Using Olfactometry to Measure Intensity and Threshold Dilution Ratio for Evaluating Swine Odor

ABSTRACT

Intensity and threshold dilution ratio are two important indices for odor control of swine buildings. Although odor threshold dilution ratio is a widely used index to describe an odor, it should be related to intensity to be more useful. A method was proposed to measure both indices simultaneously by using a dynamic forced-choice olfacto-meter. Four air samples were taken from each of four swine rooms including farrowing, finisher, gestation, and nursery. A panel of eight people was used to evaluate odor intensity. Odor threshold dilution ratios were calculated according to the American Society for Testing and Materials (ASTM) Standard Practice E679-91 to be 333, 424, 25, and 221 for samples collected from farrowing, finisher, gestation, and nursery rooms, respectively. After the samples were diluted 14.7 times, the odor intensities were evaluated to be 3.79, 3.46, 0.48, and 4.0 for the above-mentioned rooms, respectively. The data collected were used to develop a mathematical model.     

Using olfactometry to measure intensity and threshold dilution ratio for evaluating swine odor.

Intensity and threshold dilution ratio are two important indices for odor control of swine buildings. Although odor threshold dilution ratio is a widely used index to describe an odor, it should be related to intensity to be more useful. A method was proposed to measure both indices simultaneously by using a dynamic forced-choice olfactometer. Four air samples were taken from each of four swine rooms including farrowing, finisher, gestation, and nursery. A panel of eight people was used to evaluate odor intensity. Odor threshold dilution ratios were calculated according to the American Society for Testing and Materials (ASTM) Standard Practice E679-91 to be 333, 424, 25, and 221 for samples collected from farrowing, finisher, gestation, and nursery rooms, respectively. After the samples were diluted 14.7 times, the odor intensities were evaluated to be 3.79, 3.46, 0.48, and 4.0 for the above-mentioned rooms, respectively. The data collected were used to develop a mathematical 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.     

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.     

Improvement of Odor Intensity Measurement Using Dynamic Olfactometry

Abstract

Odor intensity reveals a dose-effect relationship between inhaled odor and perceived odor sensation by the receptors, while odor concentration reflects the odor strength at the emission sources. The study reports significant improvements in experimental procedures in establishing the odor concentration-intensity (OCI) relationships using a newly developed digital olfactometer. The improvements in experimental procedures have been made to meet the requirements of both the VDI guideline 3882.1 and the European standard (EN13725). Several areas which could affect the reliability of the results have been identified in some similar studies. The latest digital olfactometer was calibrated automatically to ensure accurate and repeatable dilution ratios. Cross contamination has been eliminated through the instrument design and extensive cleaning procedures, making random presentation possible. Stringent panelist screening and continuous performance monitoring ensures consistent sensitivity of the panel. The extension of odor intensity category to temperature sensation gives a reference to assist judgments of perceived odor sensation. The Dyna-Scent calculation method has simplified odor intensity calculation and can be applied to many odor samples. A total of 38 odor samples from three alumina refinery sites and two sewage treatment plants were collected for analysis. The results have confirmed the efficiency of the olfactometer. Distinct Odor Concentrations (DOCs) were calculated for each sample using both VDI and DynaScent methods. A student t test on two major odor types confirmed that there are no significant differences between two methods. The study has shown the DOCs for refinery odor and wastewater odor are in the range of 3.8-15.4 and 4.2-15.6 odor unit (OU)/m³ respectively. The study demonstrated that the improvements are critical in achieving reliable odor intensity measurement. This can lead to the setup of quantitative odor impact criteria for different industries and sites.     

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.     

Appraisal of Odor-Measurement Techniques

All odor measurement methods may be conveniently grouped into three categories: (1) threshold; (2) suprathreshold; and (3) analytical. The threshold techniques include such methods as syringe dilution, scentometer, and osmoscope. Suprathreshold techniques include direct comparison methods and dilution methods involving subjective ratings of preference as opposed to intensity. Analytical techniques involve the use of physicochemrcal methods, e.g., for monitoring of process streams or identification of individual odorants. The relative advantages and disadvantages of each method, as presently used, are discussed. Recommended applications for the various methods and suggested modifications are also presented.     

Measurement of Odor Intensity by an Electronic Nose

ABSTRACT

The possibility of using electronic noses (ENs) to measure odor intensity was investigated in this study. Two commercially available ENs, an Aromascan A32S with conducting polymer sensors and an Alpha M.O.S. Fox 3000 with metal oxide sensors, as well as an experimental EN made of Taguchi-type tin oxide sensors, were used in the experiments. Odor intensity measurement by sensory analysis and EN sensor response were obtained for samples of odorous compounds (n-butanol, CH₃COCH₃, and C₂H₅SH) and for binary mixtures of odorous compounds (n-butanol and CH₃COCH₃). Linear regression analysis and artificial neural networks (ANN) were used to establish a relationship between odor intensity and EN sensor responses.

The results suggest that large differences in sensor response to samples of equivalent odor intensity exist and that sensitivity to odorous compounds varies according to the type of sensors. A linear relationship between odor intensity and averaged sensor response was found to be appropriate for the EN based on conducting polymer sensors with a correlation coefficient (r) of 0.94 between calculated and measured odor intensity. However, the linear regression approach was shown to be inadequate for both ENs, which included metal oxide-type sensors. Very strong correlation (r = 0.99) between measured odor intensity and calculated odor intensity using the ANN developed were obtained for both commercial ENs. A weaker correlation (r = 0.84) was found for the experimental instrument, suggesting an insufficient number of sensors and/or not enough diversity in sensor responses. The results demonstrated the ability of ENs to measure odor intensity associated with simple mixtures of odorous compounds and suggest that ANN are appropriate to model the relationship between odor intensity measurement and EN sensor response.     

Characterization of livestock odors using steel plates, solid-phase microextraction, and multidimensional gas chromatography-mass spectrometry-olfactometry

Livestock operations are associated with emissions of odor, gases, and particulate matter (PM). Livestock odor characterization is one of the most challenging analytical tasks. This is because odor-causing gases are often present at very low concentrations in a complex matrix of less important or irrelevant gases. The objective of this project was to develop a set of characteristic reference odors from a swine barn in Iowa and, in the process, identify compounds causing characteristic swine odor. Odor samples were collected using a novel sampling methodology consisting of clean steel plates exposed inside and around the swine barn for < or =1 week. Steel plates were then transported to the laboratory and stored in clean jars. Headspace solid-phase microextraction was used to extract characteristic odorants collected on the plates. All of the analyses were conducted on a gas chromatography-mass spectrometry-olfactometry system where the human nose is used as a detector simultaneously with chemical analysis via mass spectrometry. Multidimensional chromatography was used to isolate and identify chemicals with high-characteristic swine odor. The effects of sampling time, distance from a source, and the presence of PM on the abundance of specific gases, odor intensity, and odor character were tested. Steel plates were effectively able to collect key volatile compounds and odorants. The abundance of specific gases and odor was amplified when plates collected PM. The results of this research indicate that PM is major carrier of odor and several key swine odorants. Three odor panelists were consistent in identifying p-cresol as closely resembling characteristic swine odor, as well as attributing to p-cresol the largest odor response out of the samples. Further research is warranted to determine how the control of PM emissions from swine housing could affect odor emissions.     

Prospects for Exhaust Control by Engine Modification

In order to assist in assessing potential odor problems arising from chemical manufacturing operations, the odor thresholds of 53 commercially important odorant chemicals have been determined using a standardized and defined procedure. The odor threshold data previously available have shown wide variation reflecting the diversity of procedures and techniques used. Factors that may affect the odor threshold measurement include the mode of presentation of the stimulus to the observer, the influence of extraneous odorants in the presentation system, the type of observer used, the definition of the odor response, the treatment of the data obtained, and the chemical purity of the odorant. The experimental approach used has minimized these variations. The odorants were presented to a trained odor panel in a static air system utilizing a low odor background air as the dilution medium. The odor threshold is defined as the first concentration at which all panel members can recognize the odor. The effect of chemical purity has been determined by measuring the odor threshold of materials representing different modes of manufacture or after purification by gas chromatographic procedures. The threshold concentrations range over six orders of magnitude. Trimethylamine exhibited the lowest threshold (0.00021 ppm volume); methylene chloride was not recognizable below 214 ppm. Of the 53 chemicals, sulfur bearing compounds exhibit low threshold values on the order of parts per billion. Aside from the sulfides, it is not possible to anticipate the odor threshold of a material based on its chemical structure or functionality.     

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.     

Modeling effects of moisture content and advection on odor causing vocs volatilization from stored swine manure

Abstract

Two models for evaluating the contents and advection of manure moisture on odor causing volatile organic compounds (VOC‐odor) volatilization from stored swine manure were studied for their ability to predict the volatilization rate (indoor air concentration) and cumulative exposure dose: a MJ‐I model and a MJ‐II model. Both models simulating depletion of source contaminant via volatilization and degradation based on an analytical model adapted from the behavior assessment model of Jury et al. In the MJ‐I model, manure moisture movement was negligible, whereas in the MJ‐II model, time‐dependent indoor air concentrations was a function of constant manure moisture contents and steady‐state moisture advection. Predicted indoor air concentrations and inhaled doses for the study VOC‐odors of p‐cresol, toluene, and p‐xylene varied by up to two to three orders of magnitude depending on the manure moisture conditions. The sensitivity analysis of both models suggests that when manure moisture movement exists, simply MJ‐I model is inherently not sufficient to represent a more generally volatilization process, which can even become stringent as moisture content increases. The conclusion illustrates how one needs to include a wide variety of manure moisture values in order to fully assess the complex volatilization mechanisms that are present in a real situation.     

Development of an Odor Panel for Evaluation of Odor Control Equipment

The odor panel using the syringe dilution technique has been successfully used to judge the effectiveness of control equipment in eliminating industrial odor problems by monitoring stack emissions. Data is presented using this odor panel method for efficiency tests of direct-flame fume incinerators performed in a large variety of industrial process applications, including pulp and paper mills, rubber processing plants, food processing plants, wire enameling plants, glass fiber manufacturing plants, paint bake ovens, brake manufacturing plants, caster manufacturing plants, rendering plants, and chemical plants. Test data shows that this method of measuring odor using the syringe dilution technique is a useful and practical tool in analyzing odor problems and determining the effectiveness of control equipment by monitoring stack emissions.     

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.     

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%).     

The Development of a Sampling System for the Determination of Odor Emission Rates from Areal Surfaces: Part II. Mathematical Model

Abstract

An improved design for an odor emission hood was examined in the laboratory using ammonia emission from a water surface. The experimental ammonia convective mass transfer coefficients from a diluted ammonia solution were determined at velocities of 0.3 m/s to 0.8 m/s using the odor emission hood. The theoretical ammonia convective mass transfer coefficients were also predicted by boundary layer theory under laminar flow conditions. It was found that experimental data were only 10% below theoretical predictions at an air velocity of 0.3 m/s to 0.6 m/s. The maximum velocity was limited to 0.6 m/s by the geometric size, shape and aerodynamic stability of the hood. At 0.33 m/s, the smallest variation of mass transfer coefficients was measured. The odor emission rate was found to be a function of air velocity to the power 0.5 in accordance with boundary layer theory. An odor sampling system based upon the odor emission hood provides a way to quantify the potential odor emission strength in sewage treatment plants, for odor dispersion modeling and odor control.