Chemical emissions from research and development (R&D) activities are difficult to estimate because of the large number of chemicals used and the potential for continual changes in processes. In this case study, stack measurements taken from R&D facilities at Pacific Northwest National Laboratory (PNNL) were examined, including extreme worst-case emissions estimates and alternate analyses using a Monte Carlo method that takes into account the full distribution of sampling results. The objective of this study was to develop techniques to estimate emissions from stack measurement data that take into account a high degree of variability in the actual emissions. The results from these analyses were then compared to emissions estimated from chemical inventories. Results showed that downwind ambient air concentrations calculated from the stack measurement data were below acceptable source impact levels (ASILs) for almost all compounds, even under extreme worst-case analyses. However for compounds with averaging periods of a year, the unrealistic but simplifying extreme worst-case analysis often resulted in calculated emissions that were above the lower level regulatory criteria used to determine modeling requirements or to define trivial releases. Compounds with 24-hr averaging periods were nearly all several orders of magnitude below all, including the trivial release, criteria. The alternate analysis supplied a more realistic basis of comparison and an ability to explore effects under different operational modes.