Advances in methodology and computing power enable the application of a quantitative approach to characterizing both variability and uncertainty in air pollutant emission inventories. 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). In this talk, we describe the current status of ongoing work at NC State to characterize variability and uncertainty in emission factors, activity factors, and emission inventories. We will also focus on a brief description of the probabilistic methodology for quantification of variability and uncertainty, and we will demonstrate the methodology and the insights obtained from it by application to a case study.