Airborne passive Fourier transform infrared remote sensing of methanol vapor from industrial emissions

Abstract

Passive Fourier transform infrared (FT-IR) remote sensing measurements are applied to the detection of methanol vapor plumes released from a chemical manufacturing facility. With the spectrometer mounted in a downward-looking mode on a fixed-wing aircraft, overflights of the facility are made during the methanol release. Signal processing and pattern recognition methods are applied to the acquired data for the purpose of constructing an automated classification algorithm for the methanol detection. The analysis is based on the use of short, digitally filtered segments of the raw interferogram data collected by the spectrometer. The classifiers are trained with data collected on the ground by use of an experimental protocol designed to simulate background conditions observed from the air. Optimization of the digital filtering and interferogram segment parameters leads to successful classifiers based on 100 or 120 interferogram points. The optimal interferogram segment location is found to be 95-points displaced from the centerburst, and the best performing digital filters are centered on the methanol C-O stretching band at 1036 cm(-1) and have a passband full-width at half-maximum of 100 to 160 cm(-1). The best classifiers achieve classification errors of less than 1% and are observed to be resistant to possible interference effects from species such as ethanol and ozone. This work demonstrates the utility of airborne passive FT-IR remote sensing measurements of volatile organic compounds under complex background conditions such as those encountered while monitoring an operating industrial facility.