Our measurements on Stromboli (Figure 1) were obtained using the same DIAL-lidar described in Aiuppa et al. (2015) and Fiorani et al. (2015, 2016), and realized within the context of the FP7-ERC project Bridge (www.bridge.unipa.it). Only key information is reported here, and the reader is referred to previous studies for a detailed description of the instrument. In brief, the Bridge lidar (Figure 1D) uses a complex transmitter that integrates (i) an injection seeded Nd:YAG laser with (ii) a double grating dye laser. This transmitter is used to generate laser radiation at ~2010 nm, a region of the electromagnetic spectrum absorbed by atmospheric CO₂, while showing minimal cross-sensitivity to H₂O (Fiorani et al., 2013). At the ON and OFF wavelengths selected for this experiment, the differential cross section of CO₂ is five orders of magnitude larger than that of H₂O (Rothman et al., 2013). Considering a CO₂ mixing ratio of 400 ppm, and with the upper and lower ranges of H₂O mixing ratios used in atmospheric models (Berk et al., 2014), i.e., from 2.59% (tropics, sea level) to 0.141% (high latitude, winter, sea level), the respective CO₂ absorption is 3 and 5 orders of magnitude larger than that of H₂O. The 2.59% H₂O mixing ratio is not far from the saturated water vapor pressure at standard atmospheric conditions. We conclude that, even in a condensing volcanic plume, H₂O absorption is negligible compared to that of CO₂.

A piezo-electric element is used to sequentially switch the wavelength of the transferred laser beam, from λ_ON (2009.537 mm: maximum CO₂ absorption) to λ_OFF (2008.484 nm: no CO₂ absorption), at 10 Hz repetition rate. These closely spaced pairs of laser beams are sequentially transmitted into the atmosphere, where they are eventually scattered back by atmospheric back-scatterers (aerosols, water droplets, particles) in either the volcanic plume or the background atmosphere. During their atmospheric propagation, the laser beams are also reflected by any obstacle encountered along the optical path, e.g., in our specific case, the Pizzo and Vancori walls/rims in front of or behind the volcanic plume (see Figures 1, 2). The returned signal is captured by the lidar receiver (a Newtonian telescope, diameter: 310 mm), and then detected and amplified by an InGaAs PIN photodiode module, directly connected with the analog-to-digital converter (ADC).

During our experiment, the DIAL-lidar operated from a small laboratory truck (Figure 1C), positioned in a fixed position at the base of the volcano in the Scari area, ~2–2.5 km from the degassing vents on the volcano's summit (Figures 1, 2). The lidar operated during June 24–29, 2015, including an initial instrumental setup phase. Stable weather conditions (temperature, 22–26 °C; no rainfall) persisted during the entire measurement window.

During operations, two large motorized elliptical mirrors (major axis: 450 mm) simultaneously aimed the laser beams and the telescope, allowing the laser beam of the lidar to scan the volcanic plume either horizontally (Figure 2A) or vertically (Figure 2B). In particular, during June 24–25, the volcanic plume was mainly dispersed northwards by gentle southerly winds. From our Scari observation point (Figure 2), the plume was seen to rise nearly vertically north of the Pizzo area (Figure 2C). The Line Of Sight (LOS) of the lidar was therefore pointed north of Pizzo and the horizontal scan mode was preferred (heading angles: 227–317°; Figure 2A). Vertical scans above the Pizzo area were also performed. For simplicity, we refer below to these June 24–25 measurements as the Pizzo scans (Figure 2A).

On June 26–29, the plume was instead transported south-southeast by the prevailing north-northwesterly winds (Figure 2D). Vertical scans were therefore preferred that were operated at constant heading angle (237.8°) and at elevation angles from 16 to 21° (Figure 2B). In such conditions, the Pizzo and Vancori peaks were intercepted at elevation angles of 16.98° and 17.78°, respectively, and the volcanic plume was in all cases encountered in the 2300–2700 m range. We hereafter refer to these scans as the Vancori scans (Figure 2B).

During each profile, 100 lidar returns, 50 at λ_ON, 50 at λ_OFF, and interlaced (OFF after ON, OFF again and so on), were emitted at a 10 Hz rate, then co-added and averaged to increase the signal-to-noise ratio, reducing the signal sampling frequency to 0.1 Hz (temporal resolution of 10 s). The spatial resolution was about 5 m (corresponding to the rise time of the detector module due to its bandwidth). Plume scans, both horizontal and vertical, were retrieved combining about 50 profiles in <10 min. Typically, 10 scans at different elevations were repeated, obtaining a three-dimensional tomography of the volcanic plume.

A cell filled with standard CO₂ gas was periodically used during operations, for check of wavelength accuracy, repeatability and stability. In brief, our calibration procedure involved measuring-by photoacoustic spectroscopy—the absorption of the CO₂ gas cell as a function of wavelength. This calibration, limited to a small interval near the predicted λ_ON, allowed identifying the wavelength at which cell absorption is maximum. The laser system was finally forced to transmit at this radiation. The CO₂ absorption cross-section used in our calculations was based on HITRAN data (Rothman et al., 2013).

Concurrently with our lidar observations, a dual-UV camera system (Kantzas et al., 2010; Tamburello et al., 2012; Burton et al., 2014) was used to monitor the temporal variations of the SO₂ flux and plume transport speed. A fully autonomous system, similar to that used in other recent work (D'Aleo et al., 2016), was mounted on the roof of the laboratory truck and operated every day from 6 am to 4 pm (Local Time). The UV camera system acquired sequential images of the plume at ~0.5 Hz using two JAI CM 140 GR cameras. Both cameras had 10-bit digitization and 1392 × 1040 pixels, using an Uka Optics UV lens with a ~37° field of view. Distinct band-pass filters, centerd at either 310 nm (where SO₂ absorbs) or 330 nm (no SO₂ absorption), were mounted on the back on the lenses of the two cameras. Each set of co-acquired images from the two UV cameras was processed using the methodology of Kantzas et al. (2010) and integrated into the Vulcamera software (Tamburello et al., 2011, 2012), to calculate an absorbance for each camera pixel. Absorbance was converted into an SO₂ column amount from readings of a co-exposed Ocean optics USB2000+ UV Spectrometer, as outlined in Lübcke et al. (2013). Cameras and spectrometer were both controlled by a mini-pc Jetway. To calculate SO₂ flux time-series, we used Vulcamera to derive temporal records of SO₂ integrated column amounts (ICAs) along a plume cross-section, perpendicular to the plume transport direction. The obtained ICA time-series were then combined with high-temporal resolution (~1 Hz) records of plume transport speed. This latter was derived using an Optical Flow sub-routine using the Lukas/Kanade algorithm (Bruhn et al., 2005; Peters et al., 2015), integrated in Vulcamera. In our specific case, the Lucas-Kanade method was used to track movements of gas fronts (e.g., gas-rich and/or ash-free portions of the plume, having well distinct absorbance features) in consecutive UV camera frames, which allowed us quantifying plume transport speed at 0.5 Hz. We tested performance of this method by using artificial images with known particle velocities, and obtained errors in estimated velocities of <5%. Table 1 lists daily means (±1 SD: standard deviation) of both SO₂ fluxes and plume transport speed (V_p) during our observational period.

According to lidar theory (Fiorani, 2007), the optical power returned to the lidar receiver at any time t is produced by back-scattering of the laser beam by an atmospheric layer at distance R (range) from the source, where R = ct/2 and c is the speed of light. As such, the lidar offers range-resolved information on atmospheric structure and properties (aerosols, particles and gas molecules) along the laser beam, in the form of an intensity (I) vs. range plot (Figure 3).

Upon its atmospheric propagation, the beam intensity decreases approximately (a) exponentially, due to atmospheric extinction, according to the Lambert-Beer law; and (b) as 1/R², because the solid angle subtended by the receiver is A/R², where A is the telescope's effective area. The two processes are superimposed. As such, in order to better observe the atmospheric back-scattering, a “range corrected signal, S” is commonly used, being given by: S = ln(I R²) (see below). Since the system works in DIAL mode, each intensity profile is in fact acquired at two distinct wavelengths, λ_ON–absorbed by CO₂–and λ_OFF–not absorbed by CO₂ (Figure 3). The two wavelengths are so close that atmospheric behavior, except from CO₂ absorption, is practically identical. The measured intensity contrast between the co-emitted λ_ON and λ_OFF signals allow range-resolved CO₂ concentrations in the volcanic plume to be obtained.

An example of a lidar-based atmospheric profile, obtained at Stromboli during a typical Vancori scan, is illustrated in Figure 3. As described above, the lidar registered one such profile every 10 s, since 100 lidar returns acquired at 10 Hz were co-added and averaged to increase the signal-to-noise ratio. Each of the atmospheric profiles (e.g., Figure 3) acquired during the Vancori scan contains the following characteristic features:

Atmospheric profiles obtained during the Pizzo scans of June 24–25 share similar characteristics, except that the Pizzo morphological peak is intercepted by the lidar beam at R ~2300 m, and the plume is encountered either before or after the Pizzo (Figure 4). The Vancori peak was obviously not encountered.

We processed each acquired atmospheric profile using a Matlab analysis routine, with the aim of calculating the CO₂ concentrations in the atmospheric background and in the volcanic plume. The data processing routine consists of the following steps, all based on the Lambert-Beer law relation:

A series of CO₂ concentration profiles (one every 10 s), all similar to those shown in Figure 3C, were obtained as the volcanic plume was sequentially scanned by our DIAL-lidar, either horizontally or vertically, during the Pizzo/Vancori scans. By interpolating all CO₂ concentration profiles obtained during a single scan, we obtained sequences of CO₂ concentration maps, examples of which are shown in Figures 4, 5. Since a full scan of the plume was completed in ~1000–2000 s, each map is in fact obtained from the combination of ~50 to ~100 atmospheric profiles.

The maps illustrate the 2D distribution of CO₂ concentrations as a function of azimuth angle [°] (X axis) and range [m] (Y axis) for horizontal scans (Figure 4); or as a function of range [m] (X axis), and elevation angle [°] (Y axis) for the vertical scans (Figure 5). In both plots, the color scales (from blue to red) illustrate the level of CO₂ concentrations (in [ppm]) in the investigated space.

Figures 4, 5 demonstrate the ability of our DIAL-lidar to resolve in-plume volcanic CO₂ from the atmospheric background CO₂ (blue colors). In the CO₂ distribution maps, clusters of peak CO₂ concentration areas (marked by red, orange and yellow colors) identify the geometry of the plume. The lidar-based plume locations are consistent with visual and UV observations of volcanic plume dispersion (Figure 2). In the Pizzo horizontal scans, the plume was intercepted north of the Pizzo peak (heading angle: 244–245°), and is identified in the maps of Figure 4 as a cluster of peak CO₂ concentrations (up to 60 ppm above ambient air) at heading angles of 245–250°. The plume was detected over a relative wide range interval (R = 2000–2400 m), relative to the Pizzo peak (R ~2300 m). This is consistent with the slightly variable plume transport directions during our June 24–25 observation period that dispersed the plume either toward (Figure 4B) or away from (Figure 4A) the lidar observation point (R = 0). A few Pizzo vertical scans (not shown) confined the vertical extension of the plume to a diagonal band, extending from R = 2300 m and elevation ~19° (the Pizzo area) to R ~2700 m and ~20° elevation. Figure 5 is an example of a CO₂ distribution map obtained during a vertical Vancori scan. The map exhibits a clear volcanic plume signal, as marked by a cluster of high CO₂ concentrations (up to 60 ppm) in the range interval 2200–2500 m and 17–19.5° elevation. CO₂ remained at background air levels for range distances <2000 and >2800 m.

The CO₂ concentration maps served as basis for calculating the CO₂ flux. To this aim, and by analogy with previous work (Aiuppa et al., 2015), we integrated the background-corrected (excess) CO₂ concentrations over the entire plume cross-sectional area covered by each scan, and multiplied this integrated column amount by the plume transport speed. Mathematically, the CO₂ flux (Φ_CO2, in kg·s⁻¹) was obtained from: (6)ΦCO2=vp·PMCO2103NA·NmolCO2−total where v_P is the plume transport speed (in [m/s]) obtained from processing of UV camera images (Table 1); N_{molCO2−total} is the total-plume CO₂ molecular density (expressed in molecules m⁻¹); and PM_CO2 and N_A are, respectively, the CO₂ molecular weight and Avogadro's constant. The term N_{molCO2−total} was obtained by integrating the effective average excess CO₂ concentrations (Cexc,i¯ [ppm]) over the entire plume cross section, according to: (7)NmolCO2−total=Nh · 10−6 · ∑iCexc,i¯ · Ai  where: N_h is the atmospheric number density (molecules m⁻³) at the crater's summit height, the term 10⁻⁶ converts Cexc,i¯ into a dimensionless quantity, and A_i represents the i-th effective plume area, given by: (8)Ai=li·ΔR where ΔR is the spatial resolution of the lidar (1.5 m) and l_i is the i-th arc of circumference (Figure 6B): (9)li=Ri·θ

In relation (9), R_i is the i-th distance vector (in meters) and θ is the angular resolution of the system expressed in radians (ranging from 0.04° π/180 = 1.75 10⁻⁴ rad to 0.1° π/180 = 0.00175 rad) (Figure 6B).

Our obtained CO₂ fluxes, shown in Figure 6A, range from 1.8 to 32.1 kg/s. The lidar-based CO₂ flux time-series (Figure 6A) has maximum temporal resolution of 16–33 min (the time required to complete a full scan of the plume for our instrumental configuration). Temporal gaps in the dataset are caused by decreases in the signal-to-noise ratio (SNR) that prevent us from accurately detecting a clear CO₂ excess. These SNR decreases are likely caused by reduction of the backscattering coefficient of the probed air parcel, reflecting temporal variations in condensation extent of the volcanic gas plume. Visual (and UV camera) observations confirmed that the plume was variably condensed during our measurement interval, possibly due to slight changes in atmospheric conditions.

We evaluate the overall uncertainty in our derived CO₂ fluxes at ± 25% at 1s (see appendix).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.