From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
Although there is a well-developed commercial offering for the detection of gaseous emissions in natural gas infrastructures, the same does not exist in the transport or transformation of liquid petroleum products. In the case of aromatics, UV DOAS using lamps and retroreflectors are amongst the only choices, along with UV-DIAL. But these are limited in sensitivity and depend on long absorption paths or are very complex. There are also large airborne lidars for the detection of liquid hydrocarbon spills on water or land that rely on UV induced fluorescence (LIF). But there is a lack of simple techniques for the remote detection of vapor plumes or spills involving liquid petroleum products. There have been proposals for the use of UV enhanced Raman for the detection of vapor plumes, but these require large laser powers and detection optics for poor sensitivity. On the other hand, recent developments in UV LEDs allows for simple techniques in the detection of aromatics, benzene and toluene in particular. These are found in most liquid petroleum products. Using these new commercially available UV LEDs and a gas correlation spectrometer set-up, benzene vapor is measured using the electronic transition at 258.9 nm and at other deep UV wavelengths. It is shown that while there is significant fluorescence in liquid benzene, oxygen in air severely quenches the fluorescence of the vapor phase benzene, rendering fluorescence unusable for the standoff detection of the vapor phase. Various implementations of standoff benzene/toluene detection using UV LEDs and gas correlation are discussed, along with pros and cons of the technique.
François Babin, Pascal Dufour, Félix Cayer, and Jean-François Y. Gravel, "UV LED based gas correlation spectrometer of aromatics for the standoff detection of industrials spills and emissions," Proc. SPIE 10215, Advanced Environmental, Chemical, and Biological Sensing Technologies XIV, 102150Q (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 10, 2017; Published: 3 May 2017); https://doi.org/10.1117/12.2263038.
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