Emission factors (EFs) and results from dispersion models are key components in the air pollution regulatory process. The EPA preferred regulatory model changed from ISCST3 to AERMOD in November, 2007. Emission factors are used in conjunction with dispersion models to predict 24-hour concentrations that are compared to National Ambient Air Quality Standards (NAAQS) for determining the required control systems in permitting sources. This change in regulatory models has had an impact on the regulatory process and the industries regulated. In this study, EFs were developed for regulated particulate matter PM10 and PM2.5 from cotton harvesting. Measured concentrations of TSP and PM10 along with meteorological data were used in conjunction with the dispersion models ISCST3 and AERMOD, to determine the emission fluxes from cotton harvesting. The goal of this research was to document differences in emission factors as a consequence of the models used. The PM10 EFs developed for two-row and six-row pickers were 154 + 43 kg/km2 and 425 + 178 kg/km2, respectively. From the comparison between AERMOD and ISCST3, it was observed that AERMOD EFs were 1.8 times higher than ISCST3 EFs for Emission factors (EFs) and results from dispersion models are key components in the air pollution regulatory process. The EPA preferred regulatory model changed from ISCST3 to AERMOD in November, 2007. Emission factors are used in conjunction with dispersion models to predict 24-hour concentrations that are compared to National Ambient Air Quality Standards (NAAQS) for determining the required control systems in permitting sources. This change in regulatory models has had an impact on the regulatory process and the industries regulated. In this study, EFs were developed for regulated particulate matter PM10 and PM2.5 from cotton harvesting. Measured concentrations of TSP and PM10 along with meteorological data were used in conjunction with the dispersion models ISCST3 and AERMOD, to determine the emission fluxes from cotton harvesting. The goal of this research was to document differences in emission factors as a consequence of the models used. The PM10 EFs developed for two-row and six-row pickers were 154 + 43 kg/km2 and 425 + 178 kg/km2, respectively. From the comparison between AERMOD and ISCST3, it was observed that AERMOD EFs were 1.8 times higher than ISCST3 EFs for absence of solar radiation. Using AERMOD predictions of pollutant concentrations off property for regulatory purposes will likely affect a source?s ability to comply with limits set forth by State Air Pollution Regulatory Agencies (SAPRAs) and could lead to inappropriate regulation of the source.

The EPA press release dated February 23, 2004 states that the three Buckeye Egg Farm facilities had the potential to emit more than a combined total of 1850 tons per year of particulate matter (PM). This number was based on flowrate calculations that were three times higher than those measured as well as a failure to include particle size distributions in the emissions calculations. The annual PM emission for each facility was approximately 35 tons per year. The EPA was unjustified in requiring Buckeye Egg Farm to obtain Title V and PSD permits as the facilities could not have met the thresholds for these permits. Engineers need to be concerned with correctly measuring and calculating emission rates in order to enforce the current regulations. Consistency among regulators and regulations includes using the correct emission factors for regulatory permitting purposes. EPA has adopted AERMOD as the preferred dispersion model for regulatory use on the premise that it more accurately models the dispersion of pollutants near the surface of the Earth than ISCST3; therefore, it is inappropriate to use the same emission factor in both ISCST3 and AERMOD in an effort to equitably regulate PM sources. For cattle feedlots in Texas, the ISCST3 emission factor is 7 kg/1000 hd-day (16 lb/1000 hd-day) while the AERMOD emission factor is 5 kg/1000 hd-day (11 lb/1000 he-day). The EPA is considering implementing a crustal exclusion for the PM emitted by agricultural sources. Over the next five years, it will be critical to determine a definition of crustal particulate matter that researchers and regulators can agree upon. It will also be necessary to develop a standard procedure to determine the crustal mass fraction of particulate matter downwind from a source to use in the regulatory process. It is important to develop a procedure to determine the particulate matter mass fraction of crustal downwind from a source before the crustal exclusion can be implemented to ensure that the exclusion is being used correctly and consistently among all regulators. According to my findings, the mass fraction of crustal from cattle feedlot PM emissions in the Texas High Plains region is 52%.

An engineering analysis of the fugitive particulate matter emissions from a feedyard is not simple. The presence of an evening dust peak in concentration measurements downwind of a feedyard complicates the calculation of an average 24-h emission flux for the feedyard. The evening dust peak is a recurring event that occurs during evening hours when particulate matter concentration measurements increase and decrease dramatically during a short period of time. The concentrations measured during the evening can be up to 8 times the concentrations measured throughout the rest of the day. There is a perception that these concentration increases are due to increases in cattle activity as the temperature decreases during the evening. The purpose of Objective 1 of this research was to quantify the changes in concentrations based on changes in meteorological conditions and/or cattle activity. Using ISCST3, a Gaussian-based EPAapproved dispersion model used to predict concentrations downwind of the feedyard , the results of this work indicate that up to 80% of the increase in concentrations can be attributed to changes in meteorological conditions (wind speed, stability class, and mixing height.)The total fugitive particulate matter emissions on a cattle feedyard are due to two sources: unpaved roads (vehicle traffic) and pen surfaces (cattle activity). Objective 2 of this research was to quantify the mass fraction of the concentration measurements that was due to unpaved road emissions (vehicle traffic). A recent finding by Wanjura et al. (2004) reported that as much as 80% of the concentrations measured after a rain event were due to unpaved road emissions. An engineering analysis of the potential of the unpaved road emissions versus the total feedyard emissions using ISCST3 suggests that it is possible for 70 to 80% of the concentration measurements to be attributed to unpaved road emissions. The purpose of Objective 3 was to demonstrate the science used by ISCST3 to predict concentrations downwind of an area source. Results from this study indicate that the ISCST3 model utilizes a form of the Gaussian line source algorithm to predict concentrations downwind of an area source.

The EPA press release dated February 23, 2004 states that the three Buckeye Egg Farm facilities had the potential to emit more than a combined total of 1850 tons per year of particulate matter (PM). This number was based on flowrate calculations that were three times higher than those measured as well as a failure to include particle size distributions in the emissions calculations. The annual PM emission for each facility was approximately 35 tons per year. The EPA was unjustified in requiring Buckeye Egg Farm to obtain Title V and PSD permits as the facilities could not have met the thresholds for these permits. Engineers need to be concerned with correctly measuring and calculating emission rates in order to enforce the current regulations. Consistency among regulators and regulations includes using the correct emission factors for regulatory permitting purposes. EPA has adopted AERMOD as the preferred dispersion model for regulatory use on the premise that it more accurately models the dispersion of pollutants near the surface of the Earth than ISCST3; therefore, it is inappropriate to use the same emission factor in both ISCST3 and AERMOD in an effort to equitably regulate PM sources. For cattle feedlots in Texas, the ISCST3 emission factor is 7 kg/1000 hd-day (16 lb/1000 hd-day) while the AERMOD emission factor is 5 kg/1000 hd-day (11 lb/1000 he-day). The EPA is considering implementing a crustal exclusion for the PM emitted by agricultural sources. Over the next five years, it will be critical to determine a definition of crustal particulate matter that researchers and regulators can agree upon. It will also be necessary to develop a standard procedure to determine the crustal mass fraction of particulate matter downwind from a source to use in the regulatory process. It is important to develop a procedure to determine the particulate matter mass fraction of crustal downwind from a source before the crustal exclusion can be implemented to ensure that the exclusion is being used correctly and consistently among all regulators. According to my findings, the mass fraction of crustal from cattle feedlot PM emissions in the Texas High Plains region is 52%.

PM2.5 variability within the neighborhood scale has not been thoroughly studied for wood burning communities. High variability in near-field PM2.5 concentration may lead to harmful public exposure since monitoring does not occur on that scale. This study measures near-field PM2.5 variability by measuring black carbon (BC), a component of PM2.5, in a 1 km2 area located in Cambria, California. BC and meteorological data (when meteorological instruments were available) were measured over thirteen 12-hour intensive operation periods (IOPs) occurring over the winters of 2009 and 2010. Near-field BC variability was measured to understand the type of exposures found in communities where many homes are burning wood simultaneously within a small area. In addition, relationships between meteorological, geographical, and burning source characteristics and BC were observed as tools for understanding BC concentration. The computer air dispersion modeling programs, ISC-PRIME and ISCST3, were also evaluated for applicability to the near field. BC concentrations were measured using 1- to 2-minute resolution aethalometers and 12 hour resolution Personal Environmental Monitors (PEMs). On average, over all IOPs and sites, aethalometer and PEM BC averages were very similar, ranging between 200 and 250 ng/m3, or 4 and 5 µg/m3 for PM2.5, and standard deviations were often high. Averaging all BC measurements, aethalometer BC standard deviation values were 360 percent of the average BC concentration and PEM BC standard deviations were 120 percent the average BC concentration. The average standard deviation detected during each IOP was 190 percent of the average BC concentration for aethalometers and 79 percent of the average BC concentration for PEMs. The average standard deviation detected at each site was 220 percent of the average BC concentration for aethalometers and 76 percent of the average BC concentration for PEMs. The larger standard deviations measured by higher resolution aethalometers demonstrated that low resolution instruments, such as PEMs, are unable to detect high concentrations that may occur. In addition to examining BC variability, multiple linear regression analyses were conducted to determine the impact of meteorological variables and geographic and burning source characteristics on BC concentration and a weighted BC deviation function (BC standard deviation divided by average BC concentration). Time impacts, humidity, and wind speed, accounted for about 50 percent of variability in aethalometer average BC and BC deviation. However, because all model assumptions were not satisfied, improvements are needed. Regression models based on PEM BC found wind speed and direction to account for about 80 percent of average PEM BC variability and number of burning sources to account for about 30 percent of PEM BC deviation. Although PEM BC models accounted for a high percentage of BC variability, few data points were available for the PEM analyses and more IOPs are needed to determine their accuracy. When evaluating correlations between geographic and burning source characteristics and PEM BC concentrations, specific IOP and PEM sampling location explained almost 70 percent of variability in BC concentration, though model residuals suggested model bias. IOP likely explained variation in burning patterns and meteorology over each night while sampling location was likely a proxy for housing density, tree coverage, and/or elevation. Because all regression model assumptions could not be satisfied, the predictors were also observed graphically. Plotting BC concentration versus the number of burning sources suggested that number of burning sources may affect BC concentration in areas of low tree coverage and high housing density and in the case that the level of surrounding vegetation and structures are minimal. More data points will be needed to determine whether or not these relationships are significant. ISC-PRIME and ISCST3 modeling overall tended to under predict BC concentrations with average modeled-to-measured ratios averaging 0.25 and 0.15, for ISC-PRIME and ISCST3, respectively. Correction factors of 9.75 and 18.2 for ISC-PRIME and ISCST3, respectively, were determined to bring modeled BC concentrations closer to unity, but the range of ratios was still high. Both programs were unable to consistently capture BC variability in the area and more investigation will be needed to improve models. The results of the study indicate high BC variability exists on the near-field scale, but that the variability is not clearly explained by existing regression and air dispersion models. To prevent public exposure to harmful concentrations, more investigation will be needed to determine factors that largely influence pollutant variability on the neighborhood scale.

No description available.

In this study, emission inventory was prepared and pollutant dispersion studies were carried out for the area around &Ccedil / ayirhan Thermal Power Plant to determine the effects of the plant on the environment. Stack gas measurement results were used for the emissions from the power plant and emission factors were used for calculating the emissions from residential sources and coal stockpiles in the study region. Ground level concentrations of SO2, NOx and PM10 were estimated by using EPA approved dispersion models / namely ISCST3 and AERMOD. The ground level concentrations predicted by two models were compared with the results of ambient air pollution measurements for November 2004. Predictions of both ISCST3 and AERMOD were underestimating the ground level SO2 concentrations. However, AERMOD predictions are better than ISCST3 predictions. The results of both models had good correlation with the results of NOx measurements. It has been shown that the contribution of the power plant to SO2, NOx and PM10 pollution in the area studied is minimal.

Odor problems are a common complaint from residents living near landfills. Many compounds can cause malodorous conditions. However, hydrogen sulfide (h2s) has been identified as a principal odorous component from construction and demolition (c&d)debris landfills. Although several studies have reported the ambient concentrations of h2s near c&d landfills, few studies have quantified emission rates of h2s. The most widely used and proven technique for measuring gas emission rates from landfills is the flux chamber method. Typically the flux chamber is a cylindrical enclosure device with a spherical top which limits the gas emission area. Pure zero grade air is introduced into the chamber, allowed to mix with emitting gases captured from the landfill surface, and then transported to the exit port where concentrations can be measured. Flux measurements using the flux chamber were performed at five different c&d landfills from june to august, 2003. The flux rates of h2s measured in this research were three to six orders of magnitude lower than the flux rates of methane reported in the literature. In addition to the h2s flux measurements, dispersion modeling was conducted, using the epa dispersion model, industrial source complex short term (iscst3), in order to evaluate impacts on landfill workers and communities around the landfills. The modeling results were analyzed to estimate the potential ground level maximum h2s concentrations for 1-hr and 3-min periods and the frequency (occurrences per year) above the h2s odor detection threshold for each landfill. Odor complaints could be expected from four among five landfills selected for this study, based on 0.5-ppb odor detection threshold.