Pyroclastic Density Currents (PDCs) have long been considered rare in mafic volcanic systems. However, over the past century, such events have been widely documented at several mafic volcanoes worldwide and, in particular, at Mount Etna, where more than fifty occurrences have been recorded in the last four decades. This study provides a comprehensive review of PDC events at Etna since 1986, assessing the diversity of processes responsible for their generation. The examined events reveal that PDCs at Etna may originate from multiple mechanisms, including instantaneous collapse of the pyroclastic mixture, gravitational remobilization of freshly deposited hot tephra, partial flank failure of pyroclastic cones, and explosive interactions between lava and snow or wet substrates. Several episodes also illustrate the superimposition of distinct triggering processes, highlighting the complexity of PDC initiation at this volcano. The overall increase in PDC frequency observed over recent decades is mainly related to the intensification of summit paroxysmal activity and the rapid growth and mechanical destabilization of the Southeast Crater, the most active of the four summit craters of Mount Etna. Although many PDCs showed minor runouts, larger events have reached runouts of 2–3 km in recent years, occasionally impacting some of the most frequented areas in the summit region of the volcano. These observations demonstrate that PDCs represent a non-negligible hazard at Etna, particularly in sectors frequently visited by tourists and hikers, where the sudden onset and rapid propagation of even small-volume PDCs may pose a significant threat.

The comprehension of the complex dynamics of degassing is critical for volcano monitoring and assessing volcanic hazards. In this study, we apply visibility graph analysis (VGA) to a decadal, high-resolution time series of daily soil CO2 flux recorded by a standardized monitoring network at Mt. Etna volcano (Italy). By mapping these time series into complex networks, we demonstrate that the connectivity degree distributions follow a power law described by the exponent γ, which reveals a self-similar behavior of gas emissions. We introduce the γ-deviation, namely the variation of the scaling exponent from its long-term site-specific baseline, as a novel proxy for degassing efficiency. The long-term baseline is interpreted as a site-specific measure of flux efficiency, while its variations are attributed to other factors, such as fluctuations in the sources or changes in the efficiency of fluids transport pathways. Our results identify a transition from a period of discordance across the monitoring sites (pre-2016) to a phase of network-wide concordance (after 2016). The striking correlation between topological γ-deviations and the established normalized network signal (Φnorm) validates the methodology, suggesting that VGA is able to capture the same underlying magmatic drivers. This study establishes VGA as a robust and reliable tool for medium- and long-term monitoring, potentially capable of identifying the occurrence of large-scale magmatic processes and refining the characterization of fluid transport dynamics in active volcanic systems.

Abstract The southeastern flank of Mt. Etna volcano slides into the Ionian Sea at rates of centimeters per year. While gravitational spreading and tectonic forces can cause volcanic flank collapse, their effects intrinsically trade off with magmatic forcing. There is still strong uncertainty regarding the processes underlying the sliding. Modeling the different forces appears to be the ideal framework to understand flank spreading; however, any model explaining the geological or dynamic features of the volcano must also explain its deformation and seismic patterns, the primary markers of volcanic activity used by monitoring institutions. Here, we present a series of new rheological models using the 3D thermomechanical code of LaMEM. The models include all the primary geological features related to gravitational and tectonic forcing, producing deformation models and observations grounded on geophysical imaging and rock physics experiments. We observed the impact of each implemented geometry and its sensitivity to parameters by comparing the model results to GPS observations quantitatively, estimating the misfit between the model and data using the coefficient of determination. The results demonstrate that the ductile rheology of the rocks under the eastern flank is essential to explain deformation patterns even in the absence of magmatic inputs. The horizontal displacement is controlled by supercritical fluids horizontally constrained by pre‐existing volcanic structures, which act as a lubricant for gravitational sliding. Their connection with deeper magmatic input must be better constrained by modeling magma transients during specific time periods and performing corresponding time‐dependent data analyses.

Abstract Fine volcanic ash can have an adverse health impact. It is readily produced during explosive eruptions but also can be generated in large quantities by secondary processes. We evaluated the production and resuspension of sub-10 μm volcanic particulate matter (PM 10 ) by road traffic as these processes may generate an increased respiratory exposure hazard. We conducted experiments on the slopes of Etna volcano, where we drove with a small SUV-type car over an area of road artificially covered with tephra and investigated the resulting grain size distribution (GSD) and concentration of PM 10 in the air as a function of (1) the number of car passages, (2) the starting thickness of the tephra deposit, and (3) vehicle speed. Our data show that increasing the number of car passages, deposit thickness, and vehicle speed rapidly induces a decrease in the GSD of tephra deposits and results in increased airborne PM 10 . We show that vehicles will cause comminution (i.e., reduction to a smaller average particle size) and resuspension of basaltic ash, so local communities can expect that, after an eruption, concentrations of PM 10 may increase with time and affect exposures close to roads.

Abstract Episodes of volcanic tremor provide valuable insights into the dynamics of subsurface processes at active volcanoes. Previous studies have suggested that evolving tremor properties may relate to changes in the stress conditions of the plumbing system. However, a strong causative link has remained elusive. At Etna volcano, we observe subtle changes in the frequency of stable spectral lines recorded during broadband tremor over 8 months in 2020. Comparison with seismic velocity changes computed using passive seismic interferometry reveals a remarkable correlation between relative frequency and seismic velocity changes. The most significant of these changes are interpreted to result from a shear‐wave velocity decrease in response to local seismicity. We argue that these observations are best explained by resonance within near‐surface structures. These findings demonstrate that monitoring frequency shifts of stable spectral lines produced during tremor can provide meaningful insights into subsurface properties.

Infrasound is a valuable tool for volcano monitoring that can offer critical insights into volcanic processes, unrest and eruption. Over the last two decades, studies have shown the benefits of using local infrasound arrays - clusters of sensors near active vents - for real-time detection, tracking, and quantifying eruption intensity. This work introduces the first open-access dataset of infrasound array waveforms recorded at Mt. Etna, Italy, from May to October 2021 during intense eruptive activity. The 6-element array was installed at Monte Conca, near a permanent seismic station operated by the Istituto di Geofisica e Vulcanologia - Osservatorio Etneo, at about 6 km from the South-East Crater. The continuous monitoring captured 39 eruptions, successfully detecting all events, tracking their progression, and distinguishing activity from multiple vents. This manuscript details the array setup and summarizes volcanic activity during deployment, supporting future research and monitoring efforts. The dataset aims to aid development of early warning systems and serves as a valuable training tool for early-career scientists in the fields of volcano monitoring.

Abstract. We present the first paleoseismological results along the Fiandaca Fault, source of the 26 December 2018, Mw 5.0 Fleri earthquake. This earthquake caused extensive damage and 8 km of surface faulting. We excavated two exploratory trenches close to the Fiandaca village, in the central segment of the 2018 coseismic rupture. Analysis of trench walls allows identifying, besides the 2018 event, two additional historical surface faulting events. Based on amount of displacement of dated stratigraphic units, including tephra from the 122 BCE eruption, these historical events were similar to the 2018 earthquake. The most recent one occurred in the period 1281–1926 CE, most likely during the1894 earthquake. The oldest one, previously unknown, occurred in the Early Middle Ages (757–894 CE). When compared with the available seismic catalogue for Mt. Etna volcano, this paleoseismic evidence might suggest increased seismic activity along the Fiandaca Fault in the last centuries. In order to test this hypothesis, we conducted detailed morphotectonic analyses and throw rate measurements across offset historical lava flows. In addition, we developed a trishear kinematic model that describes the fault zone and the morphological features of the scarps. Throw rates mean values show an increase from 3.3 mm yr−1 since the Greek-Roman period reaching 7.8 mm yr−1 since the Late Middle Ages. These findings highlight the needs of further investigations to evaluate the slip rates variations of other faults accommodating the flank instability. Our findings confirm that paleoseismological and morphotectonic studies are of critical value for defining surface faulting and seismic hazard in volcanic settings.

Insights on the multi-scale topographic features of Mt. Etna volcano (Italy)

Abstract Real‐time prediction of dike behavior during an eruption remains a significant challenge. Determining the timing and location of dike propagation arrest is crucial for hazard evaluation. The inherent complexity of eruptive phenomena makes rapid, unambiguous interpretation through modeling difficult. To overcome this drawback, identifying observables that can rapidly support prediction and decision‐making is crucial. For Etna volcano, we have moved in this direction by focusing on seismological observables, aiming to facilitate quick predictions and informed decisions. We have identified the presence of reverse focal mechanisms during the final stage of dike intrusion as a key observable that accompanies and distinguishes potential propagation arrest. This study examines the four critical case studies of dike arrest occurred in 1989, 2002, 2008, and 2018 during lateral intrusions at Mount Etna. The proposed approach proves to be a valuable tool for assessing dike propagation hazards during the early stages of an eruptive intrusion.

Abstract. In the last few decades, volcanic monitoring using remote sensing systems has become an essential tool to investigate the effects of volcanic activity on environment, climate, human health, and aviation, as well as to give insights into volcanic processes. Compared to satellite measurements, ground-based instruments offer continuous spatial and temporal coverage capable of providing high-resolution and high-sensitivity data. This work presents a new simplified prototype of a thermal infrared (TIR) system (named “VIRSO2”). The instrument comprises three cameras, one working in the visible spectrum and two in the TIR (8–14 µm). In front of one of the two TIR cameras, an 8.7 µm filter is placed. The system is designed for the detection of volcanic emission, geometry estimation, determining the columnar content of SO2 and ash, and SO2 flux retrievals. The retrieval procedures developed are detailed starting from the geometric characterisation with wind direction correction, the calibration by considering the effects of filter multireflections and temperature, and the SO2 mass by exploiting MODTRAN radiative transfer model (RTM) simulations. The SO2 flux is then computed by applying the traverse method, with the plume speed obtained from the wind speed at the crater altitude. As test cases, the measurements collected at Etna Volcano (Italy) on 1 April 2021 during a lava fountain episode and 30 August 2024 during a quiescent phase have been considered. The results show that the system can provide reliable information on plume detection, altitude, and SO2 flux. Its simplicity, its low cost, and the possibility of carrying out measurements at a safe distance from the vent during both the day and the night make this system ideal for real-time monitoring of volcanic emissions, thus helping to provide information on the state of activity of the volcano and therefore to mitigate the effect that these natural phenomena have on humans and the environment.

Abstract Volcano deformation and seismicity during basaltic explosive eruptions are the expressions of the processes that regulate magma ascent from depth to surface. Both deflation rate and seismic tremor are hypothesized to scale with magma ascent rate, which plays a significant role in the resulting eruptive style. However, the relationship among these quantities has yet to be confirmed on a large number of events. The present study analyzes the seismic and deformation motion retrieved from borehole strainmeter signals recorded during 84 lava fountains at Etna volcano in the period 2012–2023. The very wide frequency range and the high accuracy of the measurements enable: (a) to detect tiny deformation, unachievable with conventional geodetic techniques, and (b) to derive strain tremor unaffected from surface noise. The unprecedented accurate and large data set allows the exploration of the relationship between the seismic and the deformation responses and the extraction of the main features that more accurately reflect the eruptive style as determined by independent volcanological observations. Clustering analyses, conducted separately on the most relevant strain and tremor features, reveal that deformation and seismic responses are not always related each other. A closer examination of the signal evolution indicates that this relationship is not consistent in terms of amplitude or timing. The comparison of the clustering solutions has demonstrated that the strain rate signal is more in accordance with the volcanological classification of the lava fountains with significant impacts for the real‐time characterization and monitoring of basaltic explosive eruptions.

Abstract A labeled data set of 216,681 infrasound detections was compiled using data from the Central and Eastern European Infrasound Network (CEEIN). Detections associated with quarry blasts, thunderstorms, eruptions of the Etna volcano, industrial activity, and the war in Ukraine were categorized using ground truth information, such as seismic and lightning data. To establish benchmark performance, a random forest classifier and a convolutional neural network (CNN) were trained separately, achieving F1 scores of 0.8170 and 0.8248 on the test set, respectively. An ensemble model, combining both classifiers, outperformed them achieving an F1 score of 0.8773. The model, initially trained on four CEEIN arrays, was tested on data from a separate station not included in training. Although performance initially declined, transfer learning and fine‐tuning of the CNN and retraining the random forest model improved the ensemble model's F1 score to 0.9056 making it a considerable step. These results represent significant progress in automatic infrasound signal classification for monitoring the atmosphere.

Mount Etna is well known for its frequent lava fountains, or paroxysms, characterized by their intense explosive activity. Over the past decades, the Southeast Crater has been the most prolific, generating over a hundred events. More recently, three sequences of particularly powerful lava fountains were erupted from the Voragine Crater: four episodes between 3 and 5 December 2015, three between 18 and 21 May 2016, and six between 4 July and 15 August 2024. This intense eruptive activity, accompanied by significant ash dispersal and fallout, severely impacted the infrastructure and accessibility of eastern Sicily, causing disruptions to air services and the temporary closure of Catania’s international airport. In this study, we investigated these intriguing phenomena through the high-precision strain data recorded by Etna’s network of borehole dilatometers. We modelled and interpreted the source of these paroxysmal events, examining its position, the magnitude of its volumetric change during the paroxysms, and its relationship with the volcano’s plumbing system, thereby advancing our understanding of these dynamic processes.

Volcanic ballistic projectiles (VBPs) are a common hazard near volcanic vents and often threaten volcano tourists, especially at accessible volcanoes with Strombolian eruption style. Current ballistic hazard models used to estimate potential VBP impact zones often assume round and solid (fixed shape) projectiles, though the validity of these assumptions remains uncertain. In this study, we use high-speed video observations to examine the shape and size distribution of molten VBPs, termed “bombs” from Strombolian eruptions at Stromboli (in 2014 and 2017) and Etna (in 2014) volcanoes (Italy). We provide a framework for describing in-flight bomb shapes, defining three shape classes (rounded, elongate, and bilobate) and subclass end members. The ratio of rounded to total VBPs decreases with size; most bombs (71% of the total catalogued) are smaller than 0.16 m and tend to be rounded (53% at all sizes and 62% for < 0.16 m); however, 80% of the bombs larger than 0.32 m tend to be elongate or bilobate. This trend is generally consistent across Strombolian eruptions in both this study and previous studies. However, video datasets of bomb-bearing eruptions at Batu Tara (Indonesia, 2014, Strombolian to Vulcanian) and Cumbre Vieja (Canary Islands, 2021, strong Strombolian and high fire-fountaining) show fewer elongate and bilobate bombs, especially at larger sizes. This size-shape relationship presented here has not been previously accounted for in ballistic models but has the potential to provide a size-based drag coefficient within ballistic models, leading to more accurate modelling results.

Volcanoes grow and change shape by eruption, magma intrusion and by spreading outwards under their own weight. To understand the workings of a volcano, the balance of these processes must be quantified, requiring decades of observation. Here, we measure displacement of 72 survey points on and around Mt Etna to millimetre accuracy for periods of up to 42 years. We find that Etna is increasing in volume by 2.68 million cubic metres a year due to the emplacement of unerupted magma. This is an order of magnitude smaller than previously thought and implies that Etna’s magma generation system is 8 times slower than generally accepted. We also find that outward east–west displacement is creating new land surface at the summit at the rate of 1640 m2 per year and that Etna’s summit area is subsiding at rates exceeding 0.10 m per year in places.

This study investigates the integration of Earth Observation (EO) data and Machine Learning (ML) techniques for classifying volcanic activity states at Mount Etna, one of the world’s most active and monitored volcanoes. Using satellite data, including ground deformation, radiance, land surface temperature, sulfur dioxide emissions, and gravity anomalies, five volcanic activity states were identified: Quiet, Preparatory, Unrest, Eruption, and Cooling. Supervised ML algorithms, such as random forest, support vector machines, decision trees, and k-nearest neighbors, were employed to classify these states. Random forest achieved the highest accuracy, demonstrating its robustness for this application.The study addresses challenges like temporal and spatial disparities and class imbalances through data preprocessing, ensuring a reliable dataset for training and validation. A k-fold cross-validation approach was used to evaluate model performance systematically. The results underline the potential of ML techniques combined with EO data for volcanic hazard monitoring, with implications for improving risk assessment and early-warning systems. This methodology, tested on a well-instrumented volcano like Mount Etna, provides a foundation for extending the approach to other less-monitored volcanoes.These findings are one of the first attempts of integrating satellite data with Artificial Intelligence (AI) to enhance the accuracy of volcanic state predictions and mitigate risks associated with eruptions, while emphasizing the need for rigorous validation against well-documented case studies.

The Orbiting Carbon Observatory-3 (OCO-3) mission provides a new perspective for studying atmospheric carbon dioxide (CO2). Here we assess the potentiality of OCO-3 satellite acquisitions to analyse and monitor the CO2 emissions from Mt. Etna volcano. While OCO-3 data are well-suited for gas analysis on a regional spatial scale, they have not yet been widely utilised for studying volcanic carbon dioxide emissions. The Snapshot Area Map (SAM) acquisition mode enables the capture of targeted snapshots over volcanic regions, allowing for the measurement of CO2 concentrations in the vicinity of volcanic structures. In this work, we analyse 62 OCO-3 images acquired between 2020 and 2023, focusing on measurements within a 20 km radius of Mt. Etna’s summit, where the main craters are located. Atmospheric CO2 concentrations are examined as a function of distance from the summit, and assuming a linear decreasing trend, the angular coefficient is computed. Lower angular coefficient values may indicate a stronger volcanic CO2 contribution. Considering both the number of sampled pixels in each OCO-3 snapshot and the associated uncertainties in the angular coefficient calculation, we identify five days with potentially significant CO2 emissions from Mt. Etna, likely associated with specific volcanic activity phases. The eruptive activity on these five days is further investigated, revealing a possible correlation between elevated gas emissions and intense volcanic phenomena, such as lava fountains. This assessment is supported by thermal activity analyses using SEVIRI, MODIS, and VIIRS satellite data.

Between December 2020 and February 2022, Mt. Etna produced extraordinary lava fountains which developed into eruptive columns rising several kilometers above the vent. It is crucial to monitor the volcanic clouds produced during these eruptions to assess their impact on the environment, human health, and aviation. Geostationary satellite missions provide high‑frequency thermal infrared data, which are crucial for monitoring volcanic clouds during intense explosive eruptions. However, the large volume of satellite data necessitates automatic and accurate processing algorithms, especially when dealing with global‑scale observations every 5 minutes. In this work, a robust machine learning approach is developed to identify and track volcanic clouds using images from the EUMETSAT MSG SEVIRI (Meteosat Second Generation – Spinning Enhanced Visible and InfraRed Imager). This approach combines two distinct machine learning models: a deep learning (DL) model for volcanic cloud detection and a supervised machine learning (ML) model for identifying its primary components. The DL model segments volcanic clouds in SEVIRI images by analyzing both the spatial and spectral intensity data. The supervised ML model is able to distinguish the main components of a volcanic cloud by classifying the pixels as ash‑rich, SO2‑rich, or characterized by mixed components. Once an accurate mask of the volcanic cloud is obtained, the volcanic plume height is retrieved from satellite observations for further characterization. This integrated ML approach was applied to characterize the volcanic clouds produced during some of the lava fountains occurred at Etna volcano (Italy) between 2020 and 2022.

Investigating crustal stress beneath volcanoes is critical to understanding the dynamics of eruptions. To this end, seismology represents a powerful monitoring tool. The opening of fluid-filled fractures due to the interplay of different stress sources produces elastic anisotropy within the crust, affecting the propagation of seismic waves. Here we use probabilistic imaging for the inversion of P-wave travel times to map elastic anisotropy of the magmatic system beneath Mt. Etna (Italy). These images provide localized information about fracture orientations and stress below this active volcano. Comparing inferred stress with independent observations and geodynamic modeling, we show evidence of a pressurized magma storage in a radial dike network between 6 and 16 km depth under the volcano. The radial network of vertical dikes constitutes a system of oriented pathways for the upward migration of magma from the depths, leading to eruptive activity from the summit craters and lateral vents at Mount Etna.

Fe-rich filamentary textures are almost ubiquitous in products from explosive eruptions at basaltic volcanoes and, in particular, they characterize the groundmass of ash and lapilli emitted during high-energy events. Here, we present a multidisciplinary study integrating petrological analyses with computational fluid dynamics simulations to propose a new mechanism responsible for their formation. Detailed textural and compositional features of Fe-rich filaments were examined in the products of explosive eruptions with different intensities from Stromboli and Etna (Italy) volcanoes. Results reveal that they represent compositional boundary layers developed at the plagioclase-melt interface in response to the interaction between magmas with different compositions and volatile contents. Numerical simulations indicate that boundary layers can detach from crystals and disperse into resident melts due to their higher density and can survive as metastable melts for some days under magmatic conditions. We suggest that Fe-rich filaments testify to the recharging of deep magma a few days before high-energy explosive events at basaltic open-vent volcanoes, even when primitive magmas are not erupted.