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Models For Estimating Emissions

This chapter examines the approach for estimating air emissions used in the draft report to the EPA, Air Emissions from Animal Feeding Operations (EPA, 2001a), problems with the approach, and issues that must be addressed in getting supportable estimates. Model farms are used to define hypothesized relationships between air emissions and selected characteristics of various kinds of large operations that produce a large proportion of the livestock animals marketed in the United States.

Some variation of the model or average feeding operation appears to the committee as necessary as a basis for estimating air emissions from individual farms. The issue to be faced is finding the combination of characteristics of feeding operations that can be used to estimate air emissions with desired levels of accuracy and at reasonable costs. In the sections that follow, the committee assesses (1) the viability of the particular model farm approach used in the EPA draft report; (2) whether it can be improved using available data; (3) alternative approaches based on model farms constructs; (4) ways to characterize the substances emitted and the components of manure to be estimated; and (5) mitigation technologies and management practices in addition to those identified in the EPA draft report.

EPA MODEL FARM CONSTRUCT

Are the emission estimation approaches described in the EPA/OAR summary document, Air Emissions from Animal Feeding Operations, appropriate? The goal of EPA (2001a) was “to develop a method for



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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations 3 Models For Estimating Emissions This chapter examines the approach for estimating air emissions used in the draft report to the EPA, Air Emissions from Animal Feeding Operations (EPA, 2001a), problems with the approach, and issues that must be addressed in getting supportable estimates. Model farms are used to define hypothesized relationships between air emissions and selected characteristics of various kinds of large operations that produce a large proportion of the livestock animals marketed in the United States. Some variation of the model or average feeding operation appears to the committee as necessary as a basis for estimating air emissions from individual farms. The issue to be faced is finding the combination of characteristics of feeding operations that can be used to estimate air emissions with desired levels of accuracy and at reasonable costs. In the sections that follow, the committee assesses (1) the viability of the particular model farm approach used in the EPA draft report; (2) whether it can be improved using available data; (3) alternative approaches based on model farms constructs; (4) ways to characterize the substances emitted and the components of manure to be estimated; and (5) mitigation technologies and management practices in addition to those identified in the EPA draft report. EPA MODEL FARM CONSTRUCT Are the emission estimation approaches described in the EPA/OAR summary document, Air Emissions from Animal Feeding Operations, appropriate? The goal of EPA (2001a) was “to develop a method for

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations estimating [air] emissions at the individual farm level that reflects the different animal production methods that are commonly used at commercial scale operations.” The approach is intended to provide estimates of total annual air emissions from animal feeding operations (AFOs) for defined geographic areas by kind of animal and manure handling practices for each of eight kinds of emissions. It does this with a model farm construct that provides estimates of average annual emissions per animal unit (AU) for twenty-three model farms (two for beef, eight for dairy, two for poultry-broilers, two for poultry-layers, two for poultry-turkeys, five for swine, and two for veal; Appendix D). Each model is defined by three variable elements that describe manure management practices for typical large AFOs: (1) confinement and manure collection system, (2) manure management system, and (3) land application. The manure management system was further subdivided into solids separation and manure storage activities. Insofar as combinations of these elements are regionally distinctive, the model farms also reflect regional variations in air emissions. Model farms, as used by EPA (2001a), are a useful device for aggregating emission rates across diverse sets of AFOs. A model farm can be used to represent the average emissions across some geographic area over some period of time per unit capacity of a class of farms (e.g., all pig farms in the United States that use an enclosed house with pit recharge and irrigation of supernatant onto forage land; model farm S2 in Appendix D). The applicability and use of a model farm construct of the kind used by EPA (2001a) depends on: defining models in which the dependent variable, the amount of an air emission per unit of time, is closely related to independent variables that accurately depict real feeding operations, and that can explain a substantial share of the variation in the dependent variable; providing accurate estimates of the relationship between the dependent and independent variables in the model farm construct; and having estimates of the relationship between dependent and independent variables that clearly distinguish among the kinds of AFOs being modeled. A critical data requirement for estimating the appropriate emission factors is a statistically representative survey of emissions from the class of AFOs over several iterations of the time period to be represented. The size of the sample required to estimate the mean emission rate with a given degree of statistical significance increases with the variability of the factor to be measured (dependent variable) across the set of variables (independent variables) that affect it. Independent variables that have been discussed include animal type and age, diet, local climate, building type, land application method, and management. To the extent that some of these variables change over time (e.g.,

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations trends in farm organization, location, practices, and technology), updating of estimates and estimates of trends may be required. The model farm construct is represented by Equation 3-1: E = ∑ (wi × ei) (Eq. 3-1) in which the emission (E) of a particular pollutant from an AFO during a period of time is the product of the emission (ei ) from each unit on the model farm and the number of units (wi) of that type, summed over the farm. One use of model farms is to predict emissions and local effects for a single AFO or cluster of AFOs in a small area. This is a different use from that described by EPA (2001a) and requires a detailed model of the effects of selected variables on the rates of emissions and their downwind concentrations. An example of this type of model is an odor dispersion model that predicts odor intensity and frequency at various locations, given information on odor sources and local meteorological conditions. More data (perhaps hourly) and statistical analyses of the relationships between various explanatory variables and pollutant concentrations or impacts are required. A starting point for classifying types of data needed by emission type and intended use of its emission factor is shown in Table 3-1. The committee believes that EPA (2001a) fails to meet these standards. It does not provide a methodology to adequately determine air emissions from AFOs because both the model farm construct and the data are inadequate. Concerning the former, the model farm construct used by EPA (2001a) cannot be supported for estimating air emissions from an individual AFO. There is a great deal of variability among AFOs that cannot be accounted for using this approach. (See Finding 7.) In particular, additional factors not included in the EPA model that affect emissions include animal feeding and management; animal productivity; housing, including ventilation rate and confinement area; use of abatement strategies such as sprinklers to decrease dust; and physical characteristics of the site such as soil type and whether the facility is roofed. In addition, emissions are likely to differ for different climatic (long-term) and weather (short-term) conditions including temperature, wind, and humidity. Thus, accurately predicting emissions on individual AFOs would require determination of emission factors that reflect these characteristics. Accurate estimates of these emission factors would require sampling hundreds of AFOs representing different management and meteorological conditions. The cost of accurately measuring emissions on the number of AFOs (i.e., thousands) that would be needed to replicate all common situations would be very high.

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations TABLE 3-1. Classification of Emissions by Likely Intended Use of Emission Factors Emission Type Intended Use of Emission Factors   Regional Annual Inventory Local Seasonal Ambient Effects Local Transient (Hourly) Effects NH3 X X X CH4 X VOC X X X PM   X X H2S X X N2O X NO X Odor X X NOTE: CH4 = methane; H2S = hydrogen sulfide; NH3 = ammonia; NO = nitric oxide; N2O = nitrous oxide; PM = particulate matter; VOC = volatile organic compounds More specifically, improvements in the model farm construct are needed for both discrete variables (e.g., management, confinement conditions, location) and continuous variables (e.g., nutrient input, productivity, meteorology). Concerns about quality of data (use of non-peer reviewed data), lack of data, inappropriate use of data, and representativeness of the data were discussed in Chapter 2. Finding 6: The model farm construct as described in EPA (2001a) cannot be supported because of weaknesses in the data needed to implement it. Finding 7: The model farm construct used by EPA (2001a) cannot be supported for estimating either the annual amounts or the temporal distributions of air emissions on an individual farm, subregional, or regional basis because the way in which it characterizes feeding operations is inadequate. INDUSTRY CHARACTERIZATION How should industry characteristics and emission mitigation techniques be characterized? This question asks for suggestions to improve the approach described in the EPA draft report. The committee has discussed

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations several inadequacies in the EPA approach. In the next section, an alternative approach suggested by the committee is discussed in some detail. Rather than discuss possible improvements in estimating air emissions using the EPA approach and the use of possible emissions mitigation techniques based on the EPA estimates at this time, these issues are being left to the final report. Mitigation of air emissions based on best management practices, including those under comprehensive nutrient management plans (CNMPs) is an option already being used in various places. Although the effectiveness of the best management practices approaches is not wholly clear to the committee at this time, especially in the absence of research-based data on mass balance approaches, those practices that are already being used provide a basis for action until better information is available. PROCESS-BASED MODEL FARM APPROACH Should model farms be used to represent the industry? If so, how? What substances should be characterized and how can inherent fluctuations be accounted for? What components of manure should be included in the estimation approaches (e.g., nitrogen, sulfur, and volatile solids)? The committee has discussed using a process-based model farm approach to predict emissions on individual AFOs. A process-based approach would use mathematical modeling and experimental data to simulate conversion and transfer of reactants and products through the farm enterprise (Denmead, 1997; Jarvis, 1997). This alternative to EPA’s model farm approach (EPA, 2001a) would involve analysis of the farm system through study of its component parts. Rather than simply add the emissions observed from each farm element, a mathematical model would be used to represent the interactions between the system components (see Figure 3-1 for a representation of an animal production enterprise). Development of a process-based model does not obviate the need for data collection, but it enables the use of data representing only part of the farm system and will help identify gaps in the existing literature. For many pollutants (e.g., NH3 [ammonia], H2S [hydrogen sulfide], and CH4 [methane]), the quantity of emissions is likely to be proportional to the amount of material (substrate) from which the pollutant is derived. For example, the amount of NH3 emitted from a manure slurry is expected to be proportional to the amount of nitrogen in the manure (Muck and Steenhuis, 1982). With a compartmental modeling approach (Jarvis, 1993; Dou et al., 1996) and an assumed steady state, nitrogen in manure can be determined as intake nitrogen minus animal product nitrogen. Further, NH3 volatilization from manure during collection can be estimated as a fraction of manure nitrogen produced. The NH3 volatilized from storage can be represented as a fraction of nitrogen remaining after collection, and NH3 volatilization during field

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations FIGURE 3-1. A process-based model of emissions from an animal feeding operation. application can be represented as a fraction of nitrogen applied (Denmead, 1997). There appears to be a disappearance of nitrogen from manure storage or from soil in the form of harmless nitrogen gas (N2) (Thompson et al., 1987). Thus, the ratio of NH3 to N2 emissions would have to be determined under different animal management and meteorological conditions. There is little research, and even less agreement, as to what proportion of nitrogen is lost from various types of manure storage as NH3 or N2 (Harper et al., 2000). Nonetheless, much of the variation in emissions from AFOs, such as that from feeding and animal management, can be accounted for by predicting the effect on manure nitrogen production (Kohn et al., 1997). Other factors, such as climate and management conditions that affect partitioning of nitrogen from storage and land application, would be accounted for in the models as knowledge of how they influence processes becomes available.

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations Development of a process-based model of emissions will require a large amount of data, but the number of farms that would have to be represented would be reduced. Using a strictly empirical approach to estimate emissions would require measurements on farms representing the full diversity of agriculture in the United States. For example, emissions would be determined on farms using different combinations of animal, feed, and manure and crop management. With the process-based approach, emissions would be determined from different farm components and mathematical calculations used to determine emissions for different combinations of components. Furthermore, more data may be available to develop estimates of emissions from farm components than are available for whole-farm emissions. Different models would be needed to fit different objectives for the prediction estimates. Prediction of annual rates of emissions would require understanding relationships in a more aggregated way than prediction of potential short-term effects. When considering the acute health effects of emissions for nearby residents, short-term potential emissions would be needed, and a dynamic process-based model to predict emissions on a daily or more frequent basis may be recommended. When considering long-term atmospheric emissions, an aggregated model on an annual time step may be adequate. If emission rates are needed to categorize farms that may potentially emit enough pollutants to warrant extra regulation, tabular values representing typical animal, crop, feed, and manure management might be adequate, and predictions for different situations could be calculated and reported in tables for rapid referral. As with NH3, emissions of other nitrogen-containing compounds can best be estimated as a fraction of excreted nitrogen to emittant (Müller et al., 1997). However, other factors such as soil compaction and oxygen and moisture content also contribute substantially to variations in NO (nitric oxide) and N2O (nitrous oxide) emission processes (Li et al., 1992; Dendooven et al., 1996). The approach may involve modeling the ratio of N2O:N2 and factors that affect it, because generation rates of both gases are linked to rates of nitrification and denitrification (Abbasi et al., 1997), and management may be able to shift reactions to favor the more benign product, N2 (Dendooven et al., 1996). The emissions of H2S are likely to be a function of the amount of sulfur delivered to anaerobic manure storage; manure sulfur will be equivalent to the sulfur in feed and water minus the sulfur in animal products (including growth). Whereas most sulfur will be converted to H2S by microorganisms under anaerobic conditions, the rate of H2S volatilization will depend on pH and other factors. Methane emissions from a ruminant animal are proportional to the carbohydrate content of its diet, with additional effects caused by forage to concentrate ratio and the use of ionophores (Johnson and Johnson, 1995). CH4 emissions from manure storage could also be expressed as a fraction of the carbon delivered to storage, including undigested feed and bedding material.

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations Particulate matter (PM) emissions occur primarily from feeding and housing. Quantifying the total feed used may explain some variation in PM emissions from feeding, but a different means is necessary to estimate emissions from housing. Once a farm has been identified as a potentially high risk, actual farm-specific data such as feed amounts and manure analysis could be used in the models to more accurately predict emissions. This approach would reward producers who can document reducing inputs of substrates for emissions (and presumably outputs of emissions) and provide more of a performance standard rather than a prescriptive regulation, but without the cost and uncertainty involved in measuring actual emissions. Finally, certain sectors of the animal enterprise are likely to be more important for some emissions than for others. Development of a process-based model would enable system analysis and simulation for determining critical control points for emissions (Kohn et al., 1997). It would also highlight fruitful research areas, and identify knowledge gaps that need to be filled in order to improve understanding of farm processes. Finding 8: A process-based model farm approach that incorporates “mass balance” constraints for some of the emitted substances of concern, in conjunction with estimated emission factors for other substances, may be a useful alternative to the model farm construct defined by EPA (2001a). The committee plans to explore issues associated with these two approaches more fully in its final report. MITIGATION TECHNOLOGIES AND BEST MANAGEMENT PRACTICES What additional emission mitigation technologies and management practices should be considered? Previously, research on emission mitigation technologies and management practices for AFOs has been limited. However, more research in these areas is anticipated over the next several years. An exhaustive list of potential technologies would be difficult to produce, so the committee has highlighted several ongoing research efforts around the country to introduce some of the technologies and management practices that may prove useful in decreasing air emissions from AFOs. Undoubtedly there are technologies not discussed here that may prove to be as good as those listed. Lack of inclusion should not be construed as dismissing their potential. The committee will explore mitigation technologies and best management practices more thoroughly in its final report.

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations Animal Feeding Strategies Feeding livestock closer to their nutrient requirements may result in decreased nutrient content of manure and subsequent decreases in emissions of certain pollutants (e.g., NH3, H2S). Nutrient requirements of livestock species have been determined and well documented (National Research Council, 1994, 1998, 2000, 2001). Several approaches for decreasing manure nitrogen production are available. Increasing production of salable food products (meat, milk, and eggs) per animal decreases the number of animals required to fill the market demand for those products. The animal’s requirements can be divided into needs for maintenance (maintaining basal metabolism) and production (National Research Council, 1994, 1998, 2000, 2001). By meeting maintenance requirements while increasing production, nitrogen emissions from manure are decreased. Dunlap et al. (2000) showed that increasing milk production of dairy cows by administering growth hormone, increasing photoperiod using artificial lighting, and milking three times daily instead of two can decrease manure nitrogen by 16 percent for a given amount of milk produced. In addition to increasing production per animal, nitrogen excretion to manure can be decreased by feeding at a level closer to the animal’s requirements. Grouping animals with similar requirements makes it possible to feed them more precisely to meet their requirements with the same diet. For example, broilers are already separated by age, but greater homogeneity may be obtained by separating by sex as well (Fritz et al., 1969). Also, feeding broilers four different diets over the course of their lifespan, rather than the standard three, results in decreased nutrient inputs by 10 percent (Dhandu, 2001). Grouping dairy cows into separate production groups on a farm decreases nitrogen excretion by 6 percent compared to feeding all lactating cows the same ration (St-Pierre and Thraen, 1999). Of all current practices, feeding amino acid supplements has had the greatest impact on decreasing nitrogen excretion in manure. Animals require a specific profile of amino acids for optimal production, but most feeds do not provide that profile. When balancing the diets of animals, corn and legumes are typically mixed to provide a complementary set of amino acids. Corn is high in methionine but low in lysine, while legumes are high in lysine and low in methionine. Synthetic amino acid supplements can be used to further decrease protein feeding without sacrificing production or health. Sutton et al. (1996) showed that for growing pigs, corn and soybean meal diets supplemented with lysine, tryptophan, threonine, and methionine decreased NH3 and total nitrogen in freshly excreted manure by 28 percent. Using amino acids that are protected from degradation in the rumen of cattle have been shown to decrease nitrogen excretion from dairy cattle by as much as 26 percent (Dinn et al., 1998).

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations Manure Handling and Treatment Once excreted from animals, manure naturally undergoes microbial decomposition, usually anaerobic. A number of inorganic gases and organic compounds are produced during the decomposition process. Manure handling and treatment can have a great influence on the physical, chemical, and biological properties of manure and consequently on the emissions of air quality concern. Solid and liquid manure are handled differently on AFOs. There are many treatment technologies available that could play important roles in emission mitigation. However, the effectiveness of most of them is not well quantified. Standard protocols for evaluating the air quality impact of different manure handling and treatment technologies must be developed. Some technologies may reduce emissions of certain gases or compounds but increase emissions of others. Treatment technologies have to be analyzed with clear objectives as to what emissions are to be mitigated. Two recent literature summaries (Lorimor et al., 2001; Sweeten et al., 2001) reviewed various animal manure handling and treatment technologies that have been used on AFOs or extensively researched. A whole-farm approach needs to be taken when evaluating emission mitigation technologies. Knowledge of animal manure distribution on AFOs and emission source characterization from individual sources (such as animal houses, feedlots, manure storage, and land application) is important for quantifying potential emission mitigation effects of new technologies. Recently, a project was initiated by United States Department of Agriculture Natural Resources Conservation Service to identify and evaluate the emerging animal manure treatment technologies that are most likely to be used by animal producers in the next five to ten years. The project was led by Iowa State University and supported by a four-member advisory board. A preliminary list of manure handling and treatment technologies that have been identified and have relevance to air emissions includes: storage covers, anaerobic digestion, aeration, solid-liquid separation, composting, and chemical treatment for pH control (Melvin, personal communication, 2002). The potential air quality impacts of these manure treatment technologies will be analyzed in the committee’s final report based on the published information, with recommendations for further research and development. North Carolina On July 25, 2000, Smithfield Foods, Inc., entered into a voluntary agreement with the Attorney General of North Carolina to provide resources for an effort to develop innovative technologies that are determined to be technically, operationally, and economically feasible for the treatment and

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations management of swine wastes (Williams, 2001). Performance standards, along with comprehensive analyses of odor, NH3, and pathogen emissions, as well as economic analyses, are required for each technology. Currently, 16 systems are being studied: psychrophilic (unheated and unmixed) ambient temperature anaerobic digester, energy recovery, greenhouse vegetable production; thermophilic (high-temperature) anaerobic digester energy recovery; solids separation-constructed wetlands; sequencing batch reactor; upflow biofiltration; solids separation, nitrification-denitrification, soluble phosphorus removal, solids processing; belt manure removal and gasification to thermally convert dry manure to a combustible gas stream for liquid fuel recovery; ultrasonic plasma resonator; manure solids conversion to insect biomass (black soldier fly larvae) for value-added processing into animal feed protein meal and oil; solids separation-reciprocating water technology; microturbine cogeneration for energy recovery; belt system for manure removal; high-rate second-generation totally enclosed Bion system for manure slurry treatment and biosolids recovery; combined in-ground ambient digester with permeable cover or aerobic blanket, BioKinetic aeration process for nitrification-denitrification, in-ground mesophilic anaerobic digester; dewatering, drying, desalinization; and solids separation-gasification for energy and ash recovery centralized system. California The State of California recently awarded a $5 million grant (matched by $4.8 million in federal funds) to develop a centralized waste processing facility in Chino, California. Effects of this centralized treatment have not yet been evaluated. The State also provided $10 million as cost sharing for dairy farmers to build anaerobic digesters. So far there are more than 30 applications from dairy farmers interested in participating in the cost-sharing program. Research is being carried out at the University of California, Davis on alternative manure treatment technologies such as solid-liquid separation, aeration and anaerobic digestion.

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The Scientific Basis for Estimating Air Emissions from Animal Feeding Operations USDA Agricultural Research Service Air Quality National Program In January 2000, the Agricultural Research Service (ARS) met with stakeholders in Sacramento, California, to explore air quality problems associated with agriculture (USDA, 2002). This meeting was the first step in developing a list of high-priority research needs and a research program to address those needs. The Agricultural Air Quality Task Force had previously provided the Secretary of Agriculture with a list of research needs. EPA has been actively seeking ARS research in several agricultural air quality topic areas. ARS research in agricultural air quality is organized into five categories: particulate emissions, ammonia and ammonium emissions, malodorous compounds, ozone impacts, and pesticides and other synthetic organic chemicals.