Comparison of stack measurement data from R&D facilities to regulatory criteria: a case study from PNNL.

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.

Estimating air chemical emissions from research activities using stack measurement data.

UNLABELLED: Current methods of estimating air emissions from research and development (R&D) activities use a wide range of release fractions or emission factors with bases ranging from empirical to semi-empirical. Although considered conservative, the uncertainties and confidence levels of the existing methods have not been reported. Chemical emissions were estimated from sampling data taken from four research facilities over 10 years. The approach was to use a Monte Carlo technique to create distributions of annual emission estimates for target compounds detected in source test samples. Distributions were created for each year and building sampled for compounds with sufficient detection frequency to qualify for the analysis. The results using the Monte Carlo technique without applying a filter to remove negative emission values showed almost all distributions spanning zero, and 40% of the distributions having a negative mean. This indicates that emissions are so low as to be indistinguishable from building background. Application of a filter to allow only positive values in the distribution provided a more realistic value for emissions and increased the distribution mean by an average of 16%. Release fractions were calculated by dividing the emission estimates by a building chemical inventory quantity. Two variations were used for this quantity: chemical usage, and chemical usage plus one-half standing inventory. Filters were applied so that only release fraction values from zero to one were included in the resulting distributions. Release fractions had a wide range among chemicals and among data sets for different buildings and/or years for a given chemical. Regressions of release fractions to molecular weight and vapor pressure showed weak correlations. Similarly, regressions of mean emissions to chemical usage, chemical inventory, molecular weight, and vapor pressure also gave weak correlations. These results highlight the difficulties in estimating emissions from R&D facilities using chemical inventory data. IMPLICATIONS: Air emissions from research operations are difficult to estimate because of the changing nature of research processes and the small quantity and wide variety of chemicals used. Analysis of stack measurements taken over multiple facilities and a 10-year period using a Monte Carlo technique provided a method to quantify the low emissions and to estimate release fractions based on chemical inventories. The variation in release fractions did not correlate well with factors investigated, confirming the complexities in estimating R&D emissions.

Scaled tests and modeling of effluent stack sampling location mixing.

A three-dimensional computational fluid dynamics computer model was used to evaluate the mixing at a sampling system for radioactive air emissions. Researchers sought to determine whether the location would meet the criteria for uniform air velocity and contaminant concentration as prescribed in the American National Standards Institute standard, Sampling and Monitoring Releases of Airborne Radioactive Substances from the Stacks and Ducts of Nuclear Facilities. This standard requires that the sampling location be well-mixed and stipulates specific tests to verify the extent of mixing. The exhaust system for the Radiochemical Processing Laboratory was modeled with a computational fluid dynamics code to better understand the flow and contaminant mixing and to predict mixing test results. The modeled results were compared to actual measurements made at a scale-model stack and to the limited data set for the full-scale facility stack. Results indicated that the computational fluid dynamics code provides reasonable predictions for velocity, cyclonic flow, gas, and aerosol uniformity, although the code predicts greater improvement in mixing as the injection point is moved farther away from the sampling location than is actually observed by measurements. In expanding from small to full scale, the modeled predictions for full-scale measurements show similar uniformity values as in the scale model. This work indicated that a computational fluid dynamics code can be a cost-effective aid in designing or retrofitting a facility's stack sampling location that will be required to meet standard ANSI/HPS N13.1-1999.