Affiliation Organization URL Department, Purdue University, West Lafayette, Indiana

Transcription

1 E:\OD\PROJEKTE\MONSANTO\EMISSION5A.DOC Basis: ODOREMIT5.doc Author(s) First Name Middle Name Surname Role Type (Corresp) Gunther Schauberger ASAE Member, Associate Professor No Affiliation Organization URL Institute of Medical Physics and Biostatistics, University of Veterinary phone: +42 (1) ; fax: +42 (1) Medicine, Vienna, Austria URL: www-med-physik.vuwien.ac.at Author(s) First Name Middle Name Surname Role Type (Corresp) Albert J Heber ASAE Member Engineer, Professor Yes Affiliation Organization URL Agricultural and Biological Engineering Department, Purdue University, West Lafayette, Indiana phone: ; fax: URL optional heber@purdue.edu Author(s) First Name Middle Name Surname Role Type (Corresp) Teng T Lim Research Associate No Affiliation Organization URL Agricultural and Biological Engineering phone: ; fax: Department, Purdue University, West Lafayette, Indiana URL optional Author(s) First Name Middle Name Surname Role Type (Corresp) Ji-Qin Ni ASAE Member, Senior Research Associate No Affiliation Organization URL Agricultural and Biological Engineering phone: ; fax: Department, Purdue University, West Lafayette, Indiana URL optional Author(s) First Name Middle Name Surname Role Type (Corresp) Dwaine S Bundy Emeritus Professor No 12/3/ Shau Odor Emission submitted doc -Page 1

2 Affiliation Organization URL Agricultural and Biosystems Engineering, phone: ; fax: Iowa State University, Ames, Iowa URL optional Author(s) First Name Middle Name Surname Role Type (Corresp) Barry L Haymore President Affiliation Organization URL ChemLink International, St. Louis, Missouri phone: ; fax: URL optional Author(s) First Name Middle Name Surname Role Type (Corresp) Ravikrishna K Duggirala Risk Consultant Affiliation Organization URL Xcel Energy, Denver, Colorado phone: ; fax: URL optional 12/3/ Shau Odor Emission submitted doc -Page 2

3 EMPIRICAL MODEL OF ODOR EMISSION FROM SWINE FINISHING BARNS G. Schauberger, A. J. Heber, T.T. Lim, J.-Q. Ni, D.S. Bundy, B.L. Haymore, C.A. Diehl, R.K. Duggirala The authors are: Günther Schauberger, ASAE Member, Associate Professor, Institute of Medical Physics and Biostatistics, University of Veterinary Medicine, Vienna, Austria, Albert J. Heber, ASAE Member Engineer, Professor; Teng T. Lim, ASAE Member, Research Associate, Ji-Qin Ni, ASAE Member, Senior Research Associate, C.A. Diehl, Air Quality Engineer, Agricultural and Biological Engineering Department, Purdue University, West Lafayette, Indiana; Dwaine S. Bundy, Professor, Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa; Barry L. Haymore, President, ChemLink International, St. Louis, Missouri; and Ravikrishna K. Duggirala, Risk Consultant, Xcel Energy, Denver, Colorado;. Corresponding author: Albert J. Heber, Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907; phone: ; fax: ; 12/3/ Shau Odor Emission submitted doc -Page 3

4 Abstract Odor emission rates from four identical, 1,000-head, mechanically-ventilated swine finishing barns from April to August 1997 were measured. Each barn had long-term manure storage beneath a fully slatted floor, two sidewall curtains, a curtain on the west-end wall, four pit ventilation fans, and five exhaust fans on the east-end wall. Odor concentration was determined using olfactometry with four to six trained panelists. The median of the specific odor emission from the four barns was 75 OU/s per 500 kg pig live mass (P<0.05), based on 112 simultaneous odor and ventilation rate measurements. Odor emission was modeled using an exponential function that describes the influence of the indoor air temperature, and a power function that describes the influence of the ventilation airflow rate. Keywords: Ammonia, hydrogen sulfide, temperature, ventilation, airflow, air quality 12/3/ Shau Odor Emission submitted doc -Page 4

5 INTRODUCTION Public concern over air pollution from swine production, and stricter environmental regulations have created a great need for odor-related research in the swine industry (Thu, 2002). Although odor can be mitigated at the source, the most reliable method of reducing odor nuisance, is to establish a sufficient separation distance between the odor source and residential areas. There is a lack of baseline odor emission data for swine housing (Lim et al., 2001). Therefore, the objective of this paper was to analyze field data, collected at commercial swine finishing barns, and develop an empirical odor emission model. Aside from empirical guidelines for estimating a reasonable separation distance (Piringer & Schauberger, 1999), it can also be calculated using atmospheric dispersion models. In addition to dispersion models for gases, models for odor must be adapted to mimic human s odor sensation (e.g. Schauberger et al., 2000). A reliable odor emission model for the source is critical. For odor sources, the required inputs for such a model include: odor emission rate, volumetric airflow rate of the source (ventilation rate of the barn ventilation system), area and height of the ventilation exhaust, and exhaust air temperature. Until now, only very basic odor emission scenarios are considered in atmospheric dispersion models and separation distance guidelines. Constant odor emission assumed over the entire year is the most primitive pattern. Schauberger et al. (1999) advanced the science of these scenarios; by coupling a sensible heat balance model, that predicts ventilation rate and exhaust air temperature, with an odor release model based on outdoor temperature and diurnal physical activity of the animals (Schauberger et al., 1999). 12/3/ Shau Odor Emission submitted doc -Page 5

6 MATERIALS AND METHODS Model Description Indoor air temperature, indoor air velocities, and physical activity of animals, are shown to have influence on barn odor release by various authors. Using the concept that odor production is caused by sources such as slurry, feed, and the animal; a model (Eq. 1) was developed. Animal-related factors are represented by the individual live mass of the animals. Additionally, the variable E was introduced to describe the modification of odor release, R, based on various factors (Eq. 1). E = P R (1) where E is odor emission rate of the livestock barn (OU/s), P is odor production rate (OU/s), and R is an odor release modification factor. The variable P (Eq. 1), described in the following Eq. 2, represents the odor production term. P= C M (2) P tot where C P is a constant, estimated by the model, OU s -1 kg -a ; M tot a = N M, which represents the modified total live mass of the animals, kg a ; N is the number of animals; and M is the live mass of the animals, kg. The model is based on either M (a=1), the metabolic live mass (a = 0.65 to 0.75), or the number of animals independent of their mass (a=0). The modification factor R (Eq. 1) is a dimensionless function, based on various parameters that influence odor release. Each parameter s influence on odor emission can be expressed more specifically with rescaled functions, than with original parameters. The odor release modification R is expressed by a product of these functions (Eq. 3): R = CF T T CF v v CF A A (3) where F T is used to rescale the influences of the indoor temperature (T) of the livestock barn; F v, is a function showing the air velocity (v) close to the polluting surfaces and the animals; and F A is the diurnal 12/3/ Shau Odor Emission submitted doc -Page 6

7 variation of animal physical activity (A). C T, C v, and C A are coefficients of the regression. The rescale function of the indoor temperature (T) is shown using the exponential relationship FT ct ( T T0 ) = e (4) where c T is a constant; T is indoor temperature ( C); and T o is the reference temperature 15 C. The rescale function of air velocity (v) close to the slatted floor and in the animal space was parameterized by the ventilation rate of the livestock barn using the following power function: F v ( V ) = (5) n 1 b where, V = V N V (ventilation rate), is normalized to the designed number of animal stalls in the barn n d d (N d ), and the designed minimum ventilation rate of the barn (V d ). The analyses were conducted with assumptions of: N d = 1,000, and a minimum ventilation rate for growing pigs of V d = m 3 s -1 per pig (MWPS, 1990). The diurnal variation of animal activity (A) (Eqn. 3 and 6), is an important factor modifying odor release. The diurnal patterns of A can be described by a sinusoidal function with i-th harmonics (Eq. 6). n 2π i F = 1+ c sin t+, ( ϕ ) (6) A A i i i= 1 τ where c A,i is amplitude, τ is the duration (maximum of 24 h), t is time of day, and ϕ i is the time lag. Measurements Odor emission measurements were taken in four identical, 1,000-head, mechanically-ventilated, swine finishing barns (barns A, B, C and D). Each of the barns had a 2.4 m deep pit, under a fully slatted floor, with a pit surface area of 799 m 2. Detailed drawings of these barns are presented by Heber et al. (2001). The barns had curtains on both sidewalls, and the west-end wall. There were four variable-speed pit ventilation fans, five endwall ventilation fans, and two propane heaters in each barn. The four pit fans were moved to the top of 3.6 x 0.8 x 0.8 m³ wooden chimneys, to reduce wind effects and facilitate proper installation of airflow sensors (Fig 1 and 2). 12/3/ Shau Odor Emission submitted doc -Page 7

8 Odor emission samples were collected in 80-L Tedlar bags, and sent overnight to Iowa State University, Ames, Iowa, for analysis by dynamic dilution olfactometry. The dynamic olfactometer presented mixtures of odorous air samples and filtered, odor-free air, at known dilutions and controlled airflow rates. Odor concentration, in this paper, is expressed in terms of odor units (OU) per cubic meter of air (OU/m 3 ) (Lim et al., 2001). One odor unit is defined as the amount of odorants present in one cubic meter of odorous gas (under standard conditions), that elicits a physiological response from 50% of a panel (CEN, 2001). Odor emission (OU/s), in this paper, is expressed as the product of the odor concentration (OU/m³) and the barn ventilation rate (m³/s). The odor sampling procedure was modified several times during the test as follows: 1. April 7 to May 18 (n=38). A gas sampling pump drew air at 0.10 L/s to L/s from each chimney inlet (four filtered gas sampling tubes connected in parallel), through a heated Teflon sampling tube, and into a Teflon sampling manifold located in the instrument shelter. The odor samples were taken from the Teflon sampling manifold. 2. May 19 to June 22 (n=20). Two samples were collected from a location 3 m from the eastend of the barn and 15 cm above the floor. 3. June 23 to 26 (n=4). Two samples were collected from a location 30 cm in front of the inlet to the 91 cm diameter exhaust fan. 4. June 26 to August 6 (n=36). One sample was taken from inside the northeast pit fan chimney, 1.4 m above the bottom of the chimney. Another sample was taken from a location 30 cm in front of the inlet to the 91 cm diameter exhaust fan. 5. August 7 to 18 (n=4). Two samples were taken at the 91 cm diameter exhaust fan and two more samples from the chimney described in (4). 6. August 19 to 26 (n=7). The chimney sample was taken through Teflon tubing; extending from the inlet, to the exhaust chimney, to the alleyway inside the barn. A measurement of inside barn odor consisted of: the average concentration of one to four sample replications, depending on the air sampling protocol, and a number of good samples. 12/3/ Shau Odor Emission submitted doc -Page 8

9 Running averages of air temperatures and gas concentrations, inside the barn, were recorded every 20 s (Heber et al., 2001). Gas concentrations were arrived at using the following methods: 1. Ammonia (NH 3 ) was converted to nitric oxide (NO) using a solid state converter (Model 17, TEI, Inc., Franklin, MA) at 875 C. The NO was then measured using a chemiluminescence detector (Model 17C, TEI, Inc.). 2. Hydrogen sulfide (H 2 S) was converted to sulfur dioxide (SO 2 ) that was then monitored using the pulsed fluorescence method (Model 340, TEI, Inc.). Each gas analyzer was switched sequentially between four ported manifolds (two per barn) in the instrument room, and sampled from each manifold for 15 min. Data collected during the last ten minutes was averaged to determine gas concentration. Gas instruments were calibrated weekly using certified calibration gases. A total of 33, 36, 26 and 17 odor concentration measurements were conducted in barns A, B, C and D respectively. The emission of NH 3 and H 2 S was collected simultaneously 33, 35, 23 and 16 times respectively. Pit fan airflow rates were measured using full-size impeller anemometers (FanCom FMS 50) and recorded once per minute. Only one pit fan in barn A was equipped with an anemometer. Since the four pit fans were identical and controlled with the same controller, the total ventilation rate of the pit fans was taken as four times the airflow rate, measured by the one anemometer. The fan control voltage to the four pit fans in barn B was recorded, and the ventilation rate was estimated, based on an airflow/voltage relationship determined with barn A data. All pit fans in barns C and D were equipped with anemometers. Therefore, the total ventilation rate was the sum of the airflow, measured by the four individual anemometers. 12/3/ Shau Odor Emission submitted doc -Page 9

10 RESULTS Descriptive Statistics The barn inventory was above 86% (nominal 1,000-pig capacity) for 75% of all samples. Only six samples were taken when barn inventory was less than 500 pigs. About 75% of the samples were taken when the average pig mass was greater than 53 kg, which is toward the end of the fattening period. The inside barn temperature was between 17.4 C and 31.0 C, which is typically characteristic of the year s warmest temperatures. The barn ventilation rate was mostly near full capacity, due to relatively high temperatures during the study. The median (V) of 50.7 m³ /s was 15 times the recommended minimum ventilation rate (MWPS, 1990) and 86% of the recommended maximum ventilation. The four barns were occupied by 84% to 89% of the designed capacity of 1,000 pigs. In barns A and B, the median of pig live-mass was distinctly lower, than in barns C and D. The median odor concentration [lower; upper confidence limits] of the entire data set was 215 [181; 269] OU/m³. The median odor concentrations in barns A, B, C, and D were 215, 198, 198 and 181 OU/m³ respectively. The median barn and live-mass specific odor emissions were 8480 [6123;12,000] OU/s and 74.9 [52.8; 129.2] OU s -1 AU -1 respectively. Median odor emissions from the four barns were 9215, 7693, 7648 and 10,705 OU/s; or 87, 93, 59 and 57 OU s -1 AU -1,respectively. The lower specific odor emission of barns C and D, compared with barns A and B, might have been influenced by the lower ventilation rate and/or the higher total live mass of these two barns (barns C and D). The mean simultaneously-measured NH 3 concentrations for the four barns were 2.0, 2.0, 1.4 and 0.8 mg/m³ and 2.0 [1.6; 2.1] mg/m³ overall. The barn and specific NH 3 emissions were 80 [61; 102; 61] mg/s and 974 [530; 1174] ng s -1 AU -1 respectively. The barn NH 3 emissions were 96, 87, 32, and 29 mg/s and the specific NH 3 emissions were 974, 1379, 183 and 165 ng s -1 AU -1 respectively. Ammonia concentrations and emissions were significantly higher in barns A and B, compared with barns C and D. 12/3/ Shau Odor Emission submitted doc -Page 10

11 An overall mean H 2 S concentration of 95 [78; 123] ng/m³ was observed. Barn means were 123, 88, 34, and 18 ng/m³. The overall emission rate was 4.72 [3.56; 6.03] mg/s with 6.3, 4.5, 1.5, and 0.7 mg/s for barns A, B, C, and D respectively. The overall specific H 2 S emission was 46 [38; 58] ng s -1 AU -1 with medians of 59, 46, 9, and 4 ng s -1 AU -1. The comparisons of barns A and B with C and D were similar to NH 3, with higher values for A and B. The covariance between the different parameters was described using the Pearson regression coefficient (r) (Table 2). The correlation between indoor temperature and ventilation rate was caused by the commonly used temperature control of fans. The dilution of released gases inside the barn, by ventilation air, was only seen for NH 3 (r=-0.58) and H 2 S (r=-0.46) (P< 0.01). For odor, this effect was not significant. However, the increasing odor release due to a higher ventilation rate and associated increases in surface air velocities, was significant (P<0.01) for odor and NH 3 according to Eq. 4. Barn and livemass specific odor emission increased with ventilation rate (P<0.01). The correlations for NH 3 emission and NH 3 specific emission with ventilation was r=0.26 and r=0.28 respectively. Indoor temperature was correlated to the specific emission of odor, NH 3 and H 2 S. This is shown by correlations of r=0.31 (P<.01), 0.68 (P<0.1), and 0.64 (P<0.1). The negative correlation coefficient for gas concentrations can be explained by the increasing ventilation rate and temperature. This means dilution; caused by the ventilation rate, compensated for the increasing gas release caused by higher temperature. The negative correlation of NH 3 and H 2 S specific emission, might be due to the negative correlation of the total live-mass and temperature. This is because the number of animals and animal livemass were not independent of the temperature. Odor concentration was correlated to the concentration and emission of NH 3 (r=0.27 and r=0.27, both at P<0.01) and H 2 S (r=0.30 (P<0.01) and r=0.20 (P<0.01)). The live-mass specific odor emission (e O ) was strongly correlated to the emission and live-mass specific emission of NH 3 ; with r=0.33 and r=0.33 respectively (P<0.01). For H 2 S, a significant correlation with odor could only be found for emission with r=0.20 (P<0.05). 12/3/ Shau Odor Emission submitted doc -Page 11

12 Ammonia concentration was strongly correlated to H 2 S concentration (r=0.81) and emission (r=0.30) (P<0.01). Emission of NH 3 was directly proportional to H 2 S concentration (r=0.35), emission (r=0.80), and specific emission (r=0.48) (P<.01). The specific emission of NH 3 showed another strong correlation to the emission (r=0.71) and specific emission (r=.86) of H 2 S. The data space covered by the previously discussed measurements can be seen using descriptive statistics (Table 1). Percentiles were used to describe the distribution of the parameters; because they are independent from the distribution. Whereas, a normal distribution has to be assumed for arithmetic means and standard deviations. The specific odor emission (e O ) was best represented by a log-normal distribution. The high correlation coefficient of r=-0.63 showed a relationship between the total live-mass inside the barn and indoor temperature. For model development, live-mass is a cofounder with indoor temperature. Therefore, the degrees of freedom of the odor emission model (Eq. 2) were reduced by fixing the exponent (a) of the live-mass of the animals. With a=1, E is directly proportional to the total live-mass. Based on this proportionality, specific odor emission (e O ) in OU s -1 AU -1 is the ratio of odor emission (E) and total live-mass in AU (1 AU = 500 kg). The relative frequency distribution of specific odor emission (e O ) is presented in Fig. 3. Outliers of the log-normal distribution were eliminated when they were below 3 OU s -1 AU -1. The reduced data set, with a sample size of 112, is shown using hatched bars. The strong relationship between the T and V (Fig. 4), was used to divide the entire data set into three subsets. To investigate the influence of the indoor temperature (T) by eliminating the influence of the ventilation rate (V) as cofounder, two of the subsets were used. The subset V-LOW was characterized by a low ventilation rate (<13.9 m³/s) and the subset V-HIGH used a ventilation rate greater than 55.6 m³/s. The influence of ventilation rate on odor emission was investigated with the third subset (T- CONST) that included all T between 21 C and 25 C. T-CONST was characterized by low variability of indoor temperature and the full range of ventilation rate (V). 12/3/ Shau Odor Emission submitted doc -Page 12

13 Model development The exponent a, which describes the influence of pig live-mass on odor production P (Eq. 2) was preselected as a=1. This was necessary, because of the cross-correlation of M and T. The first parameter investigated was T. A regression analysis was performed with subsets V-LOW (n=15) and V-HIGH (n=39) with an exponential function (Eq. 4). The temperature ranges were 17.4 C to 22.0 C for V-LOW and 25.4 C to 31.0 C for V-HIGH. Both data sets exhibited small increases of odor emission with indoor temperature, but only the results of the V-HIGH sub-set were statistically significant (P<0.05). The coefficient C T was determined to be with a standard deviation of (P<0.001), (Eq. 7). ( T T ) F = e (r 2 = 9.5%) (7) T Ventilation rate s influence on odor emission was established using the third data subset. The regression analyses of the specific odor emission( e O ) with a normalized ventilation rate (V n ) resulted in an exponent b of 1.03 with a standard deviation of This regression was statistically significant (P<0.001), (Eq. 8). ( ) F = V 1 (r 2 = 16.4%) (8) v n The diurnal variation (Eq. 6) could not be determined from the data set, because of the limited range of odor sample times, which were between 8:50 and 17:52 (38% of 24-h day). Therefore, parameters for a mono-sinusoidal function (i=1) with one harmonic, an amplitude c A,1 =0.45, and a time lag ϕ 1 =-7.25 n were used. It was also assumed that minimum animal activity of fattening pigs occurs around 01:15 in the morning (Pedersen, 1996; Pedersen and Takai, 1997). Using three rescale functions; instead of indoor temperature (T), ventilation rate (V) and animal activity (A); the specific odor emission (e O ) can be expressed by combining Eqn. 1 and 3 to get Eq. 9. ( ) e = C C F C F C F (9) O P T T v v A A The result of the above step-wise regression (Eq. 9) is shown in Table 3. The error term and F value are presented, with the calculated regression coefficients, for the three rescaled parameters. The F value compares the variance without a model, and the variance of the selected model. 12/3/ Shau Odor Emission submitted doc -Page 13

14 Results showed that air velocity, which was parameterized by ventilation rate, resulted in the highest F value of the regression analyses. Adding other predictors, temperature and/or activity, did not improve model performance. The F value was similar for all four variants of the model (Eq. 9). Each addition of other parameters reduced the F value, therefore reducing the performance of the model. DISCUSSION Odor emission data presented in this paper was part of a comprehensive data set including other airborne emissions and parameters that describe the psychometric and thermal environments of the animals. For a more comprehensive picture of the airborne emissions of the barn, we included NH 3 as well as H 2 S. Ammonia and H 2 S are released from the same sources inside the barn. Furthermore, they are known as cofounders for odor. Nevertheless, they cannot be used as surrogates for odor measurements, because of weak correlation to odor (Fakhoury et al. 2000). Various parameters affect odor emission from swine finishing barns. Main parameters were: livestock live-mass, indoor air temperature, air velocity across inside surfaces of the barn, and diurnal variation of animal activity. The data set collected was used to calculate coefficients of the model. The univariate analyses of emission data (e.g., odor emission as a function of temperature) did not consider cofounding factors. An example is the correlation between indoor temperature and ventilation rate (Table 2). The influence of the second parameter cannot be eliminated in a univariate analysis. Therefore, overestimation can be expected in some cases (e.g. increasing indoor temperature results in increasing ventilation rate). This must be considered, according to Jeppsson (2002), and Ogink and Koerkamp (2001). If reciprocal behavior between parameters can be expected, then an effect can be weakened or completely disappear. In our analyses of the correlation coefficients (Table 2), this might be observed for: indoor temperature and concentrations of odor, NH 3 and H 2 S,; where the dilution of the ventilation rate compensates for the increasing release caused by temperature. 12/3/ Shau Odor Emission submitted doc -Page 14

15 Influences of the selected parameters were isolated by dividing the complete data-set into subsets. These subsets were used to fit the rescale functions, so the influence of the original parameters could be considered more specifically. Therefore, we were able to analyze odor emission in a bivariate manner. For odor emission model development, only indoor temperature and ventilation rate were considered; because these parameters can be assessed for a mechanically-ventilated barn using the sensible heat balance (e.g. Schauberger et al., 1999 and 2000a). For this reason, NH 3 and H 2 S were not considered, even if they showed a high correlation to odor concentration or emission. The differences between the effect of ventilation rates on H 2 S, NH 3, and odor might have caused differences in release as well. Ammonia and odor are predominantly released by animals and fresh manure on the slatted floor. In the slatted floor area, the ventilation rate directly affects air velocity. However, H 2 S is predominantly released by manure in the pit. Air velocities in the pit are influenced only slightly by the ventilation rate, due to the aerodynamic resistance of the slatted floor. Total live mass of the pigs Animal live-mass relevance can be shown using live-mass specific odor emission instead of barn odor emission. Specific odor emission (e O ) is related to the total live-mass given in AU (1 AU=500 kg). The review concerning odor emission of livestock barns by Martinec et al. (1998), showed a wide range of published data describing odor release. For example, the live-mass specific odor emission for fattening pigs varied between 38 and 495 OU s -1 AU -1 for fully slatted floors, and between 8 and 134 OU s -1 AU -1 for bedded floors. Main factors affecting the reported variation (Martinec et al., 1998) were the various methods of husbandry and the influence of overall barn management. The introduction of the modified animal live-mass (M a ) is based on the assumption that exponents other than a=1 could be used. For example, the total energy and CO 2 release is proportional to metabolic mass with an exponent a between 0.65 and 0.75 (CIGR, 1984). Ogink and Groot Koerkamp (2001) used odor emission (OU/s) per animal. It could be interpreted that emission is independent from 12/3/ Shau Odor Emission submitted doc -Page 15

16 live-mass, which means the exponent is zero. On the other hand, Ni (1998) showed an exponent of a=1.43 that describes manure production as a function of live-mass. By the cross-correlation of live-mass and indoor air temperature, it is not possible to fit the exponent using the data set. Therefore, a value of a=1 was used, which is widely reported in the literature. Indoor air temperature As odor production is a biochemical process, temperature is an important influence. Some authors selected outdoor air temperature to describe the relationship (Oldenburg, 1989; Kowalewsky, 1981). By linear regression, Oldenburg (1989) showed a 1% increase of odor release per 1 C. Huegle and Andree (2001) found temperature strongly influenced odor emission from head space of swine and cattle slurry. The selected exponential function to describe the increased odor release with increased indoor temperature is based on that used by Ni (1998) and Jeppsson (2002) for NH 3. For the exponential function describing the increased release of NH 3 with temperature, Ni (1998) used an exponent of c T =0.145 and Jeppsson (2002, 2003) used c T =0.142 respectively. The exponent of c T =0.127 from this study, compared with other studies, shows that an attempt was made to reduce the effect (bias) of other parameters (increasing ventilation rate with temperature) by grouping the data. A weak correlation for indoor temperature might be related to the interaction of temperature and total live-mass (Table 2). Therefore, the influence of temperature on odor release could not be handled independently. Total live-mass has to be seen as a cofounding factor, influencing this relationship. Air velocity Air velocity, at the surface where odor is released, is a major factor influencing the transfer coefficient. In a detailed review of NH 3 release, Ni (1999) showed the importance of convective mass transfer. The influence of air velocity, above the surface where odor is released, can be parameterized by the ventilation rate using a power function. The exponent lies between 0.8 and 0.7. For a naturally ventilated barn with deep litter, Jeppsson (2002, 2003) found an exponent of 0.69, which is far less than 12/3/ Shau Odor Emission submitted doc -Page 16

17 what we found for the mechanically-ventilated barn with a fully-slatted floor. Differences in the design could be the reason for stronger influence of the ventilation rate in the barn we measured. For VOC, Guo (2002) reviewed the mass transfer coefficient for various indoor emission source models. The exponent for air velocity ranged from 0.50 to The proposed normalization of ventilation rate by minimum ventilation flow of the barn in Eq. 5, could also be interpreted like the dimensionless jet momentum number (Heber et al., 1996). The jet momentum number gives the ratio of the product of ventilation rate and inlet air velocity, with the product of room volume and the gravitational constant. So, in both cases, the denominator is a constant value that depends on the animals. The nominator is related to the square of the ventilation rate, if the inlet air cross section is assumed to be constant. Air velocity at floor level can be expressed by the jet momentum power of 0.5 (Yu, 2001). Ogink (2001) used the same power function (Eq. 5) to describe the variation of odor emission and ventilation rate. For non-lactating sows, he found exponent b=0.57 ± 0.11, for weaners 0.81 ± 0.10, and for fattening pigs 0.60 ± These relationships were based on at least 82 samples from eight farms. By considering the influence of ventilation rate, he could reduce the coefficient of variation for non-lactating sows from 45 to 36%, for weaners from 65 to 48%, and for fattening pigs from 36 to 25% respectively. Diurnal variation Besides total energy; methane, CO 2 and NH 3 release show typical diurnal variation (Lockeryer and Champion, 2001; Pedersen and Takai, 1997; Schauberger and Pilati, 1998a and 1998b, van Ouwerkerk and Pedersen, 1993; Jeppsson, 2002; Ni et al., 2002; Sousa and Pedersen, 2003), strongly correlated to the animal physical activity (Pedersen and Pedersen, 1995). Even with the weak correlation between odor and NH 3, (Oldenburg, 1989; Fakhoury et al., 2000), the release of odor seems to be very similar to NH 3. Ammonia concentration showed a distinct day/night fluctuation. For eight different sow barns, the ratio of the mean NH 3 concentration between day and night was about 1.28 and 2.10, for the daily extremes 2.10 (Phillips et al., 1998). Jeppsson (2002) observed diurnal fluctuation around the daily 12/3/ Shau Odor Emission submitted doc -Page 17

18 mean, with values between 6% and 247% for ammonia. Sousa and Pedersen (2003) showed that animal activity is a useful predictor of NH 3 release, with a coefficient of determination above 60%. As the ventilation flow counteracts this diurnal variation, due to animal activity, a weaker diurnal variation of outlet air odor concentration can be expected. Dust, an important carrier of odorants (Hoff et al., 1997, Liao and Singh, 1998), showed the same diurnal variation (Pedersen, 1993, CIGR, 1994). For odor, Zhu et al. (2000) showed weak evidence that the concentration tended to increase in the afternoon. However, these measurements were only taken between 07:00 and 19:00. Van Ouwerkek and Pedersen (1993) suggested a parameterization of diurnal variation of activity by a sinusoidal function with only one term (period τ = 24 h). In the case of unrestricted (ad-libidum) feeding regimes of the pig-fattening unit, one sinusoidal term seems to be sufficient to describe the diurnal time pattern of the animals by the time of the day. Restricted feeding, with two feeding times per day, shows a need for two terms (i=2) to describe animal activity (Schauberger and Pilati, 1998a and 1998b). Because of the limited variation of the time of day when the data-set was sampled; it was not possible to use Eq. 6, as suggested, to rescale the time of day and obtain an appropriate assessment of animal activity. Therefore, we used the parameters for fattening pigs (Pedersen and Rom, 1998; Pedersen, 1996; Pedersen and Takai, 1997). Ogink (2001) cited the following sources that cause diurnal variations: feeding and animal activity; temperature and ventilation rate; and slurry removal (daily flushing). During manure flushing, he found an increase of the odor concentration inside the livestock barn by a factor of 1.5 to 4.0. Measurements To develop a model, the data-set should cover the entire range of parameters used as its input parameters. From this perspective, the data-set showed some weak points. First, the data-set was mainly gathered in the warm season, between April and August. Therefore, only few data are available for low indoor temperatures and low ventilation rates (Fig. 2). Second, odor samples were only taken during the 12/3/ Shau Odor Emission submitted doc -Page 18

19 day. Therefore, the influence of diurnal variation of animal activity on odor emission could not be confirmed. The well-known cross-correlation of some parameters can be seen in the matrix of correlation coefficients in Table 2. The highest correlation coefficient showed the relationship between indoor temperature and ventilation rate (r=0.69). The correlation between temperature and total live-mass is caused by the pig s weight gain from April to August. In the first case, the data of indoor temperature and ventilation rate, was divided into three subsets. By doing this, the influence of the second variable could be minimized. The limits for this division are shown in Fig. 4. In the second case, the total mass flow could not be separated from the temperature. Therefore, we could not determine whether the exponent of M a (Eq. 2) was different than 1.0 to fit the data in a more appropriate way. The reliability of the odor emission model increases when there are enough data samples to fit the suggested odor model with a greater accuracy. For this reason, complete fattening periods should be measured, during different seasons, as well as odor measurements during the night. CONCLUSIONS 1. Swine finishing barns with deep pits emitted about 75 OU s -1 AU Odor emission increases with inside temperature, explained as an exponential function. 3. Odor emission increases with inside ventilation rate of the ventilation system, explained by a power function. Acknowledgement Financial support and co-operation from Purdue University Agricultural Research Programs, Monsanto Enviro-Chem, and Heartland Pork is acknowledged. One author (GS) was a visiting research scholar at Purdue University and wants to acknowledge support from the Fulbright Commission. 12/3/ Shau Odor Emission submitted doc -Page 19

20 List of Abbreviations ϕ ι time lag, h a exponent for the modified live-mass C A coefficient for the rescale function for animal activity c A,i coefficient of Eq 6 CI upper and lower limit of the confidence interval of the median for p=0.05 C N measured NH 3 concentration of exhaust air, mg/m³ C O measured odor concentration of exhaust air, OU/m³ C p coefficient of odor production (Eq 2), OU s-1 kg-1 C S measured H 2 S concentration of exhaust air, ng/m³ C T coefficient for the rescale function for indoor temperature C v coefficient for the rescale function for air velocity inside the barn E modeled odor emission rate, OU/s E N measured NH 3 emission, mg/s e N measured specific NH 3 emission, mg/s AU E O measured odor emission, OU/s e O measured specific odor emission, OU s -1 AU -1 E S measured H 2 S emission, ng/s e S measured specific H 2 S emission, ng s -1 AU -1 F A rescale function for animal activity F T rescale function for indoor temperature F v rescale function for air velocity inside the barn M mean live-mass of N pigs M a modified live-mass, kg a M tot total live-mass of animals, AU (1 AU= 500kg) N inventory of pigs n number of samplers in data set N d designed number of animals of the barn P odor production rate, OU/s R odor release modification factor T indoor temperature, C t time of day, h V ventilation rate, m³/s V d minimum ventilation rate per pig m³/s normalized ventilation rate V n 12/3/ Shau Odor Emission submitted doc -Page 20

21 References ASTM "Standard Test Method for Measurement of Odor in Atmospheres (Dilution Method)". Annual Book of Standards. Vol. Part 26. pp CIGR Climatization of animal houses. Commission International du Genié Rural. Scottish Farm buildings Investigation Unit, Aberdeen, Scotland Fakhoury, K.J., A.J. Heber, P. Shao, and J.-Q. Ni Correlation of odor detection thresholds with concentrations of hydrogen sulfide, ammonia and trace gases emitted from swine manure. Paper No , St Joseph, MI: ASAE. Guo, H, L.D. Jacobson, D.R. Schmidt, R.E. Nicolai, and K.A. Janni Comparison of five models for setback distance determination. Paper No , St Joseph, MI: ASAE Guo, Z Review of indoor emission modlels. Part 2. Parameter estimation. Environmental Pollution 120: Hartung, E., M. Martinec, G. Brose, and T. Jungbluth Diurnal course of the odor release from livestock housings and the odor reduction of biofilters. In: Animal Production Systems and Environment. An International Conference on Odor, Water Quality, Nutrient Management and Socioeconomic Issues (Scanes C; Kanwar R eds).: Iowa State University of Science and Technology, IA. Heber, A. J., R. K. Duggirala, J. Ni, M. L. Spence, B. L. Haymore, V. I. Adamchuk, D. S. Bundy, A. L. Sutton, D. T. Kelly, and K. M. Keener (1997). "Manure treatment to reduce gas emissions from large swine houses". In: International Symposium on Ammonia and Odor Control from Animal Production Facilities. Voermans, J. A. M. and G. J. Monteny (Eds). Vinkeloord, The Netherlands, Oct pp Heber, A.J., C.R. Boon, and M.W. Peugh Air patterns and turbulence in an experimental livestock building. Journal of Agricultural Engineering Research 64: /3/ Shau Odor Emission submitted doc -Page 21

22 Heber, A.J., J.-Q. Ni, B.L. Haymore, R.K. Duggirala, and K.M. Keener Air quality and emission measurement methodology at swine finishing buildings. Transactions of ASAE 44(6): Hoff, S. J., D.S. Bundy, and X.W. Li Dust effects on odor and odor compounds. In: Ammonia and Odor Emission from Animal Production Facilities (Voermans J A M; Monteny G J eds), p , Rosmalen, The Netherlands Huegle, Th. U., and H. Andree Odor emission from cattle and swine slurry. Paper No , St Joseph, MI: ASAE Jeppsson, K.H Diurnal variation in ammonia, carbon dioxide and water vapour emission from an uninsulated, deep litter buidling for growing/finishing pigs Biosystems Engineering 81 2: doi: /bioe Jeppsson, K.H Diurnal variation in ammonia, carbon dioxide and water vapour emission from deep litter house for fattening pigs. Int. Symposium Gaseous and Odor Emissions from Animal Production Facilities, Horsens, Denmark Klarenbeek, J.V Odor emissions of Dutch agriculture. Proceedings of the Fifth International Symposium on Agricultural Waste Utilization and Management. American Society of Agricultural Engineers, pp Liao, C.M., and S. Sing Modeling dust-born odor dynamics in swine housing based on age and size distribution of airborn dust. Applied Math Model 22: Lim, T.T., A.J. Heber, J.Q. Ni, A.L. Sutton, and D.T. Kelly Characteristics and emission rates of odor from swine nursery buildings.transactions of ASAE 44(5): Lockeryer, D.R., and R.A. Champion Methane production by sheep in relation to temporal changes in grazing behaviour. Agriculture, Ecosystems and Environment 86: Martinec, M; G. Brose, and E. Hartung Course of the day of odor release from livestock housing and odor reduction with biofilters. International Conference on Agricultural Engineering, AgEng 98, Oslo, Norway. 12/3/ Shau Odor Emission submitted doc -Page 22

23 MWPS Mechanical ventilating systems for livestock housing. Midwest Plan Service: Iowa State University, Ames, p72. Ni, J.-Q., T.T. Lim, A.J. Heber, C.A. Diehl, and R.K. Duggirala Gas release from deep manure pits in emptied and cleaned swine finishing buildings. In: Animal Production Systems and the Environment: An International Conference on Odor, Water Quality, Nutrient Management and Socioeconomic Issues. Des Moines, IA, July Ni, J.-Q Emission of carbon dioxide and ammonia from mechanically ventilated pig house. PhD Thesis, Catholic University Leuven, Belgium. Ni, J.-Q Mechanistic models of ammonia release from liquid manure: a review. Journal of Agricultural Engineering Research 72:1-17. Ni, J.-Q., A.J. Heber, C.A. Diehl, T.T. Lim, R.K. Duggirala, and B.L. Haymore Characteristics of hydrogen sulfide concentrations in two mechanically ventilated swine buildings. Canadian Biosystems Engineering 44(6): Ogink, N. (2001) Diurnal and seasonal variation in odor from animal housing. Pre-Conference Workshop Odor Impact Assessment, 1st Int Conference on Odor and VOCs: Measurement, Regulation and Control Techniques. Sydney, Australia Ogink, N.W.M., and P.W.G. Groot Koerkamp Comparison of odor emissions from animal housing systems with low ammonia emission. 1st IWA International Conference on odor and VOCs: Measurement, Regulation and Control Techniques, Sydney, Australia, p Oldenburg, J Geruchs- und Ammoniak-Emissionen aus der Tierhaltung [Odor and ammonia emission of livestock husbandry]. KTBL-Schrift 333, Kuratorium für Technik und Bauwesen in der Landwirtschaft e.v. KTBL, Darmstadt Pedersen, S, and C.B. Pedersen Animal activity measured by infrared detectors. Journal of Agricultural Engineering Research 61: /3/ Shau Odor Emission submitted doc -Page 23

24 Pedersen, S, and H.B. Rom Diurnal variation in heat production from pigs in relation to animal activity. International Conference on Agricultural Engineering AgEng 98, Oslo, Norway Pedersen, S Time based variation in airborne dust in respect to animal activity. In: 4 th International Livestock Environmental Symposium (E. Collins and C. Boon, Eds), Coventry, U.K. Pedersen, S Døgnvariationer i dyrenes aktivitet i kvæg-, svine- og fjerkræstalde [Diurnal variation of the animal activity of calve, pig and poultry]. Internal report 66. Danish Institute of Animal Science. Horsens, Denmark. Pedersen, S., and H. Takai Diurnal Variaton in animal heat production in relation to animal activity. In 5 th International Livestock Environmental Symposium. Bloomington, MN Phillips, V.R., M.R. Holden, R.W. Sneath, J.L. Short, R.P. White, J. Hartung, J. Seedorf, M. Schröder, K.H. Linkert, S. Pedersen, H. Takai, J.O. Johnsen, W.G. P. Groot Koerkamp, G.H. Uenk, R. Scholtens, J.H.M. Metz, and C.M. Wathes The development of robust methods for measuring concentrations and emission rates of gaseous and particulate air pollutants in livestock buildings. Journal of Agricultural Engineering Research 70 (1): Schauberger, G, and P. Pilati. 1998a. Evaluation of a steady-state balance model to simulate the indoor climate inside livestock buildings: a comparison with measurements of a cattle house. International Conference on Agricultural Engineering, AgEng 98, Oslo, Norway. Schauberger, G., and P. Pilati. 1998b. Evaluierung eines quasi-stationären Bilanzmodells zur Stallklimasimulation: Vergleich mit Messungen eines Rindermaststalles [Evaluation of a steadystate balance model to simulate the indoor climate in livestock buildings: a comparison with measurements of a cattle house]. Wiener Tierärztliche Monatsschrift, 85, Schauberger, G., M. Piringer, and E. Petz 2000b. Steady-state balance model to calculate the indoor climate of livestock buildings demonstrated for fattening pigs. International Journal of Biometeorology 43 (4): /3/ Shau Odor Emission submitted doc -Page 24

25 Schauberger, G., M. Piringer, and E. Petz Diurnal and annual variation of odor emission of animal houses: a model calculation for fattening pigs. Journal of Agricultural Engineering Research 74 (3): Schauberger, G., M. Piringer, and E. Petz. 2000a. Diurnal and annual variation of the sensation distance of odor emitted by livestock buildings calculated by the Austrian odor dispersion model (AODM). Atmospheric Environment 34(28): Sousa, P., and S. Pedersen Ammonia emission from fattening pigs in relation to animal activity and carbon dioxide production. International Symposium on Gaseous and Odor Emissions from Animal Production Facilities, Horsens, Denmark Thu, K.M Public health concerns for neighbors of large-scale swine production operations. Journal of Agricultural Safety and Health 8(2): van Ouwerkerk, E. N. J., and S. Pedersen Application of the carbon dioxide mass balance method to evaluate ventilation rates in livestock buildings. In: 4 th International Livestock Environmental Symposium, Coventry, U.K. van Ouwerkerk, E.N.J., and S. Pedersen Application of the carbon dioxide mass balance method to evaluate ventilation rates in livestock buildings. In 4 th International Livestock Environmental Symposium, Coventry, U.K. Yu, H Experimental determination of airflow performance in ceiling slot-ventilated enclosure under isothermal conditions. Paper , St. Joseph, MI: ASAE Zhu, J., L.D. Jacobson, D. Schmidt, and R. Nicolai Daily variations in odor and gas emissions from animal facilities. Applied Engineering in Agriculture 16(2): /3/ Shau Odor Emission submitted doc -Page 25

26 Table 1. Basic statistics of measured values for entire data set and for four buildings A, B, C, and D. V T N M M tot C O E O e O C N E N e N C S E S e S m³/s C - kg AU OU/m³ OU/s OU s -1 AU -1 mg/m³ mg/s ng s -1 AU -1 ng/m³ mg/s ng s -1 AU -1 Maximum , th -percentile , th - percentile , Median th - percentile th- percentile Minimum n CI upper limit , CI lower limit Median A B C D , /3/ Shau Odor Emission submitted doc -Page 26

27 Table 2: Correlation coefficients between measured parameters (n=108). V T N M C O E O e O C N E N e N C S E S T 0.69 [a] 1 N M [c] 1 C O E O 0.31 [b] [c] 1 e O 0.30 [b] 0.31 [b] [a] 0.79 [c] 0.91 [c] 1 C N [b] [c] E N 0.26 [b] 0.37 [c] [c] 0.27 [b] 0.30 [b] 0.33 [c] 0.34 [c] 1 e N 0.28 [b] 0.68 [c] [c] [c] [c] 1 C S [c] [a] [b] [c] 0.35 [c] 0.23 [a] 1 E S [c] [c] 0.20 [a] [a] 0.30 [b] 0.80 [c] 0.71 [c] 0.57 [c] 1 e S [c] [c] [c] 0.86 [c] 0.34 [c] 0.78 [c] [a] P<0.05 [b] P<0.01 [c] P< /3/ Shau Odor Emission submitted doc -Page 27

28 Table 3: Results of the stepwise regression analyses of the specific odor emission e O and the re-scaled parameter F T, F v, and F A according to Eq 9. The cross denotes which parameter was fitted by the regression. Temperature F T Re-scaled parameter Air velocity F v Activity F A Regression coefficient (± std. error) F value + C F F F =1 C A F A =1 C P C T = 49.8 ± (p<0.002) C T F T =1 + C A F A =1 C P C V = 12.1 ± (p<0.002) + + C A F A =1 C P C T C V = 2.87 ± (p<0.003) C P C T C V C A = 2.12 ± (p<0.002) 12/3/ Shau Odor Emission submitted doc -Page 28