title: Remote detection of heated ethanol plumes by airborne passive Fourier transform infrared spectrometry.
abstract: Methodology is developed for the automated detection of heated plumes of ethanol vapor with airborne passive Fourier transform infrared spectrometry. Positioned in a fixed-wing aircraft in a downward-looking mode, the spectrometer is used to detect ground sources of ethanol vapor from an altitude of 2000-3000 ft. Challenges to the use of this approach for the routine detection of chemical plumes include (1) the presence of a constantly changing background radiance as the aircraft flies, (2) the cost and complexity of collecting the data needed to train the classification algorithms used in implementing the plume detection, and (3) the need for rapid interferogram scans to minimize the ground area viewed per scan. To address these challenges, this work couples a novel ground-based data collection and training protocol with the use of signal processing and pattern recognition methods based on short sections of the interferogram data collected by the spectrometer. In the data collection, heated plumes of ethanol vapor are released from a portable emission stack and viewed by the spectrometer from ground level against a synthetic background designed to simulate a terrestrial radiance source. Classifiers trained with these data are subsequently tested with airborne data collected over a period of 2.5 years. Two classifier architectures are compared in this work: support vector machines (SVM) and piecewise linear discriminant analysis (PLDA). When applied to the airborne test data, the SVM classifiers perform best, failing to detect ethanol in only 8% of the cases in which it is present. False detections occur at a rate of less than 0.5%. The classifier performs well in spite of differences between the backgrounds associated with the ground-based and airborne data collections and the instrumental drift arising from the long time span of the data collection. Further improvements in classification performance are judged to require increased sophistication in the ground-based data collection in order to provide a better match to the infrared backgrounds observed from the air.
authors.affiliation: Center for Intelligent Chemical Instrumentation, Department of Chemistry and Biochemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701-2979, USA.
authors.forename: Toshiyasu
authors.initials: T
authors.lastname: Tarumi
authors.forename: Gary W
authors.initials: GW
authors.lastname: Small
authors.forename: Roger J
authors.initials: RJ
authors.lastname: Combs
authors.forename: Robert T
authors.initials: RT
authors.lastname: Kroutil
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000401:Aircraft; D000465:Algorithms; D001185:Artificial Intelligence; D004784:Environmental Monitoring / Q000379:methods*; D000431:Ethanol / Q000032:analysis*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*
chemical_list: D000393:Air Pollutants; D000431:Ethanol