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Quantitative Analysis of Sulfur Dioxide with Passive Fourier Transform Infrared Remote Sensing Interferogram Data

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Abstract:

Multivariate calibration models are developed for the determination of sulfur dioxide (SO2) by passive Fourier transform infrared (FT-IR) remote sensing measurements. In a series of experiments designed to simulate the measurement of SO from industrial stack emissions, low-angle sky backgrounds are viewed through the windows of a heated flow-through gas cell. With this apparatus, infrared emission from the hot SO2 is measured against the cold background of the sky. The FT-IR interferogram data collected are analyzed directly in the construction of the calibration models. Bandpass digital filters are applied to the interferograms to isolate the modulated infrared frequencies corresponding to either the asymmetric or symmetric S-O stretching vibrations at 1361 and 1151cm -1, respectively. Quantitative calibration models are constructed by submitting short segments of the filtered interferograms to partial least-squares regression analysis. The experimental design allows the impact of variation in the temperature of the SO to be evaluated for its effect on the calibration models. Three data sets are constructed consisting of data with increasing temperature variation. When the temperature variation in the data is less than 30 C, the calibration models are able to achieve a cross-validation standard error of prediction (CV-SEP) of approximately 27 ppm-m across the 185 to 727 ppm-m range of density-corrected, path-averaged concentration. These calibration models are applied to an interferogram segment of only 250 points, and do not require any separate measurement of the infrared background. A comparison of the results from the interferogram-based analyses with those obtained in an analysis of single-beam spectral data reveals similar performances for the models computed with both types of data. Index Headings: Sulfur dioxide; Fourier transform infrared spectrometry; Remote sensing; Interferogram; Multivariate calibration. 2 2

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/0003702001949609

Publication date: March 1, 2000

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