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.     

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.     

Preventive control of odor emissions through manipulation of operational parameters during the active phase of composting

Better understanding of the effects of key operational parameters or environmental factors on odor emission is of critical importance for minimizing the generation of composting odors. A series of laboratory experiments was conducted to examine the effects of various operating conditions on odor emissions. The results revealed that airflow rates that were too high or too low could result in higher total odor emissions. An optimal flowrate for odor control would be approximately 0.6 L/min.kg dry matter with intermittent aeration and a duty cycle of 33%. Temperature setpoint at 60°C appeared to be a turning point for odor emission. Below this point, odor emissions increased with increasing temperature setpoint; conversely, odor emissions decreased with increasing temperature setpoint above this point. With regard to the composting material properties, odor emissions were greatly affected by the initial moisture content of feedstock. Both peak odor concentration and emission rate generally increased with higher initial moisture content. Odor emission was significant only at moisture levels higher than 65%. An initial moisture level below 45% is not recommended due to concern with the resulting lower degree of biodegradation. Biodegradable volatile solids content (BVS) of feedstock had pronounced effect on odor emissions. Peak odor concentration and emission rate increased dramatically as BVS increased from 45% to 65%, thus, total odor emission increased exponentially with BVS.     

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.     

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%).     

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%).     

Improvement of odor intensity measurement using dynamic olfactometry

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 DynaScent 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.     

Mapping Odor Sources from Complaint Statistics I: Identifying the Major Source

Odor is acknowledged as a major community air pollution nuisance, but the problems of analysis, particularly in Identifying the origin of odor, are also well recognised. One approach to the problem has been the development of computational schemes which use wind directions at the times of odor observations to locate the likely source of odor. An earlier technique, PONG, has now been superseded by a more sophisticated version, PONG2, which incorporates Gaussian plume modeling techniques. This paper describes the operation of PONG2 in the odor source mapping mode and provides an analysis of the Insensitivity of the technique to errors in input. Then, using specific Australian examples of community odor nuisance emanating from a large sewage treatment complex near Melbourne, and an irrigated golf course within metropolitan Darwin, the paper outlines the utility of PONG2 in resolving problem odor sources at a range of scales and levels of complexity.     

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.     

Quality assured measurements of animal building emissions: odor concentrations

Standard protocols for sampling and measuring odor emissions from livestock buildings are needed to guide scientists, consultants, regulators, and policy-makers. A federally funded, multistate project has conducted field studies in six states to measure emissions of odor, coarse particulate matter (PM(10)), total suspended particulates, hydrogen sulfide, ammonia, and carbon dioxide from swine and poultry production buildings. The focus of this paper is on the intermittent measurement of odor concentrations at nearly identical pairs of buildings in each state and on protocols to minimize variations in these measurements. Air was collected from pig and poultry barns in small (10 L) Tedlar bags through a gas sampling system located in an instrument trailer housing gas and dust analyzers. The samples were analyzed within 30 hr by a dynamic dilution forced-choice olfactometer (a dilution apparatus). The olfactometers (AC'SCENT International Olfactometer, St. Croix Sensory, Inc.) used by all participating laboratories meet the olfactometry standards (American Society for Testing and Materials and European Committee for Standardization [CEN]) in the United States and Europe. Trained panelists (four to eight) at each laboratory measured odor concentrations (dilution to thresholds [DT]) from the bag samples. Odor emissions were calculated by multiplying odor concentration differences between inlet and outlet air by standardized (20 degrees C and 1 atm) building airflow rates.     

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.     

Comments

Regarding the TT-4 Odor Committee paper on Odor Regulation, we believe that the basic goal of a regulation should be to prevent ambient odors that are a nuisance to the community and that control methods to reach these goals should not be specified by regulatory bodies. In enforcing odor regulations, the cost effectiveness of alternate solutions should be considered. For example, we consider a limit on the total odor emission to be a penalty on large installations which is not cost effective.     

Odor emission rate estimation of indoor industrial sources using a modified inverse modeling method

Odor emission rates are commonly measured in the laboratory or occasionally estimated with inverse modeling techniques. A modified inverse modeling approach is used to estimate source emission rates inside of a postdigestion centrifuge building of a water reclamation plant. Conventionally, inverse modeling methods divide an indoor environment in zones on the basis of structural design and estimate source emission rates using models that assume homogeneous distribution of agent concentrations within a zone and experimentally determined link functions to simulate airflows among zones. The modified approach segregates zones as a function of agent distribution rather than building design and identifies near and far fields. Near-field agent concentrations do not satisfy the assumption of homogeneous odor concentrations; far-field concentrations satisfy this assumption and are the only ones used to estimate emission rates. The predictive ability of the modified inverse modeling approach was validated with measured emission rate values; the difference between corresponding estimated and measured odor emission rates is not statistically significant. Similarly, the difference between measured and estimated hydrogen sulfide emission rates is also not statistically significant. The modified inverse modeling approach is easy to perform because it uses odor and odorant field measurements instead of complex chamber emission rate measurements.     

The development of industry-specific odor impact criteria for feedlots using models

Emissions from feedlot operations are known to vary by environmental conditions and few if any techniques or models exist to predict the variability of odor emission rates from feedlots. The purpose of this paper is to outline and summarize unpublished reports that are the result of a collective effort to develop industry-specific odor impact criteria for Australian feedlots. This effort used over 250 olfactometry samples collected with a wind tunnel and past research to develop emission models for pads, sediment basins, holding ponds, and manure storage areas over a range of environmental conditions and tested using dynamic olfactometry. A process was developed to integrate these emission models into odor dispersion modeling for the development of impact criteria. The approach used a feedlot hydrology model to derive daily feedlot pad moisture, temperature, and thickness. A submodel converted these daily data to hourly data. A feedlot pad emissions model was developed that predicts feedlot pad emissions as a function of temperature, moisture content, and pad depth. Emissions from sediment basins and holding ponds were predicted using a basin emissions model as a function of days since rain, inflow volume, inflow ratio (pond volume), and temperature. This is the first attempt to model all odor source emissions from a feedlot as variable hourly emissions on the basis of climate, management, and site-specific conditions. Results from the holding pond, sediment basin, and manure storage emission models performed well, but additional work on the pad emissions model may be warranted. This methodology mimics the variable odor emissions and odor impact expected from feedlots due to climate and management effects. The main outcome of the work is the recognition that an industry-specific odor impact criterion must be expressed in terms of all of the components of the assessment methodology.     

Estimating middle-, neighborhood-, and urban-scale contributions to elemental carbon in Mexico City with a rapid response aethalometer.

A successive moving average subtraction method is developed and applied to black carbon measured over 5-min intervals at a downtown location near many small emitters and at a suburban residential site within the urban plume but distant from specific emitters. Short-duration pulses assumed to originate from nearby sources are subtracted from the concentrations at each site and are summed to estimate middle-scale (approximately 0.1-1 km) contributions. The difference of the remaining baselines at the urban and suburban monitors is interpreted as the contribution to the downtown monitor from source emissions mixed over a neighborhood scale (1-5 km). The baseline at the suburban site is interpreted as the contribution of the mixture of black carbon sources for the entire city. When applied to a 24-day period from February and March 1997 in Mexico City, the analysis showed that 65% of the 24-hr black carbon was part of the urban mixture, 23% originated in the neighborhood surrounding the monitor, and only 12% was contributed from nearby sources. These analyses indicate that a fixed-site monitor can reasonably represent exposures in its surrounding neighborhood even when many local sources, such as exhaust from diesel buses and trucks, affect the monitor.     

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.     

Industrial odor sources and air pollutant concentrations in Globeville,a Denver,Colorado neighborhood

An odor of unknown origin described as a “tar” or “asphalt” smell has become unbearable for many of Globeville, CO, residents over the past few years. Residents report during odor events burning eyes and throat, headaches, skin irritation, and problems sleeping. This study was undertaken to identify the potential sources of the odor and the concentrations of air pollutants making up the odor by conducting meteorological correlations and sampling for a panel of volatile organic compounds (VOCs), sulfur gases, and polycyclic aromatic hydrocarbons (PAHs) in the neighborhood and near suspected sources. Wind speed and direction data collected every 1 min in the neighborhood indicate that when the odor is noticed, the community is directly downwind of a wood preservation facility and an asphalt roofing facility. Air samples collected during high-intensity odor events have shown concentrations of methylene chloride, hexane, toluene, naphthalene, dibenz[a,h]anthracene, benzo[g,h,i]perylene, and indeno[1,2,3-cd]pyrene, each at least two times higher than background concentrations. Naphthalene and the other PAHs are known pollutants emitted from wood treatment processes, and are known to have a coal tar odor. Naphthalene was present in a sample collected directly adjacent to the Koppers facility and was not present in any background samples. Single-compound odor and health thresholds, however, were never surpassed. Given the technical and regulatory challenges of sampling odors and controlling emissions, it is recommended that Globeville residents and neighboring industry pursue a “good neighbor policy” to solve the odor issue. Specific offending industrial processes could be identified for which there exist cost-effective control technologies that would reduce exposure to odors and air toxics in Globeville.

Implications: Meteorological correlations and samples of volatile organic compounds (VOCs), sulfur gases, and polycyclic aromatic hydrocarbons (PAHs) in the Globeville, CO, neighborhood and near suspected sources during odor events indicate potential industrial sources of a transient and noxious odor. Legislative approaches have proven unfruitful and no health or odor thresholds were typically violated. New approaches are warranted to address odor mixture effects in neighborhoods near industrial facilities.     

Fine particulate matter emissions inventories: comparisons of emissions estimates with observations from recent field programs

Emissions inventories of fine particulate matter (PM2.5) were compared with estimates of emissions based on data emerging from U.S. Environment Protection Agency Particulate Matter Supersites and other field programs. Six source categories for PM2.5 emissions were reviewed: on-road mobile sources, nonroad mobile sources, cooking, biomass combustion, fugitive dust, and stationary sources. Ammonia emissions from all of the source categories were also examined. Regional emissions inventories of PM in the exhaust from on-road and nonroad sources were generally consistent with ambient observations, though uncertainties in some emission factors were twice as large as the emission factors. In contrast, emissions inventories of road dust were up to an order of magnitude larger than ambient observations, and estimated brake wear and tire dust emissions were half as large as ambient observations in urban areas. Although comprehensive nationwide emissions inventories of PM2.5 from cooking sources and biomass burning are not yet available, observational data in urban areas suggest that cooking sources account for approximately 5-20% of total primary emissions (excluding dust), and biomass burning sources are highly dependent on region. Finally, relatively few observational data were available to assess the accuracy of emission estimates for stationary sources. Overall, the uncertainties in primary emissions for PM2.s are substantial. Similar uncertainties exist for ammonia emissions. Because of these uncertainties, the design of PM2.5 control strategies should be based on inventories that have been refined by a combination of bottom-up and top-down methods.