Quantification of Variability and Uncertainty in Emission Factors and Emission Inventories

The quality of emission factors is typically described using data quality ratings. Such ratings are qualitative and provide no indication of the precision of the emission factor for an average emission source, nor of the variability in emissions from one source to another within a category. Advances in methodology and computing power enable the application of a quantitative approach to characterizing both variability and uncertainty in emission factors. Variability refers to actual differences in emissions from one source to another due to differences in feedstock composition, design, maintenance, and operation. Uncertainty refers to lack of knowledge regarding the true emissions because of measurement errors (both random and systematic), limited sample sizes (statistical random sampling error), and non-representativeness (which can introduce additional errors, including systematic errors). The set of numerical methods generically known as bootstrap simulation are a powerful tool for characterization of both variability and random sampling error. In this paper, the authors demonstrate the use of bootstrap simulation and related techniques for the quantification of variability and uncertainty for selected examples, including NO{sub x} emissions from coal-fired power plants and CO, NO{sub x}, and hydrocarbon emissions for light duty gasoline vehicles. While examples are focused upon emission factors formore » selected criteria pollutants or their precursors, the same methodology can be applied to other pollutants (e.g., hazardous air pollutants, greenhouse gases). The work described here was conducted at North Carolina State University in a project sponsored by the US Environmental Protection Agency.« less