Incorporating Uncertainties in Emission Inventories Into Air Quality Modeling

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dc.contributor.advisor E. Downey Brill, Committee Member en_US
dc.contributor.advisor H. Christopher Frey, Committee Chair en_US
dc.contributor.advisor Hugh Devine, Committee Member en_US
dc.contributor.advisor Dan Loughlin, Committee Member en_US
dc.contributor.author Abdel-Aziz, Amr Mohamed Osama en_US
dc.date.accessioned 2010-04-02T18:30:30Z
dc.date.available 2010-04-02T18:30:30Z
dc.date.issued 2003-01-13 en_US
dc.identifier.other etd-01122003-143835 en_US
dc.identifier.uri http://www.lib.ncsu.edu/resolver/1840.16/3479
dc.description.abstract In modeling ambient ozone concentrations, NOx emissions estimated based on emission inventories are used as input to air quality models. A concern regarding the quality of ozone predictions is the uncertainty inherent in emission inventories. This work aims at developing new methodologies for quantifying uncertainty in emission inventories and propagating the uncertainty through a photochemical grid air quality model. Time series techniques were used to develop new methodologies for developing probabilistic emission inventories. These methodologies were applied to a case study for NOx emissions for each of 32 units of 9 coal-fired power plants in the Charlotte domain, North Carolina. Probabilistic inventories for a near-term future episode based on the data of the year 1995 and for a distant future episode in 2007 based on the data of the year 1998 were developed. In order to investigate how much of an effect does correlation between emissions from different units has on the developed probabilistic inventory, two different approaches were used. Univariate time series techniques were applied in the first where each unit is assumed to be dispatched independently of all other units. Multivariate time series techniques were applied in the second in order to account for the inter-unit dependence in developing the inventory. A methodology for accounting for intra-unit dependence between variables used in estimating the inventory was also developed. For the first approach, the 1995 case showed that the 95% confidence interval for the daily inventory lied between 562 t/d and 698 t/d. This represents approximately ±10% uncertainty range from the average value which is 639 t/d. The daily inventory for the 2007 case showed an uncertainty range of ±8% of the average value which is 192 t/d. The second approach showed that the total daily inventory for the year 1995 had a 95% confidence interval of 548 to 778 t/d, corresponding to an uncertainty range of -15% to +22% of the average value while the 2007 case showed an uncertainty range of -8% to +15% of the average value. Comparison of the simulated results of the two approaches with observations showed that the dependent approach produced a distribution for uncertainty that more accurately represents the observed data. Both inventories were used as input to an air quality model to investigate the propagation of uncertainties in emission inputs through the model. Uncertainties in the maximum 1-hour and 8-hour ozone predictions were estimated at different locations in the modeling domain. Forty-three grid cells were estimated to have a probability greater than 0.9 that the maximum hourly ozone concentration exceeds the 120 ppb 1-hour ozone standard. A similar analysis was conducted for the 8-hour ozone standard where 1654 grid cells showed a probability greater than 0.9 of exceeding the 80 ppb standard. The results of the case study demonstrate that the range of hourly variability in power plant emissions is sufficient large to justify a quantitative analysis of uncertainty, and that the range of uncertainty in air quality predictions is large enough to imply ambiguity regarding development of control strategies. The developed methodologies are very useful tools for decision making. These methodologies can be employed to develop control strategies to achieve attainment with an acceptable degree of confidence, such as 90 or 95 percent. en_US
dc.rights I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. en_US
dc.subject Ozone en_US
dc.subject NOx Emissions en_US
dc.subject Emissions Modeling en_US
dc.subject Emission Inventories en_US
dc.subject Air Quality Modeling en_US
dc.subject Uncertainty en_US
dc.title Incorporating Uncertainties in Emission Inventories Into Air Quality Modeling en_US
dc.degree.name PhD en_US
dc.degree.level dissertation en_US
dc.degree.discipline Civil Engineering en_US


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