| The use of probabilistic analysis methods for dealing with variability and uncertainty is being more widely recognized and recommended in the development of emission factor and emission inventory. Probabilistic analysis provides decision-makers with quantitative information about the confidence with which an emission factor may be used. Variability refers to the heterogeneity of a quantity with respect to time, space, or different members of a population. Uncertainty refers to the lack of knowledge regarding the true value of an empirical quantity. Ignorance of the distinction between variability and uncertainty may lead to erroneous conclusions regarding emission factor and emission inventory. This dissertation extensively and systematically discusses methodologies associated with quantification of variability and uncertainty in the development of emission factors and emission inventory, including the method based upon use of mixture distribution and the method for accounting for the effect of measurement error on variability and uncertainty analysis. A general approach for developing a probabilistic emission inventory is presented. A few example case studies were conducted to demonstrate the methodologies. The case studies range from utility power plant emission source to highway vehicle emission sources. A prototype software tool, AUVEE, was developed to demonstrate the general approach in developing a probabilistic emission inventory based upon an example utility power plant emission source. A general software tool, AuvTool, was developed to implement all methodologies and algorithms presented in this dissertation for variability and uncertainty analysis. The tool can be used in any quantitative analysis fields where variability and uncertainty analysis are needed in model inputs. |
