Made available in DSpace on 2018-08-24T22:53:14Z (GMT). No. of bitstreams: 1 tese_4955_.pdf: 2595696 bytes, checksum: 9e5cf0372c78efe4f4b26fe602407955 (MD5) Previous issue date: 2011-04-15 / A presença de casas e prédios e outras construções próximas de fontes emissoras afeta o padrão de escoamento de ar e a dispersão dos contaminantes na camada limite superficial. Além disto, o tempo de média para o qual as concentrações médias do contaminante são determinadas depende da composição química do contaminante e do tempo de impacto causado. Por exemplo, para compostos odorantes, o tempo de média deve ser relacionado ao intervalo de tempo de uma inspiração (1 à 5s) ou ao intervalo de tempo para o qual os compostos odorantes causam efetivamente o incômodo. Uma das ferramentas empregadas nos estudos desses impactos são os modelos matemáticos que tem a capacidade de incluir o efeito da presença de obstáculos e de representar concentrações de curto período no escoamento. Devido à facilidade e rapidez em sua aplicação, os modelos gaussianos são muito empregados com adaptações incorporando o efeito de obstáculos e a representação de concentrações de curto período. O presente trabalho tem como objetivo avaliar os modelos CALPUFF e AERMOD, utilizando o modelo PRIME para considerar os efeitos da presença do obstáculo, e duas metodologias para a obtenção de concentrações de pico, uma que aplica um fator de ajuste no coeficiente de dispersão e outra que aplica um fator de ajuste diretamente nas concentrações para pequenos intervalos de tempo. Os resultados das modelagens foram comparados com dados obtidos em experimentos de túnel de vento, e mostraram os que os modelos tenderam a subestimar os valores de concentração próximos aos obstáculos, com o modelo AERMOD superestimando seus resultados em relação ao CALPUFF. Além disso, foi possível inferir que o modelo CALPUFF melhora o seu desempenho à medida que a distância em relação ao obstáculo aumenta. Já com relação ao modelo AERMOD, constatou-se que seus resultados melhores ocorrem parte nas regiões próximas do obstáculo, sendo superior ao CALPUFF na maioria dos casos. Entretanto, para maiores distâncias, os dois modelos estimaram resultados semelhantes. A análise das concentrações máximas médias para intervalos de tempo de curto período sugeriu que o ajuste aplicado diretamente nas concentrações nas previsões dos modelos AERMOD e CALPUFF não diferem substancialmente. Porém quando as duas metodologias são analisadas no CALPUFF, as maiores concentrações de pico são encontradas com o fator de ajuste aplicado diretamente nas concentrações, com uma diminuição da diferença entre as metodologias à medida que se tem intervalos de tempos maiores.

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Luftföroreningar skadar människors hälsa och orsakar miljöproblem. Luftföroreningar kommer från många olika källor och ett stort bidrag står transporter och industrier för. Förbränningsanläggningar är en sorts industrier som bidrar med föroreningar av exempelvis kväveoxider, partiklar och koldioxid. För att motverka att skadliga halter uppkommer finns miljökvalitetsnormerna som innehåller gränsvärden för olika luftföroreningar. För att en verksamhet ska få släppa ut föroreningar måste de utföra en tillståndsansökan. I ansökan ska det bevisas att verksamheten inte kommer bidra till att något av miljökvalitetsnormernas gränsvärden överskrids. Det utförs ofta spridningsberäkningar i form av simuleringar för att beräkna vilket haltbidrag utsläppen kommer generera. Dessa utredningar kan vara tidskrävande och i många fall överflödiga eftersom resultatet ofta visar att haltbidraget blir väldigt litet. Haltbidraget av kvävedioxid från mindre värmeverk och kraftvärmeverk har beräknatsmed hjälp av programmet AERMOD. Simuleringar har utförts för två olika platser i Sundsvall och det har undersökts hur mycket haltbidraget förändras när värden för olika parametrar varierar. Påverkan av rökgasens hastighet och temperatur har studerats tillsammans med egenskaper hos skorstenen i form av skorstenshöjd och diameter. I det här projektet har olika fall där spridningsberäkningar inte är nödvändiga identifierats för att förenkla processen för utredningar. Ett flödesdiagram tagits fram med syfte att kunna användas vid bedömning om beräkningar är nödvändiga eller inte för värmeverk. Beroende på värden för olika parametrar ger flödesdiagrammet en hänvisning om beräkningar krävs, noggrannare utredning behövs utföras eller om beräkningar inte krävs. Efter att olika fall studerats kunde slutsatser dras att för värmeverk och kraftvärmeverk med en skorstensdiameter större än 0,5 m, rökgashastighet över 10 m/s, rökgastemperatur över 50 ◦C och massflöde under 1,2 g/s krävs inga beräkningar då skorstenshöjden är 45 m eller högre. För dessa fall påverkade en förändring av någon parameter haltbidraget ytterst lite och alla haltbidrag blev väldigt låga. När skorstenshöjden var lägre än 15 m ansågs det att beräkningar alltid är nödvändiga eftersom små förändringar av någon parameter gav stora skillnader i haltbidraget. För skorstenshöjder mellan 15 och 45 m ansågs det att beräkningar inte alltid är nödvändiga men att mer noggranna utredningar krävs. Topografins och meteorologins påverkan på haltbidraget har också studerats genom att simuleringar utförts för två platser med olika omgivande topografin. Det kunde konstateras att med en komplex topografi blev haltbidraget överlag högre och sambandet mellan olika parametrar och haltbidraget frångick ibland den allmänna trenden. Slutsatsen drogs att noggranna beräkningar alltid bör utföras om topografin är komplex. / Air pollutions generate health problems to humans and have negative impact on the environment. Environmental quality standards in Sweden are based on requirements on various European Community directives. Combustion is a source for air pollution and a new plant must always prove that the pollution will not exceeded environmental quality standards. An investigation is therefore made and often modeling is performed to calculate how high the contribution from the plant will be. The process can be time consuming and sometimes not necessary because the result often shows that the contribution is very low. In this project a way to simplify the investigation by trying to find cases when calculation is not necessary have been made. The dispersion of nitrogen dioxide from a heating plant have been studied by simulations in the program AERMOD for two different places in Sundsvall. The impact of different parameters as gas velocity, gas temperature, stack height and stack diameter on the result have been studied together with the effect of different topographies. A flow chart has been constructed with the result from the simulations. The flow chart can be used to decide if calculation is necessary or not for different heating plants. From the results the conclusion was that for a heating plant with a stack diameter larger than 0.5 m, a gas velocity higher than 10 m/s, gas temperature over 50 ◦C and a mass flow lower than 1.2 g/s calculations are not needed for a stack higher than 45 m. For these cases the contribution from the plant is very low and also the risk for exceeding limits. A stack height lower than 15 m was considered as a case where calculation always is needed. For a height between 15 and 45 m the conclusion was made that more investigation is needed before a decision can be made. The study of how the topography affecting the dispersion of the pollution resulted in the conclusion that for a complex topography a careful investigation always is needed.

Sulfur dioxide is a primary pollutant and a known respiratory irritant. While there is a small level of background SO2, elevated concentrations are caused by industrial emissions. Muscatine, IA was designated as an area of nonattainment due to the persistent elevated levels of SO2 in the area. There are currently no available methods for predicting potential SO2 violations in Muscatine, and very little research was found investigating predictive modeling efforts. This thesis examines atmospheric conditions in Muscatine caused by SO2 emissions from facilities near the city. The main goals were to examine the plume dispersion model AERMOD for its ability to accurately map pollution levels, and to determine whether AERMOD could be used to predict SO2 concentrations when using meteorological forecast models as weather inputs. An historical analysis was performed using meteorological records from 2007 and AERMOD. The maximum emission limit was used in AERMOD. The resulting predicted concentrations were compared with concentrations reported at a monitoring site within the city. A forecasting analysis was also completed using two weather model forecasts (WRF and NAM) from March 2012 as meteorological input for AERMOD. Accurate daily SO2 emissions were obtained from each facility, and the corresponding rates were used in AERMOD. The resulting predicted concentrations were compared with monitored concentrations during the same time period. Overall, the historical analysis showed AERMOD's tendency to overestimate SO2 concentrations, particularly on days that also resulted in high monitored levels. The forecasting analysis resulted in favorable results with respect to the WRF weather forecast, but the NAM forecast created concentrations in AERMOD that were poorly correlated with monitored values. AERMOD still was likely to overestimate concentrations, but these overestimations were lessened due to more accurate emission information. Further research will be needed to further advance the prediction of pollution levels.

Impact of odour emissions from livestock operation sites on the air quality of neighboring areas has raised public concerns. A practical means to solve this problem is to set adequate setback distance. Air dispersion modeling was proved to be a promising method in predicting proper odour setback distance. Although a lot of air dispersion models have been used to predict odour concentrations downwind agricultural odour sources, not so much information regarding the capability of these models in odour dispersion modeling simulation could be found because very limited field odour data are available to be applied to evaluate the modeling result. A main purpose of this project was evaluating AERMOD and CALPUFF air dispersion models for odour dispersion simulation using field odour data.<p> Before evaluating and calibrating AERMOD and CALPUFF, sensitivity analysis of these two models to five major climatic parameters, i.e., mixing height, ambient temperature, stability class, wind speed, and wind direction, was conducted under both steady-state and variable meteorological conditions. It was found under steady-state weather condition, stability class and wind speed had great impact on the odour dispersion; while, ambient temperature and wind direction had limited impact on it; and mixing height had no impact on the odour dispersion at all. Under variable weather condition, maximum odour travel distance with odour concentrations of 1, 2, 5 and 10 OU/m3 were examined using annual hourly meteorological data of year 2003 of the simulated area and the simulation result showed odour traveled longer distance under the prevailing wind direction.<p> Evaluation outcomes of these two models using field odour data from University of Minnesota and University of Alberta showed capability of these two models in odour dispersion simulation was close in terms of agreement of modeled and field measured odour occurrences. Using Minnesota odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 3.6% applying conversion equation from University of Minnesota and 3.1% applying conversion equation from University of Alberta between two models. However, if field odour intensity 0 was not considered in Minnesota measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 3.1% applying conversion equation from University of Minnesota and 1.6% applying conversion equation from University of Alberta between two models. Using Alberta odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 0.7% applying conversion equation from University of Alberta and 1.2% applying conversion equation from University of Minnesota between two models. However, if field odour intensity 0 was not considered in Alberta measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 0.4% applying conversion equation from University of Alberta and 0.7% applying conversion equation from University of Minnesota between two models. Application of scaling factors can improve agreement of modeled and measured odour intensities (including all field odour measurements and field odour measurements without intensity 0) when conversion equation from University of Minnesota was used.<p> Both models were used in determining odour setback distance based on their close performance in odour dispersion simulation. Application of two models in predicting odour setback distance using warm season (from May to October) historical annul hourly meteorological data (from 1999 to 2002) for a swine farm in Saskatchewan showed some differences existed between models predicted and Prairie Provinces odour control guidelines recommended setbacks. Accurately measured field odour data and development of an air dispersion model for agricultural odour dispersion simulation purpose as well as acceptable odour criteria could be considered in the future studies.

Impact of odour emissions from livestock operation sites on the air quality of neighboring areas has raised public concerns. A practical means to solve this problem is to set adequate setback distance. Air dispersion modeling was proved to be a promising method in predicting proper odour setback distance. Although a lot of air dispersion models have been used to predict odour concentrations downwind agricultural odour sources, not so much information regarding the capability of these models in odour dispersion modeling simulation could be found because very limited field odour data are available to be applied to evaluate the modeling result. A main purpose of this project was evaluating AERMOD and CALPUFF air dispersion models for odour dispersion simulation using field odour data.<p> Before evaluating and calibrating AERMOD and CALPUFF, sensitivity analysis of these two models to five major climatic parameters, i.e., mixing height, ambient temperature, stability class, wind speed, and wind direction, was conducted under both steady-state and variable meteorological conditions. It was found under steady-state weather condition, stability class and wind speed had great impact on the odour dispersion; while, ambient temperature and wind direction had limited impact on it; and mixing height had no impact on the odour dispersion at all. Under variable weather condition, maximum odour travel distance with odour concentrations of 1, 2, 5 and 10 OU/m3 were examined using annual hourly meteorological data of year 2003 of the simulated area and the simulation result showed odour traveled longer distance under the prevailing wind direction.<p> Evaluation outcomes of these two models using field odour data from University of Minnesota and University of Alberta showed capability of these two models in odour dispersion simulation was close in terms of agreement of modeled and field measured odour occurrences. Using Minnesota odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 3.6% applying conversion equation from University of Minnesota and 3.1% applying conversion equation from University of Alberta between two models. However, if field odour intensity 0 was not considered in Minnesota measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 3.1% applying conversion equation from University of Minnesota and 1.6% applying conversion equation from University of Alberta between two models. Using Alberta odour plume data, the difference of overall agreement of all field odour measurements and model predictions was 0.7% applying conversion equation from University of Alberta and 1.2% applying conversion equation from University of Minnesota between two models. However, if field odour intensity 0 was not considered in Alberta measured odour data, the difference of overall agreement of all field odour measurements and model predictions was 0.4% applying conversion equation from University of Alberta and 0.7% applying conversion equation from University of Minnesota between two models. Application of scaling factors can improve agreement of modeled and measured odour intensities (including all field odour measurements and field odour measurements without intensity 0) when conversion equation from University of Minnesota was used.<p> Both models were used in determining odour setback distance based on their close performance in odour dispersion simulation. Application of two models in predicting odour setback distance using warm season (from May to October) historical annul hourly meteorological data (from 1999 to 2002) for a swine farm in Saskatchewan showed some differences existed between models predicted and Prairie Provinces odour control guidelines recommended setbacks. Accurately measured field odour data and development of an air dispersion model for agricultural odour dispersion simulation purpose as well as acceptable odour criteria could be considered in the future studies.

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.

This study tested the precision and accuracy of a triethanolamine (TEA) absorbent in the OgawaTM Passive Sampling Device (PSD) for detection of ambient hydrogen fluoride (HF). The project was initiated to develop a method to verify compliance with emissions regulations for Coronet. Field and laboratory trials were conducted. Mixed cellulose ester filters were saturated with 70% TEA and placed in the PSDs. Aermod ISCT3 modeled ambient HF concentrations at Coronet to guide deployment of PSDs at 28 sampling stations, 3 PSDs per station, 500 to 3500 meters from Coronet. After 30 days of sampling, ambient HF concentrations were calculated from ion chromatographic (IC) analysis (NIOSH Method 7906/AS14 column) results to be in the low parts per billion (ppb) range. Concentration increased with proximity to Coronet as predicted by Aermod ISCT3. Average precision for collocated PSDs was less than 5%. Laboratory validation of the method used a HF permeation tube in a Teflon and high density polyethylene (HDPE) sampling train with silica-dried ultra zero air and crushed sodium hydroxide (NaOH) reference samplers. PSD accuracy was a constant 23% average and average precision was 32%, dropping 50% with minor procedural improvements. Validated field results verified compliance with HF emissions regulations for Coronet.

Ambient monitoring and air quality modeling of air toxics concentrations at the neighborhood-scale level is a key element for human exposure and health risk assessments. Since 2005, The University of Texas at Austin (UT) has operated a dense ambient monitoring network that includes both hourly automated gas chromatographs as well as threshold triggered canister samples and meteorological data in the Corpus Christi area. Although Corpus Christi is in attainment with the National Ambient Air Quality Standards for both ozone and fine particulate matter, its significant petroleum refining complex has resulted in concerns about exposure to air toxics. The seven site network, incorporating both the industrial and residential areas in Corpus Christi, provided a unique opportunity to further the development and understanding of air quality modeling for toxic air pollutants at the neighborhood-scale level. Two air dispersion models, AERMOD and CALPUFF, were used to predict air concentrations of benzene for one of the UT operated monitoring sites (Oak Park monitoring site: C634) and the predictions were compared to the observed benzene concentration data at the Oak Park monitoring site to evaluate model performance. AERMOD and CALPUFF were also used to predict benzene concentrations in populated areas and at sensitive receptor locations such as schools and hospitals. Both AERMOD and CALPUFF were able to reproduce the early morning high benzene concentration and the northern wind effect except under strong NNE wind conditions, where the observed data indicated elevated high benzene concentration which AERMOD and CALPUFF failed to predict. These under-predictions could be due to the NNE strong wind condition at that time of these occurrences or could be attributed to different types of emissions other than the point sources emissions from the 2005 TCEQ Photochemical Modeling inventory, such as mobile sources or accidental emission events. These preliminary analyses could be expanded by modeling longer periods, by including other emission sources and by inter-comparisons with observed data from other CCNAT monitoring sites. In addition, fundamentally different modeling approaches (eulerian, rather than lagrangian) could be considered. / text