Tropospheric Delay in the Neapolitan and Vesuvius Areas (Italy) by Means of a Dense GPS Array: A Contribution for Weather Forecasting and Climate Monitoring

Studying the spatiotemporal distribution and motion of water vapour (WV), the most variable greenhouse gas in the troposphere, is pivotal, not only for meteorology and climatology, but for geodesy, too. In fact, WV variability degrades, in an unpredictable way, almost all geodetic observation based on the propagation of electromagnetic signal through the atmosphere. We use data collected on a dense GPS network, designed for the purposes of monitoring the active Neapolitan (Italy) volcanoes, to retrieve the tropospheric delay parameters and precipitable water vapour (PWV). This study has two main targets: (a) the analysis of long datasets (11 years) to extract trends of climatological meaning for the region; (b) studying the main features of the time evolution of the PWV during heavy raining events to gain knowledge on the preparatory stages of highly impacting thunderstorms. For the latter target, both differential and precise point positioning (PPP) techniques are used, and the results are compared and critically discussed. An increasing trend, amounting to about 2 mm/decades, has been recognized in the PWV time series, which is in agreement with the results achieved in previous studies for the Mediterranean area. A clear topographic effect is detected for the Vesuvius volcano sector of the network and a linear relationship between PWV and altitude is quantitatively assessed. This signature must be taken into account in any modelling for the atmospheric correction of geodetic and remote-sensing data (e.g., InSAR). Characteristic temporal evolutions were recognized in the PWV in the targeted thunderstorms (which occurred in 2019 and 2020), i.e., a sharp increase a few hours before the main rain event, followed by a rapid decrease when the thunderstorm vanished. Accounting for such a peculiar trend in the PWV could be useful for setting up possible early warning systems for those areas prone to flash flooding, thus potentially providing a tool for disaster risk reduction

Using ground motion prediction equations to monitor variations in quality factor due to induced seismicity: a feasibility study

Sub-surface operations for energy production such as gas storage, fluid reinjection or hydraulic fracking may modify the physical properties of the rocks, in particular the seismic velocity and the anelastic attenuation. The aim of the present study is to investigate, through a synthetic test, the possibility of using empirical ground-motion prediction equations (GMPEs) to observe the variations in the reservoir. In the synthetic test, we reproduce the expected seismic activity (in terms of rate, focal mechanisms, stress drop and the b value of the Gutenberg-Richter) and the variation of medium properties in terms of the quality factor Q induced by a fluid injection experiment. In practice, peak-ground velocity data of the simulated earthquakes during the field operations are used to update the coefficients of a reference GMPE in order to test whether the coefficients are able to capture the medium properties variation. The results of the test show that the coefficients of the GMPE vary during the simulated field operations revealing their sensitivity to the variation of the anelastic attenuation. The proposed approach is suggested as a promising tool that, if confirmed by real data analysis, could be used for monitoring and interpreting induced seismicity in addition to more conventional techniques

Decentralised cooperation and its potential for local democratic governance: the experience of Trentino decentralised cooperation

Since the early 1990s Local Authorities (LAs) have stepped into the international development cooperation arena and in many European countries Decentralised Cooperation (DC) has become increasingly relevant as a new development cooperation modality that has great potential in terms of promoting sustainable development and local democratic governance. DC in international development is a relatively new phenomenon and its full potential is not yet fully known. Based on the findings of a two year long evaluative research of four DC programmes implemented by Trentino in Northern Italy in partnership with three municipalities in the Balkans and a rural district in Mozambique, this paper contributes to the literature on DC policies and practices in highlighting a scarcely analysed issue: the contribution of DC to local democratic governance and the possible reasons as to why DC, despite its potential, faces challenges that hinder its capacity to effectively contribute to decentralised governanc

Decentralised cooperation and its potential for local democratic governance: the experience of Trentino decentralised cooperation

Since the early 1990s Local Authorities (LAs) have stepped into the international development cooperation arena and in many European countries Decentralised Cooperation (DC) has become increasingly relevant as a new development cooperation modality that has great potential in terms of promoting sustainable development and local democratic governance. DC in international development is a relatively new phenomenon and its full potential is not yet fully known. Based on the findings of a two year long evaluative research of four DC programmes implemented by Trentino in Northern Italy in partnership with three municipalities in the Balkans and a rural district in Mozambique, this paper contributes to the literature on DC policies and practices in highlighting a scarcely analysed issue: the contribution of DC to local democratic governance and the possible reasons as to why DC, despite its potential, faces challenges that hinder its capacity to effectively contribute to decentralised governanc

The ARGO Project: assessing NA-TECH risks on offshore oil platforms

Abstract Analysis of natural and anthRopoGenic risks on Offshore platforms (ARGO) is a 3-years project, funded by the Italian Ministry of Economic Development. The project, coordinated by AMRA, a permanent Research Centre for the development of innovative technologies applied to environmental problems, aims at providing technical-support for the analysis of natural and anthropogenic risks on offshore oil-platforms. ARGO has developed methodologies for the probabilistic analysis of industrial accidents triggered by natural events (NA-TECH) on offshore platforms. The final analysis of the ARGO Project suggest a constant monitoring of exploitation activity, fluids re-injection and storage using high technology networks

Heterozygous missense variants of SPTBN2 are a frequent cause of congenital cerebellar ataxia

Heterozygous missense variants in the SPTBN2 gene, encoding the non-erythrocytic beta spectrin 2 subunit (beta-III spectrin), have been identified in autosomal dominant spinocerebellar ataxia type 5 (SCA5), a rare adult-onset neurodegenerative disorder characterized by progressive cerebellar ataxia, whereas homozygous loss of function variants in SPTBN2 have been associated with early onset cerebellar ataxia and global developmental delay (SCAR14). Recently, heterozygous SPTBN2 missense variants have been identified in a few patients with an early-onset ataxic phenotype. We report five patients with non-progressive congenital ataxia and psychomotor delay, 4/5 harboring novel heterozygous missense variants in SPTBN2 and one patient with compound heterozygous SPTBN2 variants. With an overall prevalence of 5% in our cohort of unrelated patients screened by targeted next generation sequencing (NGS) for congenital or early-onset cerebellar ataxia, this study indicates that both dominant and recessive mutations of SPTBN2 together with CACNA1A and ITPR1, are a frequent cause of early-onset/congenital non-progressive ataxia and that their screening should be implemented in this subgroup of disorders

The active portion of the Campi Flegrei caldera structure imaged by 3-D inversion of gravity data

Publisher's Version/PDFWe present an improved density model and a new structural map of the Neapolitan Yellow Tuff caldera, the active portion of the nested Campi Flegrei caldera. The model was built using a new 3-D inversion of the available high-precision gravity data, and a new digital terrain and marine model. The inversion procedure, based on a variable-depth lumped assembling of the subsurface gravity distribution via cell aggregation, gives better defined insights into the internal caldera architecture, that well agree with the available geological, geophysical, and geochemical data. The adopted 3-D gravity method is highly efficient for characterizing the shallow caldera structure (down to 3 km depth) and defining features related to regional or volcano tectonic lineaments and dynamics. In particular, the resulting density distribution highlights a pronounced density low in correspondence of the central portion of the caldera with a detail not available till now. The joint interpretation of the available data suggests a subsurface structural setting that supports a piecemeal collapse of the caldera, and allows the identification of its headwall. Positive gravity anomalies localize dense intrusions (presently covered by late volcanic deposits) along the caldera marginal faults, and the main structural lineaments both bordering the resurgent block and cutting the caldera floor. These results allow us to both refine the current geological-structural framework and propose a new structural map that highlights the caldera boundary and its internal setting. This map is useful to interpret the phenomena occurring during unrest, and to improve both short-term and long-term volcanic hazards assessment

Induced seismicity response of hydraulic fracturing: results of a multidisciplinary monitoring at the Wysin site, Poland

Shale oil and gas exploitation by hydraulic fracturing experienced a strong development worldwide over the last years, accompanied by a substantial increase of related induced seismicity, either consequence of fracturing or wastewater injection. In Europe, unconventional hydrocarbon resources remain underdeveloped and their exploitation controversial. In UK, fracturing operations were stopped after the Mw 2.3 Blackpool induced earthquake; in Poland, operations were halted in 2017 due to adverse oil market conditions. One of the last operated well at Wysin, Poland, was monitored independently in the framework of the EU project SHEER, through a multidisciplinary system including seismic, water and air quality monitoring. The hybrid seismic network combines surface mini-arrays, broadband and shallow borehole sensors. This paper summarizes the outcomes of the seismological analysis of these data. Shallow artificial seismic noise sources were detected and located at the wellhead active during the fracturing stages. Local microseismicity was also detected, located and characterised, culminating in two events of Mw 1.0 and 0.5, occurring days after the stimulation in the vicinity of the operational well, but at very shallow depths. A sharp methane peak was detected ~19 hours after the Mw 0.5 event. No correlation was observed between injected volumes, seismicity and groundwater parameters

CFM: a convolutional neural network for first-motion polarity classification of seismic records in volcanic and tectonic areas

First-motion polarity determination is essential for deriving volcanic and tectonic earthquakes’ focal mechanisms, which provide crucial information about fault structures and stress fields. Manual procedures for polarity determination are time-consuming and prone to human error, leading to inaccurate results. Automated algorithms can overcome these limitations, but accurately identifying first-motion polarity is challenging. In this study, we present the Convolutional First Motion (CFM) neural network, a label-noise robust strategy based on a Convolutional Neural Network, to automatically identify first-motion polarities of seismic records. CFM is trained on a large dataset of more than 140,000 waveforms and achieves a high accuracy of 97.4% and 96.3% on two independent test sets. We also demonstrate CFM’s ability to correct mislabeled waveforms in 92% of cases, even when they belong to the training set. Our findings highlight the effectiveness of deep learning approaches for first-motion polarity determination and suggest the potential for combining CFM with other deep learning techniques in volcano seismology