Evaluating Draft EPA Emissions Models for Laying Hen Facilities

Yijie Xiong^1,2,*, Guoming Li³, Brett C. Ramirez⁴, Robert T. Burns⁵, Richard S. Gates⁶

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Published in Journal of the ASABE 66(4): 851-863 (doi: 10.13031/ja.15237). Copyright 2023 American Society of Agricultural and Biological Engineers.

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¹ Animal Science, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.

²Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska, USA.

³Poultry Science, University of Georgia, Athens, Georgia, USA.

⁴Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa, USA.

⁵Biosystems Engineering and Soil Science, University of Tennessee, Knoxville, Tennessee, USA.

⁶Egg Industry Center, Iowa State University, Ames, Iowa, USA.

^*Correspondence: yijie.xiong@unl.edu

The authors have paid for open access for this article. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creative commons.org/licenses/by-nc-nd/4.0/

Submitted for review on 13 June 2022 as manuscript number PAFS 15237; approved for publication as a Research Article by Associate Editor Dr. Sanjay Shah and Community Editor Dr. Jun Zhu of the Plant, Animal, & Facility Systems Community of ASABE on 3 April 2023.

Highlights

• Draft EPA emission models for laying hen facilities were systematically evaluated.
• The models performed poorly on predicting the air pollutants when input variables were out of the NAEMS data range.
• A key finding was the unanticipated sensitivity of the draft model outputs to bird inventory and climate zones.
• Further revision and improvement may be necessary for draft models before they can be adopted by the egg industry.

Abstract. In August 2021, the U.S. Environmental Protection Agency (EPA) released draft models to estimate daily NH3, H2S, PM10, PM2.5, and TSP emissions from egg-layer houses (high-rise and manure-belt) and manure storage using inputs of daily mean ambient temperature, relative humidity (RH), and hen inventory. These models were developed from refined datasets generated by the National Air Emissions Monitoring Study fieldwork completed in 2009. Notably, they do not include data for cage-free housing. Currently, 66% of U.S. laying hens are housed in cages; thus, these models, if adopted, will have a substantial impact on the U.S. egg industry. This study evaluated the EPA draft models’ robustness and assessed model outputs for egg production systems under differing climate scenarios. The EPA draft models distort emission factors for bird inventories to be lower or higher than those used to develop the models. With inventory held constant, the marginal influence of ambient temperature and RH on daily emissions varied substantially, with some values falling below the measurement detection threshold while others exceeding literature findings. For twelve representative U.S. locations representing differing climates, substantial differences in emission factors were found for bird inventories outside the range in the database. Annual emissions estimated from inventories used to develop the EPA models also varied by location. We conclude that the current draft EPA emission models cannot be used to the degree of precision that is suitable to apply to a wide range of layer facilities, particularly cage-free systems. Revisions are suggested to accommodate a greater range of climates, laying hen facility types, and inventories for practical emission estimations.

Keywords. Air quality, Ammonia, Egg production, Emission model, Hydrogen sulfide, Particulate matter, Poultry.

Controlling the indoor environment, particularly the thermal components and air quality (for example, carbon dioxide [CO₂], hydrogen sulfide [H₂S], ammonia [NH₃], and particulate matter [PM] levels), is crucial to the health, welfare, and productivity of laying hens (Dawkins et al., 2004; Naseem and King, 2018; Webster and Czarick, 2000). These pollutant emissions can pose environmental concerns if they exceed accepted thresholds. A collaborative effort between the National Academy of Sciences and the U.S. Environmental Protection Agency (EPA) was established in 2002 to provide scientifically credible methodologies to estimate emissions from animal feeding operations. This effort was known as the National Air Emissions Monitoring Study (NAEMS) (USEPA, 2022). As part of the NAEMS study, a total of six high-rise and two manure-belt egg-laying barns in California, Indiana, and North Carolina were monitored to quantify air emissions. Although the emission data was not reported for these egg-layer operations in the 2012 NAEMS final draft, several refereed journal articles were published from this multistate endeavor. Li et al. (2013a) and Wang-Li et al. (2013a) monitored the NC2B site for NH₃ and PM emissions over a two-year period. They reported a combined average PM₁₀ and TSP (total suspended particle) emission rate of 17.8±14.9 and 43.1±35.5 mg d^-1 hen^-1, respectively. For NH₃, they reported an average concentration of 21.8 ppm at the exhaust for the two houses combined, and a daily mean emission rate of 0.6±0.20 g d^-1 hen^-1 (Wang-Li et al., 2013b).

Currently, approximately 66% of hens are housed in cage systems in the U.S., with 34% of the U.S. flock in cage-free production (UEP, 2022). While enriched colony cages have been shown to provide better bird welfare metrics compared to cage-free environments, including vertical aviaries with litter-floor (Hardin et al., 2019; Janczak and Riber, 2015; Lay et al., 2011), recent retailer and supply chain pledges (Graber and Keller, 2020) and individual state mandates are driving a transition towards vertical aviaries for cage-free production. Challenges with vertical aviaries include lower bird density, which requires supplemental heating, reduced indoor air quality (Zhao et al., 2015a,b; Shepherd et al., 2015), and greater emissions of PM, NH₃ and greenhouse gases (Zhao et al., 2014). Both enriched colony and vertical aviary systems require a better understanding of NH₃, and PM emissions as a key component to comply with the air emission practices for laying hen facilities.

In 2021 August, EPA released draft laying hen housing and manure storage emissions models (called “draft models” hereafter) for public review (USEPA, 2021a). Each model was derived from the NAEMS data collected starting in 2007 over a two-year period from five sites in three states (USEPA, 2022). Draft models by housing style (high-rise, manure-belt, and manure storage) were provided in the following general form for NH₃, H₂S, PM₁₀, PM_2.5, and TSP.

(1)

(2)

where

ln(Y) = natural log transformed mass of pollutant from a house on a daily basis

Intercept = model specific coefficient determined by the EPA

C_Inventory = model specific coefficient for bird inventory

Inventory = bird number in an egg production facility, divided by 1,000

AmbT and C_AmbT = daily mean ambient temperature (°C) and corresponding coefficient

AmbRH and C_AmbRH = daily mean ambient RH (%) and corresponding coefficient

ER = emission rate (in mass pollutant per day)

E₀ and E₁ = and C in General Report (USEPA, 2021b) and Layer Report (USEPA, 2021a), used to adjust bias and offset.

Note that in equation 2 we use the plus sign for E₁ (which is either 0 or a negative value) rather than a minus sign, as indicated in the reports (USEPA 2021a, b).

The draft models further refined the datasets generated by the NAEMS study to account for background concentrations. The summarized specifics for model development and evaluation for two housing types (high-rise and manure-belt, both caged) are provided in Appendix 1. Notably, the proposed models and the refined dataset do not include cage-free housing. While the majority of U.S. laying hens are currently housed in caged manure-belt housing, this is changing rapidly towards cage-free production; thus, these models, if adopted, will have a substantial impact on the U.S. egg industry, and their lack of applicability to the current U.S. housing systems and sizes is a concern.

Rationale and Objectives

The rationale of this effort is to provide a scientific and professional review and evaluation of the EPA’s draft emission models for laying hen facilities. Particularly, the objectives of this investigation were to (1) stress test the draft models using single state point checks for each input (i.e., temperature, RH, bird inventory) to assess the feasibility and limitations; and (2) use typical meteorological year input data for a range of current U.S. housing bird inventories to estimate annual contributions for representative locations in the U.S.

Table 1. Combinations of a range of ambient daily temperature and relative humidity (RH) used in EPA draft emission model stress-test evaluations.
Temperature (°C)		RH (%)
		10	50	90
-40			X
-20			X
0		X	X	X
20		X	X	X
40		X	X

Materials and Methods

All analyses presented in this manuscript are based on results derived from the EPA draft models. The authors did not develop any new or different models. The EPA draft models were applied based on the following criteria: to resemble current laying hen facility conditions, ensuring a comprehensive assessment of the model's robustness.

Code Development

Each draft emission model identified by the EPA was coded in Python (Version 3.9.7) with the open-source Pandas and Numpy libraries. The program was designed so that model-designated parameters (i.e., housing type, bird inventory, ambient temperature, and ambient RH) can be inputted via the command line for simple point checks or from a comma-separated variable (CSV) file when evaluating multiple combinations of inputs. Model coefficients (tables A1-A3) were stored in a separate input file. This data processing framework was used to derive emission factors from the EPA draft models for all analyses.

Sensitivity Analysis

A two-step stress test was accomplished by (1) testing the influence of a single input variable while holding others over a range of constant values; and (2) assessing predictions derived from EPA models on a per hen-day basis, according to the common emission factor methodology, rather than per house.

Single Input Variable

Ten combinations of environmental input variables were selected based on a range of typical ambient conditions at locations where major egg production systems in the U.S. are located (table 1) and were used to stress-test the emission models using a range in house inventory numbers as listed in table 2. For each housing system, the bird inventory numbers that matched the previously reported NAEMS sites were included along with additional values representative of different size egg producing operations. The draft models for emissions from manure storage sheds were developed from a facility accepting manure from two manure-belt houses in the previous NAEMS sites (Ni et al., 2010). The bird inventory supplying the manure storage is approximately the same as the manure-belt housing (two 280,000 hen houses); thus, we used a range of bird numbers from 10,000 to 580,000, representing small to large manure storage facilities, for the stress test.

Table 2. Hen inventory values used in EPA draft emission model stress-test evaluations.
Housing System		Laying Hen Inventory Tested
High-Rise		38,000[a]	95,000[a]	125,000	218,050[a]	400,000	600,000	-	-
Manure-Belt		10,000	40,000	250,000	280,000[a]	400,000	600,000	-	-
Manure Storage		10,000	40,000	100,000	250,000	280,000	400,000	580,000	600,000
[a] Approximate value in NAEMS study.

For each housing system and each temperature/RH combination in table 1, the model outputs (in mass per day) were divided by the inventory number to report the daily emission factors as mass per hen-day (g d^-1 hen^-1 for NH₃ and mg d^-1 hen^-1 for H₂S, PM₁₀, PM_2.5, and TSP) and were plotted against the selected house inventory numbers (table 2). The inventories that were reported by NAEMS were indicated on the plots. For better data visualization, a log₁₀ scale was used for the y-axis of the PM_2.5 plot for manure-belt housing.

Emission Factors Derived from the EPA Draft Models

The marginal effects of a range of temperatures (-40°C to 40°C with a 10°C interval) at 50% RH and a range of RH (0% to 100% with a 10% interval) at 20°C were evaluated using bird inventory corresponding to the NAEMS dataset as reported by the USEPA (2021a) and as shown in table 2. The incremental emission rates between two adjacent temperature or RH settings were calculated and then expressed as the marginal change in daily emissions (per °C at a constant 50% RH and per 1% RH at a constant 20°C).

Model Evaluation for Annual Emission Estimation

Selection of Representative Weather Locations

Regions of the U.S. with major egg production were identified from our experience, USDA data, and conversations with egg industry stakeholder leaders. Twenty-three locations were selected and reviewed for similarities and differences in weather patterns, and the availability of a representative weather dataset (Appendix 2). The climate for each location was characterized using Typical Meteorological Year 3 (TMY3) data from the National Renewable Energy Lab (Wilcox and Marion, 2008). Each TMY3 file contains hourly values of various independent variables (including ambient dry-bulb temperature and RH), and the TMY3 location file represents the statistically selected months having the most typical weather data from the last 30 years or so for a location. Each month in the TMY3 dataset can consist of records generated from a different year.

Locations with TMY3 files available within the selected regions of interest were then reviewed for similarities in either temperature or RH ranges, and consistency with the International Energy Conservation Code (IECC) Climate Zone classification and the U.S. Department of Energy Building America classification scheme (Baechler et al., 2015). TMY3 files were categorized according to IECC Climate by temperature (Zones 2 to 6) and RH conditions (A: Dry, B: Moist, and C: Marine). The Building America Climate Zone classification scheme integrates both temperature and RH, including zones: Hot-humid, Hot-dry, Mixed-humid, Mixed-dry, Marine, and Cold.

All candidate weather stations (table A4) were sorted and grouped based on the following criteria: representative of egg production, representative of the range in annual ambient temperature across the U.S., and representative moisture zones as denoted by the Building America classification. Twelve sites with different zonal classifications were sorted by extremity of temperature and RH, and those with similar characteristics were eliminated.

Geographical Variation in Annual Emission Estimates

Twelve TMY3 stations and a selected set of bird inventories (table 2, approximate values* in NAEMS Study) were used with each EPA draft model to compute annual emissions. In addition, inventory for two manure storage sheds, estimated at 519,580 according to the EPA draft report and a previous NAEMS study (Ni et al., 2010), was included. The draft models were tested with the above stations’ climate files, inventory as listed in table 2, and three methods of summarizing the ambient characteristics by averaging the ambient temperature and RH to daily, monthly, or annual values prior to input to the EPA model.

A density map depicting the relative magnitude of annual NH₃ emission factors (g hen^-1 yr^-1) for the 12 selected TMY3 sites was generated using ArcGIS Pro (version 10.3), with ambient temperature and RH computed using the annual averaging method. Emission factor magnitudes were illustrated by circles of different diameters, representing values from smallest to largest. These emission factors were overlaid on another density map that depicted the number of total poultry (chickens only, excluding turkeys, including egg production farms) operations in each county across the continental U.S. (USDA-NASS, 2015). This dataset is the best available resource before the USDA publishes its 2017 Census statistics for egg-laying farms. This information was then superimposed over the Building America Climate Zone. This graphical perspective illustrates the relative effects of the climate and poultry farm density on the annual emission factors using the EPA draft models.

Comparison to Literature Values

Emission factors for laying hen facilities from studies conducted between 2003 and 2021 were reviewed for comparison to EPA models derived values. The emission factors are organized per pollutant and per housing type. Values for other facilities, such as houses with pits, composting facilities, with tunnel or cross ventilation, are also included for reference. To compare draft model outputs to reference records, summary statistics for manure-belt housing NH₃ emission factors from three representative references (two sites in IA and one site in IN; all with more than 200,000 hens per house) were calculated and compared to draft emissions for Fort Dodge, IA, for 280,000 hens. Summary statistics for manure-belt housing NH₃ emission factors from two other references (IA and PA; 100,000 to 150,000 hens) were calculated and compared to the daily average value for Fort Dodge, IA, for a 100,000-hen inventory.

Results and Discussion

Sensitivity Analysis

Single Input Variable

The draft models predict daily building emissions, which, when divided by bird inventory for each specific case, yields daily emission factors. Figures 1 to 3 illustrate daily emission factors for high-rise, manure-belt, and manure storage as affected by bird inventory. For each inventory and housing type, ten combinations of ambient temperature and RH that span very cold to very hot, as well as humid to dry environments, are plotted.

A key finding is the unanticipated strong influence of bird inventory on the draft model emission factor. As a reasonable first approximation, building emissions were expected to be proportional to inventory, indicating a relatively constant emission factor. However, figures 1 to 3 reveal a strong tendency for per bird emission factors to increase at the lower and upper ranges of the simulated bird inventory. Most models exhibited an increase of emission factor at lower bird inventories. All but two PM models for manure storage exhibited substantial increases at higher bird inventories. At least one model, PM_2.5 for manure-belt housing, was unstable and extremely sensitive at higher inventories (fig. 2). These sensitivities noted for the emission factor with changing bird inventory are likely because the inventory values used resulted in draft model extrapolations outside the narrow range in which they were developed (table 2). Note that TSP emission factors for manure-belt housing and manure storage were always less than those of PM₁₀ or PM_2.5, which is of course physically impossible.

Figure 1. High-Rise Housing: Single input variable stress-test results expressed in daily emission factor of each pollutant (mass per day per hen). EPA draft models were established from the capacities of 38,000, 95,000, and 218,000 hens per house.

For high-rise housing (fig. 1), the draft NH₃ model provided higher emission factors at the lowest bird inventory of 38,000 hens, with a subsequent drop of over 100% at the higher bird inventories used to develop the models. The PM models also showed substantially greater emission factors at lower bird inventories for this housing type.

For manure-belt housing, EPA draft models were developed from a bird house inventory of about 280,000 hens. Any other bird inventory presented to these models represented an extrapolation; the plots in figure 2 demonstrate that NH₃ and H₂S models were reasonably stable between 250,000 and 280,000 birds but increased substantially at lower or greater bird inventories. The plot for PM₁₀ in manure-belt housing is an example of poor model performance outside the range it was developed from. The PM₁₀ emission factor increases exponentially with bird inventory, contrary to expectations. Similarly, the PM_2.5 plot clearly shows the draft model is unstable above the inventory of 280,000 birds.

Figure 2. Manure-Belt House: Single input variable stress-test results expressed in daily emission factor of each pollutant (mass per day per hen). EPA draft model was established from a flock size of 280,000 hens.

Predicted NH₃ and H₂S emission factors increased linearly with bird inventory above 100,000; this is unexpected since the emission from stacked manure is mostly proportional to emitting surface (which does not change appreciably as a manure pile grows only in height) and manure moisture content (not measured in the NAEMS study). Negative emission factors were observed for NH₃ and H₂S. These emission factors increased many-fold at lower or higher bird inventories and are clearly inappropriate for use.

Figures 1 to 3 also demonstrate the impact of different combinations of average daily temperature and RH. These had no impact on predicted PM emissions (PM₁₀, PM_2.5, and TSP) for manure-belt and manure storage because those terms were not included in the models. For high-rise housing, the predicted NH₃ emission factor ranged over an order of magnitude between the coldest and hottest combinations, which is unlikely to be substantiated by the underlying data. By contrast, H₂S emission factors were relatively stable with an inventory of up to 218,050 birds, varying by about 50% across the range of conditions tested. All three PM models behaved like the NH₃ model, but with a less clear distinction between different temperature and RH combinations. Trends for NH₃ and H₂S emission factors for manure-belt housing (fig. 2) were similar to those for high-rise housing (fig. 1); the NH₃ emission factor was much less sensitive to the range in temperature and RH than it was for the high-rise housing, whereas the H₂S emission factor was more sensitive. Particulate matter emissions were insensitive to environmental conditions.

Emission Factors Derived from the EPA Draft Models

The marginal change in daily emissions and emission factors are listed per °C at 50% RH in table 3, and per 1% RH at 20°C in table 4, respectively. PM emissions did not depend on temperature or RH for either manure-belt housing or manure storage, and those for NH₃ and H₂S did not depend on RH for manure storage. An increase in gaseous emissions with increasing temperature may be expected, although the magnitudes predicted (2.5% to 4.7% per °C) appear substantial for bird housing. A decrease in emissions with increasing temperature was found for manure storage (-1.6% to -2.5% per °C), which is unlikely to occur. All PM emissions (except for PM_2.5 for the CA high-rise inventory) increased with ambient temperature, ranging from 1.5% to 29.2% per °C. Gaseous emissions also increased with increased RH (0.18% to 0.60% per%RH), and particulate emissions all decreased (-0.31% to -2.46% per%RH).

Model Evaluation for Annual Emission Estimation

Selection of Representative Weather Locations

Table 5 lists the 12 representative stations and the corresponding TMY3 site identification, IECC Climate Zone, Building America Climate Zone, and annual average and standard deviation of the TMY3 ambient temperature and RH. Other TMY3 sites with similar ambient conditions are listed in table A4.

Figure 3. Manure Storage: Single input variable stress-test results expressed in daily emission factor of each pollutant (mass per day per hen). EPA model was established with manure storage from up to two manure-belt houses, total estimated at 580,000 hens.

Geographical Variation in Annual Emission Estimates

Annual emission factors from daily, monthly, and annual averaging methods were tabulated for all 12 selected TMY locations (table 6). There was a negligible or small reduction in the annual emission factor as the computations went from daily to monthly to annual averages input to the models, suggesting that a simple, single value for the annual average would suffice. Because the ambient temperature and RH terms were not included in the annual emission calculation models for particulate matter (PM₁₀, PM_2.5, and TSP), a single value was obtained across all TMY3 stations.

Figure 4 depicts the relative magnitude of annual NH₃ and H₂S emission factors (g hen^-1 yr^-1 and mg hen^-1 yr^-1, respectively) for the 12 selected TMY3 sites, computed using annually averaged ambient temperature and RH (table 6). Figure 4 was comprised of three data layers, and the darker shade under the same Building America Climate Zone color category indicates a greater poultry farm operation density in the area. Figure 4 thus demonstrates the ranges in annual NH₃ emission factors within the framework of poultry production density and national climate categories. Colder locations had substantially lower emission factors, while hotter locations showed higher emission factor values, with humidity further increasing the magnitude of emission factors.

NH₃ emissions simulated for a manure-belt house with 280,000 birds, were predicted to be the greatest for the Orlando, FL site and the least for the Minneapolis, MN site, demonstrating a positive relation to increased daily average temperature and, to a lesser extent, RH. As table 6 and figure 4 demonstrate, the temperature and RH likely had greater effects on the magnitude of the NH₃ emission factors, computed on a per hen basis, than the inventory sizes. In this analysis, the three leading egg production states [i.e., IA, IN, and OH (USDA-NASS, 2015)] had substantially lower annual NH₃ emission factor values than those producing fewer eggs but with higher temperatures and humidity (e.g., FL, TX, AZ).

Comparison to Literature Values

The emission factors were compared with published values in the literature summarized in tables 7 to 9, which include studies reported from the NAEMS project for NH₃ (table 7), H₂S (table 8), and particulate matters (table 9). Overall, the EPA draft models overestimated the emission factors for all pollutants. As an example, figure 5 shows the comparison of NH₃ emission factors derived from EPA draft models and literature (table 7) for a manure-belt housing with an inventory of 280,000 and 100,000 hens. The EPA draft model was evaluated using TMY3 data from Fort Dodge, IA, and the literature values (table 7) were from three studies across IA and IN with inventories of unknown (Liang et al., 2003), 250,000 (Ni et al., 2017a), and 200,000 (Shepherd et al., 2015). Average emission factors from the EPA draft model and literature were 0.31 and 0.17 g hen^-1d^-1 for a 280,000-hen facility, whereas the values were 0.37 and 0.074 g hen^-1 d^-1 for a 100,000-hen facility. Results show that EPA draft models have no variability (other than climate) because of the lack of incorporation of different management practices, housing styles, diets, etc. These practices are better represented in the literature, but there are limited studies. For a 280,000-hen facility, EPA draft models overestimate all mean literature values by 53% and by 132% for a 100,000-hen facility.

Further Considerations

The bird inventory range used for EPA draft model development was limited compared to that normally encountered in industry. An alternate approach, such as using the given bird inventory from the modified NAEMS datasets (e.g., 280,000 for manure-belt), computing the emission factor by dividing the estimated building emission by inventory, and simply multiplying by the actual bird inventory, may be advisable. If other factors, such as climate, are desired to be included, the weather data ranges in the NAEMS dataset must match the broad range found in the U.S. climate categories where egg production is significant. In fact, this cannot be assured, so an alternative with a simple marginal change in emission factor per °C change or per% RH change (using simple mean annual ambient conditions) may be suggested. These marginal changes should be derived from the NAEMS dataset and not extrapolated climate values; if the extremes are not available in the NAEMS data, then no further marginal changes should be entertained.

Figure 4. Density map showing the annual NH3 emission factor (g hen-1 yr-1) for the 12 U.S. locations selected (table 6), for a manure-belt layer house with 280,000 hens. The map shows county level poultry operation numbers and the Building America climate zones. County level poultry (chicken only) operation numbers are from the 2012 Census (USDA-NASS, 2015).

The underlying NAEMS dataset was derived from measurements made on systems operating nearly 15 years ago. But the industry has changed and will continue to change. High-rise housing has been phasing out for over a decade, and manure-belt housing for conventional (caged) layers took its place, which in turn is now also being supplemented with cage-free aviary housing. The NAEMS dataset has no estimates for cage-free housing, and the trend is for this sector to expand from current levels of about 34% of production upwards to 75% to meet state regulations and customer pledges by the end of 2026 (UEP, 2022). If adopted, these draft models based on dated facilities in a rapidly changing industry are of questionable value.

The draft models themselves have substantial implementation challenges. First, they are too complicated mathematically for an average person to implement. Second, they erroneously offer impossibly large changes in emission factors for most pollutant/facility combinations as bird inventory (an independent input) is varied over reasonable values. Third, the resultant regional variation in predicted emissions exceeds that seen in the published scientific literature and is fundamentally unvalidated. The selection of emission models for the U.S. egg industry should be made based on the intended use of the models. The draft models would be challenging for the industry to adopt, and as currently written, are open to misapplication. Examples of misapplication include siting new facilities or verifying the compliance of an existing facility with the EPA Clean Air Act.

The use of an emission model that includes bird inventory as an independent variable is not warranted unless the range of allowable bird inventories is also specified. There is no appropriate inventory range for manure-belt housing or manure storage because the underlying EPA draft models were developed from a single site, with no substantive range in bird inventory. Thus, any inventory input other than that used to develop the models is an extrapolation. For high-rise housing, three sites with three different bird inventories were used by the EPA to develop the draft models. While this does provide a range of reasonable bird inventories for application, the behavior of the EPA draft models when extrapolating to lower or higher bird inventories is not reasonable.

The model for manure storage was from (up to) two manure-belt houses and used 5-day lag bird inventory as an estimate of manure loading rate; yet, emissions from manure piles depend substantially on the emitting surface area, the ventilation rate of the structure, and the moisture content of the manure. Further, other manure characteristics such as pH, moisture content, and total ammonium nitrogen are also important factors that affect emission factors from layer facilities and manure storages. Since none of these key factors are available for that site, a much simpler emission factor approach is recommended that might be adjusted for manure moisture content.

Table 7. Reference summary of NH3 emission factors.
	Housing Type		Site Location of Study		Bird Inventory		Ambient Temperature (°C)		Ambient RH (%)		NH3 Emission Factor (g d-1 hen-1)	Source of Reference
	High-rise		Central CA		32,500		5 - 35		-		0.95 ± 0.67	Lin et al. (2012)
			Eastern IN		218,000		12.4 ± 11.3		68.2 ± 13.3		1.08 ± 0.42	Ni et al. (2017b)
			North-central IA		-		-		-		1.03	Liang et al. (2003)
			North Carolina		95,000		16.5		-		0.599 ± 0.620	Wang-Li et al. (2013b)
			Iowa		73,938 - 82,219		-		-		0.90 ± 0.027 0.81 ± 0.044	Liang et al. (2005a)
			Pennsylvania		93,974 - 95,984		-		-		0.83 ± 0.099
			Iowa		73,938 - 82,219		-		-		0.87 ± 0.29			Liang et al. (2005b)
			North-central IA		73,938 - 82,219		-		-		0.69 - 1.48			Liang et al. (2006)
			California		-		-		-		0.94 - 0.95			Liang et al. (2013)
			Indiana		-		-		-		1.03 - 1.13
			North Carolina		-		-		-		0.59
	High-rise deep pit		Forli´, Italy		60,000		6.6 - 34.5		-		0.03 - 1.61[a]	Fabbri et al. (2007)
	High-rise manure pit		North Carolina		103,000		12 - 22		-		0.52 - 0.66	Li et al. (2013a)
	Manure-belt		North-central IA		-		-		-		0.168	Liang et al. (2003)
			West Lafayette, IN		250,000		12.0 ± 10.9		-		0.28	Ni et al. (2017a)
			Iowa		200,000		-		-		0.082	Shepherd et al. (2015)
			Iowa		104,860		-		-		0.054 ± 0.0035	Liang et al. (2005b)
			Pennsylvania		157,822 - 158,117		-		-		0.094 ± 0.019
			Iowa		104,860		-		-		0.094 ± 0.062, 0.054 ± 0.026			Liang et al. (2005a)
			North-central IA		104,860		-		-		0.044 - 0.172			Liang et al. (2006)
			Ohio		180,000		-17.2 - 32.8		17 - 100		0.07 - 0.37			Wang et al. (2009)
			Ontario, Canada		70,600		-4.1 - 19.4		-		0.06 ± 0.05			Morgan et al. (2014)
			Forli´, Italy		60,000		3.6 - 33.2		-		0 - 0.3[a]			Fabbri et al. (2007)
			European Union		12,500		-		-		0.1 ± 0.003			Hayes et al. (2006)
			Ireland		42,000		3.67 - 15.46		-		0.25			Kelleghan et al. (2021)
	Manure-belt (enriched colony)		Spain		52,000		15.7 ± 4.8		77.3 ± 11.8		0.11 ± 0.08	Alberdi et al. (2016)
	Manure-belt (enriched colony)		Spain		38,000		15.9 ± 4.7		74 ± 5		0.05 ± 0.15	Rosa et al. (2020)
	Manure-belt		Ohio		170,000		23.0 ± 2.9 summer		72.7 ± 11.7		0.081 ± 0.004	Tong et al. (2021)
	(Mixed tunnel and cross ventilation)		-0.1 ± 5.6 winter		0.099 ± 0.004
	Manure-belt storage		Iowa		200,000		-		-		0.21	Shepherd et al. (2015)
	Manure-belt (Composting facility)		Ohio		830,000		14.6 ± 9.5		74.7 ± 18.6		0.72 ± 0.13	Zhao et al. (2016)
	Composting facility		Ohio		1,000,000		-20 - 35		64 - 78		0.32 ± 0.14	Zhao et al. (2008)
	Deep litter (deep pit)		European Union		5,000		-		-		0.5 ± 0.006	Hayes et al. (2006)
[a] Unit is g d-1 hen space-1 .
Table 8. Reference summary of H2S emission factors.
Housing Type		Site Location of Study		Bird Inventory		Ambient Temperature (°C)		Ambient RH (%)		H2S Emission Factor (mg d-1 hen-1)			Source of Reference
High-rise		Central California		32,500		5 - 35		-		1.27 ± 0.78			Lin et al. (2012)
		Eastern Indiana		218,000		12.4 ± 11.3		68.2 ± 13.3		1.37 ± 0.83			Ni et al. (2017b)
		North Carolina		95,000		-		-		0.618 ± 0.517 0.698 ± 0.620			Wang et al. (2016)
Manure-belt		West Lafayette, IN		250,000		12.0 ± 10.9		-		1.952			Ni et al. (2017a)

Broad ranges in emissions estimates from the EPA draft models for different regions in the U.S. that are relevant to the egg industry were noted. These occur because of the sensitivity to average temperature and RH. The ranges in estimates exceed those found in recent literature and suggest a revision in this approach.

While a substantial database was created as part of the underlying NAEMS project, it is not credible to justify a national emissions model based on three (high-rise), one (manure-belt), and one (manure storage) sites for a country such as the U.S. with its tremendous variation in climate. Using the emissions estimates for manure-belt housing for different climates, such as Minneapolis (MN), Modesto (CA), or Orlando (FL), suggests a need for an alternative approach and an assessment and validation of climate effects. Further, these draft EPA models do not have any means of accounting for the advances made in managing both manure-belt housing and manure storage since the data underlying these draft models was collected.

Table 9. Reference summary of particulate matter emission factors.
	Housing Type	Site Location of Study	Bird Inventory	Ambient Temperature (°C)	Ambient RH (%)	Emission Factor (mg d-1 hen-1)	Source of Reference
PM2.5	High-rise	Central California	32,500	5 - 35	-	5.9 ± 12.6	Lin et al. (2012)
		North Carolina	103,000	-	-	0.37 ± 3.06	Li et al. (2013a)
		North Carolina	100,000	19.3 ± 7.07	65.6 ± 14.4	7.86 - 11.4	Li et al. (2011a)
	High-rise manure pit	North Carolina	103,000	12 - 22	-	0.27 - 2.4	Li, et al. (2013b)
	High-rise in-house manure storage	Central Iowa	248,814	-	-	3.6 ± 3.7	S. Li et al. (2011b)
	High-rise deep pit	Forli´, Italy	60,000	6.6 - 34.5	-	0.72 – 115.92[a]	Fabbri et al. (2007)
	Manure-belt	Iowa	200,000	-	-	0.9	Shepherd et al. (2015)
		Ontario, Canada	70,600	-4.1 - 19.4	-	1.3 ± 1.0	Morgan et al. (2014)
		Forli´, Italy	60,000	3.6 - 33.2	-	0.24 - 53.28[a]	Fabbri et al. (2007)
	Manure-belt (Mixed tunnel and cross ventilation)	Ohio	170,000	-13.3 - 29.1	29.9 - 99.1	1.41 ± 1.53	Knight et al. (2021)
PM10	High-rise	Central California	32,500	5 - 35	-	33.4 ± 27.4	Lin et al. (2012)
		Eastern Indiana	218,000	12.4 ± 11.3	68.2 ± 13.3	20.6 ± 22.5	Ni et al. (2017b)
		North Carolina	103,000	-	-	17.8 ± 14.9	Li et al. (2013a)
		Ohio	169,000	9.2 ± 9.9	-	34.8 ± 33.3	Lim et al. (2007)
	High-rise manure pit	North Carolina	103,000	12 - 22	-	4.41 - 31.5	Li et al. (2013b)
	High-rise in-house manure storage	Central Iowa	248,814	-	-	26.1 ± 15.8	S. Li et al. (2011b)
	High-rise deep pit	Forli´, Italy	60,000	6.6 - 34.5	-	5.52 - 414.48 [a]	Fabbri et al. (2007)
	Manure-belt	West Lafayette, IN	250,000	12.0 ± 10.9	-	25.2	Ni et al. (2017a)
		Iowa	200,000	-	-	15.7	Shepherd et al. (2015)
		Ontario, Canada	70,600	-4.1 - 19.4	-	4.5 ± 3.7	Morgan et al. (2014)
		Forli´, Italy	60,000	3.6 - 33.2	-	1.92 - 117.12[a]	Fabbri et al. (2007)
	Manure-belt (tunnel and cross ventilation)	Ohio	-	-13.3 - 29.1	29.9 - 99.1	17.9 ± 9.6	Knight et al. (2021)
	Belt battery	Central Ohio	168,000	-13.7 - 26.2	48 - 98	20	Zhao et al. (2005)
TSP	High-rise	Central California	32,500	5 - 35	-	78.0 ± 42.7	Lin et al. (2012)
		Central Ohio	168,000	-13.7 - 26.2	48 - 98	146	Zhao et al. (2005)
		Ohio	169,000	9.2 ± 9.9	-	81.2 ± 65.4	Lim et al. (2007)
		North Carolina	103,000	-	-	43.1 ± 35.5	Li et al. (2013a)
	High-rise manure pit	North Carolina	103,000	12 - 22	-	9.55 - 64.7	Li et al. (2013b)
	Belt battery	Central Ohio	168,000	-13.7 - 26.2	48 - 98	168	Zhao et al. (2005)
* Unit is mg d-1 hen space-1 .

Conclusions

A set of stress tests with single input variables was performed with the EPA draft emission models for laying hen facilities and manure storage sheds. A key finding was the unanticipated sensitivity of draft model outputs to bird inventory. A strong tendency to increase per bird emissions at the lower or upper ranges of the simulated bird inventory was obvious. Deploying the draft models to estimate annual effective emission factors and farm emissions from a dozen select areas of the U.S. demonstrated substantial differences in emission predictions with inventories smaller or greater than those on which they were developed. With inventory held constant, the marginal influence of ambient temperature and RH on daily emissions varied from relatively low, which is likely below any measurement detection threshold and within the model’s prediction uncertainty, to relatively large, which is not corroborated in the literature. The models, notably, would not be applicable to cage-free systems. The draft models are not advised to be used before the EPA draft model equations allow for estimates and assessments that permit a precision level suitable for a wide range of layer facilities.

Figure 5. Comparison of NH3 emission factors derived from EPA draft models and literature (table 7; Liang et al., 2003; Ni et al., 2017a; Shepherd et al., 2015) for a manure-belt housing with 280,000 and 100,000 hens for Fort Dodge, IA.

Acknowledgments

The authors are grateful to the American Egg Board (AEB) for funding this project and to the Egg Industry Center for putting together a task force and allocating funds for this joint effort. The authors also thank Tom Hebert from Bayard Ridge Group, LLC, for reviewing this effort and providing constructive suggestions. B.C.R. and R.S.G.: This work is also a product of the Iowa Agriculture and Home Economics Experiment Station, Ames, Iowa. Project Number IOW05590 is sponsored by the Hatch Act and State of Iowa funds.

Disclosures

The analysis was commissioned by the American Egg Board (AEB) to evaluate these draft models and yield information regarding their suitability for use by the US egg industry. AEB had no role in data collection or analysis contained within this report. Additionally, any conclusions or opinions expressed within the report are those of the investigator and/or collaborators, and not of AEB. No funds provided by AEB were or will be used for the purposes of influencing legislation, government policy, or any related action.

Appendix 1: Summary of Draft EPA Models by Housing Type

High-rise Housing

EPA developed and evaluated ten models (G-1 to G-10) for NH₃ and H₂S emissions for high-rise houses. EPA selected model G-8 (input variables include intercept, inventory, ambient temperature, and RH) for further analysis of NH₃ and H₂S as it had the best normalized mean bias compared to other models. All regression parameters of model G-8 were significant for NH₃ and H₂S (P < 0.0001).

Fifteen models (P-1 to P-15) were developed and evaluated by the EPA for PM₁₀, PM_2.5, and TSP. Model P-4 (input variables include intercept, inventory, ambient temperature, and RH) was selected for further analysis of PM₁₀ as it had the lowest mean error and one of the lowest normalized mean biases among all models. For model P-4, all regression coefficients were significant for PM₁₀ analysis (P < 0.0001) and had reasonable performance for PM_2.5 and TSP. Therefore, the EPA selected model P-4 for PM_2.5 and TSP as a particulate matter emission estimation method. The P-values of regression coefficients of P-4 were <0.05 for PM_2.5 and were <0.05 for all parameters except for ambient temperature (P=0.1417) for TSP. Table A1 lists the EPA model parameters for calculating air pollutant emission in high-rises.

Table A1. Parameters for high-rise systems.
	NH3	H2S	PM10	PM2.5	TSP
Intercept	2.6598	2.7231	6.8702	4.6219	7.5995
CInventory	0.0059	0.0098	0.0077	0.008	0.0079
CAmbT	0.0387	0.0210	0.0145	0.051	0.0137
CambRH	0.0018	0.0038	-0.0030	-0.0181	-0.0058
E0	1.58238	1.24359	1.11745	1.51089	1.11429
E1	0	-15	-494	-37	0

Manure-Belt Housing

EPA developed and evaluated 12 models (G-1 to G-12) for NH₃ and H₂S emissions from manure-belt houses. For NH₃ emissions, EPA selected model G-7 (input variables include intercept, inventory, ambient temperature, and ambient RH) for further evaluation as all parameters analyzed were statistically significant (P<0.05). For H₂S emissions, the same model was selected as it had one of the lowest normalized mean biases compared to other models. Using the same model also reduces the burden of data collection. All regression analysis parameters for H₂S were statistically significant (P<0.0001).

The EPA then developed and evaluated 16 models (P-1 to P-16) for PM₁₀, PM_2.5, and TSP. The model fitting statistics for PM₁₀ indicated models P-1 and P-16 performed similarly. Between the two, EPA selected model P-1, which had fewer parameters (input variables include intercept and inventory), for further PM₁₀ analysis. The parameters were statistically significant (P<0.005). Model P-1 was also selected for both PM_2.5 and TSP emissions, although the model fitting statistics for PM_2.5 and TSP did not show significant differences for both the intercept and inventory (P_intercept = 0.0681 and P_inventory = 0.0604 for PM_2.5; P_intercept = 0.4404 and P_inventory = 0.7855 for TSP). Table A2 shows the model parameters for calculating the air pollutant emission in manure-belt housing.

Table A2. Parameters for manure-belt systems.
	NH3	H2S	PM10	PM2.5	TSP
Intercept	2.4392	3.7391	6.631005	-127.4489	6.936206
CInventory	0.0047	0.0073	0.007205	0.534577	0.00987
CAmbT	0.0294	0.0222	0	0	0
CambRH	0.0019	0.0048	0	0	0
E0	1.27315	1.09812	1.45218	2.97703	1.34146
E1	0	-39	-1045	-108	-696

Manure Storage

For manure storage sheds, the EPA developed and evaluated 20 models (G-1 to G-20) for NH₃ and H₂S emissions. Models G-16 and G-17 had statistically significant parameters. Model G-16 requires another input variable, airflow (building ventilation rate), which needs hourly windspeed measurements from several openings of the facility and is thus cumbersome for a producer to quantify. Model G-17 requires an estimate of the manure production volume and manure accumulation time. Due to the elevated requirements to calculate these parameters, EPA selected model G-2 with easily obtainable parameters (input variables include intercept, inventory (5-day lag), and ambient temperature) for NH₃ and H₂S emissions. Model fitting results show that all parameters had a P-value < 0.001, except for the intercept (P-value = 0.7116), while those for H₂S were all statistically significant (P<0.05).

For PM₁₀, PM_2.5, and TSP emissions from manure storage sheds, the model was selected from 13 models (P-1 to P-13) developed by the EPA. The EPA disregarded the models that require airflow inputs, and selected model P-11 (input variables include intercept and the 5-day-lag inventory) for further consideration. Model fitting results for PM₁₀ showed that the P-value was 0.0007 for the intercept and 0.7853 (i.e., not significant) for the 5-day-lag inventory. This indicated that the 5-day-lag bird inventory was not a significant input factor, and thus, only the intercept (= 4.5366) became the dominant log-transformed emission estimate for manure storage sheds across the entire dataset. Including bird inventory as an input variable is thus, not justified, for PM₁₀ emission estimations. For model fitting analyses, the P-value was 0.0505 for intercept and 0.0334 for inventory (5-day lag) for PM_2.5 and nonsignificant for TSP. Table A3 shows the coefficients for calculating the air pollutant emission in manure storage.

Table A3. Parameters for manure storage shed.
	NH3	H2S	PM10	PM2.5	TSP
Intercept	-0.194945	1.295775	4.5366	-30.57734	4.041666
CInventory	0.003927	0.004976	0.000732	0.067599	0.002286
CAmbT	-0.013752	-0.024164	0	0	0
CambRH	0	0	0	0	0
E0	1.28615	1.36619	1.68902	1.68697	2.01361
E1	-1.3	-6.0	-54.0	0	-30

Appendix 2: Selection of Representative Weather Locations

The candidate locations, IECC Climate Zone, and Building America Climate Zone information pertinent to the U.S. egg industry that were selected are given in table A4.

Table A4. Candidate Weather Stations IECC and Building America Climate Classifications.
TMY3 Station	IECC Climate Zone (Temperature/Moisture[a])	Building America Climate Zone
Indianapolis IN	5A	Cold
Grand Rapids MI	5A	Cold
Detroit MI	5A	Cold
Franklin PA	5A	Cold
Pittsburgh PA	5A	Cold
Columbus OH	5A	Cold
Spokane WA	5B	Cold
Fort Dodge IA	6A	Cold
Spencer IA	6A	Cold
Litchfield MN	6A	Cold
Minneapolis MN	6A	Cold
Salt Lake City UT	5B	Cold
Phoenix AZ	2B	Hot-Dry
Modesto CA	3B	Hot-Dry
Bakersfield CA	3B	Hot-Dry
San Antonio TX	2A	Hot-Humid
Orlando FL	2A	Hot-Humid
Jackson MS	3A	Hot-Humid
Portland OR	4C	Marine
Athens GA	3A	Mixed-Humid
Charlotte NC	3A	Mixed-Humid
Lancaster PA	3A	Mixed-Humid
Columbia MO	4A	Mixed-Humid
[a]A=Moist, B=Dry, C=Marine.

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