Search

Volcanic plume height is one the most important features of explosive activity; thus, it is a parameter of interest for volcanic monitoring that can be retrieved using different remote sensing techniques. Among them, calibrated visible cameras have demonstrated to be a promising alternative during daylight hours, mainly due to their low cost and low uncertainty in the results. However, currently these measurements are generally not fully automatic. In this paper, we present a new, interactive, open-source MATLAB tool, named 'Plume Height Analyzer' (PHA), which is able to analyze images and videos of explosive eruptions derived from visible cameras, with the objective of automatically identifying the temporal evolution of eruption columns. PHA is a self-customizing tool, i.e., before operational use, the user must perform an iterative calibration procedure based on the analysis of images of previous eruptions of the volcanic system of interest, under different eruptive, atmospheric and illumination conditions. The images used for the calibration step allow the computation of ad hoc expressions to set the model parameters used to recognize the volcanic plume in new images, which are controlled by their individual characteristics. Thereby, the number of frames used in the calibration procedure will control the goodness of the model to analyze new videos/images and the range of eruption, atmospheric, and illumination conditions for which the program will return reliable results. This also allows improvement of the performance of the program as new data become available for the calibration, for which PHA includes ad hoc routines. PHA has been tested on a wide set of videos from recent explosive activity at Mt. Etna, in Italy, and may represent a first approximation toward a real-time analysis of column height using visible cameras on erupting volcanoes. [ABSTRACT FROM AUTHOR]

Lack of interregional trade data is often a major obstacle when doing economic analysis at the subnational level. This paper discusses a calibration procedure for estimating bilateral trade between the regions of a country. Our approach can be equivalently characterized as an application of the gravity-RAS or the doubly constrained gravity model method. Either way, a crucial element is represented by the distance elasticity parameter, which the user is expected to provide exogenously. We propose a way of estimating that parameter using standard econometric methods with readily available data and demonstrate our calibration procedure in a case study of Italy. [ABSTRACT FROM AUTHOR]

Accurate modeling of the patient flow within an Emergency Department (ED) is required by all studies dealing with the increasing and well-known problem of overcrowding. Since Discrete Event Simulation (DES) models are often adopted with the aim of assessing solutions for reducing the impact of this worldwide phenomenon, an accurate estimation of the service time of the ED processes is necessary to guarantee the reliability of the results. However, simulation models concerning EDs are frequently affected by missing data, thus requiring a proper estimation of some unknown parameters. In this paper, a simulation-based optimization approach is used to estimate the incomplete data in the patient flow within an ED by adopting a model calibration procedure. The objective function of the resulting minimization problem represents the deviation between simulation output and real data, while the constraints ensure that the response of the simulation is sufficiently accurate according to the precision required. Data from a real case study related to a big ED in Italy is used to test the effectiveness of the proposed approach. The experimental results show that the model calibration allows recovering the missing parameters, thus leading to an accurate DES model. [ABSTRACT FROM AUTHOR]

Robustness of source parameter estimates is a fundamental issue in understanding the relationships between small and large events; however, it is difficult to assess how much of the variability of the source parameters can be attributed to the physical source characteristics or to the uncertainties of the methods and data used to estimate the values. In this study, we apply the coda method by Mayeda et al. using the coda calibration tool (CCT), a freely available Java-based code (https://github.com/LLNL/coda-calibration-tool) to obtain a regional calibration for Central Italy for estimating stable source parameters. We demonstrate the power of the coda technique in this region and show that it provides the same robustness in source parameter estimation as a data-driven methodology [generalized inversion technique (GIT)], but with much fewer calibration events and stations. The Central Italy region is ideal for both GIT and coda approaches as it is characterized by high-quality data, including recent well-recorded seismic sequences such as L'Aquila (2009) and Amatrice–Norcia–Visso (2016–2017). This allows us to apply data-driven methods such as GIT and coda-based methods that require few, but high-quality data. The data set for GIT analysis includes ∼5000 earthquakes and more than 600 stations, while for coda analysis we used a small subset of 39 events spanning 3.5 <  M w < 6.33 and 14 well-distributed broad-band stations. For the common calibration events, as well as an additional 247 events (∼1.7 <  M w < ∼5.0) not used in either calibration, we find excellent agreement between GIT-derived and CCT-derived source spectra. This confirms the ability of the coda approach to obtain stable source parameters even with few calibration events and stations. Even reducing the coda calibration data set by 75 per cent, we found no appreciable degradation in performance. This validation of the coda calibration approach over a broad range of event size demonstrates that this procedure, once extended to other regions, represents a powerful tool for future routine applications to homogeneously evaluate robust source parameters on a national scale. Furthermore, the coda calibration procedure can homogenize the M w estimates for small and large events without the necessity of introducing any conversion scale between narrow-band measures such as local magnitude (M L) and M w, which has been shown to introduce significant bias. [ABSTRACT FROM AUTHOR]

Seismic events that struck Italy in the last years had a dramatic impact on historical and cultural heritage. Damage occurred on existing masonry buildings, in particular on churches, stressed out the need to better understand the seismic behaviour of this type of constructions as well as to define more appropriate mitigation strategies to be applied at large scale. This study deals with a predictive methodology for vulnerability assessment of churches in large territorial areas. Focusing the attention on three naves churches of Abruzzi, the paper proposes modifications and integrations of an existing methodology taken by literature. These modifications are defined through a calibration procedure based on damage observation of 64 three naves churches located at different distances from the epicentre of the 2009 earthquake. The proposed model is employed to predict damage level distribution for different earthquake intensities, allowing fragility curves to be plotted and therefore potential damage scenarios to be represented. [ABSTRACT FROM AUTHOR]

Featured Application: This work could contribute to evidence strengths and weaknesses of manual and automatic calibration of building dynamic simulation models leading at improving the quality of building retrofit solutions investigation. Energy reduction can benefit from the improvement of energy efficiency in buildings. For this purpose, simulation models can be used both as diagnostic and prognostic tools, reproducing the behaviour of the real building as accurately as possible. High modelling accuracy can be achieved only through calibration. Two approaches can be adopted—manual or automatic. Manual calibration consists of an iterative trial and error procedure that requires high skill and expertise of the modeler. Automatic calibration relies on mathematical and statistical methods that mostly use optimization algorithms to minimize the difference between measured and simulated data. This paper aims to compare a manual calibration procedure with an automatic calibration method developed by the authors, coupling dynamic simulation, sensitivity analysis and automatic optimization using IDA ICE, Matlab and GenOpt respectively. Differences, advantages and disadvantages are evidenced applying both methods to a dynamic simulation model of a real office building in Rome, Italy. Although both methods require high expertise from operators and showed good results in terms of accuracy, automatic calibration presents better performance and consistently helps with speeding up the procedure. [ABSTRACT FROM AUTHOR]

A portable analyser is described for simultaneous detection of CO2 and HF emitted by volcanoes and fumaroles. The system is based on two fibre-coupled distributed feedback lasers and only one multipass cell, and provides the absolute concentration values of the two gases, without requiring a calibration procedure, at a maximum rate of 4 Hz. The spectrometer can operate both in a closed-cell configuration and in an open-cell set-up, with the latter mitigating problems associated with chemisorption of the HF molecule. The concept, practical realization and laboratory performance of the device are presented, together with results from a first test campaign measuring volcanic gases emitted from the crater of Vulcano, Italy. We obtained an in-field sensitivity of 320 ppb for CO2 and 20 ppb for HF at 2 s integration time. [ABSTRACT FROM AUTHOR]

An accurate modeling of the ionosphere electron density is pivotal to guarantee the effective operation of communication and navigation systems, particularly during Space Weather events. Despite the crucial contribution of empirical models like the International Reference Ionosphere (IRI), their limitations in predicting ionospheric variability, especially under geomagnetically disturbed conditions, are acknowledged. The solution proposed in this work involves integrating real‐time, spatially distributed ionospheric measurements into climatological models through data assimilation. To enhance our predictive capabilities, we present an upgrade of the IRI UP data‐assimilation method, incorporating real‐time vertical total electron content (vTEC) maps from the IONORING algorithm for nowcasting ionospheric conditions over Italy. This approach involves updating the IRI F2‐layer peak electron density description through ionospheric indices, to finally produce real‐time maps over Italy of the ordinary critical frequency of the F2‐layer, foF2, which is crucial for radio‐propagation applications. The IRI UP–IONORING method performance has been evaluated against different climatological and nowcasting models, and under different Space Weather conditions, by showing promising outcomes which encourages its inclusion in the portfolio of ionospheric real‐time products available over Italy. The validation analysis highlighted also what are the current limitations of the IRI UP–IONORING method, particularly during nighttime for severely disturbed conditions, suggesting avenues for future enhancements. Plain Language Summary: Ensuring the smooth operation of communication and navigation systems based on both High‐Frequency and GNSS radio signals relies on accurately modeling the density of electrons in the ionosphere. Existing empirical models, like the International Reference Ionosphere (IRI), have limitations in predicting small‐scale spatial and temporal changes in the ionosphere, especially during severe Space Weather events. This work proposes a solution by incorporating real‐time ionospheric measurements into a background model through a process called data assimilation. Specifically, the IRI UP method has been upgraded by assimilating real‐time vertical total electron content maps from the IONORING algorithm for nowcasting ionospheric conditions over Italy. The performance of this upgraded method has been tested against different models and for different Space Weather conditions, showing promising results. These findings open avenues for future improvements contributing to enhancing our ability to predict and adapt to dynamic changes in the Earth's ionosphere, of which various communication and navigation technologies would benefit. Key Points: IRI UP method upgrade through the assimilation of real‐time vTEC maps from IONORING for nowcasting ionospheric conditions over ItalyIRI UP outputs real‐time foF2 maps over Italy through the ingestion of ionospheric effective indices maps in the background IRI modelIt has been successfully tested against several climatological and nowcasting models, and under different Space Weather conditions [ABSTRACT FROM AUTHOR]

Since the beginning of the 20th century, many studies have focused on the possibility of considering the sky as a body characterized by an apparent temperature, and several correlations to quantify the apparent sky temperature have been proposed. However, the different models were obtained for specific meteorological conditions and through measurements at specific sites. The available models do not cover all locations in the world, although the evaluation of the sky temperature is fundamental for estimating the net radiative heat transfer between surfaces and the sky. Here, experimental data logged from a regional micrometeorological network (in Italy, within the Lazio region) were processed and used to identify an empirical model for the estimation of the sky temperature in the Mediterranean area. Data relating to atmospheric infrared radiation were used to compute the sky temperature, aiming at identifying a direct correlation with the ambient temperature. Climatic data acquired during 2022 were processed. The proposed correlations were compared with other models available in the literature, including the standard ISO 13790. This study proposes an annual-based direct correlation in its initial phase, demonstrating a superior fit to the measured data compared to well-known direct empirical models from the literature. Subsequently, quarterly-based correlations are introduced further in a secondary phase of the work to improve the model's adaptation to experimental observations. The results reveal that quarterly-based correlations improve goodness-of-fit indexes compared to annual-based and well-known direct empirical correlations. Finally, a detached building was modeled via a dynamic code to highlight the influence of different correlations on annual energy needs. [ABSTRACT FROM AUTHOR]

In this paper the actual dynamic behavior of the civic Clock tower of Rotella, a little village in central Italy heavily damaged by the recent 2016 seismic sequence, is thoroughly investigated by means of a detailed numerical model built and calibrated using the experimental modal properties obtained through Ambient Vibration Tests. The goal is to update the uncertain parameters of two behavioral material models applied to the Finite Element Model (elastic moduli, mass densities, constraints, and boundary conditions) to minimize the discrepancy between experimental and numerical dynamic features. A sensitivity analysis was performed with the definition of a metamodel to reduce the computational strain and try to define the necessary parameters to use for the calibration process. Due to the high nonlinear dependency of the objective function of this optimization problem on the parameters, and the likely possibility to get trapped in local minima, a machine learning approach was meant. A fully automated Finite Element Model updating procedure based on genetic algorithms and global optimization is used, leading to tower uncertain parameters identification. The results allowed to create a reference numerical replica of the structure in its actual health state and to assess its dynamic performances allowing better control over their future evolution. [ABSTRACT FROM AUTHOR]

We employ a new macroeconomic–epidemiological agent-based model to evaluate the "lives vs livelihoods" trade-off brought to the fore by coronavirus disease (Covid-19). An infectious disease spreads across the network of agents' social and economic contacts and feeds back on the economic dimension of the model through various channels such as employment and consumption demand. Under a lockdown (LD) scenario, the model is able to closely reproduce the epidemiological dynamics of the first wave of the Covid-19 epidemic in the Lombardy region of Italy. We find that, in the presence of non-pharmaceutical interventions, there is no trade-off between lives and macroeconomic losses as a stricter LD eventually leads to superior outcomes along both dimensions. We also evaluate the efficacy of various macroeconomic stabilization policies designed to counteract the downturn generated by the epidemic and LD measures. In an agent-based setting we gain additional insights on the way in which such policies impact not only on gross domestic product but also, for instance, on firms' defaults and relative prices. Liquidity support for firms, a short-time working scheme with compensation for workers, and direct transfer to households stand out as the most effective policy tools. [ABSTRACT FROM AUTHOR]

This work presents the new model called CRHyME (Climatic Rainfall Hydrogeological Modelling Experiment), a tool for geo-hydrological hazard evaluation. CRHyME is a physically based and spatially distributed model written in the Python language that represents an extension of the classic hydrological models working at the basin scale. CRHyME's main focus consists of simulating rainfall-induced geo-hydrological instabilities such as shallow landslides, debris flows, catchment erosion and sediment transport into a river. These phenomena are conventionally decoupled from a hydrological routine, while in CRHyME they are simultaneously and quantitatively evaluated within the same code through a multi-hazard approach. CRHyME is applied within some case studies across northern Italy. Among these, the Caldone catchment, a well-monitored basin of 27 km 2 located near the city of Lecco (Lombardy), was considered for the calibration of solid-transport routine testing, as well as the spatial-scale dependence related to digital terrain resolution. CRHyME was applied across larger basins of the Valtellina (Alps) and Emilia (Apennines) areas (∼2600 km 2) which have experienced severe geo-hydrological episodes triggered by heavy precipitation in the recent past. CRHyME's validation has been assessed through NSE (Nash–Sutcliffe efficiency) and RMSE (root mean square error) hydrological-error metrics, while for landslides the ROC (receiver operating characteristic) methodology was applied. CRHyME has been able to reconstruct the river discharge at the reference hydrometric stations located at the outlets of the basins to estimate the sediment yield at some hydropower reservoirs chosen as a reference and to individuate the location and the triggering conditions of shallow landslides and debris flows. The good performance of CRHyME was reached, assuring the stability of the code and a rather fast computation and maintaining the numerical conservativity of water and sediment balances. CRHyME has shown itself to be a suitable tool for the quantification of the geo-hydrological process and thus useful for civil-protection multi-hazard assessment. [ABSTRACT FROM AUTHOR]

The Ansanto Valley's Mefite, one of the Earth's largest non‐volcanic CO2 gas emissions, is distinguished by its cold natural carbon dioxide springs. These emissions originate from the intricate tectonics and geodynamics of the southern Apennines in Italy. Known for over two millennia for its lethal concentration of CO2 and other harmful gases, the Mefite has a reputation for being toxic and dangerous. Despite its historical significance and unique geological features, there is a lack of information on the microbial diversity associated with the Mefite's gas emissions. This study presents an integrated exploration of the microbial diversity in the mud soil, using high‐throughput sequencing of 16S rRNA (Prokaryotes) and ITS2 (Fungi), alongside a geochemical site characterisation. Our findings reveal that the Mefite's unique environment imposes a significant bottleneck on microbial diversity, favouring a select few microbial groups such as Actinobacteria and Firmicutes for Prokaryotes, and Basidiomycota for Fungi. [ABSTRACT FROM AUTHOR]

In-line valves are devices typically used for isolation or flow regulation in pipe systems, playing a key role in the operational management of transmission mains (TM). However, there is no fast and expeditious procedure available for checking the efficacy of the sealing mechanism, and its ability to prevent leakage, unwanted flow or partial blockages, which is a crucial action for any maintenance operation. Due to the different values of the conveyed discharge, the diameter changes along the TM at a series junctions which therefore makes diameter changes a very common singularity. This paper has two aims. The first one is to evaluate the feasibility of Inverse Transient Analysis (ITA) for checking the sealing of in-line valves. In particular, the primary objective of the numerical model is to identify the distinctive features of the measured pressure signals that correspond to the status of an in-line valve, discerning whether it is fully sealed or partially closed. The second objective is to use Direct Analysis (DA) of the pressure signals to appropriately capture the transient response of the series junctions. To address these issues, safe transients have been generated in a real TM by means of a Portable Pressure Wave Maker (PPWM) device, refined at the Water Engineering Laboratory (WEL) of the University of Perugia, Italy. The results of the field tests and numerical model point out that the positive pressure wave reflected by the in-line valve is smaller than the one expected if it were perfectly sealed. Moreover, the transient response of the series junction has been properly captured by the DA of the pressure signal. Accordingly, the proposed procedures have been demonstrated to be suitable tools for the management of long transmission pipelines. [ABSTRACT FROM AUTHOR]

In this paper, an analysis of air quality data is provided for the municipal area of Taranto (southern Italy) characterized by high environmental risks as formally decreed by the Italian government in the 1990s with two administrative measures. This is due to the massive presence of industrial sites with elevated environmental impact activities along the NW boundary of the city conurbation. The aforementioned activities have effects on the environment and on public health, as a number of epidemiological researches concerning this area reconfirm. The present study is focused on particulate matter as measured by PM10 concentrations at 13 monitoring stations, equipped with analogous instruments based on the Beta absorption technology, either reporting hourly, two-hourly, or daily measurements. Daily estimates of the PM10 concentration surfaces are obtained in order to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities. Preliminary analysis involved addressing several data problems: (1) due to the use of two different validation techniques, a calibration procedure was devised to allow for data comparability; (2) imputation techniques were considered to cope with the large number of missing data, due to both different working periods and occasional malfunctions of PM10 sensors; and (3) reliable weather covariates (wind speed and direction, pressure, temperature, etc.) were obtained and considered within the analysis. Spatiotemporal modelling was addressed by a Bayesian kriging-based model proposed by Le and Zidek () characterized by the use of time varying covariates and a semiparametric covariance structure. Advantages and disadvantages of the model are highlighted and assessed in terms of fit and performance. Estimated daily PM10 concentration surfaces are suitable for the interpretation of time trends and for identifying concentration peaks within the urban area. [ABSTRACT FROM AUTHOR]

This paper presents a parametric automatic procedure to calibrate the multichannel Rayleigh–Mie–Raman lidar at the Institute for Atmospheric Science and Climate of the Italian National Research Council (ISAC-CNR) in Tor Vergata, Rome, Italy, using as a reference the operational 0000 UTC soundings at the WMO station 16245 (Pratica di Mare) located about 25 km southwest of the lidar site. The procedure, which is applied to both channels of the system, first identifies portions of the lidar and radiosonde profiles that are assumed to sample the same features of the water vapor profile, taking into account the different time and space sampling. Then, it computes the calibration coefficient with a best-fit procedure, weighted by the instrumental errors of both radiosounding and lidar. The parameters to be set in the procedure are described, and values adopted are discussed. The procedure was applied to a set of 57 sessions of nighttime 1-min-sampling lidar profiles (roughly about 300 h of measurements) covering the whole annual cycle (February 2007–September 2008). A calibration coefficient is computed for each measurement session. The variability of the calibration coefficients (∼10%) over periods with the same instrumental setting is reduced compared to the values obtained with the previously adopted, operator-assisted, and time-consuming calibration procedure. Reduction of variability, as well as the absence of evident trends, gives confidence both on system stability as well as on the developed procedure. Because of the definition of the calibration coefficient and of the different sampling between lidar and radiosonde, a contribution to the variability resulting from aerosol extinction and to the spatial and temporal variability of the water vapor mixing ratio is expected. A preliminary analysis aimed at identifying the contribution to the variability from these factors is presented. The parametric nature of the procedure makes it suitable for application to similar Raman lidar systems. [ABSTRACT FROM AUTHOR]

• Procedure based on continuous ground albedo measurements and discrete satellite calibration. • Tests on a high-albedo surface (HAS) of 900 m2 treated with a high-reflective paint. • Developed a Calculus Unit to calculate the RF time history and the compensated CO 2. • The maximum difference between albedometer and satellite measurements is 13%. • An amount of CO 2 compensated by HAS equal to 73 kgCO 2 eq/m2. The increase of terrestrial albedo may be considered a key strategy to mitigate global warming, since it produces a reduction of the radiative forcing (RF). The RF concept, as defined by IPCC, is used in literature for calculations of the CO 2 compensation due to albedo increase, which can be achieved by the development of high-albedo solutions (cool materials, retro -reflective materials, green infrastructures, etc.). The authors have previously proposed a new procedure, called RF-meter, to measure the continuous time history of the RF due to an albedo increase (RF Δα) and so to calculate the related CO 2 offset potential by high-albedo solutions. RF-meter procedure is based on the continuous albedo measurements at ground level and discrete satellite calibration. The proposed method is tested on a high-albedo surface (HAS) of 900 m2 treated with a high-reflective paint installed on the roof of CIRIAF building, University of Perugia, Italy. The experimental field is equipped with an albedometer, a weather station and a Calculus Unit. The measured data are elaborated by the Calculus Unit to calculate the RF time history and the compensated CO 2. Data from albedo monitoring exhibited that albedo of HAS was always higher than albedo before the treatment (α 0) and the average Δα is equal to 0.3. The calibration procedure allows to reduce the errors on RFΔ α calculation. The maximum difference between albedo values from albedometer and satellite measurements resulted equal to 3.9%. Results from the Calculus Unit showed an amount of CO 2 compensated by HAS equal to 73 kg CO2eq /m2. [ABSTRACT FROM AUTHOR]

Colored dissolved organic matter (CDOM) is a significant constituent of aquatic systems and biogeochemical cycles. Satellite CDOM retrievals are challenging in inland waters, due to overlapped absorption properties of bio-optical parameters, like Total Suspended Matter (TSM). In this framework, we defined an accurate CDOM model using Sentinel2-MSI (S2-MSI) data in Pertusillo Lake (Southern Italy) adopting a classification scheme based on satellite TSM data. Empirical relationships were established between the CDOM absorption coefficient, aCDOM (440), and reflectance band ratios using ground-based measurements. The Green-to-Red (B3/B4 and B3/B5) and Red-to-Blue (B4/B2 and B5/B2) band ratios showed good relationships (R2 ≥ 0.75), which were further improved according to sub-region division (R2 up to 0.93). The best accuracy of B3/B4 in the match-ups between S2-MSI-derived and in situ band ratios proved the exportability on S2-MSI data of two B3/B4-based aCDOM (440) models, namely the fixed (for the whole PL) and the switching one (according to sub-region division). Although they both exhibited good agreements in aCDOM (440) retrievals (R2 ≥ 0.69), the switching model showed the highest accuracy (RMSE of 0.0155 m−1). Finally, the identification of areas exposed to different TSM patterns can assist with refining the calibration/validation procedures to achieve more accurate aCDOM (440) retrievals. [ABSTRACT FROM AUTHOR]

Featured Application: Use of enzymatic gels to clean wall paintings. Application of multispectral imaging to evaluate the effect of the cleaning operations on artwork surfaces. A 17th century wall painting, representing the Virgin between two Saints, in a noble Italian renaissance palace, the Palazzo Gallo in Bagnaia (Viterbo, Italy), was restored in 2021 in the context of a wider restoration campaign involving the main room of the palace built by cardinal Sansoni Riario. Diagnostic analyses performed using traditional characterization techniques (optical microscopy on micro-stratigraphic sections, X-ray fluorescence spectroscopy and Fourier transform infrared spectroscopy) provided the identification of both the original painting and its restoration materials, while imaging investigations using the ultraviolet fluorescence photography, false color images and multispectral mapping provided by the hypercolorimetric multispectral imaging (HMI) technique enabled the evaluation of the state of conservation, the location of restoration interventions and supported the monitoring of the cleaning procedure. An altered protective Paraloid®-based coating dating from the early 2000s had to be removed due to the unpleasant glossy finishing it had given to the painted surface, making the scene barely readable. To pursue a restoration protocol based on environmental sustainability and green chemistry, enzyme-based gels marketed by the Nasier-Brenta© and CTS© companies were tested in different protocols for the cleaning of the mixture (known as beverone) covering the painting. Although some interesting results were observed, the enzymatic cleaning had limited effectiveness, and was more timing-consuming than was reasonable. Traditional chemical solvents such as Dowanol PM (methoxy propanol) and benzyl alcohol were necessary to complete the cleaning of the painting surface. [ABSTRACT FROM AUTHOR]

Considering future tasks in soil health, resource management, and environmental protection, farmers are challenged to develop sustainable strategies for managing soil and land resources. In this study, the long-term sustainability of two fertilization strategies—current, with synthetic fertilizers (SYN) vs. conservative, with organic sources of nitrogen (organic amendments plus green manure with a legume, CONS)—was assessed in a processing tomato/durum wheat rotation. The EPIC model was used, validated with field data, and then run to simulate the management for 30 years under three current and future climates. Yield, soil organic carbon (SOC) stock change, nitrogen use efficiency (NUE), water use efficiency (WUE), and nitrate leaching were considered sustainability indicators. Under all of the future climate scenarios, tomato yield increased with CONS, remaining almost stable with SYN. Wheat yield increased both with CONS and SYN; however, the average yield with CONS was considerably lower than with SYN. NUE and nitrate leaching followed the same trend, both decreasing with CONS, while WUE was higher with CONS compared to SYN. The effect of CONS on SOC was always positive. Thus, the alternative N fertilization strategy proposed can be a favorable option for maintaining soil health and sustainable crop production. [ABSTRACT FROM AUTHOR]

The objective of this study is to test the feasibility of time-lapse GPR measurements for the quality control of repairing operations (i.e., injections) on marble blocks. For the experimental activities, we used one of the preferred repairing fillers (epoxy resin) and some blocks from one of the world's most famous marble production area (Carrara quarries in Italy). The selected blocks were paired in a laboratory by overlapping one over the other after inserting very thin spacers in order to simulate air-filled fractures. Fractures were investigated with a 3 GHz ground-penetrating radar (GPR) before and after the resin injections to measure the amplitude reduction expected when the resin substitutes the air. The results were compared with theoretical predictions based on the reflection coefficient predicted according to the thin bed theory. A field test was also performed on a naturally fractured marble block selected along the Carrara shore. Both laboratory and field tests validate the GPR as an effective tool for the quality control of resin injections, provided that measurements include proper calibration tests to control the amplitude instabilities and drift effects of the GPR equipment. The method is accurate enough to distinguish the unfilled fractures from the partially filled fractures and from the totally filled fractures. An automatic algorithm was developed and successfully tested for the rapid quantitative analysis of the time-lapse GPR profiles collected before and after the injections. The whole procedure is mature enough to be proposed to the marble industry to improve the effectiveness of repair interventions and to reduce the waste of natural stone reserves. [ABSTRACT FROM AUTHOR]

The Soil and Water Assessment Tool (SWAT) model is generally applied in alpine catchments using a unique set of snow parameters for the entire basin, and calibration is based on discharge records only. This technical note presents three calibration procedures for snow parameters of SWAT considering snow water equivalent (SWE) values computed using a dense network of snow depth measurement stations available in the Upper Adige River basin, Italy. The first two procedures calibrate snow parameters according to the average sub-basin SWE: the first one defines a unique set of parameters for the entire basin, while the second allows for sub-basin variability. The last approach includes the elevation band SWE output in the calibration for each sub-basin and qualitatively compares it to the SWE computed from the available snow depth monitoring stations. This last method provides the best agreement between SWAT model results and SWE data. [ABSTRACT FROM AUTHOR]

The new mineral batoniite, [Al 8 (OH) 14 (H 2 O) 18 ](SO 4)5 ⋅ 5H 2 O, was discovered in the Cetine di Cotorniano Mine, Chiusdino, Siena, Tuscany, Italy. It occurs as hemispherical aggregates composed of brittle tabular crystals, up to 1 mm in size, white to colorless in color, with a white streak and a vitreous to greasy luster. Batoniite is biaxial negative, with α= 1.4833(6), β= 1.4948(6), γ= 1.5019(5) (589 nm), and 2 V(meas.)= 71(1) ∘. Electron microprobe analysis, affected by the dehydration of batoniite under the chamber vacuum, gave (in wt %) the following: Al 2 O 3 33.48, Fe 2 O 3 0.05, SO 3 33.00, and H 2 O calc 44.41, total 110.94. It corresponds to the chemical formula (Al 7.98 Fe 0.013+)Σ7.99 (SO 4)5.01 (OH) 13.95 (H 2 O) 18 ⋅ 5H 2 O. Batoniite is triclinic, belonging to space group P1‾ , with a= 9.1757(6), b= 12.0886(9), c= 20.9218(15) Å, α= 82.901(3), β= 87.334(3), γ= 86.999(2) ∘ , V= 2297.8(3) Å 3 , and Z= 2. The crystal structure was refined to R1= 0.0916 for 8118 unique reflections with Fo>4σ(Fo) and 811 refined parameters and 60 restraints. Batoniite is characterized by isolated [Al 8 (OH) 14 (H 2 O) 18 ] 10+ polyoxocations, H-bonded to five interstitial (SO 4) 2- and five H 2 O groups. In type material, it is associated with gypsum and a poorly crystallized Al–Fe sulfate. The crystallization of batoniite is probably due to the action of H 2 SO 4 on Al-bearing rocks of Paleozoic age cropping out in the Garibaldi Tunnel, the lowest mining level of the Cetine di Cotorniano Mine. [ABSTRACT FROM AUTHOR]

In the context of the Covid-19 pandemic, we evaluate the effects of vaccines and virus variants on epidemiological and macroeconomic outcomes by means of Monte Carlo simulations of a macroeconomic-epidemiological agent-based model calibrated using data from the Lombardy region of Italy. From simulations we infer that vaccination plays the role of a mitigating factor, reducing the frequency and the amplitude of contagion waves and significantly improving macroeconomic performance with respect to a scenario without vaccination. The emergence of a variant, on the other hand, plays the role of an accelerating factor, leading to a deterioration of both epidemiological and macroeconomic outcomes and partly negating the beneficial impacts of the vaccine. A new and improved vaccine in turn can redress the situation. Vaccinations and variants, therefore, can be conceived of as drivers of an intertwined cycle impacting both epidemiological and macroeconomic developments. [ABSTRACT FROM AUTHOR]

Injecting volcanic gas into the air leads to an increase in carbon dioxide (CO2) levels compared with background concentrations and may establish gas hazard conditions. This study reports the results of five stable isotope (i.e., δ13C‐CO2 and δ18O‐CO2) surveys of airborne CO2 on Vulcano from August 2020 to November 2021. To measure CO2 in the air, a mobile laboratory was equipped with a laser‐based spectrophotometer that can selectively detect different CO2 isotopologues. Volcanic CO2 has a different isotopic signature than atmospheric CO2 and both δ13C‐CO2 and δ18O‐CO2 can help trace the injections of volcanic gases into the air. An isotopic mass balance model was developed for partitions CO2 between atmospheric background and volcanic CO2. The results of these studies show that volcanic CO2 emissions and atmospheric circulation deeply affected the concentration of CO2 in the air at Vulcano Porto. Studies of δ13C‐CO2 and δ18O‐CO2 provide an estimate of volcanic CO2 in the air. These results help identify spatially some points of interest for mitigating volcanic gas emission‐related hazards on Vulcano. Plain Language Summary: In volcanic areas, the concentration of CO2 in the air increases due to the dispersion of volcanic gases, as CO2 dominates among the local gas source components. Identifying variations in gas hazard due to changes in volcanic degassing is difficult when estimates of volcanic gases in air are based only on measurements of CO2 concentration. In this study, the effects of volcanic degassing on airborne CO2 are thoroughly evaluated by analyzing the isotopic composition of airborne CO2 during five onsite measurement surveys between August 2020 and November 2021. To quantify the contribution of volcanic CO2 to total CO2 in air, we developed a model based on the collected data using mass balance calculations. In 2021, a massive increase in volcanic degassing caused a clear increase of airborne CO2 concentration at Vulcano. We find that the effects of volcanic degassing depend on air turbulence, which changes throughout the day. The spatial variations in CO2 allow us to track the dispersion of volcanic gases in the air and their effects on gas hazards and atmospheric composition with unprecedented accuracy. Key Points: Spatial isotope monitoring enables the identification of the origin of CO2 in the airCalculating the stable isotope mass balances enables quantifying the volcanic CO2 in the total CO2 in the airSignificant changes in volcanic degassing increased air CO2 concentration and gas hazard on Vulcano—Italy—in 2021 [ABSTRACT FROM AUTHOR]

As the urgency to mitigate climate change intensifies, the achievement of zero carbon targets in the built environment has become a critical objective. Building retrofitting plays a vital role in reducing energy consumption and carbon emissions in existing buildings. This paper presents an approach that combines Building Information Modeling (BIM) with multiple domains to strive for zero carbon targets in building retrofit projects. The proposed approach is based on a dynamic multi-parameter analysis that integrates indoor comfort, energy savings, CO2eq reduction, and the social cost of carbon while considering investment costs. Renewable energy technologies, such as photovoltaic panels and solar thermal systems, are emphasized to achieve the desired zero-carbon outcomes. Real-time monitoring mechanisms enable continuous performance evaluation and adaptive retrofit strategies for further energy savings. This approach is validated through a case study of an existing thermal bath building, known as "Terme Lucane", located in southern Italy. The results of the study demonstrate significant reductions in energy consumption and carbon emissions, highlighting the potential of the proposed approach to achieve zero carbon targets through the integration of multi-data BIM implementation. These findings offer a promising pathway for building retrofit projects aiming for zero carbon targets through multi-data BIM modeling. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has led to the unprecedented challenge of devising massive vaccination rollouts, toward slowing down and eventually extinguishing the diffusion of the virus. The two‐dose vaccination procedure, speed requirements, and the scarcity of doses, suitable spaces, and personnel, make the optimal design of such rollouts a complex problem. Mathematical modeling, which has already proved to be determinant in the early phases of the pandemic, can again be a powerful tool to assist public health authorities in optimally planning the vaccination rollout. Here, we propose a novel epidemic model tailored to COVID‐19, which includes the effect of nonpharmaceutical interventions and a concurrent two‐dose vaccination campaign. Then, we leverage nonlinear model predictive control to devise optimal scheduling of first and second doses, accounting both for the healthcare needs and for the socio‐economic costs associated with the epidemics. We calibrate our model to the 2021 COVID‐19 vaccination campaign in Italy. Specifically, once identified the epidemic parameters from officially reported data, we numerically assess the effectiveness of the obtained optimal vaccination rollouts for the two most used vaccines. Determining the optimal vaccination strategy is nontrivial, as it depends on the efficacy and duration of the first‐dose partial immunization, whereby the prioritization of first doses and the delay of second doses may be effective for vaccines with sufficiently strong first‐dose immunization. Our model and optimization approach provide a flexible tool that can be adopted to help devise the current COVID‐19 vaccination campaign, and increase preparedness for future epidemics. [ABSTRACT FROM AUTHOR]

This work represents a first step in the definition of a framework aimed at finding, by means of efficient global optimization based on metamodels, an optimal configuration of physical parameters for the ICON (ICOsahedral Nonhydrostatic) Limited Area Mode at high resolution (about 1.1 km) over Southern Italy, to be used for operational runs. The objective of the optimization is to reduce the distance between observed meteorological variables and modeled data. This distance is measured by an opportunely designed objective function. This work represents a preparatory step, since the input parameters considered are only a reduced number with respect to the huge amount of parameters potentially involved. First, domain size sensitivity was performed to choose the optimal domain. Then, the optimization was conducted by means of an Efficient Global Optimization algorithm relying on a Gaussian-based metamodel. The four parameters considered control the heat transfer in the turbulent layer, the laminar resistance and the snow vertical velocity. They were optimized over a week in November 2018, a period characterized by extreme events in the region considered. The results demonstrated the effectiveness of the proposed approach, reducing the distance from observed data, and the method can be considered promising from the perspective taking into account a larger set of physical parameters, and validation over a wider time-window. [ABSTRACT FROM AUTHOR]

Future climate change is expected to significantly alter the growth of vegetation in grassland systems, in terms of length of the growing season, forage production, and climate-altering gas emissions. The main objective of this work was, therefore, to simulate the future impacts of foreseen climate change in the context of two pastoral systems in the central Italian Apennines and test different adaptation strategies to cope with these changes. The PaSim simulation model was, therefore, used for this purpose. After calibration by comparison with observed data of aboveground biomass (AGB) and leaf area index (LAI), simulations were able to produce various future outputs, such as length of growing season, AGB, and greenhouse gas (GHG) emissions, for two time windows (i.e., 2011–2040 and 2041–2070) using 14 global climate models (GCMs) for the generation of future climate data, according to RCP (Representative Concentration Pathways) 4.5 and 8.5 scenarios under business-as-usual management (BaU). As a result of increasing temperatures, the fertilizing effect of CO2, and a similar trend in water content between present and future, simulations showed a lengthening of the season (i.e., mean increase: +8.5 and 14 days under RCP4.5 and RCP8.5, respectively, for the period 2011–2040, +19 and 31.5 days under RCP4.5 and RCP8.5, respectively, for the period 2041–2070) and a rise in forage production (i.e., mean biomass peak increase of the two test sites under BaU: +53.7% and 62.75% for RCP4.5. and RCP8.5, respectively, in the 2011–2040 period, +115.3% and 176.9% in RCP4.5 and RCP8.5 in 2041–2070, respectively,). Subsequently, three different alternative management strategies were tested: a 20% rise in animal stocking rate (+20 GI), a 15% increase in grazing length (+15 GL), and a combination of these two management factors (+20 GI × 15 GL). Simulation results on alternative management strategies suggest that the favorable conditions for forage production could support the increase in animal stocking rate and grazing length of alternative management strategies (i.e., +20 GI, +15 GL, +20 GI × 15 GL). Under future projections, net ecosystem exchange (NEE) and nitrogen oxide (N2O) emissions decreased, whereas methane (CH4) rose. The simulated GHG future changes varied in magnitude according to the different adaptation strategies tested. The development and assessment of adaptation strategies for extensive pastures of the Central Apennines provide a basis for appropriate agricultural policy and optimal land management in response to the ongoing climate change. [ABSTRACT FROM AUTHOR]

Many countries exposed to high levels of seismic risk, including Italy, are facing a huge challenge in promptly quantifying post-earthquake damages to their built historical heritage. In this context, structural health monitoring plays a fundamental role allowing to continuously track changes in selected damage-sensitive features. However, monitoring data interpretation is often not univocal and may be affected by large uncertainty, provoking false positives and false negatives. Hence, this research proposes a novel approach for post-earthquake structural condition assessment by exploiting the aggregation of different sources of information, notably steering from both monitoring and visual inspection campaigns, in order to take risk-informed decisions. More in depth, an automatic tool is proposed to detect and locate structural damages in monumental structures with the aid of a data fusion approach including vibration-based system identification, static and dynamic measurements, finite element (FE) and surrogate modeling, Bayesian-based model updating and visual inspections. In a preliminary phase, potential damage-sensitive regions in the structure are identified through FE-based numerical analysis and engineering judgment. Then, the solution of the inverse problem aimed at deriving the Bayesian posterior statistics of the uncertain parameters is entrusted to a computational-effective surrogate model (SM). Finally, the Bayesian-based updated parameters are adjusted considering the different allowable sources of information to achieve a final assessment. The effectiveness of the proposed approach is demonstrated by using the recorded data acquired in the Consoli Palace, an historical building located in Umbria, central Italy, which has been continuously monitored since 2017 using dynamic, static and environmental sensors and which has been hit by low-intensity earthquakes in 2021. [ABSTRACT FROM AUTHOR]

Estimating the hydraulic properties of the vadose zone is essential to understand soil‐water dynamics and achieving appropriate water management in agricultural lands. Inverse modelling methods are commonly used to estimate hydraulic properties from field observations. Unlike the extensively applied local search methodologies, data assimilation techniques can fully account for multiple uncertainties and are becoming a widely used tool for estimating hydraulic parameters. However, only few applications on real field tests are available. The main objective of this study was to estimate the van Genuchten‐Mualem (VGM) parameters and the saturated hydraulic conductivity (Ks$$ {K}_s $$) of a heterogeneous low‐lying farmland at the margin of the Venice Lagoon, Italy, characterized by high peat content, sandy drifts, and a very shallow water table. To this end, two methods were tested, that is, the Ensemble Smoother (ES) and the Levenberg–Marquardt (LM) algorithm associated with hydrological modelling performed with Hydrus‐1D. Volumetric water content (VWC) observations were collected at three monitoring sites from May to September 2011. Results on parameters highlighted that the ES technique effectively reduced the uncertainty of α$$ \alpha $$ and n$$ n $$, but it was less effective on θr$$ {\theta}_r $$ and Ks$$ {K}_s $$. The results on VWC showed that the ES efficiency decreased with the increasing non‐linearity of the system (e.g., higher sand content) and when the variability of the experimental data was lower (e.g., deepest soil layers where saturation remained permanently close to 1). Both LM and ES allowed to reproduce the VWC observations in the calibration and validation phases, with the former and the latter performing better in the case of sandy and peat soils, respectively. As concerns the method applicability, the ES was less time‐demanding as it efficiently updated all the parameters at once and was less dependent on the user choices. Finally, the paper points out the importance of previous knowledge of the VGM parameters (e.g., from lab hydraulic analyses) in defining the constraints for the optimization. [ABSTRACT FROM AUTHOR]

The agricultural sector, the largest and least efficient water user, is facing important challenges in sustaining food production and careful water use. The objective of this study is to improve farm and irrigation district water use efficiency by developing an operational procedure for smart irrigation and optimizing the exact water use and relative water productivity. The SIM (smart irrigation monitoring and forecasting) optimization irrigation strategy, based on soil moisture (SM) and crop stress thresholds, was implemented in the Chiese (North Italy) and Capitanata (South Italy) Irrigation Consortia. The system is based on the energy–water balance model FEST-EWB (Flashflood Event-based Spatially distributed rainfall runoff Transformation Energy–Water Balance model), which was pixelwise calibrated with remotely sensed land surface temperature (LST), with mean areal absolute errors of approximately 3 °C, and then validated against local measured SM and latent heat flux (LE) with RMSE values of approximately 0.07 and 40 Wm−2, respectively. The effect of the optimization strategy was evaluated on the reductions in irrigation volume and on the different timing, from approximately 500 mm over the crop season in the Capitanata area to approximately 1000 mm in the Chiese district, as well as on cumulated drainage and ET fluxes. The irrigation water use efficiency (IWUE) indicator appears to be higher when applying the SIM strategy than when applying the traditional irrigation strategy: greater than 35% for the tomato fields in southern Italy and 80% for maize fields in northern Italy. [ABSTRACT FROM AUTHOR]

Sustainable forest land management requires measuring and monitoring soil organic carbon. Visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR, 350–2500 nm), although it has become an important method for predicting soil organic carbon (SOC), requires further studies and methods of analysis to realize its full potential. This study aimed to determine if the size of the study area and soil sampling density may affect the performance of Vis-NIR diffuse reflectance spectroscopy in the prediction of soil organic carbon. Two forest sites in the Calabria region (southern Italy), which differ in terms of area and soil sampling density, were used. The first one was Bonis catchment area (139 ha) with a cover consisting mainly of Calabrian pine, while the second was Mongiana forest area (33.2 ha) within the "Marchesale" Biogenetic Nature Reserve, which is covered by beech. The two study areas are relatively homogeneous regarding parent material and soil type, while they have very different soil sampling density. In particular, Bonis catchment has a lower sampling density (135 samples out of 139 ha) than Mongiana area (231 samples out of 33.2 ha). Three multivariate calibration methods (principal component regression (PCR), partial least square regression (PLSR), and support vector machine regression (SVMR)) were combined with different pretreatment techniques of diffuse reflectance spectra (absorbance, ABS, standard normal variate, SNV, and Savitzky–Golay filtering with first derivative (SG 1st D). All soil samples (0–20 cm) were analyzed in the laboratory for SOC concentration and for measurements of diffuse reflectance spectra in the Vis-NIR region. The set of samples from each study area was randomly divided into a calibration set (70%) and a validation set (30%). The assessment of the goodness for the different calibration models and the following SOC predictions using the validation sets was based on three parameters: the coefficient of determination (R2), the root mean square error (RMSE), and the interquartile range (RPIQ). The results showed that for the two study areas, different levels of goodness of the prediction models depended both on the type of pretreatment and the multivariate method used. Overall, the prediction models obtained with PLSR and SVMR performed better than those of PCR. The best performance was obtained with the SVMR method combined with ABS + SNV + SG 1st D pretreatment (R2 ≥ 0.77 and RPIQ > 2.30). However, there is no result that can absolutely provide definitive indications of either the effects of the study area size and soil sampling density in the prediction of SOC by vis-NIR spectroscopy, but this study fostered the need for future investigations in areas and datasets of different sizes from those in this study and including also different soil landscapes. [ABSTRACT FROM AUTHOR]

Satellite precipitation observations represent the future of climatology, as they allow continuous assessment of climate parameters over the entire earth's surface. Today, climate detection methods are in a transitional period where satellite data are not yet perfectly calibrated, while ground-based weather stations are still the most reliable detection methods. Precipitation is one of the most important climatic parameters, but at the same time it is complex to measure accurately by satellite. In this context it is essential to evaluate the reliability of a very useful and universally recognised satellite product, the IMERG V06 Final Run with validated weather stations in Central Italy. This could allow, on the one hand, to assess the accuracy of this satellite product compared to rain gauges, and on the other hand a better calibration at local scale. The analysis involved as many as 154 rain gauges in an area of about 12,600 Km2, so as to cover the area in a punctual way; in this context, two different comparison procedures were tested, pixel-to-point and pixel-to-pixel at annual and monthly scale, in the period 2001–2021. The results showed an overestimation of rainfall by the IMERG product, compared to rain gauges of about 200 mm per year, while in the coastal and hilly areas, IMERG's performance is even worse, overestimating precipitation by almost 1000 mm. This study showed that the pixel-to-pixel procedure proved to be the most reliable, showing the existence of a linear relationship between altitude and the differences between rain gauges and IMERG data, with an annual linear regression explaining up to 82% of the overall variance, allowing a possible calibration of the satellite product in the area. [Display omitted] • Assessment of the reliability of the IMERG V06 Final Run product in Central Italy. • Pixel-to-point calibration procedure compared with the new pixel-to-pixel procedure. • Pixel-to-pixel procedure from interpolated rain gauges with altitude-based cokriging. • Detection of systematic errors in rain gauges and IMERG data. • Calibration of IMERG data in Central Italy. [ABSTRACT FROM AUTHOR]

Satellite-based Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolutions, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based EO data in hydrological modeling. In a set of six experiments, the distributed hydrological model Continuum is set up for the Po River basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture (SM) and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation, and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling–Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGE mean= 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite data on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications. [ABSTRACT FROM AUTHOR]

Climate change impact studies on hydrological extremes often rely on hydrological models with parameters inferred through calibration procedures using observed meteorological data as input forcing. We show that this procedure can lead to a biased evaluation of the probability distribution of high streamflow extremes when climate models are used. As an alternative approach, we introduce a methodology, coined "Hydrological Calibration of eXtremes" (HyCoX), in which the calibration of the hydrological model, as driven by climate model output, is carried out by maximizing the probability that the modeled and observed high streamflow extremes belong to the same statistical population. The application to the Adige River catchment (southeastern Alps, Italy) by means of HYPERstreamHS, a distributed hydrological model, showed that this procedure preserves statistical coherence and produces reliable quantiles of the annual maximum streamflow to be used in assessment studies. [ABSTRACT FROM AUTHOR]

This paper presents an insight on the seismic response and seismic risk of base-isolated structures performed within the RINTC project, considering different configurations of rubber-based isolation systems. This represents an in-depth revision of past works due to the adoption of more robust and accurate isolator model. Four archetype 6-storeys RC frame buildings are examined, representing both new designed isolated buildings and existing buildings retrofitted with seismic isolation, differing in age of construction and location (the cities of L'Aquila (central Italy) and Naples (southern Italy)). All the isolation systems are designed in accordance with the current Italian Seismic Code. Seismic performance assessment is performed by multi-stripe nonlinear time history analysis. The annual failure rates for two different performance levels, namely Global Collapse and Usability Preventing Damage, are computed. The results point out that all isolation systems work effectively in limiting damage while they show a quite low margin with respect to collapse, regardless of the dominant failure mode, which is governed by the more fragile component among isolation system and superstructure. The annual failure rates derived for base-isolated buildings are critically reviewed and then compared with those obtained for similar new designed fixed-base RC buildings. [ABSTRACT FROM AUTHOR]

Using a multiple indicators and multiple causes (MIMIC) model, this paper estimates the extent of illegal gambling in Italian regions over the period 2013–18. By treating illegal gambling as an unobserved latent variable directly related to its causes and effects, this model gives information about the relationship between cause and indicator variables and the latent variable from covariance structures. From the analysis, it emerges that the share of illegal gambling increases with the value of the winnings paid; it decreases when the number of authorized machines increases. We also find that individuals with low levels of education and low income show a greater propensity to gamble illegally. [ABSTRACT FROM AUTHOR]

The Gas Absorption Spectrometer Correlating Optical Difference—New Generation 4 (GASCOD/NG4) is a multi-axis differential optical absorption spectroscopy (MAX-DOAS) instrument which measures diffuse solar spectra at the Environmental-Climate Observatory (ECO) of the Italian research institute CNR-ISAC, near Lecce. The high-resolution spectra measured in zenith-sky configuration were used to retrieve the NO 2 and O 3 vertical column densities (VCDs) from March 2017 to November 2019. These good-quality data, proven by the comparison with the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) satellite measurements, were used to characterize the ECO site by exploiting the sinergy with in situ NO 2 and O 3 concentrations and meteorological data. Although stratospheric processes seem to be the main forces behind the NO 2 and O 3 VCDs seasonal trends, diurnal variabilities revealed the presence of a tropospheric signal in the NO 2 VCDs, which had significant lower values during Sundays. Comparison with wind data acquired at the ECO observatory, at 20 m above the ground, revealed how NO 2 VCDs are influenced by both tropospheric local production and transport from the nearby city of Lecce. On the other hand, no significant tropospheric signal was contained in the O 3 VCDs. [ABSTRACT FROM AUTHOR]

Landslide hazard management usually requires time-consuming campaigns of data acquisition, elaboration, and modeling. However, in the post-emergency phase management, time is a factor, and simpler but faster methods of analysis are needed to support decisions even in the short-term. This paper analyzes the Theilly landslide (Western Italian Alps), which was recently affected by a series of reactivations. While some instrumental campaigns are being carried out to support the design of protection measures, simple tools are also needed to assess the hazard of future reactivations and to evaluate the possibility of damming the torrent at the footslope. Therefore, state-of-the-art empirical methods were used and customized for the specific case study: a set of intensity–duration rainfall thresholds depicting increasing hazard levels was defined to monitor and forecast possible reactivations, while a methodology based on hydro-morphometric indices was applied to the case of study, to assess the possible evolution scenarios (landslide that does not dam the river, formation of a stable dam, formation of an unstable dam), based on the landslide volume. The proposed empirical methodologies have the advantage of requiring only ready-available input data and quick elaborations, thus allowing the rapid set up of tools that could be used for hazard management. [ABSTRACT FROM AUTHOR]

This paper reports the first results obtained thanks to the installation of a dedicated one‐km fiber‐optic cable, integrated within the Irpinia Near Fault Observatory regional network in Southern Italy. The cable was installed in a dry lake, located near the active faults responsible for the M6.9 earthquake that occurred in 1980. A distributed acoustic sensing (DAS) interrogator was deployed over a period of 4 months and a half and allowed to record tens of local events. To model the seismic phases observed in the recordings, simple seismic refraction experiments were conducted along with analyses of earthquakes and numerical simulations. Results show that in this peculiar geological context, DAS is mainly sensitive to waves guided horizontally by the subsurface low‐velocity layered structure of the site. This leads to considerable site amplification but also wavefront deformation and allows to detect local microearthquakes without any stacking or other array processing techniques. Magnitude estimation was performed using a dedicated amplitude correction process, along with detection threshold estimation. The benefit of using stacking of DAS channels for improving signal‐to‐noise ratio was also estimated. Plain Language Summary: A fiber‐optic cable was buried near the fault responsible for the devastating earthquake that struck the Irpinia region (Southern Italy) in 1980. A specialized instrument, called distributed acoustic sensing (DAS) interrogator, was used to measure micrometric deformations of the cable due to small earthquakes regularly produced by the faults. The ground where the cable was installed is composed by stratified and very soft materials which trap and amplify seismic waves. Through analysis and modeling, we demonstrated that DAS is particularly sensitive to those effects because of its measuring principle. We showed that wave amplification allows to record small earthquakes otherwise impossible to detect and that their magnitude can be estimated if corrections are applied. We characterized the wave deformation process, a first important step to enable the use of conventional detection and location techniques and unleash the true potential of DAS deployments in geological context presenting soft materials. Key Points: An active tectonic area was monitored by a new dedicated fiber‐optic cable deployed in a dry lake near a major faultRecordings were dominated by highly amplified waves with slow apparent velocitiesModeling suggests that distributed acoustic sensing is particularly sensitive to site effects whereas energy analysis enables magnitude estimation [ABSTRACT FROM AUTHOR]

Mycotoxin represents a significant concern for the safety of food and feed products, and wheat represents one of the most susceptible crops. To manage this issue, fast, reliable, and low-cost test methods are needed for regulated mycotoxins. This study aimed to assess the potential use of the electronic nose for the early identification of wheat samples contaminated with deoxynivalenol (DON) above a fixed threshold. A total of 214 wheat samples were collected from commercial fields in northern Italy during the periods 2014–2015 and 2017–2018 and analyzed for DON contamination with a conventional method (GC-MS) and using a portable e-nose "AIR PEN 3" (Airsense Analytics GmbH, Schwerin, Germany), equipped with 10 metal oxide sensors for different categories of volatile substances. The Machine Learning approach "Classification and regression trees" (CART) was used to categorize samples according to four DON contamination thresholds (1750, 1250, 750, and 500 μg/kg). Overall, this process yielded an accuracy of >83% (correct prediction of DON levels in wheat samples). These findings suggest that the e-nose combined with CART can be an effective quick method to distinguish between compliant and DON-contaminated wheat lots. Further validation including more samples above the legal limits is desirable before concluding the validity of the method. [ABSTRACT FROM AUTHOR]

Landslide mapping techniques have had many improvements in recent decades, the main field of development has been on traditional cartographic techniques and to a lesser extent on indirect numerical cartography. As for Direct Numerical Cartography (DNC), only a few improvements have been made due to the complexity and economic cost of the new technologies. To meet this lack in DNC techniques GOGIRA (Ground Operative-system for GIS Input Remote-data Acquisition), a new system following the GIS (Geographic Information System) scheme, was developed. It is a suite of hardware and software tools, algorithms, and procedures for easier and cheaper DNC. Initial tests conducted on the Quincinetto landslide system (north-western Italy) demonstrated good results in terms of morphometric coherence and precision. A geomorphological map made with GOGIRA was compared with a highly detailed geomorphological map developed with modern tested methods. In conclusion GOGIRA proved to be a valid system for geomorphological DNC when applied to a complex landslide system, considering the early stage of developing results for linear and point mapping was excellent, as for polygonal elements more studies must be conducted to improve accuracy and precision. [ABSTRACT FROM AUTHOR]

This paper concerns a stepwise modelling procedure for groundwater flow simulation in a folded and faulted, multilayer carbonate aquifer, which constitutes a source of good quality water for human consumption in the Apennine Range in Central Italy. A perennial river acts as the main natural drain for groundwater while sustaining valuable water-related ecosystems. The spatial distribution of recharge was estimated using the Thornthwaite–Mather method on 60 years of climate data. The system was conceptualized as three main aquifers separated by two locally discontinuous aquitards. Three numerical models were implemented by gradually adding complexity to the model grid: single layer (2D), three layers (quasi-3D) and five layers (fully 3D), using an equivalent porous medium approach, in order to find the best solution with a parsimonious model setting. To overcome dry-cell problems in the fully 3D model, the Newton–Raphson formulation for MODFLOW-2005 was invoked. The calibration results show that a fully 3D model was required to match the observed distribution of aquifer outflow to the river baseflow. The numerical model demonstrated the major impact of folded and faulted geological structures on controlling the flow dynamics in terms of flow direction, water heads and the spatial distribution of the outflows to the river and springs. [ABSTRACT FROM AUTHOR]

In many developing countries, due to economic constraints, a single station on a river reach is often equipped to record flow variables. On the other hand, hydrographs at the upstream sections may also be needed for especially assessing flooded areas. The upstream flow hydrograph prediction is called the reverse flood routing. There are some reverse flood routing pocedures requiring sophisticated methods together with substantial data requirements. This study proposes a new reverse flood routing procedure, based upon the simple kinematic wave (KW) equation, requiring only easily measurable downstream stage data. The KW equation is first averaged along a channel length at a fixed time, t, assuming that channel width is spatially constant, and then the spatially averaged equation is averaged in time, Δt. The temporally averaged terms are approximated as the arithmetical mean of the corresponding terms evaluated at time t and t + Δt. The Chezy roughness equation is employed for flow velocity, and the upstream flow stage hydrograph is assumed be described by a two parameter gamma distribution (Pearson Type III). The spatially averaged mean flow depth and lateral flow are related to the downstream flow stage. The resulting routing equation is thus obtained as a function of only downstream flow stage, meaning that the method mainly requires measurements of downstream flow stage data besides the mean values of channel length, channel width, roughness coefficient and bed slope. The optimal values of the parameters of reverse flood routing are obtained using the genetic algorithm. The calibration of the model is accomplished by using the measured downstream hydrographs. The validation is performed by comparing the model-generated upstream hydrographs against the measured upstream hydrographs. The proposed model is applied to generate upstream hydrographs at four different river reaches of Tiber River, located in central Italy. The length of river reaches varied from 20 to 65 km. Several upstream hydrographs at different stations on this river are generated using the developed method and compared with the observed hydrographs. The method predicts the time to peak with less than 5% error and peak rates with less than 10% error in the short river reaches of 20 km and 31 km. It also predicts the time to peak and peak rate in other two brances of 45 km and 65 km with less than 15% error. The method satisfactorily generates upstream hydrographs, with an overall mean absolute error (MAE) of 42 m3/s. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of river dynamics requires the grain size distribution of bed sediments and its variation across different temporal and spatial scales. Several techniques are already available for grain size assessment based on field and remotely sensed data. However, the existing methods are only applicable on small spatial scales and on short time scales. Thus, the operational measurement of grain size distribution of riverbed sediments at the catchment scale remains an open problem. A solution could be the use of satellite images as the main imaging platform. However, this would entail retrieving information at sub‐pixel scales. In this study, we propose a new approach to retrieve sub‐pixel scale grain size class information from Copernicus Sentinel‐2 imagery building upon a new image‐based grain size mapping procedure. Three Italian gravel‐bed rivers featuring different morphologies were selected for unmanned aerial vehicle (UAV) acquisitions, field surveys and laboratory analysis meant to serve as ground truth grain size data, ranging from medium sand to coarse gravel. Grain size maps on the river bars were generated in each study site by exploiting image texture measurements, upscaled and co‐registered with Sentinel‐2 data resolution. Relationships between the grain sizes measured and the reflectance values in Sentinel‐2 imagery were analysed using a machine learning framework. Results show statistically significant predictive models (MAE of ±8.34 mm and R2 = 0.92). The trained model was applied on 300 km of the Po River in Italy and allowed us to identify the gravel–sand transition occurring along this river length. Therefore, the approach presented here—based on freely available satellite data calibrated by low‐cost drone‐derived imagery—represents a promising step towards an automated surface mean grain size mapping over long river length, easily repeated through time for monitoring purposes. [ABSTRACT FROM AUTHOR]

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km2, Central Italy), where most landslides are slow-moving. QRA is based on the application of the equation risk = hazard (H) × vulnerability (V) × exposure (E) and on the use of open data with uniform characteristics at the national scale. The study area was divided into a grid with a 1 km2 cell size, and for each cell, the parameters necessary for the risk assessment were calculated. The obtained results show that the total risk of the study area amounts to approximately 7 billion €. The proposed methodology presents several novelties in the risk assessment for the regional/national scale of the analysis, mainly concerning the identification of the datasets and the development of new methodologies that could be applicable over such large areas. The present work demonstrates the feasibility of the methodology and discusses the obtained results. [ABSTRACT FROM AUTHOR]

During the last three decades, ensemble modelling has switched the focus from deterministic to probabilistic outcomes after its successful application in meteorological forecasting. This work involves the application of Ensemble Prediction System (EPS)-based results as forcing for a coastal EWS employing the morphodynamic model XBeach in a so-called (semi-)probabilistic way. First, calibration following the GLUE approach is performed for a profile in Cesenatico (Emilia-Romagna coast, Italy), while the (semi-)probabilistic system is implemented subsequently for two nearby locations. Ensemble mean and standard deviation from the Transnational Multi-Model Ensemble (TMES) forecasting system are combined in varied ways and used to force XBeach. A testing period of two months is analyzed (March and April 2020) together with the already operational deterministic implementation with one specific day of high sea conditions being used to assess the performance of the system. The deterministic results present higher outcome variability compared to the usage of the TMES mean and mean plus/minus one standard deviation (SD). Adding two SDs to the TMES mean results in higher variability than the deterministic approach. The (semi-)probabilistic system shows high potential as it provides more information on possible outcomes. However, its implementation has to be carefully designed as the application of the TMES mean plus SDs might result in false threshold exceedance and unproportionate responses. • A (semi-)probabilistic XBeach-based EWS is tested with multi-model ensemble outputs. • XBeach calibration is fundamental to avoid modeled erosion overestimation. • A GLUE-like calibration procedure yields an optimized parameter set. • The new EWS approach provides additional information to the forecaster/decision maker. [ABSTRACT FROM AUTHOR]

Despite the debate about the introduction of an official absolute poverty line is growing in the EU, at the moment Italy is the only EU country providing an official measure of absolute poverty. Absolute poverty is estimated in Italy with reference to household consumption using the Household Budget Survey (HBS), but it can be estimated also relying on incomes. Focusing on the Italian experience, this article contributes to the literature and to the national and European policy debate about poverty measurement in three ways. First, a detailed review of the methodology adopted in Italy to compute absolute poverty is presented. Second, the article investigates what changes when absolute poverty is assessed relying on income (using EU-SILC data) instead than on consumption (using HBS). Third, a comparison between income-based absolute poverty and the two main indicators of poverty and social exclusion used at the EU level—at risk of poverty rate, AROP, and severe material deprivation index, SMD—is shown. Main findings are: (1) the level and the characteristics of the poor change when absolute poverty is measured with reference to income rather than to consumption; (2) the incidence of income-based absolute poverty has risen more steeply than the incidence of the AROP since the upsurge of the economic crisis in 2008; (3) a very low correlation at the individual level between the income-based absolute poverty status and the SMD status emerges, thus strongly questioning the idea of using SMD as a proxy of absolute poverty. [ABSTRACT FROM AUTHOR]