Sulfur Dioxide (SO2) is a difficult air pollutant to characterize spatially since it is primarily emitted from a few point sources typically having tall stacks. A better comprehension of the behavior and advection of ambient SO2 in metro Atlanta will help in the interpretation of epidemiologic analyses as well as establish an understanding of the source contributions to ambient SO2 in Atlanta. The operation and SO2 emission characteristics of four coal-fired power plants and a coal-fired cement kiln, all of which lie in the vicinity of Atlanta, were examined. Data retrieved from three downtown Atlanta monitoring stations that record ambient SO2 concentrations were also examined. Trends from ambient SO2 data agree with emission trends from the four coal-fired power plants, suggesting that one or more of the power plants are contributing to the ambient SO2 in Atlanta. SO2 rose plots using concentration and wind direction data from downtown monitoring stations were developed to identify from which direction the elevated levels of ambient SO2 were originating. A strong peak in the northwest direction of Atlanta suggests that Plant Bowen, Plant McDonough, or Lafarge Building Materials may be contributing to high concentrations of ambient SO2 in Atlanta. Further analysis concluded that Lafarge was not a likely contributor to the northwest peak. The plumes of Plant Bowen and Plant McDonough were modeled using air parcel trajectories and the Gaussian dispersion model. The results suggest that, when the wind is blowing from the northwest direction, Plant McDonoughs plume is the primary contributor to the elevated levels of SO2 recorded by downtown Atlanta monitoring stations.

Houston-Galveston-Brazoria area is in severe non-attainment status for ozone compliance. Source-oriented mechanistic modeling was used to determine the major sources of VOCs that contributes to ozone formation during the Texas Air Quality Study (TexAQS) from August 16, 2000 to September 7, 2000. Environmental Protection Agency (EPA)?s Community Scale Air Quality Model (CMAQ) version 4.6 was used as a host model to include a revised Statewide Air Pollution Research Center (SAPRC99) photochemical mechanism with source-oriented extensions to track the contributions of Volatile Organic Compounds (VOCs) emissions from diesel engines, biogenic sources, highway gasoline vehicles, fuel combustion, off-highway gasoline engines, solvent utilization and petrochemical industries to ozone formation in the atmosphere. Source-oriented emissions needed to drive the model were generated using a revised Sparse Matrix Operator Kernel Emissions (SMOKE) model version 2.4. VOC/NOx ratios are found to be a critical factor in the formation of ozone. Highest ozone formation rates were observed for ratios from 5-15. The contributions of VOC to ozone formation were estimated based on the linear relationship between the rate of NO to NO2 conversion due to radicals generated from VOC oxidation and the rate of net ozone formation. Petroleum and other industrial sources are the largest anthropogenic sources in the urban Houston region and contribute to 45% of the ozone formation in the HGB area. Highway gasoline vehicles make contributions of approximately 28% to ozone formation. Wildfires contribute to as much 11% of ozone formation on days of high wildfire activity. The model results show that biogenic emissions account for a significant amount of ozone formation in the rural areas. Both highway and off-highway vehicles contribute significantly to ozone formation especially in the downwind region. Diesel vehicles do not contribute significantly to ozone formation due to their low VOC emissions.

The air quality in Kaohsiung and Ping-Dong district is the worst in Taiwan. The air pollution episodes in Kaohsiung are attributed to high concentrations of PM10 and O3. Among them, over half of the episodes result from PM10. In addition to Pollutant Standards Index (PSI), atmospheric visibility is also an indicator of ambient air quality. Citizens always complain about the impairment of visibility because it can be visualized directly. Visibility is closely correlated to both air pollutants and meteorological condition. Extinction of visible light by fine particles is the major reason for visibility impairment. In this study, an artificial neural network was applied to predict the concentration of PM10 and atmospheric visibility. The objectives of this study were to investigate the effects of meteorological factor and air pollutants on visibility and to apply artificial neural network to predict the concentration of PM10 and atmospheric visibility. The measured PM10 data were divided into two parts (i.e. summer and winter, ) to understand whether different season affect the prediction of PM10 concentration. The modeling results showed that the optimum input variables included the PM10 concentration, atmospheric pressure, surface radiation, relative humidity, atmospheric temperature, and cloud condition. The network outputs showed high correlation with measured PM10 concentration (R=0.876) in the whole-year set. Furthermore, the prediction of summer set also showed high correlation with measured PM10 concentration (R=0.753). The winter set demonstrated the worse prediction among three sets, and showed medium correlation with measured PM10 concentration (R=0.553). The visibility network test was conducted by two stages. The first stage (set-1~set-3) showed that relative humidity, atmospheric temperature, and cloud condition were the most important meteorological factors, while PM10, O3, and NO3 were the most important air pollutants on the prediction of atmospheric visibility. The prediction of set-1 considering only meteorological factors was the worst (R=0.586), while set-3 was the best and showed medium correlation with measured atmospheric visibility (R=0.633). The second stage (set-4 and set-5) increased the hidden neuron numbers and input variables, and added atmospheric visibility in the input variables. Although the correlation coefficients between predicted and measured data did not increase, the prediction of atmospheric visibility had significant improvement. Finally, a short-term prediction of PM10 and atmospheric visibility was conducted and validated by the level of PSI values and atmospheric visibility. Prediction results showed that the accuracy of PM10 prediction was 76.9%, while the prediction of atmospheric visibility by set-3 network demonstrated an accuracy of 76.9%. Moreover, no significant difference of prediction was detected by using either three-level or five-level visibility systems.

Thesis (Ph.D.)--University of Texas at Arlington, 2009.

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International.

Thesis (M.S.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains xi, 96 p. : ill. (some col.), map. Includes abstract. Includes bibliographical references (p. 75).

Thesis (Ph. D.)--York University, 1999. Graduate Programme in Earth and Space Science. / Typescript. Includes bibliographical references (leaves 249-265). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ39304.

Thesis (M.A.)--University of Hong Kong, 2001. / Includes bibliographical references.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 76-77). Also available in electronic version. Access restricted to campus users.

Not available / text