Ammonia Emissions from Swine Waste Lagoons in the Utah Great ...

Published online January 5, 2006

Ammonia Emissions from Swine Waste Lagoons in the Utah Great Basin Lowry A. Harper,* Kim H. Weaver, and Richard A. Dotson the industry and transportation sectors, can become part of an airshed impacting very distant ecosystems. Early USDA estimates (Hatfield et al., 1993) suggested that 89 to 90% of the N inputs to anaerobic lagoons in confined animal feeding operations (CAFOs) were lost to the atmosphere. These suggested NH3 emissions represented about 60% of the total feed N input. Current estimates by the USEPA (2004) and by the state of North Carolina (Doorn et al., 2002) suggest that 71 and 36% of the N going into CAFOs is volatilized as NH3 gas, respectively. However, other studies in the North Carolina and Georgia Coastal Plains region of the United States (Harper and Sharpe, 1998; Harper et al., 2000) have shown that lagoons emit significantly less NH3 than previously thought. Harper et al. (2004) found that only about 7.5% of N entering into a swine production operation as feed left the lagoon as NH3. They found another 7.3% was emitted as NH3 from the production houses and another 2% from field application of waste effluent to nearby crops (Sharpe and Harper, 2002). Much of the N (about 43% of input feed) that entered into the lagoon was found to be denitrified to N2 (Harper and Sharpe, 1998; Harper et al., 2000, 2004) by microbial and/or chemical denitrification. To evaluate the effect of animal concentrations on the region’s ecosystems, emissions type and amounts must be accurately evaluated from these systems. Emission factors currently in use, developed mainly from data of Northern Europe (Battye et al., 1994), are variable and questionable for use in the semiarid Great Basin of the United States. [The U.S. emission factors were developed from Northern European emission factors (Asman, 1992); however, a conversion error by Battye et al. for swine resulted in the U.S. factors developed being too large by about a factor of two (Asman, personal communication, 2000).] Seasonal variations are also inconsistent and must be properly considered when calculating annual emissions. Consequently, emission factors must be used with caution because of variability induced by geography and meteorology, methodology for measurement (Denmead and Raupach, 1993; Harper, 2005), type and weight of animals (Harper and Sharpe, 1998), N content of feedstuffs, housing and management, and other factors. Even emissions and emission factors determined from the same lagoon at the same time using different technologies have shown considerable variation. On a swine farm in the Coastal Plains of North Carolina, several studies of NH3 emissions were performed during the same period. It is interesting that for the same lagoon, the NH3 estimates using floating chambers (Aneja et al., 1999) and a Gausian dispersion model (McColloch, 1999) are similar but considerably larger, by 1.9 times, than the microclimate studies by Harper et al. (2004). Also, as part of this study, researchers using tracer techniques (Todd et al., 2001) obtained annual emissions 4.0 times

Reproduced from Journal of Environmental Quality. Published by ASA, CSSA, and SSSA. All copyrights reserved.

ABSTRACT In animal production systems (poultry, beef, and swine), current production, storage, and disposal techniques present a challenge to manage wastes to minimize the emissions of trace gases within relatively small geographical areas. Physical and chemical parameters were measured on primary and secondary lagoons on three different swine farming systems, three replicates each, in the Central Great Basin of the United States to determine ammonia (NH3) emissions. Nutrient concentrations, lagoon water temperature, and micrometeorological data from these measurements were used with a published process model to calculate emissions. Annual cycling of emissions was determined in relation to climatic factors and wind speed was found the predominating factor when the lagoon temperatures were above about 3°C. Total NH3 emissions increased in the order of smallest to largest: nursery, sow, and finisher farms. However, emissions on an animal basis increased from nursery animals being lowest to sow animals being highest. When emissions were compared to the amount of nitrogen (N) fed to the animals, NH3 emissions from sows were lowest with emissions from finisher animals highest. Ammonia emissions were compared to similar farm production systems in the humid East of the United States and found to be similar for finisher animals but had much lower emissions than comparable humid East sow production. Published estimates of NH3 emissions from lagoons ranged from 36 to 70% of feed input (no error range) compared to our emissions determined from a process model of 9.8% with an estimated range of 64%.

T

HE INCREASED AWARENESS of the contribution of N compounds to the total deposition of pollutants is becoming more important in air and water quality and in global climate change. In animal production systems (poultry, beef, and swine), current production, storage, and disposal techniques present a challenge to manage wastes so as to minimize the emissions of each trace gas [NH3, nitrous oxide (N2O), methane (CH4), and others] without impacting the combined emissions of the other gases. A portion of emitted NH3 reacts with acidic gases including nitric (HNO3), hydrochloric (HCl), and sulfuric acid (H2SO4) present as aerosols, converting the NH3 to NH41 salt particles. With significant contributions of available acid gases from industry and transportation, their neutralization with NH3 forms particulates that may create hazes, which are not easily dry-deposited and can be transported over long distance before they are removed by precipitation (wet deposition) (Asman et al., 1998). Thus, animal production enterprises, along with

L.A. Harper, Southern Piedmont Conservation Research Unit JPCSNRCC, USDA-ARS, 1420 Experiment Station Road, Watkinsville, GA 30677. K.H. Weaver and R.A. Dotson (retired), Division of Chemistry, Southern Utah University, Cedar City, UT 84720. Received 16 July 2004. *Corresponding author ([email protected], lowry.harper@ gmail.com). Published in J. Environ. Qual. 35:224–230 (2006). Technical Reports: Atmospheric Pollutants and Trace Gases doi:10.2134/jeq2004.0288 ª ASA, CSSA, SSSA 677 S. Segoe Rd., Madison, WI 53711 USA

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HARPER ET AL.: AMMONIA EMMISSIONS FROM SWINE WASTE LAGOONS

higher than microclimate techniques. [For a discussion of probable causes of emissions determinations discrepancies between the different techniques, see Denmead and Raupach (1993), Harper (1988), or Harper (2005).] For that farm, a total farm N balance by Harper et al. (2004) of individually measured components accounted for about 95% of the feed input N. Larger estimates of NH3 emissions suggested by some reports would result in more N leaving the farm [as animal N plus volatile N gases (NH3 1 N2O 1 N2)] than as feed N entering the farm. To obtain realistic emissions in relation to variable climatic and management conditions where animal production occurs, specialized equipment and transport technology is required. Recent non-interference measurements have been made (Harper and Sharpe, 1998; Harper et al., 2000, 2004) in the Southeastern Coastal Plains area of the United States in three types of production systems and with varied seasonal emissions. These measurements showed widely variable rates as would be expected with different lagoon characteristics and climatic conditions. Because lagoons are difficult to measure, requiring specialized equipment and appropriate atmospheric transport technology, a statistical model (Harper et al., 2004) based on lagoon ammonium (NH41) content, temperature, and pH, along with wind speed, explained 78% of the variability in emissions. Also, a process model (De Visscher et al., 2002), based also on the same physical and chemical factors, as used above, was developed. The objective of this process model development was to have a research tool that was transferable to other regions. The statistical model is location-specific and has limitations. Also, input physical and chemical parameters outside the ranges of the statistical model development would produce questionable results. The process model predicted emissions, as measured by micrometeorological techniques, with an accuracy explaining 70% of the variability of the data using average daily input values. The process model more reliably predicted measured emissions than the statistical model, which generally underestimated emissions. Use of the U.S., humid-Southeast, location-specific model in the semiarid West would be questionable, thus the process model was used for calculating emissions. The purpose of this research was to calculate lagoon NH3 emissions from lagoons receiving swine feeding operation waste. Measurements of the necessary chemical and physical factors were made to calculate emissions with the process model. The model was used to reduce the expense of determining emissions on the large number of lagoons for an extended period of time. MATERIALS AND METHODS The model of De Visscher et al. (2002) was used to estimate NH3 emissions from the lagoons. The process model provided a more suitable technique for estimating emissions because it should have wide geographical and management applicability compared to the region-specific model of Harper et al. (2004). Processes in the model leading to emission estimations included the determination of the total free ammoniacal nitrogen [AN] (NH3 1 NH41) in the lagoon liquid as well as the dissociation of NH3 from suspended organic material. The emission of NH3 is modeled as a two-film model where

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the NH3 diffuses from the bulk liquid to the air–water interface through a thin boundary layer, evaporates, and diffuses to the bulk air through a gaseous boundary layer, each layer being characterized by a transfer coefficient. By combining Henry’s law with the diffusion relationships and the mass-transfer coefficient in the boundary layer (derived from a relationship of wind speed measured at a specified height), a process relationship from NH3 emission was derived which should be geographically widely acceptable. [An electronic copy of the mathematical model may be obtained from L.A. Harper ([email protected]) or A. De Visscher ([email protected]. be).] The model-simulated emissions, as compared to noninterference micrometeorological measurement of Harper et al. (2000, 2004), gave an accuracy of 70% of the data variability using average daily emissions and 50% of the data variability using 4-h average data. The higher accuracy with average daily input values is probably due to the reduction of stochastic variability of the 4-h data. The process model did not show increased accuracy over the above statistical models, but the deviations between model calculations and actual NH3 emission measurements were distributed more evenly in the case of the process model. More detailed discussion of model development is available in De Visscher et al. (2002). Three representative types of production farms with three individual farms per production type were selected in the overall operation. The nine production sites with primary and secondary waste storage lagoons in each site were instrumented resulting in 18 instrumented lagoons. Necessary information for determining emission factors and emissions per feed input was provided by the host management. Table 1 provides information on farm type, lagoon type, lagoon area, effluent characteristics, animal numbers, and live weights. Temperature measurement in each of the lagoons was accomplished using small temperature recorders (HOBO underwater data logger; Onset, Bourne, MA), which stored data collected at 10-min intervals for 1 mo. Data were downloaded regularly and processed into continuous files for each lagoon. Nutrient samples were collected from the water–air surface layers on a monthly basis. The samples were collected in the top 2 cm of the surface into plastic sample bottles (in duplicate) in three locations, frozen immediately after collection, and stored until sufficient samples were obtained for analysis (normally 1 mo). Three samples were sufficient as the lagoons were well-mixed horizontally due to wind-shear at the water–air interface. Nutrient analyses included NH41–N and pH (for a description of analysis procedures see Harper et al., 2000). Micrometeorological measurements were made with a portable meteorological station that sampled air temperature, wind speed, and wind direction at a 2-m height. Data were sampled at 15-min intervals and collected every 2 wk and processed into continuous files for each lagoon along with the lagoon temperature data. Occasional missing wind speed data were obtained from a nearby airport meteorological station located about 18 km from the research site. Comparison of wind speeds suggested that site wind speeds and airport wind speeds were not significantly different for long-term (daily) average wind speeds. In total, about 3 mo of airport data were used due to site equipment malfunction. Data were averaged into monthly values for emissions evaluation. All 18 lagoon data sets were compiled into a single data set for determinations of annual emissions, emission factors, and emissions as a percentage of feed N.

RESULTS AND DISCUSSION Monthly average emissions varied in all production farm types (sow, nursery, and finisher) on an annual cycle

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Table 1. Animal and lagoon average characteristics.† Effluent


Farm type

Lagoon type

Farm average number of animals

Total live animal weight

Surface area

Temperature

NH4

pH

Sow 1 Sow 2 Sow 3 Mean SD

primary primary primary

5000 5000 5000

kg 922902 922902 922902

ha 1.93 1.93 1.93

°C 12.20 12.57 11.74 12.17 0.34

mg g (ppm) 1048.9 869.8 912.9 943.9 76.3

7.93 7.86 7.89 7.89 0.03


secondary secondary secondary

5000 5000 5000

922902 922902 922902

1.87 1.87 1.87

– 8.36 10.68 9.52 1.16

– 367.9 342.3 355.1 12.8

– 8.12 8.23 8.18 0.06

Nursery 1 Nursery 2 Nursery 3 Mean SD


12000 12000 12000

163265 163265 163265

0.50 0.50 0.50

12.06 12.27 12.51 12.28 0.18

1311.5 1303.4 1236.1 1283.7 33.8

7.90 7.87 7.88 7.88 0.01

Nursery 1 Nursery 2 Nursery 3


12000 12000 12000

163265 163265 163265

0.68 0.68 0.68

– – 9.29

– – 1194.5

– – 7.73

Finisher 1 Finisher 2 Finisher 3 Mean SD


11520 11520 11520

783674 783674 783674

1.69 1.69 1.69

12.41 12.11 18.57 14.36 2.98

1730.8 1720.5 1811.8 1754.4 40.8

7.88 7.90 8.09 7.95 0.09



11520 11520 11520

783674 783674 783674

0.59 0.59 0.59

– 18.11 16.90 17.51 0.60

– 1138.8 689.0 913.9 224.9

– 8.10 8.05 8.07 0.02

21

† All data are the annual average of monthly averages.

(Fig. 1A) with higher emissions during the summer and autumn. However, the autumn emissions were higher in 2001 than 2000 due to much higher wind speeds. Figures 1B and 1C give typical examples of annual cycling of meteorological and production variables. Table 1 gives a comparison of animal and lagoon characteristics between farms. Animal wastes were removed from the each of the animal houses on a weekly basis from the below-floor containment (often called the ‘‘pull-plug’’ system). With six units in the building, one unit was emptied each day making the waste input into the lagoon reasonably continuous. The liquid waste was processed in the primary lagoon, but an overflow secondary lagoon was available for periods when the primary lagoon became full. Ammonium concentrations in the primary lagoons were higher than in the secondary lagoons since only effluent (at the primary lagoon surface) entered the secondary lagoon with little of the animal waste which would decompose providing additional NH41 to offset volatilized NH3 and perhaps some biological and/or chemical denitrification (Harper et al., 2000, 2004). The pH values of the secondary lagoons were higher than primary lagoons possibly due to higher organic N in primary than in the secondary lagoons. Also, the lack of waste processing in the secondary lagoon would cause the secondary lagoons to have a higher pH because of reduced CO2 production. Furthermore, there was little methanogenesis in the secondary lagoons, which may have an acidifying effect as a result of the Gibbs free

energy change of the oxidation reaction of NH41 to N2 (Harper et al., 2004): 4(12a)NH41(aq) 1 4aNH3(aq) 1 3O2(g) ! 2N2(g) 1 6H2 O(1) 1 4(12a)H1(aq)

[1]

where a (the fractional contribution of the alkaline form of NH41) and (1 2 a) (the fractional contribution of the acid form) are given by K[K 1 (H1)]21 and (H1)(K 1 [H1])21, with K being the protolytic constant (5.5 3 10210) (see Harper et al., 2004). Ammonium concentrations varied between farms’ lagoons as a result of differences in waste loading rates, with the finisher farm lagoons almost twice as concentrated as the sow farms. There was little difference in pH between all the farmtype lagoons even though there were significant differences in waste loading rates. This can be explained by the buffer systems that exist in the lagoons such as ammonium bicarbonate (NH4HCO3) (Vlek and Craswell, 1981). For example, an NH4HCO3 buffer composed of equal moles of NH41 and bicarbonate (HCO32) in distilled water results in a pH (7.7) near the average pH (8.0) of the lagoons. A mass-balance calculation using the dissociation constants of carbonic acid (H2CO3) and the NH41 ion predicts nearly the same pH (7.8). When the calculation is repeated with more HCO32 than NH41, a pH of 8 is predicted. Other buffer systems also exist in the lagoons (H2PO42/HPO422, pH 5 7.2 and NH41/NH3, pH 5 9.2) that have maximum buffering capacity near the pH of the



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Fig. 1. Annual emissions of NH3 from a sow lagoon in the Central Great Basin of Utah as determined by process and statistical models (A); chemical and physical parameters used in model determinations are shown in (B) and (C) and include air (Tair) and effluent (T effl) temperatures, wind speed (u), effluent ammonium concentration (NH4), and effluent pH. Dashed lines show the minimum and maximum range for which the statistical model is valid.

lagoons, which would also stabilize the pH and make them resistant to pH change. There was variability in calculated emissions between lagoons (Table 2). Since the primary lagoons were the main processing facilities and the secondary lagoons were mainly overflow facilities, the primary lagoon emissions for each farm type were quite constant because of steady-state input from the houses and biological processing in the lagoons. The three primary lagoons’ emissions had a combined average standard deviation of about 15% of the mean. The secondary lagoons’ emissions were quite variable since some of the lagoons were dry and emitted no gases; furthermore, those secondary lagoons with effluent had little organic matter addition and consequently little processing. In the sow secondary lagoons, the emissions were highly variable since one lagoon had very high emissions and one had zero emis-

sions. Since all the operations were designed to be similar, effluent in the secondary lagoons was a singular management factor. None of the nursery farms had effluent in the secondary lagoons and one finisher farm had no effluent in the secondary lagoon. Similar to the sow and nursery farms, the finisher operations emissions from the secondary lagoons were approximately 10% of the primary lagoon emissions. Measured NH3 emission factors for lagoons have been reported by Harper et al. (2001) on a per-animal basis ranging in Europe from 0.7 to 5.3 kg NH3–N animal21 yr21 for farrow-to-finish farms (FF) and 1.8 to 13.5 kg NH3–N animal21 yr21 for farrow-to-wean farms (FW). Emission factors for the United States ranged from 0.8 to 1.8 kg NH3–N animal21 yr21 for FF farms and 1.2 kg NH3– N animal21 yr21 for a FW farm. These emission factors were determined by many techniques. Many of the

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Table 2. Ammonia emissions and emissions factors for a swine feeding operation. Emissions were determined from a process model by De Visscher et al. (2002).


Farm type

Lagoon

Lagoon surface area

Average number of animals per house

Average animal live weight

Lagoon emissions

Lagoon emission factor



ha 1.93 1.93 1.93

5000 5000 5000

kg 184.6 184.6 184.6

kg NH3–N lagoon21 yr21 9940 11613 5342 8965 2651

kg NH3–N animal21 yr21 1.99 2.32 1.07 1.79 0.53



1.87 1.87 1.87

5000 5000 5000

184.6 184.6 184.6

0 3602 8128 3910 3325

0 0.72 1.63 0.78 0.67

Nursery 1 Nursery 2 Nursery 3 Mean SD


0.5 0.5 0.5

12000 12000 12000

13.6 13.6 13.6

4011 3414 3655 3693 245

0.33 0.28 0.30 0.31 0.02

Nursery 1 Nursery 2 Nursery 3


0 0 0

12000 12000 12000

13.6 13.6 13.6

0 0 0



1.69 1.69 1.69

11520 11520 11520

68.0 68.0 68.0

18900 15715 20864 18493 2122

1.64 1.36 1.81 1.61 0.18



0.59 0.59 0.59

11520 11520 11520

68.0 68.0 68.0

0 4675 3066 2580 1939

0 0.41 0.27 0.22 0.17

0 0 0

method for expression of emissions is as a percentage of feed N provided to the animals. When expressed as a percentage of feed input, sow farms emit the least and the finisher farms emit the largest amount of NH3 from lagoons. Many factors result in differences in comparative emissions (including feed conversion, surface area of the lagoons, etc.) between production animal types, and the average of individual composite emissions cannot be used to develop a total system composite emission factor. The accuracy of the emissions determinations using this process model depends on model error, input error, and parameter error (Loague and Corwin, 1996). Earlier verification of this model showed an overall uncertainty of 30% (De Visscher et al., 2002). Variance of spatial measurements within lagoons was small due to wind shear causing spatial mixing. Spatial sampling (plus analysis) variability within the lagoons for NH41 ranged from 10 to 15% with pH variability of less than 1%. Lagoon temperature spatial variability was found to be less than

factors were obtained using appropriate non-interference techniques. Our emission factors per animal, developed from the process model of De Visscher et al. (2002) at the Central Great Basin site, ranged from 0.31 for nursery farms to 1.94 and 2.97 kg NH3–N animal21 yr21 for sow and finisher farms, respectively. A composite emission factor will not be presented for this site because of the inappropriateness of combining different stage animals into a single factor. Emissions or emission factors may be expressed on a total farm emission, on a peranimal, on a per-unit weight, or on an animal-unit basis (AU, animal unit based on an animal weight of 500 kg). Table 3 gives an example of the confusion and/or possible error of expressing emissions on different bases. When emissions are compared on a per-animal basis, emissions from production animals (sows) discharge the largest emissions. However, when compared on a weight basis, sows emit only a fraction of the emissions of the nursery or finisher animals. Perhaps the most appropriate

Table 3. Ammonium emissions, emissions factors, and emissions as percent of feed input for an animal feeding operation. Emissions were determined from the process model of De Visscher et al. (2002). Lagoon emission factor Farm type

Emissions animal

21 21

Sow Nursery Finisher Average

kg NH3–N animal 2.58 0.31 1.83 NA‡

yr

21

yr

21

† Animal unit based on an animal weight of 500 kg. ‡ Not applicable.

Emissions kg animal21 yr21 g NH3–N kg animal 13.5 22.1 31.5 NA

21

yr

21

Emissions AU21† yr21

Lagoons emissions: percent of feed

g NH3–N AU21 yr21 6.8 11.1 15.7 NA

% 5.8 9.9 11.7 9.8



5%. Wind speed was measured in the general vicinity of the lagoons and since the terrain and vegetation was uniform, daily average wind speed error over the lagoons was assumed to be the same as the measurement error (about 1%). There was an emissions replication variability between lagoons for 12 mo of 30, 7, and 11% for the sow, nursery, and finisher farm primary lagoons, respectively, by the De Visscher model. Monte Carlo analysis is a stochastic technique for characterizing the uncertainty in model simulations (Loague and Corwin, 1996). The uncertainty of this model due to input error was evaluated using Monte Carlo simulations using the variance of each input parameter for the emission determinations. The analysis considers each model input parameter to be a random variable with a probability density function and is based on a large number of realizations. Monte Carlo simulations (1000 simulations) using sampling (and chemical analysis) variability within lagoons as identified above showed an estimated variance of 11%. Similarly, simulations were run including both spatial uncertainty within lagoons plus between lagoons on an annual basis. Monte Carlo analysis suggested a combined annual variance of 16% with a monthly range of 15%. The input uncertainty represents about 53% of the total model error suggested by De Visscher et al. (2002). Secondary lagoons were not included in the input variability estimates since many of the lagoons were dry, which would produce high variations when zero emissions were included. While early emission estimates of lagoon emissions as a percentage of feed input by the USDA of 60% (Hatfield et al., 1993), current estimates by the USEPA of 71% (USEPA, 2004), and current estimates by the state of North Carolina of 36% (Doorn et al., 2002) do not provide error limits on their published emissions, we calculate the average model emissions for the three types of farms is 9.8% of feed input with an estimated variability of about 4%. [Note: approximately 30% of feed input in swine leaves as protein N (Hall et al., 1988; Jongbloed and Lenis, 1992).] Harper et al. (2004) presented emissions as a percent of feed input from sow and finisher farms in the humid East (North Carolina). Finisher farms in the Central Great Basin were determined to emit 11.7% (Table 3) of feed input compared to 7.5% in the humid East. Because of the expected error due to model determinations and replication variability, there is no statistical difference between finisher emissions between the humid East and the semiarid West. Sow farms in the Central Great Basin were determined to emit 5.8% of feed input compared to 19.8% in the humid East. Emissions from nursery farms were 9.9% of feed input, but there were no comparable results in the humid East for comparison.

CONCLUSIONS Physical and chemical factors were measured on primary and secondary lagoons on three production farm types, three replicates each, in the Central Great Basin of the United States to calculate NH3 emissions from swine waste processing lagoons. Nutrient concentration,

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lagoon water temperature, and micrometeorological data were used with published process and statistical models to calculate emissions. Annual cycling of emissions was observed in relation to climatic factors with wind speed having the predominating effect when lagoon temperatures were above about 38C. Total NH3 emissions increased in the order of smallest to largest: nursery, sow, and finisher farms. Emissions on an animal basis increased from nursery animals being lowest to sow animals being highest. When emissions were compared to the amount of N fed to the animals, NH3 emissions from sows were lowest with emissions from finisher animals highest. Ammonia emissions were compared to similar farm production systems in the humid East of the United States and found to be not different for finisher animals but much lower than for comparable humid East sow production. Published estimates of NH3 emissions from lagoons range from 36 to 70% of feed input to the operations (no error range) and compare very differently to our emissions determined from a process model of 9.8% with an estimated range of 64%. ACKNOWLEDGMENTS The authors wish to express their appreciation to the project technical assistants, J.E. Scarbrough and M.A. Thornton, and Southern Utah University undergraduate assistants for their efforts in these studies. A special thanks is extended to the Global Climate Change Program, USDA-ARS; the Utah Pork Producers Association; and to the host swine producers who helped make these studies possible. Appreciation is extended to W. Asman, J. Hatfield, and R. Sharpe for constructive comments on the manuscript.

REFERENCES Aneja, V.P., J.P. Chauhan, and J. Walker. 1999. Characterization of ammonia emissions from swine waste lagoons. Report to DAQ, NCDR, Raleigh, NC, Contract #EA8001 and Water Resources Research Institute Contract #EA7003. North Carolina State Univ., Raleigh. Asman, W.A.H. 1992. Ammonia emissions in Europe: Updated emission and emission variations. Rep. 228471008. Natl. Inst. of Public Health and Environ. Protection, Bilthoven, the Netherlands. Asman, W.A.H., P. Cellier, S. Genermont, N.J. Hutchins, and S.G. Sommer. 1998. Ammonia emission research: From emission factors to process descriptions. EUROTRAC Newsl. 20:2–10. Battye, R., W. Battye, C. Overcash, and S. Fudge. 1994. Development and selection of ammonia emission factors. EPA Contract no. 68D3-0034, Work Assign. 0-3. USEPA, Research Triangle Park, NC. Denmead, O.T., and M.R. Raupach. 1993. Methods for measuring atmospheric gas transport in agricultural and forest systems. p. 19–43. In L.A. Harper, A.R. Mosier, M.M. Duxbury, and D.E. Rolston (ed.) Agricultural ecosystem effects of trace gases and global climate change. ASA Spec. Publ. 55. ASA, Madison, WI. De Visscher, A., L.A. Harper, P.W. Westerman, R.R. Sharpe, and O. Van Cleemput. 2002. Ammonia emissions from anaerobic swine lagoons: Model development. J. Appl. Meteorol. 41:426–433. Doorn, M.R.J., D.F. Natschke, and P.C. Meuwissen. 2002. Review of emission factors and methodologies to estimate ammonia emissions from animal waste handling. EPA-600/R-02/-17. USEPA, Washington, DC. Hall, D.D., A. Madsen, and H.P. Mortensen. 1988. Protein og aminosyrer til slagtesvin. 3 Balanceforsog med forskellige frohold mellem treonin og lysin. Beretning fra Satens Husdyrbrugsforsog 639. Harper, L.A. 1988. Comparisons of methods to measure ammonia volatilization in the field. p. 93–109. In B.R. Bock and D.E. Kissel (ed.) Ammonia volatilization from urea fertilizers. Tennessee Valley Authority, Muscle Shoals, AL.


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Harper, L.A. 2005. Ammonia: Measurement issues. p. 345–379. In J. Hatfield (ed.) Micrometeorological measurements in agricultural systems. ASA, CSSA, and SSSA, Madison, WI. Harper, L.A., and R.R. Sharpe. 1998. Ammonia emissions from swine waste lagoons in the Southeastern U.S. Coastal Plains. Final grant report to Division of Air Quality, North Carolina Department of Environment and Natural Resources, Raleigh, NC, USDA-ARS Agreement no. 58-6612-7M-022, December 1998. USDA-ARS, Watkinsville, GA. Harper, L.A., R.R. Sharpe, and F.M. Byers. 2001. Nitrogen cycling in swine production systems: Independent measurement techniques for system mass balance. p. 4. In Proc. 2001 Int. Conf. on Future Directions in Air Quality Res. 12–15 Feb. 2001. North Carolina State Univ., Raleigh. Harper, L.A., R.R. Sharpe, and T.B. Parkin. 2000. Gaseous nitrogen emissions from anaerobic swine lagoons: Ammonia, nitrous oxide, and dinitrogen gas. J. Environ. Qual. 29:1356–1365. Harper, L.A., R.R. Sharpe, T.B. Parkin, A. De Visscher, O. Van Cleemput, and F.M. Byers. 2004. Nitrogen cycling through swine production systems: Ammonia, dinitrogen, and nitrous oxide emissions. J. Environ. Qual. 33:1189–1201. Hatfield, J.C., M.C. Brumm, and S.W. Melvin. 1993. Swine manure management. p. 40–57. In Agricultural utilization of municipal, animal, and industrial waste. USDA-ARS, Washington, DC.

Jongbloed, A.W., and N.P. Lenis. 1992. Alteration of nutrition as a means to reduce environmental pollution by pigs. Livest. Prod. Sci. 31:75–94. Loague, K., and D.L. Corwin. 1996. Uncertainty in regional-scale assessments of non-point source pollutants. p. 131–152. In D.L. Corwin and K. Loague (ed.) Applications of GIS to the modeling of non-point source pollutants in the vadose zone. SSSA Spec. Publ. 48. SSSA, Madison, WI. McColloch, R.B. 1999. Estimating ammonia emissions from a swine production facility in North Carolina using an inverted Gaussian plume model. M.S. thesis. North Carolina State Univ., Raleigh. Sharpe, R.R., and L.A. Harper. 2002. Nitrous oxide and ammonia fluxes in a soybean field irrigated with swine effluent. J. Environ. Qual. 31:524–532. Todd, L.A., M. Ramanathan, K. Mottus, R. Katz, A. Dodson, and G. Mihlan. 2001. Measuring chemical emissions using open-path Fourier transform infrared (OP-FTIR) spectroscopy and computerassisted tomography. Atmos. Environ. 35:1937–1947. USEPA. 2004. National Emission Inventory—Ammonia emissions from animal husbandry operations (Draft Report). Available at http:// www.epa.gov/ttn/chief/ap42/ch09/related/nh3inventorydraft_ jan2004.pdf (verified 17 Oct. 2005). USEPA, Washington, DC. Vlek, P.L.G., and E.T. Craswell. 1981. Ammonia volatilization from flooded soils. Fert. Res. 2:227–245.