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Mount Melbourne and Mount Rittmann are quiescent, although potentially explosive, alkaline volcanoes located 100 km apart in Northern Victoria Land quite close to three stations (Mario Zucchelli Station, Gondwana and Jang Bogo). The earliest investigations on Mount Melbourne started at the end of the 1960s; Mount Rittmann was discovered during the 1988–89 Italian campaign and knowledge of it is more limited due to the extensive ice cover. The first geophysical observations at Mount Melbourne were set up in 1988 by the Italian National Antarctic Research Programme (PNRA), which has recently funded new volcanological, geochemical and geophysical investigations on both volcanoes. Mount Melbourne and Mount Rittmann are active, and are characterized by fumaroles that are fed by volcanic fluid; their seismicity shows typical volcano signals, such as long-period events and tremor. Slow deformative phases have been recognized in the Mount Melbourne summit area. Future implementation of monitoring systems would help to improve our knowledge and enable near-real-time data to be acquired in order to track the evolution of these volcanoes. This would prove extremely useful in volcanic risk mitigation, considering that both Mount Melbourne and Mount Rittmann are potentially capable of producing major explosive activity with a possible risk to large and distant communities.

In recent years we have witnessed a growing concern in scientific research to understand and improve actionable analytics-driven decision processes, mostly focused on online data. Many researchers have focused their attention on computational and Information and Communications Technology issues in this matter. Only a small share of literature is concerned with how indicators can be improved by Big Data analytics. In this paper, we propose an innovative methodological approach to building indicators by combining Big Data analytics with the analysis of network patterns. Our study aims to define relational structures in a Big Data set, implementing measurements and clustering methods by Network Analysis in order to build decision-making indicators. We describe an audience model both to collect a large amount of online data from large online newspapers and to structure those in a relational form. By analysing readers’ comments, we can derive proxies of reliable indicators about specific topics discussed on an online newspaper blog. We show the effectiveness of such an approach in detecting and building indicators to support policy-makers in complex decision-making processes.

The balance between the amount of gas coexisting with mantle-derived magmas at depth and that emitted during intereruptive phases may play a key role in the eruptive potential of volcanoes. Taking the December 2018 eruption at Mt. Etna volcano as a case study, we discuss the geochemical data streams observed. The signals indicate a long-lasting prelude stage to eruption, starting in 2017 and involving magma-fluid accumulation in the deep (7 km bsl) reservoir, followed by pressure buildup in the system at intermediate depth (5 to 2 km bsl), 6 to 7 months before the eruption. A brief preeruptive phase marks the pressurization at 2 to 3 km below the craters. By comparing the magma and fluid recharge at depth to the measured volcanic degassing from the plume, we provide evidence that Mt. Etna was in a state of extreme overpressurization in the weeks before the onset of the eruption.

International audience; Mount Etna volcano is well-known for its frequent eruptions and high degassing rates from its summit craters and flanks. The geochemical monitoring network on Mt. Etna that measures soil CO2 flux and in-plume CO2/SO2 ratio recorded very important degassing variations from the flank and the summit craters during the second half of 2018. In this area several significant volcanic events occurred in October and December 2018 and in January 2019. Past observations have distinguished a tendency for wide variations in degassing rates, marked by a sharp increase preceding the onset of volcanic activity. However, this is the first time that three earthquakes of magnitude M>4 have been registered since the inception of the geochemical network in January 2001. Of particular interest is the CO2/SO2 ratio in plumes recorded by the monitoring station sited at the summit crater of Voragine showed very significant degassing variations, which were comparable with those recorded for the soil CO2 flux. This paper focuses on the combination of events occurring on Mt. Etna and their relationship with degassing rates. The most remarkable results can be summarized as follow: i) the networks recorded high variations of soil CO2 flux and CO2/SO2 ratio, which assisted in identifying distinctive phases of pressurization of Mt. Etna plumbing system and ii) all earthquakes occurred during phases of minimum gas rate, which in turn followed stages of pressurization involving different portions of the plumbing system. The 2018 period of high volcanic activity and the corresponding seismic episodes provided an invaluable case study for Mt. Etna, which allowed to combine seismic events and geochemical signal variations.

Co-auteur étranger; International audience; Preliminary interpretation of geological processes during field measurement campaigns require fast data analysisto adapt ongoing target strategies. It is the case of soil investigations where coupling geochemical and geophysicalrecords favors a better understanding of subsurface processes. This task requires (i) statistical analysisto identify areas of interest during spatial surveys and (ii) signal processing to analyze temporal series. Here wepresent SoilExp, an open-source Python-based Graphical User Interface (GUI) that permits to process spatial andtemporal surveys of soil gases (e.g. soil CO2 flux) combined with common physical parameters (e.g. self-potential,temperature) that are synchronously recorded on the field. SoilExp mixes innovative algorithms with the morecommon tools used for the analysis of both spatial surveys or temporal series. It offers the possibility to displaydistribution plots, maps, comparative plots, spectra and spectrograms, as well as data statistical analysis, in orderto deal efficiently with datasets acquired on the field. Field measurements performed at Stromboli (Italy) supportsthat such software solution facilitates a quick visualization of the data output and is a powerful tool on thegeochemical and geophysical analysis.

[EN] Competing newspapers, tend to publish the same information in a given time frame. However, each editor tends to aggregate and present the news according to certain criteria such as editorial policies, filtering strategies, readers base, etc. Thus, the proper choice and filtering of information makes one newspaper different from the other and, the proper management of such criteria, may deem the success or failure of a newspaper. From the editor’s perspective, the news selection process is a trade-off between informativeness and attractiveness, as determined by the readership. Moreover, is it possible that cultural and political inputs from social media may impact the news selection process? Political news on social networks represent nowadays a valuable informative asset that gives the possibility to correlate newspaper information with public request expressed on social networks. We believe that it is possible to develop a theory to mitigate the newspaper’s cultural identity with the public information needs collected on social media. In our work, we show how to measure the society's request for information through the analysis of public reaction to certain articles on social networks, in particular we present how studying the hashtags and articles shared can be conveyed to understand social dynamics in nowadays discussion.

Among major volatiles released from the Earth’s interior, CO2 is an important target for the international community. The interest is keenly motivated by the contribution of CO2 in the Earth’s carbon budget and its role on past, current, and future climate dynamics. In particular, the isotopic signature of CO2 is fundamental to characterize the source of this gas and its evolution up to the atmosphere. The recent development of new laser-based techniques has marked an important milestone for the scientific community by favoring both high-frequency and in situ stable isotope measurements. Among them, the Delta Ray IRIS (Thermo Scientific Inc., Waltham, USA) is one of the most promising instruments thanks to its high precision, its limited interferences with other gaseous species (such as H2S and/or SO2), and its internal calibration procedure. These characteristics and the relative easiness to transport the Delta Ray IRIS have encouraged its use on the field to analyze volcanic CO2 emissions in recent years but often with distinct customized protocols of measurements. In this study, various tests in the laboratory and on the field have been performed to study the dependence of CO2 isotope measurements on analytical, instrumental, and environmental conditions. We emphasize the exceptional ability of the Delta Ray IRIS to perform isotope measurements for a large range of CO2 concentration (200 ppm–100%) thanks to a dilution system and to get a reliable estimation of the real CO2 content from the diluted one. These tests lead to point out major recommendations on the use of Delta Ray IRIS and allow the development of adapted protocols to analyze CO2 emissions like in volcanic environments.

In this work, we propose a wavelet-based filtering for soil CO2 flux time series. The filter relies on the detection of the periodic components achieved by means of the long-term time-frequency characterization of the time series. For this purpose, we exploited the vast data set coming from the monitoring network installed at Mt. Etna volcano (Italy). The network provides hourly measure of CO2 flux together with the measure of the climatic variables. These data allow to investigate the relationships between CO2 time series and the potentially influencing meteorological factors. This has been assessed calculating the wavelet coherence between CO2 time series against air temperatures, atmospheric pressure, and relative humidity in all the sites where these information were available. Results highlight the occurrence of marked cycles at about ∼1 year for the most of the sites while shorter cycles occur only at some sites. From these cycles a periodic signal can be calculated, and therefore opportunely removed from the time CO2 series to enhance the volcano-related anomalies. We found also common cycles among CO2 and the climatic variables, which synchronicity is constant over time but it is site-specific. Starting from this consideration, we calculated a reference signal for CO2 combining analytically the temperature, the pressure, and the humidity cycles: this model of the climatic effect has been used to predict the seasonal trend of the CO2 output.

In recent years, large volumes of data are generated by automatic extraction of information, innovative data mining, and predictive analytics. This paper proposes an innovative approach by combining Big Data with the analysis of relational structures in order to improve actionable analytics-driven decision patterns. From the website of one of the largest online Italian newspapers, interactions among users and their comments about a 2016 Italian constitutional review bill are organized in a Big Data audience model. Readers’ sentiments are measured and relational patterns are classified by descriptive measurements and clustering structures implemented in Network Analysis methods.

The main aim of this article is to improve knowledge on applicability of Big Data (BD) techniques in social research, by exploring the validity of using BD as an approach in emerging news contexts. In particular, we constructed and examined a large database of historical data of public online comments on a recent constitutional bill review. We using BD technology in order to analyze people’s opinions to this particular reform.

The issue of antimicrobial resistance (AMR) is a focus of the World Health Organization, which proposes educational interventions targeting the public and healthcare professionals. Here, we present the first attempt at a regionwide multicomponent campaign in Sicily (Italy), called &ldquo, Obiettivo Antibiotico&rdquo, which aims to raise the awareness of prudent use of antibiotics in the public and in healthcare professionals. The campaign was designed by an interdisciplinary academic team, and an interactive website was populated with different materials, including key messages, letters, slogans, posters, factsheets, leaflets, and videos. The campaign was launched in November 2018 and, as of 21 December 2018, the website had a total of 1159 unique visitors, of which 190 became champions by pledging to take simple actions to support the fight against AMR. Data from social media showed that the audience was between 18 and 54 years of age, with a high proportion of female participants (64%). Interestingly, the LinkedIn page received more than 1200 followers, and Facebook 685 followers. The number of actions taken (pledges) by the audience was 458, evenly divided between experts (53%) and the general public (47%). Additional efforts are needed to reach more people, thus future efforts should focus on further promotion within the Sicilian region to sustain the engagement with the campaign.

Lava lakes provide a direct observation window into processes which usually remain hidden, such as magma convection and outgassing dynamics. We here report a coupled analysis of thermal infrared footage and infrasound array recordings at Mount Nyiragongo (D. R. Congo), and derive a conceptual model of the lava lake's convective system and outgassing mechanism. We suggest that surface flow results from a horizontal pressure gradient at the surface of the lake, driving the crust from high-pressure regions where hot upwelling magma impinges the surface, to low-pressure regions where cold downwelling magma pulls away from the surface. The ascending current of this convection cell carries gas pockets, which once at the surface, are dragged across the lake into downwelling sinks. Such sinks are characterized by persistent chaotic bubble bursting (spattering), whose intensity and position are tracked from infrasound array analysis. Fluctuations of these are observed, but have not been correlated with oscillations of the lava lake level, nor with the variations of surface velocities, both recorded from infrared footage. We also report the activity of a new eruptive vent, which opened early 2016 near an inner circular fracture of the crater's third terrace. We show that the vent's activity was intermittent, alternating between explosive strombolian activity and effusive activity. The latter produced lava flows which spread on the crater's last terrace before cascading into the active lava lake. Although no significant change in the lake behavior was witnessed while the new eruptive vent was active, increased attention should be addressed as this new activity could reflect over-pressurization of the shallow magmatic system. The variety of phenomena captured by this study complements and expands observations reported at other low-viscosity lava lakes, chiefly Kilauea (Hawai'i) and Erta Ale (Ethiopia). Despite Nyiragongo's more vigorous convective regime (where multiple convective cells can operate simultaneously), we suggest that the mechanisms controlling the surface motion and outgassing are similar at all three systems, pointing to generic processes governing the dynamics of low-viscosity lava lakes.

San Miguel volcano, El Salvador, erupted on 29 December 2013, after a 46year period characterized by weak activity. Prior to the eruption a trend of increasing SO2 emission rate was observed, with all values measured after mid-November greater than the average value of the previous year (similar to 310td(-1)). During the eruption, SO2 emissions increased from the level of similar to 330td(-1) to 2200td(-1), dropping after the eruption to an average level of 680td(-1). Wind measurements and SO2 emission rates during the preeruptive, syneruptive, and posteruptive stages were used to model SO2 dispersion around the volcano. Atmospheric SO2 concentration exceeded the dangerous threshold of 5 ppm in the crater region and in some sectors with medium elevation of the highly visited volcanic cone. Combining the SO2 emission rate with measured CO2/SO2, HCl/SO2, and HF/SO2 plume gas ratios, we estimate the CO2, HCl, and HF outputs for the first time on this volcano.