Volcanic Plume CO2 Flux Measurements at Mount Etna by Mobile Differential Absorption Lidar

Santoro, S.; Parracino, Stefano; Fiorani, Luca; D'Aleo, Roberto; Ferdinando, E. Di; Giudice, Gaetano; Maio, Giovanni; Nuvoli, Marcello; Aiuppa, Alessandro

doi:10.3390/geosciences7010009

Cited by 11 publications

(45 citation statements)

References 32 publications

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“…Optical flow analysis is becoming increasingly popular in conjunction with UV-sensitive cameras to measure SO 2 fluxes in volcanology [32][33][34] and has recently been used in the visible region for CO 2 flux estimation [5]. OF calculates the displacement of image features between subsequent video frames, yielding a displacement vector field for each analyzed pixel by solving…”

Section: Plume Speed Retrieval Using Optical Flow Analysismentioning

confidence: 99%

Ground-Based Remote Sensing of Volcanic CO2 Fluxes at Solfatara (Italy)—Direct Versus Inverse Bayesian Retrieval

et al. 2018

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CO 2 is the second most abundant volatile species of degassing magma. CO 2 fluxes carry information of incredible value, such as periods of volcanic unrest. Ground-based laser remote sensing is a powerful technique to measure CO 2 fluxes in a spatially integrated manner, quickly and from a safe distance, but it needs accurate knowledge of the plume speed. The latter is often difficult to estimate, particularly for complex topographies. So, a supplementary or even alternative way of retrieving fluxes would be beneficial. Here, we assess Bayesian inversion as a potential technique for the case of the volcanic crater of Solfatara (Italy), a complex terrain hosting two major CO 2 degassing fumarolic vents close to a steep slope. Direct integration of remotely sensed CO 2 concentrations of these vents using plume speed derived from optical flow analysis yielded a flux of 717 ± 121 t day −1 , in agreement with independent measurements. The flux from Bayesian inversion based on a simple Gaussian plume model was in excellent agreement under certain conditions. In conclusion, Bayesian inversion is a promising retrieval tool for CO 2 fluxes, especially in situations where plume speed estimation methods fail, e.g., optical flow for transparent plumes. The results have implications beyond volcanology, including ground-based remote sensing of greenhouse gases and verification of satellite soundings.

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Section: Plume Speed Retrieval Using Optical Flow Analysismentioning

confidence: 99%

Ground-Based Remote Sensing of Volcanic CO2 Fluxes at Solfatara (Italy)—Direct Versus Inverse Bayesian Retrieval

et al. 2018

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“…The Bridge Volcanic LiDAR (BILLI), recently developed at ENEA Research Center of Frascati, successfully retrieved threedimensional tomographic measurements of volcanic CO 2 in the plumes, at Italian volcanoes Pozzuoli Solfatara (Naples, Italy), [3][4][5] Stromboli volcano (Sicily, Italy), 6,7 and Mount Etna (Sicily, Italy). 8 To our knowledge, this was the first time that CO 2 in a volcanic plume was retrieved by LiDAR. BILLI opens unprecedented possibilities in measuring volcanic CO 2 concentrations in excess of a few tens of ppm that can be clearly resolved at a distance of ≥3 km with a spatial resolution of ≤5 m and temporal resolution of ≤10 s.…”

Section: Introductionmentioning

confidence: 99%

“…Wind profiles from a significant dataset, acquired during the experimental campaign of Stromboli in June 2015, will be presented and discussed. The analysis and conclusions will show that the performance of our system has made it possible to build a methodology for the fast tracking of not only the CO 2 concentration and flux [3][4][5][6][7][8] but also the wind speed.…”

Section: Introductionmentioning

confidence: 99%

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Fast tracking of wind speed with a differential absorption LiDAR system: first results of an experimental campaign at Stromboli volcano

et al. 2017

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Abstract. Carbon dioxide (CO 2 ) is considered a precursor gas of volcanic eruptions by volcanologists. Monitoring the anomalous release of this parameter, we can retrieve useful information for the mitigation of volcanic hazards, such as for air traffic security. From a dataset collected during the Stromboli volcano field campaign, an assessment of the wind speed, in both horizontal and vertical paths, performing a fast tracking of this parameter was retrieved. This was determined with a newly designed shot-per-shot differential absorption LiDAR system operated in the near-infrared spectral region due to the simultaneous reconstruction of CO 2 concentrations and wind speeds, using the same sample of LiDAR returns. A correlation method was used for the wind speed retrieval in which the transport of the spatial inhomogeneities of the aerosol backscattering coefficient, along the optical path of the system, was analyzed.

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“…The emission rate data have been largely focused on sulphur dioxide (SO 2 ), which is straightforward to remotely sense in volcanic plumes due to its strong UV absorption bands and low ambient concentrations. There have also been exciting recent developments concerning laser LIght Detection And Ranging (LIDAR) remote sensing of carbon dioxide (CO 2 ) emissions, (e.g., [4,5]) from volcanoes.…”

Section: Introductionmentioning

confidence: 99%

Ultraviolet Imaging of Volcanic Plumes: A New Paradigm in Volcanology

et al. 2017

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Ultraviolet imaging has been applied in volcanology over the last ten years or so. This provides considerably higher temporal and spatial resolution volcanic gas emission rate data than available previously, enabling the volcanology community to investigate a range of far faster plume degassing processes than achievable hitherto. To date, this has covered rapid oscillations in passive degassing through conduits and lava lakes, as well as puffing and explosions, facilitating exciting connections to be made for the first time between previously rather separate sub-disciplines of volcanology. Firstly, there has been corroboration between geophysical and degassing datasets at ≈1 Hz, expediting more holistic investigations of volcanic source-process behaviour. Secondly, there has been the combination of surface observations of gas release with fluid dynamic models (numerical, mathematical, and laboratory) for gas flow in conduits, in attempts to link subterranean driving flow processes to surface activity types. There has also been considerable research and development concerning the technique itself, covering error analysis and most recently the adaptation of smartphone sensors for this application, to deliver gas fluxes at a significantly lower instrumental price point than possible previously. At this decadal juncture in the application of UV imaging in volcanology, this article provides an overview of what has been achieved to date as well as a forward look to possible future research directions.

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Volcanic Plume CO2 Flux Measurements at Mount Etna by Mobile Differential Absorption Lidar

Cited by 11 publications

References 32 publications

Ground-Based Remote Sensing of Volcanic CO2 Fluxes at Solfatara (Italy)—Direct Versus Inverse Bayesian Retrieval

Ground-Based Remote Sensing of Volcanic CO2 Fluxes at Solfatara (Italy)—Direct Versus Inverse Bayesian Retrieval

Fast tracking of wind speed with a differential absorption LiDAR system: first results of an experimental campaign at Stromboli volcano

Ultraviolet Imaging of Volcanic Plumes: A New Paradigm in Volcanology

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