We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardy, Piedmont, and Veneto regions. We focused on the application of a stochastic approach in fitting the model parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyzed the official data and the predicted evolution of the epidemic in the Italian regions, and we compared the results with the data and predictions of Spain and South Korea. We linked the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discussed the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.

Ground-penetrating radar (GPR) is one of the most commonly used instruments to map the Snow Water Equivalent (SWE) in mountainous regions. However, some areas may be difficult or dangerous to access; besides, some surveys can be quite time-consuming. We test a new system to fulfill the need to speed up the acquisition process for the analysis of the SWE and to access remote or dangerous areas. A GPR antenna (900 MHz) is mounted on a drone prototype designed to carry heavy instruments, fly safely at high altitudes, and avoid interference of the GPR signal. A survey of two test sites of the Alpine region during winter 2020–2021 is presented, to check the prototype performance for mapping the snow thickness at the catchment scale. We process the data according to a standard flow-chart of radar processing and we pick both the travel times of the air–snow interface and the snow–ground interface to compute the travel time difference and to estimate the snow depth. The calibration of the radar snow depth is performed by comparing the radar travel times with snow depth measurements at preselected stations. The main results show fairly good reliability and performance in terms of data quality, accuracy, and spatial resolution in snow depth monitoring. We tested the device in the condition of low snow density (<200 kg/m³) and this limits the detectability of the air–snow interface. This is mainly caused by low values of the electrical permittivity of the dry soft snow, providing a weak reflectivity of the snow surface. To overcome this critical aspect, we use the data of the rangefinder to properly detect the travel time of the snow–air interface. This sensor is already installed in our prototype and in most commercial drones for flight purposes. Based on our experience with the prototype, various improvement strategies and limitations of drone-borne GPR acquisition are discussed. In conclusion, the drone technology is found to be ready to support GPR-based snow depth mapping applications at high altitudes, provided that the operators acquire adequate knowledge of the devices, in order to effectively build, tune, use and maintain a reliable acquisition system.

We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardia, Piemonte, and Veneto regions. We focus on the application of a stochastic approach in fitting the model numerous parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyze the official data and the predicted evolution of the epidemic in the Italian regions, and we compare the results with data and predictions of Spain and South Korea. We link the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discuss the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.

Proglacial lakes are distinctive features of deglaciated landscapes and often act as sediment sinks, collecting solid material from subglacial erosion or washout of deglaciated areas. The solid transport flow, strongly linked to the glaciers and periglacial landforms, may rise due to the rapid changes driven by climate warming, causing deep transformations in the basin hydrology, and even the appearance or disappearance of lakes at a decadal timescale.The goal of this study was to present a geophysical–geotechnical approach that integrates several techniques, to quantify the sediment distribution in a proglacial lake. A geophysical survey is performed with ground‐penetrating radar (GPR) installed on a boat, whereas a time‐domain reflectometer (TDR) measures the electrical conductivity and permittivity of the lakebed sediments. Unperturbed samples are collected and analyzed to measure the main geotechnical properties of the sediment: grain‐size distribution, plastic limit, and liquid limit. Such properties support the interpretation of the GPR data and the detection of spatial variations of the sediment facies.To validate the proposed methodology, field tests were carried out at Lake Seracchi, the largest lake of the Rutor glacier, Italian Alps. It formed around 1880 because of the recent glacier shrinkage, as chronicled by valuable historical documents. Its greyish waters carry a significant amount of suspended sediment recognized as glacial flour, which gradually accumulates on the bottom of the lake.The obtained bathymetry and sediment thickness maps of Lake Seracchi show the strength of the approach: from only a few manual samples, it is possible to extrapolate the geotechnical properties of interest, such as friction angle or hydraulic conductivity, to wider areas, surveyed by the geophysical techniques. This is achieved by investigating the spatial distribution of key geophysical properties linked to the geotechnical properties of interest.

In the bioremediation field, geophysical techniques are commonly applied, at lab scale and field scale, to perform the characterization and the monitoring of contaminated soils. We propose a method for detecting the dielectric properties of contaminated soil during a process of bioremediation. An open-ended coaxial probe measured the complex dielectric permittivity (between 0.2 and 20 GHz) on a series of six soil microcosms contaminated by diesel oil (13.5% V_oil/V_tot). The microcosms had different moisture content (13%, 19%, and 24% V_w/V_tot) and different salinity due to the addition of nutrients (22 and 15 g/L). The real and the imaginary component of the complex dielectric permittivity were evaluated at the initial stage of contamination and after 130 days. In almost all microcosms, the real component showed a significant decrease (up to 2 units) at all frequencies. The results revealed that the changes in the real part of the dielectric permittivity are related to the amount of degradation and loss in moisture content. The imaginary component, mainly linked to the electrical conductivity of the soil, shows a significant drop to almost 0 at low frequencies. This could be explained by a salt depletion during bioremediation. Despite a moderate accuracy reduction compared to measurements performed on liquid media, this technology can be successfully applied to granular materials such as soil. The open-ended coaxial probe is a promising instrument to check the dielectric properties of soil to characterize or monitor a bioremediation process.