Global radiant flux from active volcanoes: the 2000–2019 MIROVA database

Since 2000, the Moderate Resolution Imaging Spectroradiometer (MODIS) has acquired infrared images of the Earth’s surface daily. These data have made it possible to measure the thermal energy radiated by the world’s most famous volcanoes and also to discover and track eruptions in remote and poorly monitored regions. In this work, we present the database of Volcanic Radiative Power (VRP, in W) time series, recorded by the MIROVA (Middle Infrared Observation of Volcanic Activity) system over 2 decades of MODIS observations (2000–2019) at 111 active volcanoes. The database reveals that globally, the number of thermally active volcanoes each year varies between 60 and 80, almost equally partitioned between volcanoes with a basic (50%) and intermediate (45%) composition, while only 5% is represented by volcanoes erupting acidic lavas. Within the investigated period, the global-scale heat flux was almost stationary, and occasionally punctuated by peaks associated with the largest effusive eruptions (e.g., Bardarbunga and Kilauea). The Volcanic Radiative Energy (VRE, in J) emitted by basic volcanoes (~1.8 × 1018 J) in 20 years constitutes 91% of the total, while intermediates and acids contribute only 8% (~1.8 × 1017 J) and 1% (~1.7 × 1016 J), respectively. A comparison with the volume of lava erupted effusively by the same volcanoes reveals that this difference is attributed to the lower efficiency in radiating thermal energy of increasingly acidic (viscous) lava bodies. Each compositional group is associated with a specific relationship between VRE and erupted volume which characterises most of the effusive volcanoes. On the other hand, some open-vent volcanoes reveal that much more heat is released than that theoretically radiated by the erupted lava. This imbalance (hereby called excess radiation) is attributed to an additional heat source, likely associated with an underlying convective magma column and/or to outgassing through a permeable conduit. We are convinced that the database presented in this work will be useful to support new emerging studies on globalscale volcanism and will contribute to a better understanding of each volcanic system

Presence and correlates of autistic traits among patients with social anxiety disorder

Introduction: Due to their similar behavioral presentation, it can sometimes be challenging to distinguish between a social anxiety disorder (SAD) and the social avoidance that is frequently described in autism spectrum disorder (ASD). Moreover, a growing body of evidences is reporting that a significant proportion of subjects with ASD also meet the requirements for SAD and, vice versa, subjects with SAD tend to exhibit a higher prevalence of autistic traits. Aim: In this framework, the current study aims to evaluate prevalence and correlates of autistic traits in a sample of adult subjects diagnosed with SAD and healthy controls (HC), also evaluating which autism spectrum dimensions may statistically predict higher SAD symptoms. Methods: 56 subjects with a clinical diagnosis of SAD and 56 gender and age matched HC were recruited from the Psychiatric Clinic of the University of Pisa. Subjects were assessed with the SCID-5, the Social Anxiety Spectrum – Short Version (SHY- SV) and the Adult Autism Subthreshold Spectrum (AdAS Spectrum). Results: SAD group scored significantly higher in all AdAS Spectrum and SHY-SV domains and total score compared to the HC group with no significant gender difference. SHY-SV total and domain scores, were strongly and positively and strongly correlated with all AdAS Spectrum domains and total score. AdAS Spectrum total score and Childhood/Adolescence, Non-Verbal Communication, Empathy and Restricted interests and Rumination domain scores score were significant predictors of higher SHY-SV score. Conclusion: Our results confirm the link between SAD and autistic traits also in adult population, describing not only high levels of autistic traits in SAD adults, but also significant correlations between many core features of the two disorders and a predictive role of autistic traits on higher SAD symptoms

Towards global volcano monitoring using multisensor sentinel missions and artificial intelligence: The MOUNTS monitoring system

Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018–2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards

Volcanic CO2 tracks the incubation period of basaltic paroxysms

The ordinarily benign activity of basaltic volcanoes is periodically interrupted by violent paroxysmal explosions ranging in size from Hawaiian to Plinian in the most extreme examples. These paroxysms often occur suddenly and with limited or no precursors, leaving their causal mechanisms still incompletely understood. Two such events took place in summer 2019 at Stromboli, a volcano otherwise known for its persistent mild open-vent activity, resulting in one fatality and damage to infrastructure. Here, we use a post hoc analysis and reinterpretation of volcanic gas compositions and fluxes acquired at Stromboli to show that the two paroxysms were preceded by detectable escalations in volcanic plume CO2 degassing weeks to months beforehand. Our results demonstrate that volcanic gas CO2 is a key driver of explosions and that the preparatory periods ahead of explosions in basaltic systems can be captured by precursory CO2 leakage from deeply stored mafic magma

Mood symptoms and suicidality across the autism spectrum

Abstract Background Autism spectrum is a psychopathological dimension which encompasses a wide range of clinical presentations: from subthreshold forms and autistic traits (AT), that can be found in the general population, to full-blown autism spectrum disorder (ASD). Many studies reported high rates of comorbidity between both ASD and AT and mood disorders, as well as a high prevalence of suicidal ideation among patients with ASD/AT. The aim of this study was to investigate the presence of mood symptoms and suicidal ideation and behaviors in patients with full-blown ASD and in subjects with AT, as well in a healthy control (HC) group, with a specific focus on which of the autistic features may be predictive of suicidal ideation and behaviors. Methods We recruited 262 adult subjects: 34 with ASD without intellectual impairment or language disability (ASD group), 68 fulfilling only one symptom criterion for ASD according to DSM-5 but who do not meet criteria for a full-blown diagnosis of ASD (AT group), and 160 HC. All subjects were assessed with the Structured Clinical Interview for DSM-5 (SCID-5); in addition, they were asked to fill two questionnaires: The Mood Spectrum, Self-report (MOODS-SR) and the Adult Autism Subthreshold Spectrum (AdAS Spectrum). Results ASD subjects reported significantly higher AdAS Spectrum and MOODS-SR total scores, as well as higher MOODS-SR depressive component total scores, when compared with AT and HC subjects. AT subjects scored significantly higher than the HC group. No significant differences were reported between ASD and AT subjects for the suicidality score according to MOODS-SR, despite both groups scored significantly higher than the HC group. The strongest predictor of suicidality score were MOODS-SR depressive component score and AdAS Spectrum Restricted interests and rumination domain score. Conclusions Our results highlight a correlation between autism and mood spectrum, as well as between suicidality and both ASD and AT. Subthreshold forms of ASD should be accurately investigated due to their relationship with suicidal thoughts and behaviors

Ground deformation reveals the scale-invariant conduit dynamics driving explosive basaltic eruptions

The mild activity of basaltic volcanoes is punctuated by violent explosive eruptions that occur without obvious precursors. Modelling the source processes of these sudden blasts is challenging. Here, we use two decades of ground deformation (tilt) records from Stromboli volcano to shed light, with unprecedented detail, on the short-term (minute-scale) conduit processes that drive such violent volcanic eruptions. We find that explosive eruptions, with source parameters spanning seven orders of magnitude, all share a common pre-blast ground inflation trend. We explain this exponential inflation using a model in which pressure build-up is caused by the rapid expansion of volatile-rich magma rising from depth into a shallow (<400m) resident magma conduit. We show that the duration and amplitude of this inflation trend scales with the eruption magnitude, indicating that the explosive dynamics obey the same (scale-invariant) conduit process. This scale-invariance of pre-explosion ground deformation may usher in a new era of short-term eruption forecasting

Excess degassing drives long-term volcanic unrest at Nevado del Ruiz

This study combines volcanic gas compositions, SO2 flux and satellite thermal data collected at Nevado del Ruiz between 2018 and 2021. We find the Nevado del Ruiz plume to have exhibited relatively steady, high CO2 compositions (avg. CO2/ST ratios of 5.4 ± 1.9) throughout. Our degassing models support that the CO2/ST ratio variability derives from volatile exsolution from andesitic magma stored in the 1–4 km depth range. Separate ascent of CO2-rich gas bubbles through shallow (< 1 km depth), viscous, conduit resident magma causes the observed excess degassing. We infer that degassing of ~ 974 mm3 of shallow (1–4 km) stored magma has sourced the elevated SO2 degassing recorded during 2018–2021 (average flux ~ 1548 t/d). Of this, only < 1 mm3 of magma have been erupted through dome extrusion, highlighting a large imbalance between erupted and degassed magma. Escalating deep CO2 gas flushing, combined with the disruption of passive degassing, through sudden accumulation and pressurization of bubbles due to lithostatic pressure, may accelerate volcanic unrest and eventually lead to a major eruption