Abstract
We report here on the results of a proof-of-concept study aimed at remotely sensing the volcanic CO2 flux using a Differential Adsorption lidar (DIAL-lidar). The observations we report on were conducted on June 2014 on Stromboli volcano, where our lidar (LIght Detection And Ranging) was used to scan the volcanic plume from ~ 3 km distance from the summit vents. The obtained results prove that a remotely operating lidar can resolve a volcanic CO2 signal of a few tens of ppm (in excess to background air) over km-long optical paths. We combine these results with independent estimates of plume transport speed (from processing of UV Camera images) to derive volcanic CO2 flux time-series of ≈16-33 minutes temporal resolution. Our lidar-based CO2 fluxes range from 1.8±0.5 to 32.1±8.0 kg/s, and constrain the daily averaged CO2 emissions from Stromboli at 8.3±2.1 to 18.1±4.5 kg/s (or 718-1565 tons/day). These inferred fluxes fall within the range of earlier observations at Stromboli. They also agree well with contemporaneous CO2 flux determinations (8.4-20.1 kg/s) obtained using a standard approach that combines Multi-GAS-based in-plume readings of the CO2/SO2 ratio (≈ 8) with UV-camera sensed SO2 fluxes (1.5-3.4 kg/s). We conclude that DIAL-lidars offer new prospects for safer (remote) instrumental observations of the volcanic CO2 flux.
Highlights
A major step forward in ground-based volcano monitoring has recently arisen from the advent of modern instrumental techniques and networks for volcanic gas observations (Galle et al, 2010; Oppenheimer et al, 2014; Saccorotti et al, 2014; Fischer and Chiodini, 2015)
According to lidar theory (Fiorani, 2007), the optical power returned to the lidar receiver at any time t is produced by backscattering of the laser beam by an atmospheric layer at distance R from the source, where R = ct/2 and c is the speed of light
The two wavelengths are so close that atmospheric behavior, except from CO2 absorption, is practically identical
Summary
A major step forward in ground-based volcano monitoring has recently arisen from the advent of modern instrumental techniques and networks for volcanic gas observations (Galle et al, 2010; Oppenheimer et al, 2014; Saccorotti et al, 2014; Fischer and Chiodini, 2015). Such technical advances provide improved temporal resolution relative to traditional direct sampling techniques (Symonds et al, 1994; Giggenbach, 1996).
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