Source sampling was conducted over a four-day period at a Texas cattle feed yard. Twenty four tests were conducted to measure the TSP and PM10 concentrations at five locations around the feed yard. These concentrations were used in a semi-iterative process with ISCST3 to back-calculate 17 daytime average emission fluxes and 4 average nighttime emission fluxes. The results of the study yielded a 24-hour average PM10 emission factor of 19 kg/1000head-day (42 lbs/1000hd-day) including unpaved road dust emissions. Due to moist pen conditions, the distinction between the road dust fraction and pen surface fraction of the emission factor was made. The PM10 emission factor from the cattle pens was determined to be on the order of 3 kg/1000hd-day (6

The description of odor concentrations is critical to odor dispersion modeling. In the literature, odor concentrations are characterized by odor units (OU), or odor units per cubic meter (OU/m3). It is recommended that a more appropriate description of odor intensity as determined by dilution-to- threshold (DT) is OU. Units and protocols for odor modeling are discussed in this paper. Odor emission rates for area source modeling should have unit of OU*m/s, which will yield downwind odor concentrations in units of OU The US Environmental Protection Agency (EPA) approved ISCST3 Gaussian dispersion model was evaluated for odor modeling in predicting downwind concentrations and back calculating area source odor emission rates (flux). The comparison between ISC predicted downwind concentrations and field sampled downwind concentrations indicated that ISC can be used to predict average odor downwind concentrations, but failed to predict peak odor concentrations. ISC also had difficulty predicting downwind concentrations at wind speeds higher than 6 m/s. The comparison of odor emission rates between that obtained from back calculating using the ISC model with field sampled downwind odor concentrations and flux chamber source measurements show that flux chamber results for odor source sampling tended to under-estimate odor emission rates.

Agriculture is currently facing more restrictive emission limits as a consequence of air quality regulations. A number of State Air Pollution Regulatory Agencies (SAPRAs) are focusing their efforts on particulate matter (PM) emissions from field operations. In particular, PM emissions from concentrated animal feeding operations (CAFO) are of concern. The problem with regulating air emissions from ground-level area sources (GLAS) is the lack of accurate emission flux data. This is partially due to the difficulties of directly measuring the emission rate of dust from a given source. In order to quantify the emissions from agricultural operations, net downwind concentrations are measured and the resulting concentrations are used to back into an emission flux using dispersion modeling. Emission fluxes can subsequently be reported as emission factors. One use of emission factors is for permitting new and existing sources. According to the EPA, "Modeling is the preferred method for determining emissions limitations for both new and existing sources," (EPA, 1999). It should be expected that emission factors will be used to estimate downwind concentrations using dispersion modeling and that the resulting estimated concentrations will be used in the air pollution regulatory process. The box model has been used in the past to estimate fluxes with the resulting fluxes being used in Industrial Source Complex-Short Term, Version 3 (ISC) to estimate downwind concentrations for regulatory purposes. This paper presents a proposed process for estimating fluxes using the box model with the criterion that the subsequent concentrations with ISC will be conservative. The goal of this study was to develop a process whereby the box model could be used to determine emission fluxes based upon measured concentrations and ISC could be used to predict downwind concentrations with the resulting fluxes. For the process to be acceptable, the predicted ISC concentrations were required to be larger than the measured concentrations used in the box model to determine the flux. The concept was a GLAS with concentration measurements being made at the edge of the GLAS. It was assumed that the emission flux would be uniform in the GLAS. The following data were used: • the measured concentration was a constant 200µg/m3; • the area source was a square with dimensions of 100m, 200m, and 500m on a side; • the fixed box height was 4 meters; • the sampler was located in the center of the area source 2 meters from the downwind edge and 1 meter high; • the wind speed was a varied based on the stability class and assumed to come from an ideal direction. The fluxes calculated with the box model were subsequently used as input for ISC to predict concentrations for the measured concentration location. The fluxes derived from use of the box model were considered to be conservative if the ISC results yielded concentrations higher than 200µg/m 3 .

Tests in a controlled laboratory environment were performed on three PM2.5 samplers: a FRM sampler with Wells Impactor Ninety-Six, a FRM sampler with Sharp-Cut Cyclone, and a High-Volume PM2.5 Sampler. Three dusts were used for sampling: alumina, corn starch, and wheat flour. Ten replications were performed for each sampler in each dust for a total of ninety replications. Concentration measurements for the test samplers were compared to the "true" PM2.5 concentrations, determined by multiplying the fraction less than 10 µm from the Coulter Counter PSD times the TSP concentration. The results showed the percent error of the PM2.5 samplers increased with the MMD of the dust sampled. The hypothesis was that the PM2.5 samplers used to monitor PM2.5 concentrations in the ambient air will not accurately perform in an agricultural environment. It was concluded that the use of these PM2.5 samplers would result in unfair regulation of the agricultural industry.

Cotton production continues to remain steady in Texas. Gin facilities have decreased in number through the decades. Fewer gins mean longer distances to haul seed cotton modules from field to gin. Transport of modules is limited to certain roads due to axle weight restrictions of module trucks. A study of alternative transport systems to include semi-tractor trailers is underway at Texas A&M University. Fewer gins also mean more bales to throughput per facility and longer ginning seasons or increased ginning rate. Efficiencies of operation will be essential for gin owners to con tinue making profits. A second study of three practical scenarios for a new seed cotton handling, storage and ginning system that would result in extended ginning seasons and reductions in production costs is underway at Texas A&M University.

Summary: Of the many environmental factors and stressors associated with clinical illness in cattle feedyards, fugitive dust is recognized as an important but ill-defined contributor. To date, little research has been conducted to quantify accurately the contribution of fugitive dust to the onset, duration and severity of respiratory disease in feedyard cattle. As a result, the art and science of conducting controlled experiments to quantify such effects are poorly developed. We report preliminary results of an attempt to correlate two independent means of estimating the cumulative exposure of livestock to dust in a semi-enclosed environment. In this experiment, a known quantity of simulated feedyard dust was manufactured from dried, sieved feedyard manure. The dust was delivered via a Venturi device into a leaky tent constructed over two sorting pens at a research feedyard in Bushland, TX. Particle-size distributions of the manufactured dust were determined using a Coulter Counter. High-volume PM10 samplers operated inside the tent for the duration of dust delivery. An analytical model of the tent system was derived to predict the average concentration of PM 10 in the air during the dust event. Model-predicted concentrations based on Coulter Counter analysis were a factor of 28.2 greater than the concentrations measured with EPA-designated Federal Reference Method PM10 samplers. The disparity between measured and predicted concentrations appears to be a result, in part, of the use of ultrasonic energy to suspend the dust samples in electrolyte for Coulter Counter analysis.

A biomass fueled fluidized bed gasifier was developed in the late 1980's. A patent was issued to Parnell and Lepori in 1988 at Texas A&M University for a system based upon fluidized bed gasification (FBG). Cotton gin trash and other biomass feedstock were used as fuel to generate heat energy for power production. These biomass fuels have low melting points and thus, gasification rather than combustion was deemed necessary. The relatively low price of conventional fuels in the 1990's resulted in limited progress in advancing gasification studies. The TAMU FBG can be operated on pyrolysis mode to produce liquid fuels such as bio-oils. With the price of conventional fuels steadily rising in recent years, attention has been focused on the use of the TAMU FBG for the production of valuable liquid fuels from biomass and particularly from cotton gin trash. The goal of this study was to evaluate the feasibility of producing high value liquid fuels such as kerosene (JP-8), diesel, and gasoline-like fuels from fluidized bed gasification of cotton gin trash. A 305 mm (1') diameter laboratory scale fluidized bed gasifier was used to produce the syngas for liquid fuel production. Novel zeolite catalysts will be used for the reforming process of the low calorific value gas in addition to using steam under high temperature and pressure. The liquid fuels produced will be analyzed using a gas chromatograph.