
wind speed, and precipitation) for use in generating daily FEM EFs for over 3,100 counties in the U.S.
The FEM code has also been enhanced to accommodate and read in these newly designed county-level
daily average meteorological data

10.4.1.3	Farm Manure Management Practices Information Improvements

In addition to local meteorology effects, NH3 EFs from livestock waste is also a strong function of
manure management practices employed by the producers (i.e. what housing, storage and application
methods are used). It can also significantly impact the conditions of the manure and waste (e.g. water
content, total ammoniacal nitrogen concentration, pH) and as a result, it can increase or reduce the
emissions of ammonia from these sources.

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The FEM model requires county-level farm manure management inputs which describe the type of
animal housing, manure storage, and application methods used for a particular location. Each location
is expected to have some combination of practices; for example, in a single county, some of swine farms
may employ deep-pit housing, lagoon storage, and irrigation application while other farms use shallow-
pit housing with lagoon storage and injection application. In order to understand the differences in
regional preferences for particular manure management strategies, information was extracted from the
most recent National Animal Health Monitoring Surveys (NAHMS) from the U.S. Department of
Agriculture (USDA) [ref 1,2, 9-29], as described earlier.

Though the basic NAHMS data we access for the 2020 NEI is the same as that accessed for earlier NEIs,
we have tried in the 2020 process to help improve accounting for these farm management process and
to enable easy review and edits by our stakeholders, by developing a Python-based Farm Practices
Probability Tool (FP2). This tool will allows user to generate the FEM-ready county-level farm manure
management practice configuration probability table based on a combination of manure management
practices distribution within the county, state, or region from the USDA-based NAHMS [ref 9-29]
reports. The format of farm configuration probability table is described in Table 5. For each county, the
FP2 tool generates a default probability table that attempts to represent all types of manure
management practices for that county based on NAHMS data. A farm configuration is a unique
combination of manure management practices that describe the operation of the farm. Each farm
configuration is executed by the FEM, and the county-level daily NH3 emission factor is the average of all
farm configuration FEM simulations, weighted by farm size and probability of occurrence. In future NEI
cycles, EPA expects to update these farm configuration probability tables with the latest and most
accurate animal manure management practices information from updated NAHMS data and/or inputs
from SLTs as the tool is flexible enough to allow SLTs to enter values that would supercede the default
values that have been established for all the operations shown in Table 10. A value of 1 indicates that
configuration exists for a county, a value of 0 indicates it does not.

Table 10-9: FEM farm manure management practice configuration probability table.

FEM Submodel

Configuration

Value

Description

Grazing

Confined summer

lorO

Seasonal summer Grazing

Confined winter

lorO

Seasonal Winter Grazing

Pasture

lorO

Pasture resistance

Drylot

lorO

Beef=Drylot, Poultry-Litter

Housing

Tiestall

lorO

Dairy=Tiestall, Swine=Deep-Pit, Poultry=High-Rise

Freestall

lorO

Dairy=Freestall, Swine=Shallow-Pit, Poultry=Manure Belt

Nohousing

lorO

No enclosed housing:

Liquid

lorO

Liquid phase animal waste

Solid

lorO

Dry phase animal waste

Storage

Lagoon

lorO

Lagoon storage

Earthbasin

lorO

Earth basin storage

Slurrytank

lorO

Slurry tank storage

Application

Irrigation

lorO

Irrigation application

Injection

lorO

Injection application

Trailinghose

lorO

Trailinghose application

Broadcast

lorO

Broadcast application

Summer_application

lor 4

Summer: [l=daily, 2=weekly, 3=monthly, 4=seasonal]

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Winter_application

lor 4

Winter: [l=daily, 2=weekly, 3=monthly, 4=seasonal]

Farm practice

Probability

Fraction

Probability of occurrence (e.g., 0.1, 0.2„) (this represents
the probability for any county that a particular type of
farm practice exists. Using the "1" s in a county over the
total number of "l"s for a practice across the nation.

10.4.1.4 Continued use of National Air Emissions Monitoring Study (NAEMS) data to tune the models

While this is not exactly an improvement in the 2020 NEI process, it's is important to point out how
important this dataset is for developing emission estimates from the CMU FEM model. It is difficult to
characterize NH3 emission factors from agricultural livestock waste due to the many sources of
emissions variability, such as local meteorology, farm management practices, and nutritional feed used
in the farms, as well as difficulties in long-term monitoring of emissions from various processes (housing,
storage, application and/or grazing) within a farm. Previously many evaluations and tuning of the FEM
were performed based on short-term measurements mostly from the literature. In 2016, research at
CMU expanded the applications of FEM [ref 1,2] to other animal types beyond dairy cattle (to beef
cattle, broilers, layers, and swine) and helped develop the 2014 NEI livestock waste emission estimates
for use in the NEI. In this 2014 NEI development, the FEM was first evaluated with the long-term NH3
monitoring campaign study, National Air Emissions Monitoring Study (NAEMS), that robustly represents
the seasonal and regional differences in emissions from livestock production in the United States [ref
30], The NAEMS farms were selected to span a range of practices as well as locations and emission
measurements were conducted from 2007 to 2010. The reader is referred to the references listed in this
document and in the 2017 NEI TSD on the NAEMS measurement campaign, but Table 11 shows the list
of all farms [ref 30] that participated in the NAEMS long-term monitoring campaign. Emissions
measurements were taken at a total of 17 (2 livestock barn sites (5 swine, 5 dairy cattle, 4 layer, and 3
broiler barn sites) and 10 manure storage facility sites (5 swine lagoons, 1 swine basin, 1 dairy cow
manure lagoon, 2 dairy basins, 1 dairy drylot) for anywhere from 1.5 to 2.5 years, beginning in late 2007
and continuing through early 2010. While the NAEMS monitoring locations covers most of the housing
application it is limited in its coverage of storage processes within farms. Storage and application of
poultry, as well as beef cattle were not a part of the NAEMS study and as a result those emission
measurements had to be found elsewhere for use in the FEM modeling. As further improvements are
made in assessing the NAEMS data via development of alternative emission estimation processes for
farms, the NEI will continue to draw upon such analyses for its development and for QA.

Table 10-10: Description of farms in NAEMS including managment practices by animal type.

Animal Type

State

Process

Management Practice

Broiler

California

Housing

Litter-based

Kentucky(2)

Liter-based

Layer

California

Housing

High-Rise (HR)

North Carolina

High-Rise (HR)

Indiana

High-Rise (HR)

Indiana

Manure-belt (MB)

Swine

Iowa

Housing

Deep Pit

Indiana

Deep Pit

North Carolina

Shallow Pit/Flush

North Carolina

Shallow Pit/Flush

Oklahoma

Shallow Pit/Flush

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Iowa

Storage

Manure Basin

Indiana

Lagoon

North Carolina

Lagoon

North Carolina

Lagoon

Oklahoma

Lagoon

Oklahoma

Lagoon

Dairy

California

Housing

Free-stall Barn

Indiana

Free-stall Barn

New York

Free-stall Barn

Washington

Free-stall Barn

Wisconsin

Free-stall Barn

Indiana

Storage

Lagoon

Texas

Feedlot (housing)

Washington

Manure Basin

Wisconsin

Manure Basin

10.5 References

1.	A. M. McQuilling and P. J. Adams, "Semi-empirical process-based models for ammonia emissions
from beef, swine, and poultry operations in the United States," Atmos. Environ., vol. 120, pp. 127-
136, Nov. 2015.

2.	A. McQuilling, 2016, Ammonia Emissions from Livestock in the United States: From Farm-Level
Models to a New National Inventory, Thesis, Carnegie Mellon University, available at:

https://kilthub.cmu.edu/articles/Ammonia Emissions from Livestock in the United States From
Farm-Level Models to a New National Inventory/6714665

3.	Inventory of Greenhouse Gas Emissions and Sinks, 1990-2020. Chapter 5.2, Manure Management.
EPA 430-R-18-003. https://www.epa.gov/system/files/documents/2022-04/us-ghg-inventory-2022-
main-text.pdf

4.	United States Department of Agriculture National Agricultural Statistics Service Quick Stats.
https://quickstats.nass.usda.gov/

5.	R. W. Pinder, N. J. Pekney, C. I. Davidson, and P. J. Adams, "A process-based model of ammonia
emissions from dairy cows: improved temporal and spatial resolution," Atmos. Environ., vol. 38, no.
9, pp. 1357-1365, Mar. 2004.

6.	R. W. Pinder, R. Strader, C. I. Davidson, and P. J. Adams, "A temporally and spatially resolved
ammonia emission inventory for dairy cows in the United States," Atmos. Environ., vol. 38, no. 23,
pp. 3747-3756, Jul. 2004.

7.	R. W. Pinder, P. J. Adams, S. N. Pandis, and A. B. Gilliland, "Temporally resolved ammonia emission
inventories: Current estimates, evaluation tools, and measurement needs," J. Geophys. Res.
Atmospheres, vol. Ill, no. D16, p. D16310, Aug. 2006.

8.	Battye, R. W. Battye, C. Overcash, S. Fudge, 1994. Development and Selection of Ammonia Emission
Factors, Final Report. EPA Contract 68-D3-0034, Work Assignment 0-3.

9.	USDA-APHIS, "Feedlot 2011 - Part I: Management Practices on US Feedlots with a Capacity of 1000
or More Head," 2013. https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-
surveillance/nahms

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10.	USDA-APHIS, "Feedlot 2011 - Part II: Management Practices on US Feedlots with a capacity of
Fewer than 1000 Head/' 2013.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

11.	USDA-APHIS, "Beef 2007-2008 - Part I: Reference of Beef Cow-calf Management Practices in the
United States, 2007-08/' 2009.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

12.	USDA-APHIS, "Beef 2007-2008- Part II: Reference of Beef Cow-calf Management Practices in the
United States, 2007-08," 2009.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

13.	USDA-APHIS, "Beef 2007-08 - Part III: Changes in the US Beef Cow-calf Industry, 1993-2008," 2009.

14.	USDA-APHIS, "Dairy 2007- Part V: Changes in Dairy Cattle Health and Management Practices in the
United States, 1996-2007," 2007.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

15.	USDA-APHIS, "Dairy 2007- Part III: Reference of Dairy Cattle Health and Management Practices in
the United States, 2007," 2007.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

16.	USDA-APHIS, "Dairy 2007- Part II: Changes in the US Dairy Cattle Industry, 1991-2007," 2007.
https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

17.	USDA-APHIS, "Dairy 2002- Part II: Changes in the United States Dairy Industry, 1991-2002," 2002.
https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

18.	USDA-APHIS, "Dairy 2002- Part 1: Reference of Dairy Health and Management in the United States,
2002," 2002. https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-
surveillance/nahms

19.	USDA-APHIS, "Swine 2006 - Part I: Reference of Swine Health and Management Practices in the
United States, 2006," 2007. https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-
and-surveillance/nahms

20.	USDA-APHIS, "Swine 2006 - Part II: Reference of Swine Health and Health Management Practices in
the United States, 2006," 2008.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

21.	USDA-APHIS, "Swine 2006 - Part III: Reference of Swine Health, Productivity, and General
Management in the United States, 2006," 2008.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

22.	USDA-APHIS, "Swine 2006 - Part IV: Changes in the US Pork Industry, 1990-2006," 2008.
https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

23.	USDA-APHIS, "Poultry '04 - Part II: Reference of Health and Management of Gamefowl Breeder
Flocks in the United States, 2004," 2005.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

24.	USDA-APHIS, "Poultry '04 - Part III: Reference of Management Practices in Live-Poultry Markets in
the United States, 2004," 2005.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

25.	USDA-APHIS, "Poultry 2010 - Reference of Health and Management Practices on Breeder Chicken
Farms in the United States, 2010," 2005.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

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26.	USDA-APHIS, "Poultry 2010: Structure of the US Poultry Industry, 2010/' 2011.
https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

27.	USDA-APHIS, "Part II: Reference of 1999 Table Egg Layer Management in the US/' 2000.
https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

28.	USDA-APHIS, "Layers 2013-Part 1: Reference of Health and Management Practices on Table-Egg
Farms in the United States 2013," 2014.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

29.	USDA-APHIS, "Layers 2013 - Part III: Trends in Health and Management Practices on Table-Egg
Farms in the United States, 1999-2013," 2014.

https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/monitoring-and-surveillance/nahms

30.	US EPA's National Air Emissions Monitoring Study (NAEMS), https://www.epa.gov/afos-air/national-
air-emissions-monitoring-studv

31.	N. Hutchings, S. Sommer, and S. Jarvis, "A model of ammonia volatilization from a grazing livestock
farm," Atmos. Environ., vol. 30.4, pp. 589-599, 1996.

32.	R. Pinder, N. Pekney, C. Davidson, and P. Adams, "A process-based model of ammonia emissions
from dairy cows: improved temporal and spatial resolution," Atmos. Environ., vol. 38.9, pp. 1357-
1365,2004

33.	M. Wesely and B. Hicks, "Some factors that affect the deposition rates of sulfur dioxide and similar
gases on vegetation," J. Air Pollut. Control Assoc., vol. 27.11, pp. 1110-1116, 1977.

34.	S. Sommer and N. Hutchings, "Ammonia emission from field applied manure and its reduction-
invited paper," Eur. J. Agron., vol. 15.1, pp. 1-15, 2001.

35.	J. Olesen and S. Sommer, "Modelling effects of wind speed and surface cover on ammonia
volatilization from stored pig slurry," Atmos. Environ., vol. 27.16, pp. 2567-2574, 1993.

36.	https://www.cmascenter.org/smoke/

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United States	Office of Air Quality Planning and Standards	Publication No. EPA-454/R-23-001j

Environmental Protection	Air Quality Assessment Division	March 2023

Agency	Research Triangle Park, NC

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