Bryan W. Shaw

PARTICLE SIZE DISTRIBUTION OF CATTLE FEEDLOT DUST EMISSION

The cattle feedlot industry is under increased scrutiny and regulatory involvement at state and national levels with regard to particulate matter (PM) emissions from fugitive sources. Concentrations of total suspended particulate matter (TSP) and PM less than 10 micrometers (PM10) aerodynamic equivalent diameter (AED) were measured, using high volume samplers and Sierra Andersen samplers, respectively. Particle size distributions of dust captured on sampler filters were measured with a Coulter Counter model TAII. Mass median diameters for high volume and PM10 samplers averaged 9.5 ± 1.5 and 6.9 ± 0.8 µm (AED), respectively. Three cattle feedlots (17,000 to 40,000 head capacity) in the Southern Great Plains were used in the study.

DESIGN AND EVALUATION OF A LOW-VOLUME TOTAL SUSPENDED PARTICULATE SAMPLER

The regulation of particulate matter (PM) emitted by agricultural sources, e.g., cotton gins, feed mills, and concentrated animal feeding operations (CAFOs), is based on downwind concentrations of particulate matter less than 10 and 2.5 .m (PM10 and PM2.5) aerodynamic equivalent diameter (AED). Both PM10 and PM2.5 samplers operate by pre-separating PM larger than the size of interest (10 and 2.5 .m) prior to capturing the PM on the filter. It has been shown that Federal Reference Method (FRM) PM10 and PM2.5 samplers have concentration measurement errors when sampling PM with mass median diameters (MMD) larger than the size of interest in ambient air. It has also been demonstrated that most PM from agricultural sources typically have particle size distributions with MMDs larger than 10 .m AED. The PM10 concentration measurement error can be as much as 343% for ambient PM with MMD = 20 .m. These errors are a consequence of the PM10 pre-separator allowing a larger mass of PM greater than 10 .m to penetrate to the filter than the mass of PM less than 10 .m captured by the pre-separator. The mass of the particles greater than 10 .m that are allowed to penetrate to the filter introduces a substantial error in the calculated concentration of PM10. Researchers have reported that sampling PM larger than 2.5 .m AED resulted in a shift in the cutpoint of the pre-separator. If this is true for all PM10 and PM2.5 samplers, then the resulting errors in measurement of ambient concentrations could be even larger. One solution to this problem is to measure the concentration of total suspended particulate (TSP) matter and calculate the concentration of PM10 by determining the mass fraction of PM less than size of interest from the particle size distribution (PSD). The “standard” high-volume TSP sampler operates at a volume rate-of-flow in excess of 1.13 m3 min-1 (40 ft3 min-1). Most of the current PM10 and PM2.5 samplers operate at 1 m3 h-1 (0.589 ft3 min-1). Other researchers reported that TSP samplers have a cutpoint of a nominal 45 .m AED. The U.S. EPA specifies the engineering design parameters for TSP samplers. This article reports the engineering design and evaluation of a low-volume (1 m3 h-1) TSP sampler (TSPLV). The results suggest that the new TSPLV may be more robust and more accurate than the “standard” high-volume TSP sampler.

Particulate matter sampler errors due to the interaction of particle size and sampler performance characteristics : Background and theory

The National Ambient Air Quality Standards (NAAQS) for particulate matter (PM), in terms of PM10 and PM2.5, are ambient air concentration limits set by the EPA that should not be exceeded. Further, state air pollution regulatory agencies (SAPRAs) utilize the NAAQS to regulate criteria pollutants emitted by industries by applying the NAAQS as a property-line concentration limit. The primary NAAQS are health-based standards; therefore, an exceedance implies that it is likely that there will be adverse health effects for the public. Prior to and since the inclusion of PM10 and PM2.5 into the EPAs regulation guidelines, numerous journal articles and technical references have been written to discuss the epidemiological effects, trends, regulations, methods of determining PM10 and PM2.5, etc. A common trend among many of these publications is the use of samplers to collect information on PM10 and PM2.5. Often, the sampler data are assumed to be an accurate measure of PM10 and PM2.5. The fact is that issues such as sampler uncertainties, environmental conditions, and characteristics of the material that the sampler is measuring must be incorporated for accurate sampler measurements. The purpose of this article is to provide the background and theory associated with particle size distribution (PSD) characteristics of the material in the air that is being sampled, sampler performance characteristics, the interaction between these two characteristics, and the effect of this interaction on the regulatory process. The results show that if the mass median diameter (MMD) of the PM to which the sampler is exposed is smaller than the cutpoint of the sampler, then under-sampling occurs. If the MMD of the PM is greater than the cutpoint of the sampler, then over-sampling occurs. The information presented in this article will be utilized in a series of articles dealing with the errors associated with particulate matter measurements.

Particulate Matter Sampler Errors Due to the Interaction of Particle Size and Sampler Performance Characteristics: Ambient PM2.5 Samplers

The National Ambient Air Quality Standards (NAAQS) for particulate matter (PM) in terms of PM2.5 are ambient air concentration limits set by the EPA to protect public health and well-being. Further, some state air pollution regulatory agencies (SAPRAs) utilize the NAAQS to regulate criteria pollutants emitted by industries by applying the NAAQS as property-line concentration limits. Prior to and since the inclusion of the PM2.5 standard, numerous journal articles and technical references have been written to discuss the epidemiological effects, trends, regulation, and methods of determining PM2.5. A common trend among many of these publications is the use of samplers to collect PM2.5 concentration data. Often, the sampler data are assumed to be accurate concentration measures of PM2.5. The fact is that issues such as sampler uncertainties, environmental conditions, and characteristics of the material that the sampler is measuring must be incorporated for accurate sampler measurements. The focus of this article is on the errors associated with particle size distribution (PSD) characteristics of the material in the air that is being sampled, the PM2.5 sampler performance characteristics, the interaction between these two characteristics, and the effect of this interaction on the regulatory process. Theoretical simulations were conducted to determine the range of errors associated with this interaction for the PM2.5 ambient air samplers. Results from the PM2.5 simulations indicated that a source emitting PM characterized by a mass median diameter (MMD) of 20 µm and a geometric standard deviation (GSD) of 1.5 could be forced to comply with a PM2.5 standard that is 14 times more stringent than that required for a source emitting PM characterized by an MMD of 10 µm and a GSD of 1.5, and 59 times more stringent than that required for a source emitting PM characterized by an MMD of 5.7 µm and a GSD of 1.5. Therefore, in order to achieve equal regulation among differing industries, PM2.5 measurements must be based on true concentration measurements.

Comparison of Dispersion Models for Ammonia Emissions from a Ground-Level Area Source

Dispersion models are important tools for determining and regulating pollutant emissions from many sources, including ground-level area sources such as feedyards, dairies, and agricultural field operations. This study compares the calculated emission fluxes of ammonia from a feedyard in the Texas panhandle using four dispersion models: Industrial Source Complex Short Term Version 3 (ISCST3), AERMOD-PRIME, WindTrax, and AUSTAL. ISCST3 and AERMOD are Gaussian plume models, while WindTrax and AUSTAL are backward and forward Lagrangian stochastic models, respectively. Identical measured downwind ammonia concentration data were entered into each model. The results of this study indicate that calculated emission rates and/or emission factors are model specific, and no simple conversion factor can be used to adjust emission rates and/or factors between models. Therefore, emission factors developed using one model should not be used in other models to determine downwind pollutant concentrations.

A THEORETICAL APPROACH FOR PREDICTING NUMBER OF TURNS AND CYCLONE PRESSURE DROP

A new theoretical method for computing travel distance, number of turns, and cyclone pressure drop has been developed and is presented in this article. The flow pattern and cyclone dimensions determine the travel distance in a cyclone. The effective number of turns was calculated based on the travel distance. Cyclone pressure drop is composed of five pressure loss components. The frictional pressure loss is the primary pressure loss in a cyclone. This new theoretical analysis of cyclone pressure drop for 1D2D, 2D2D, and 1D3D cyclones was tested against measured data at different inlet velocities and gave excellent agreement. The results show that cyclone pressure drop varies with the inlet velocity, but not with cyclone diameter.

Inherent biases of PM10 and PM2.5 samplers based on the interaction of particle size and sampler performance characteristics

Agricultural operations across the United States are encountering difficulties in complying with the current air pollution regulations for particulate matter. EPA has interpreted that the property line concentration limit must be less than the National Ambient Air Quality Standards (NAAQS). For PM10 and PM2.5, the 24-hour NAAQS are 150 and 65 mg/m 3 , respectively. Compliance with the PM NAAQS is determined by property line sampling, using EPA approved samplers, or dispersion modeling. Ultimately, these samplers would produce an accurate measure of the pollutant indicator for instance, a PM10 sampler would produce an accurate measure of PM less than or equal to 10 mm. However, samplers are not perfect and biases are introduced due to the interaction of the particle size and sampler performance characteristics. The focus of this manuscript is to theoretically simulate these biases and demonstrate how these biases result in unequal regulation between industries.

COMPARISON OF CONTINUOUS MONITOR (TEOM) AND GRAVIMETRIC SAMPLER PARTICULATE MATTER CONCENTRATIONS

Tapered element oscillating microbalance (TEOM) samplers may offer significant advantages to state air pollution regulatory agencies and air quality researchers in terms of reduced labor and data processing requirements through an automated particulate matter (PM) monitoring system. However, previous research has shown that TEOM samplers may not report accurate PM concentrations due to the operating characteristics of the automated system. This article presents the results of a multiyear study using collocated TEOM and gravimetric samplers configured to measure TSP concentrations from a Texas cattle feedlot. The objective of this work was to define the relationship between PM concentrations measured by TEOM and gravimetric samplers and characterize the influence of concentration intensity and particle size on that relationship. The results show that there was a significant positive linear relationship between the concentrations measured by the TEOM and gravimetric TSP samplers (p-values < 0.001). It was observed that in general, the TEOM samplers reported lower TSP concentrations than the collocated gravimetric TSP sampler. Further investigation into these results indicated that the difference in the concentration measured by the TEOM sampler versus the gravimetric TSP sampler (known as the TEOM measurement error) is correlated with the concentration measured by the gravimetric TSP sampler, but the nature of that relationship varies by location. However, linear relationships were observed between the measurement error of the TEOM samplers and the mass median diameter and geometric standard deviation of the collocated gravimetric TSP sample.

Effects of Cyclone Diameter on Performance of 1D3D Cyclones: Collection Efficiency

Cyclones are a common air pollution abatement device for separating particulate matter (PM) from air streams in industrial processes. Several mathematical models have been proposed to predict the performance of cyclones as cyclone diameter varies. The objective of this research was to determine the relationship between cyclone diameter and collection efficiency based on empirical data and to compare the results to those of four mathematical models. Tests were performed comparing cyclone collection efficiency of 15.24, 30.48, 60.96, and 91.44 cm (6, 12, 24, and 36 in.) diameter cyclones with poly-disperse PM having an aerodynamic mass median diameter (MMD) near 10 m. The PM chosen for this study was selected to magnify any differences in cyclone collection efficiency due to differences in cyclone barrel diameter. The mass of PM collected by the cyclones and the mass of PM that penetrated the cyclones was used to determine the collection efficiency of each cyclone. The collection efficiency of cyclones decreased nonlinearly as cyclone diameter increased, with statistically different collection efficiencies observed among the 30.48, 60.96, and 91.44 cm (12, 24, and 36 in.) diameter cyclones. None of the mathematical models analyzed in this article accurately predicted cyclone efficiency.

A process-based approach for ammonia emission measurements at a free-stall dairy

A protocol using flux chambers was employed to determine ammonia emission rates from different low level area sources (LLAS) including free stalls, open lots, manure composting areas, lagoons and separated solids in a central Texas dairy. Data including ammonia emissions from these sources were collected for summer and winter seasons of 2003. Ammonia concentration measurements were made using chemiluminescence-based analyzers. The estimated emission rates for the facility were 24.7±25.4 kg.day-1 for winter and 63.1 ±31.1 kg.day-1 for summer. This difference was due to temperature, loading rate of dairy waste, and bacterial activity of LLAS. The uncertainty analysis showed that 9.4% of ammonia sampling uncertainty was attributed to ammonia sensors, calibration gas impurity and air flow controllers. In winter, the compost and the free-stall contributed about 77% to the total emission rates for the facility. But in summer, 65% of overall ammonia emissions were contributed by two lagoons at the dairy. These results suggest that seasonally dependent best management practices may be needed to reduce annual average ammonia emissions from free stall dairies.