Quantitative determination of ethanol in heated plumes by passive Fourier transform infrared remote sensing measurements

Abstract

Quantitative calibration models are developed for passive Fourier transform infrared (FT-IR) remote sensing measurements of open-air-generated vapors of ethanol. These experiments serve as a feasibility study for the use of passive FT-IR measurements in quantitative determinations of industrial stack emissions. A controlled-temperature plume generator is used to produce plumes of known concentrations of pure ethanol and mixtures of ethanol and methanol. Analyte plumes are generated over the path-averaged concentration range of 20-300 ppm-m and stack temperatures of 125, 150, 175, and 200 degrees C. A novel experimental setup is employed in which an ambient temperature polyvinyl chloride backdrop is placed behind the emission stack and used as a target for the passive IR measurements. An emission FT-IR spectrometer with telescope entrance optics is then employed to view the generated plumes against the backdrop. Signal processing techniques based on signal averaging and bandpass digital filtering are applied to both interferogram and single-beam spectral data obtained from these measurements, and the resulting filtered signals are used as inputs into the generation of multivariate partial least-squares (PLS) calibration models. Successful calibration models are obtained with both interferogram and spectral data, and neither analysis requires the collection of separate IR background data. For a set of validation data collected on a different day from the calibration measurements, standard errors of prediction of 30.6 and 32.2 ppm-m ethanol are obtained for the PLS models based on interferogram and spectral data, respectively.

Line-averaging measurement methods to estimate the gap in the CO<sub>2</sub> balance closure – possibilities, challenges and uncertainties

“…Furthermore, the shown example results were measured near the ground during stable stratification with remarkable amounts of temperature gradient as well as during low wind conditions. Several authors, e.g., Sun et al (2007), Kutsch et al (2008), and Siebicke et al (2012), found maximum advection during such conditions especially near the ground surface (Feigenwinter et al, 2008). The analysis of further data sets with additional concentration measurements and for additional time periods should confirm the derived results so far and the possibility of applying spatially averaging methods to measure advection of CO 2 .…”

“…Siebicke et al (2012) found an additional second maximum for stable stratification and low air temperature due to radiative cooling at the end of the night. Sun et al (2007) also reported significant horizontal CO 2 advection during transition periods in the early evening and early morning when turbulence intensity is low.…”

Line-averaging measurement methods to estimate the gap in the CO<sub>2</sub> balance closure – possibilities, challenges, and uncertainties

“…In summary, the total uncertainty represents the maximum error estimation, which is valuable for the validation of the method in terms of applicability to determine spatial concentration variations for the micrometeorological investigations addressed by this study. The estimated range of maximum concentration uncertainty for our experiment was confirmed by other passive OP-FTIR investigations (e.g., Allard et al, 2005;Sulub and Small, 2007;Kira et al, 2015). However, most of these studies are based on hot gases with high temperature contrasts between background and target gas compounds (volcanic gases, exhaust gases) or on the determination of non-atmospheric GHGs (industrial gases, aerosols).…”

Improving the Robustness and Stability of Partial Least Squares Regression for Near‐infrared Spectral Analysis

“…1 The multivariate calibration technique most commonly used for processing NIR spectra is partial least squares (PLS) regression. [2][3][4][5][6][7] Building high-quality PLS models depends on the execution of several steps, and one of the important steps is the outlier detection. [8][9][10] Like all least-squares (LS) regression methods, PLS is not robust, even one outlier contained in calibration set may spoil the regression model.…”