Development of a livestock odor dispersion model: part I. Model theory and development

A livestock odor dispersion model (LODM) was developed to predict mean odor concentration, odor frequency, instantaneous odor concentration, and peak odor concentration from livestock operations. This model is based on the Gaussian fluctuating plume model and has the ability to consider the instantaneous concentration fluctuations and the differences between odor and traditional air pollutants. It can predict odor frequency from the routine hourly meteorological data input and deal with different types of sources and multiple sources. Also, the relationship between odor intensity and odor concentration was incorporated into the model.     

Determination of Setback Distances for Livestock Operations Using a New Livestock Odor Dispersion Model (LODM)

ABSTRACT

Setback distance has been used as an effective tool to avoid odor nuisance from livestock operations. Many setback distances were guidelines that were determined by empirical methods that are considered to be lack of science base. Air dispersion models have been used to determine setback distances; however, these models do not consider the short-time fluctuations of odor. A livestock odor dispersion model (LODM) was developed to consider the short-time variations of odor and predict occurrence frequency for certain levels of odor. In this study, this model was used to predict the occurrence frequency for various levels of odor in the vicinity (10 km) of a swine farm. Using selected odor criteria, setback distances between the swine farm and nearby communities were defined. Results indicate that the LODM can be used as an effective tool to determine setback distances.

IMPLICATIONS One of the more important applications of odor dispersion models is to determine setback distances for major odor sources, such as intensive livestock operations, from nearby communities. This study provided a case study in determining directional setback distances from a typical swine farm using a newly developed livestock odor dispersion model (LODM). It is also the first study in using hourly odor frequency to determine setback distances.     

Determination of setback distances for livestock operations using a new livestock odor dispersion model (LODM)

Setback distance has been used as an effective tool to avoid odor nuisance from livestock operations. Many setback distances were guidelines that were determined by empirical methods that are considered to be lack of science base. Air dispersion models have been used to determine setback distances; however, these models do not consider the short-time fluctuations of odor. A livestock odor dispersion model (LODM) was developed to consider the short-time variations of odor and predict occurrence frequency for certain levels of odor. In this study, this model was used to predict the occurrence frequency for various levels of odor in the vicinity (10 km) of a swine farm. Using selected odor criteria, setback distances between the swine farm and nearby communities were defined. Results indicate that the LODM can be used as an effective tool to determine setback distances.     

Investigating the capacity of an activated sludge process to reduce volatile organic compounds and odor emissions.

The effects of hydrogen sulfide (H2S) diffusion into activated sludge (AS) on odor and volatile organic compound (VOC) concentrations in offgas were studied over an 8-week period. Most VOCs detected in the offgas of both aeration tanks were aromatic hydrocarbons. The VOC concentrations generally decreased when H2S was introduced to the AS compared with the control, indicating a negative effect of H2S on VOC removal. Two volatile organic sulfur compounds present in the test AS offgas showed an increase followed by a decrease during H2S peak loads. Six VOCs and odor concentration increased during the introduction of an H2S peak; however no correlation was observed between H2S and odor concentration. The increase in odor concentration resulted from the increase in the concentration of six aromatic VOCs, which had their removal slowed down during a 100-ppmv H2S peak. Activated sludge diffusion provides effective H2S removal with minimal affect on odor emissions.     

Which meteorological conditions produce worst-case odors from area sources?

Two competing meteorological factors influence atmospheric concentrations of pollutants from open liquid area sources such as wastewater treatment plant units: temperature and stability. High temperatures in summer produce greater emissions from liquid area sources because of increased compound volatility; however, these emissions tend to disperse more readily because of frequent occurrence of unstable conditions. An opposite scenario occurs in winter, with lesser emissions due to lower temperatures, but also frequently less dispersion, due to stable atmospheric conditions. The primary objective of this modeling study was thus to determine whether higher atmospheric concentrations from open liquid area sources occur more frequently in summer, when emissions are greater but so is dispersion, or in winter, when emissions are lesser but so is dispersion. The study utilized a rectangular clarifier emitting hydrogen sulfide as a sample open liquid area source. Dispersion modeling runs were conducted using ISCST3 and AERMOD, encompassing 5 yr of hourly meteorological data divided by season. Emission rates were varied hourly on the basis of a curve-fit developed from previously collected field data. Model output for each season was used to determine (1) maximum 2-min average concentrations, (2) the number of odor events (2-min average concentrations greater than odor detection thresholds), and (3) areas of impact. On the basis of these 3 types of output, it was found that the worst-case odors were associated with summer, considering impacts of meteorology upon both emissions and dispersion. Not accounting for the impact of meteorology on emissions (using a constant worst-case emission rate) caused concentrations to be overpredicted compared with a variable emission rate case. The highest concentrations occurred during stability classes D, E, and F, as anticipated. A comparison of ISCST3 and AERMOD found that for the area source modeled, ISCST3 predicted higher concentrations and more odor events for all seasons.     

Correlating emissions with time and temperature to predict worst-case emissions from open liquid area sources

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry's law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H2S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H2S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85-90 degrees F), the constant emission rate underestimated odor impact significantly (by 73%).     

Development of the Relationship between Odor Intensity and Threshold Dilution Ratio for Swine Units

ABSTRACT

The following models of odor intensity for swine units were evaluated: the Weber-Fechner law model, the power law model, the Stevens model, and the Beidler model. Data were collected from four swine rooms (farrowing, finisher, gestation, and nursery) and odor threshold dilution ratios were measured by a panel using a dynamic forced-choice olfactometer. Odor intensity scales were determined by eight panelists using a six-point category scale method. A nonlinear parameter estimation method was used to estimate the parameters in each of the models. The widely used Weber-Fechner law did not adequately fit the data of odor intensity and threshold. Both the power law and the Beidler models described the data effectively, but the Beidler model showed the best fit of the data and was used as the model to represent the relationship between odor intensity and threshold dilution ratio for swine buildings.     

Diurnal and seasonal variations of odor and gas emissions from a naturally ventilated free-stall dairy barn on the Canadian prairies

This study characterized the seasonal concentration (C) and emission (E) patterns of odor, ammonia (NH₃), and hydrogen sulfide (H₂S) over the course of a whole year and their diurnal patterns in cold, warm, and mild seasons for a naturally ventilated free-stall dairy barn. It was found that seasonal odor and NH₃ and H₂S emissions varied greatly: from 17.2 to 84.4 odor units (OU) sec^?1 AU^?1 (AU: animal unit, 500 kg of animal body mass), from 0.27 to 0.92 mg sec^?1 AU^?1, and from 3 to 105 μg sec^?1 AU^?1, respectively. The overall concentrations of odor and NH₃ were higher in the winter, whereas the emissions were higher in the mild and warm seasons. Diurnal variation was most significant for odor emission (OE) in the mild season when the ratio of maximum (279.2 OU sec^?1 AU^?1) to minimum value (60.5 OU sec^?1 AU^?1) was up to 4.6. The indoor air quality was also evaluated by considering not only the health effect of individual gases, but also the additive effect of NH₃ and H₂S. Results showed that the indoor air quality was poorest in cold seasons when NH₃ C could exceed the threshold limit set out in occupational health regulation, and in fact could worsen due to the additive effect of the two gases. Further, it was suggested NH₃ was a good indicator for predicting odor concentration (OC) or OE. The impact of climatic parameters on odor and gases were also examined, and it was found ventilation rate (VR) negatively affected OC and NH₃ C, but positively impacted OE and NH₃ E. Using 70% of the total data, a multilinear model for OE was developed as a function of VR and indoor relative humidity and was validated to be acceptable using the rest of the data.

Implications: Diurnal and seasonal variations of odor, NH₃, and H₂S concentrations and emissions were monitored for a naturally ventilated dairy barn in a cold region. The emission factors were calculated and indoor air quality was evaluated. The overall odor and NH₃ concentrations were higher in winter, whereas emissions were higher in the mild and warm seasons. Diurnal variation was most significant for odor emission in the mild season, when the ratio of maximum to minimum value was up to 4.6. The results can be used to estimate odor and gas emissions from other dairy barns in Canada and other cold regions.     

Measurement of biosolids compost odor emissions from a windrow, static pile, and biofilter.

A pilot study was conducted to compare odor emissions from a windrow process and an aerated static pile and to determine the odor reduction efficiency of a pilot two-phase biofilter for odor control of biosolids composting. Chemical compounds identified as responsible for odors from biosolids composting include ammonia, dimethyl disulfide, carbon disulfide, formic acid, acetic acid, and sulfur dioxide (or carbonyl sulfide). Aeration was found to reduce the concentration of ammonia, formic acid, and acetic acid by 72, 57, and 11%, respectively, compared with a nearby windrow, while dimethyl sulfide, carbon disulfide, and sulfur dioxide (or carbonyl sulfide) concentrations were below detection limits. Using dilution-to-threshold olfactometry, aeration followed by biofiltration was found to reduce the odor from biosolids composting by 98%. Biofiltration also altered the character of odor emissions from biosolids composting, producing a less offensive odor with an earthy character. Biofiltration was found to reduce the concentration of ammonia, dimethyl disulfide, carbon disulfide, formic acid, acetic acid, and sulfur dioxide (or carbonyl sulfide) by 99, 90, 32, 100, 34, and 100%, respectively. The concentrations of those odorants were estimated to be 3700, 110000, 26,37,5, and 1.2 times reported human detection limits before the two-phase biofilter, respectively, and 42,9600,18,0,3, and 0 times human detection limits after the biofilter, respectively.     

Accounting for averaging time in air pollution modeling

This paper examines the validity of a commonly used expression to estimate concentrations for averaging times that are much smaller than that corresponding to the model estimate. These “peak” concentrations, which can be several times larger than the model estimate, are required in applications such as odor assessment. We show that this expression cannot be justified, and that information on short-term concentrations is only contained in the probability distribution of time-averaged concentrations. The paper proposes a simple model of concentration time series to examine the effect of averaging time on concentration probability distributions. The results from the model are compared with previous theoretical and experimental studies.     

Correlating Emissions with Time and Temperature to Predict Worst-Case Emissions from Open Liquid Area Sources

Abstract

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry’s law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H₂S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H₂S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85–90 °F), the constant emission rate underestimated odor impact significantly (by 73%).     

Annual variations of odor concentrations and emissions from swine gestation, farrowing, and nursery buildings

To obtain annual odor emission profiles from intensive swine operations, odor concentrations and emission rates were measured monthly from swine nursery, farrowing, and gestation rooms for a year. Large annual variations in odor concentrations and emissions were found in all the rooms and the impact of the seasonal factor (month) was significant (P < 0.05). Odor concentration was low in summer when ventilation rate was high but high in winter when ventilation rate was low, ranging from 362 (farrowing room in July) to 8934 (nursery room in December) olfactory unit (OU) m(-3). This indicates that the air quality regarding odor was significantly better in summer than that in winter. Odor emission rate did not show obvious seasonal pattern as odor concentration did, ranging from 2 (gestation room in November) to 90 (nursery room in April) OU m(-2) sec(-1); this explains why the odor complaints for swine barns have occurred all year round. The annual geometric mean odor concentration and emission rate of the nursery room was significantly higher than the other rooms (P < 0.05). In order to obtain the representative annual emission rate, measurements have to be taken at least monthly, and then the geometric mean of the monthly values will represent the annual emission rate. Incorporating odor control technologies in the nursery area will be the most efficient in reducing odor emission from the farm considering its emission rate was 2 to 3 times of the other areas. The swine grower-finisher area was the major odor source contributing 53% of odor emission of the farm and should also be targeted for odor control. Relatively positive correlations between odor concentration and both H2S and CO2 concentrations (R(2) = 0.58) means that high level of these two gases might likely indicate high odor concentration in swine barns.     

Predictions by the Odor From Feedlots, Setback Estimation Tool (OFFSET) compared with observations by neighborhood monitors

A comparison between predicted and observed odor intensities at 20 neighborhood residences in the vicinity of seven various livestock farms in five different Minnesota counties was made to evaluate the Odor From Feedlots, Setback Estimation Tool (OFFSET) developed by the University of Minnesota. Observations by neighborhood monitors suggest that the OFFSET-predicted separation distances for annoyance-free frequencies of 99, 98, and 97% are large enough. The observations additionally indicate that predicted distances to obtain 94 and 91% annoyance-free frequencies may be large enough for some farms, but for other farms, greater distances may be needed. For two farms in the study, no significant difference between all of the observed and predicted intensities could be found. At four sites, a significant difference was found, and at three of these the difference was considerable. Odor emission rates used in the OFFSET model seem to describe the average emission fairly well for many odor sources, but improvement may be needed for some types of sources. Possible reasons for observations of annoying odor when not predicted include fluctuations in the odor emissions, wind speed fluctuations, topographic variation between sites, sensitivity differences by neighborhood monitors, and background emissions from other sources.     

Dispersion modeling to compare alternative technologies for odor remediation at swine facilities

The effectiveness of 18 alternative technologies for reducing odor dispersion at and beyond the boundary of swine facilities was assessed in conjunction with an initiative sponsored through agreements between the Attorney General of North Carolina and Smithfield Foods, Premium Standard Farms, and Frontline Farmers. The trajectory and spatial distribution of odor emitted at each facility were modeled at 200 and 400 m downwind from each site under two meteorological conditions (daytime and nighttime) using a Eulerian-Lagrangian model. To predict the dispersion of odor downwind, the geographical area containing the odorant sources at each facility was partitioned into 10-m2 grids on the basis of satellite photographs and architectural drawings. Relative odorant concentrations were assigned to each grid point on the basis of intensity measurements made by the trained odor panel at each facility using a 9-point rating scale. The results of the modeling indicated that odor did not extend significantly beyond 400 m downwind of any of the test sites during the daytime when the layer of air above the earth's surface is usually turbulent. However, modeling indicated that odor from all full-scale farms extended beyond 400 m onto neighboring property in the evenings when deep surface cooling through long-wave radiation to space produces a stable (nocturnal) boundary layer. The results also indicated that swine housing, independent of waste management type, plays a significant role in odor downwind, as do odor sources of moderate to moderately high intensity that emanate from a large surface area such as a lagoon. Human odor assessments were utilized for modeling rather than instrument measurements of volatile organic compounds (VOCs), hydrogen sulfide, ammonia, or particulates less than 10 microm in diameter (PM10) because these physical measurements obtained simultaneously with human panel ratings were not found to accurately predict human odor intensity in the field.     

Predictions by the Odor From Feedlots,Setback Estimation Tool (OFFSET) Compared with Observations by Neighborhood Monitors

Abstract

A comparison between predicted and observed odor intensities at 20 neighborhood residences in the vicinity of seven various livestock farms in five different Minnesota counties was made to evaluate the Odor From Feedlots, Setback Estimation Tool (OFFSET) developed by the University of Minnesota. Observations by neighborhood monitors suggest that the OFFSET-predicted separation distances for annoyance-free frequencies of 99, 98, and 97% are large enough. The observations additionally indicate that predicted distances to obtain 94 and 91% annoyance-free frequencies may be large enough for some farms, but for other farms, greater distances may be needed. For two farms in the study, no significant difference between all of the observed and predicted intensities could be found. At four sites, a significant difference was found, and at three of these the difference was considerable. Odor emission rates used in the OFFSET model seem to describe the average emission fairly well for many odor sources, but improvement may be needed for some types of sources. Possible reasons for observations of annoying odor when not predicted include fluctuations in the odor emissions, wind speed fluctuations, topographic variation between sites, sensitivity differences by neighborhood monitors, and background emissions from other sources.     

Consultant Guide/1986

ABSTRACT

To obtain annual odor emission profiles from intensive swine operations, odor concentrations and emission rates were measured monthly from swine nursery, farrowing, and gestation rooms for a year. Large annual variations in odor concentrations and emissions were found in all the rooms and the impact of the seasonal factor (month) was significant (P < 0.05). Odor concentration was low in summer when ventilation rate was high but high in winter when ventilation rate was low, ranging from 362 (farrowing room in July) to 8934 (nursery room in December) olfactory unit (OU) m^?3. This indicates that the air quality regarding odor was significantly better in summer than that in winter. Odor emission rate did not show obvious seasonal pattern as odor concentration did, ranging from 2 (gestation room in November) to 90 (nursery room in April) OU m^?2 sec^?1; this explains why the odor complaints for swine barns have occurred all year round. The annual geometric mean odor concentration and emission rate of the nursery room was significantly higher than the other rooms (P < 0.05). In order to obtain the representative annual emission rate, measurements have to be taken at least monthly, and then the geometric mean of the monthly values will represent the annual emission rate. Incorporating odor control technologies in the nursery area will be the most efficient in reducing odor emission from the farm considering its emission rate was 2 to 3 times of the other areas. The swine grower-finisher area was the major odor source contributing 53% of odor emission of the farm and should also be targeted for odor control. Relatively positive correlations between odor concentration and both H₂S and CO₂ concentrations (R ² = 0.58) means that high level of these two gases might likely indicate high odor concentration in swine barns.

IMPLICATIONS The emissions of air pollutants including odors, greenhouse gases, and toxic gases have become a major environmental issue facing animal farms in the U.S.A. and Canada. To ensure the air quality in the vicinity of intensive livestock farms, air dispersion models have been used to determine setback distances between livestock facilities and neighboring residences based on certain air quality requirement on odor and gases. Due to the limited odor emission data available, none of the existing models can take account of seasonal variations of odor emissions, which may result in great uncertainties in setback distance calculations. Therefore, the obtained seasonal odor and gas emission rates by this study can be used by the government regulatory organizations and researchers in air dispersion modeling to get improved calculation of setback distances.     

Characterization of odor released during handling of swine slurry: Part I. Relationship between odorants and perceived odor concentrations

Odor emission from livestock production systems is a major nuisance in many rural areas. This study aimed at determining the major airborne chemical compounds responsible for the unpleasant odor perceived in swine facilities during slurry handling, and at proposing predictive models of odor concentration (OC) based on the concentrations of specific odorants in the air. A multivariate data analysis strategy involving principal components analysis and multiple linear regressions was implemented to analyze the relationships between concentration of 35 gases (measured by GC/MS or gas detection tubes), and the overall OC perceived by sensory analysis. The study compiled data on the concentration of odor and odorants, measured in the headspace of 24 unstored and stored slurry samples collected from three different types of production units on 8 commercial swine farms. Among all the measured constituents, OC was found to have the highest correlation with the sulfur containing compounds (i.e. hydrogen sulfide, dimethylsulfide, dimethyldisulfide, dimethyltrisulfide). The concentration of hydrogen sulfide accounted for 68% of the variation in OC above the stirred slurry samples. The highest concentrations of volatile organic compounds were observed for phenols and indoles, which made a significant contribution to the overall OC when the slurry was fresh. The contribution of ammonia to the OC was only significant in the absence of hydrogen sulfide. The precision of predictive models of OC based on the concentration of specific odorants in the air was satisfactory (R² between 0.66 and 0.89). Hence, this study suggests that monitoring of specific odor compounds released from agitated swine slurry can be used to predict the concentration of odor perceived close to the source (e.g. at storage units), allowing the assessment of odor nuisance potentials.     

Odor measurements for manure spreading using a subsurface deposition applicator

Odor emissions during manure spreading events have become a source of concern, particularly where farms are located nearby urban areas. The objective of the present study was to compare odor concentrations and odor emission rates due to pig manure application using two different types of applicators, a sub-surface deposition system and a conventional splash-plate applicator. Air samples were collected using a Surface Isolation Flux Chamber and the "bag-in-vacuum chamber" techniques, at 0.5, 1.5 and 2.5 hours after manure application. A three-station forced-choice dynamic dilution olfactometer was used by an odor panel for determining odor concentration. Preliminary results indicated that with the sub-surface deposition system applicator odor emission rate was reduced by 8% to 38% compared to that of the conventional splash-plate applicator. The highest reduction in odor strength and odor emission rate was observed in the most offensive period after manure application. The sub-surface deposition system may be a solution for hog producers who wish to reduce odor complaints from applying manure without the cost and problems associated with deep injection systems.