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Quantitative Determination of Methanol and Ethanol with Synthetic Calibration Spectra in Passive Fourier Transform Infrared Remote Sensing Measurements

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Abstract:

A spectral synthesis strategy is introduced to help obtain estimates of path-integrated concentrations in passive Fourier transform infrared (FT-IR) remote sensing measurements conducted during field-monitoring experiments. Obtaining quantitative information from passive infrared data is challenging because of the joint effects of temperature and concentration on spectral intensities. The collection of calibration data for use in modeling spectral intensities for a given set of experimental conditions is also costly and labor intensive. In the work presented here, a radiance model is defined for use in synthesizing calibration spectra that serve as inputs to partial least-squares (PLS) models that relate spectral intensities to path-integrated concentrations. The field data for which quantitative estimates are desired are used to estimate the background temperature associated with a given time and set of experimental conditions. Sample temperatures can be obtained through either experimental measurement or by estimating one calibration release. Given these temperatures, calibration data can be synthesized and the PLS model developed. This methodology is tested with stack monitoring data obtained from controlled releases of pure and mixture samples of heated ethanol and methanol. Experiments were conducted across 6 days with stack temperatures of 150 to 200 °C and with path-integrated concentrations in the range of 10 to 300 parts per million meters. Median relative errors in the estimates of path-integrated concentration were typically in the range of 20% or less, with the best results observed at the higher stack temperatures.

Keywords: Calibration; Partial least-squares; Passive infrared; Remote sensing; Volatile organic compounds

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/12-06807

Affiliations: Department of Chemistry and Optical Science and Technology Center, University of Iowa, Iowa City, IA 52242 USA

Publication date: August 1, 2013

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