PNNL

Abstract

Emissions from research and development (R&D) facilities are difficult to characterize due to the wide variety of processes used, changing nature of research, and large number of chemicals. Positive matrix factorization (PMF) was applied to volatile organic compounds (VOCs) concentrations measured in the main exhaust stacks of four different R&D buildings to identify the number and composition of major contributing sources. PMF identified from 9-11 source-related factors contributing to the stack emissions depending on the building. The factors that were similar between buildings were major contributors to trichloroethylene (TCE), acetone, and ethanol emissions. Several other factors had similar profiles for two or more buildings but not for all four. One factor for each building was a combination of p/m-xylene, o-xylene and ethylbenzene. At least one factor for each building was identified that contained a broad mix of many species and constraints were used in PMF to modify the factors to resemble more closely the off-shift concentration profiles. PMF accepted the constraints with little decrease in model fit. Although the PMF model predicted the profiles of the off-shift samples, the percent of total emissions was under-predicted by the model versus the measured data.