Paper
31 May 1995 Estimation of VOC emission rates from FTIR measurements and whole-air canister data
Ray E. Carter Jr., Dennis D. Lane, Glen A. Marotz, Michael F. Davis, Jody L. Hudson, Charles T. Chaffin Jr., Timothy L. Marshall, Robert M. Hammaker, William G. Fateley
Author Affiliations +
Proceedings Volume 2365, Optical Sensing for Environmental and Process Monitoring; (1995) https://doi.org/10.1117/12.210797
Event: Optical Sensing for Environmental and Process Monitoring, 1994, McLean, VA, United States
Abstract
Methods of estimating VOC emission rates from a point source are being field tested by the University of Kansas, in cooperation with Region VII of the U.S. EPA and Kansas State University. The methods use path-integrated VOC concentrations, meteorological data, and a form of the Gaussian dispersion equation. VOC concentrations were derived both from a whole-air canister sampling method, with subsequent GC analysis, and from open-path FTIR measurements; estimated emission rates produced from the two analytical methods were compared. Canister-derived concentrations provided higher mean estimation accuracies than did FTIR measurements for both 1, 1, 1-Trichloroethane and toluene; however, for a third data set consisting of all other compounds released, FTIR measurements provided higher values. Estimation accuracy also was evaluated as a function of atmospheric stability and downwind distance; accuracy generally increased and variability decreased as stability increased; accuracy was better at longer than at 50 meters.
© (1995) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ray E. Carter Jr., Dennis D. Lane, Glen A. Marotz, Michael F. Davis, Jody L. Hudson, Charles T. Chaffin Jr., Timothy L. Marshall, Robert M. Hammaker, and William G. Fateley "Estimation of VOC emission rates from FTIR measurements and whole-air canister data", Proc. SPIE 2365, Optical Sensing for Environmental and Process Monitoring, (31 May 1995); https://doi.org/10.1117/12.210797
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KEYWORDS
Data modeling

FT-IR spectroscopy

Atmospheric modeling

Meteorology

Performance modeling

Statistical modeling

Statistical analysis

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