On groundwater flow and shallow geothermal potential: A surrogate model for regional scale analyses.

Many cities worldwide lay upon alluvial aquifers which have a great potential for low temperature geothermal installations thanks to the thermal diffusive properties of saturated porous media and the constant temperature of the subsurface. In addition, aquifers with fast moving groundwater have a higher potential due to the additional energy replenishment by advection, which is often underestimated. This work aims at bridging the gap between quantitative hydro-thermal numerical analysis and regional scale assessment developing a process-based surrogate model for the estimation of the thermal exchange (geothermal) potential of ground source heat pumps (GSHP) considering groundwater advection. The proposed method is based on a synthetic 3D FEM model reproducing the infinite line source configuration and introducing groundwater advection. Conductive/advective g-functions were derived from the numerically simulated space-time thermal perturbation for a comprehensive set of hydrogeological regimes, and a surrogate model was developed by a machine learning (ML) regression of the thermal response of the system. This solution, beyond the run time of the numerical study and the ML training phase, is very fast, applicable at any scale and scalable to any desired depth. The trained model can be used to predict the geothermal potential of GSHP for almost all sedimentary basins around the world upon the availability of the required input data (aquifer thickness and saturation, aquifer porosity and groundwater flow velocity). In this study, large scale geothermal potential maps were generated from input layers implemented in a GIS, for a demonstrative area in northern Italy showing highly variable groundwater flow (Darcy velocity from 10-3 to 10+3 m/y). A promising increase (up to +250 %) in the thermal exchange potential of GSHP due to the contribution of advection was highlighted discussing the benefits of groundwater flow and the amount of usable potential with implications on shallow geothermal energy management and development.

Triggering and recovery of earthquake accelerated landslides in Central Italy revealed by satellite radar observations.

Earthquake triggered landslides often pose a great threat to human life and property. Emerging research has been devoted to documenting coseismic landslides failed during or shortly after earthquakes, however, the long-term seismic effect that causes unstable landslides only to accelerate, moderately or acutely, without immediate failures is largely neglected. Here we show the activation and recovery of these earthquake accelerated landslides (EALs) in Central Italy, based on satellite radar observations. Unlike previous studies based on single or discrete landslides, we established a large inventory of 819 EALs and statistically quantified their spatial clustering features against a set of conditioning factors, thus finding that EALs did not rely on strong seismic shaking or hanging wall effects to occur and larger landslides were more likely to accelerate after earthquakes than smaller ones. We also discovered their accelerating-to-recovering sliding dynamics, and how they differed from the collapsed 759 coseismic landslides. These findings contribute to a more comprehensive understanding of the earthquake-triggering landslide mechanism and are of great significance for long-term landslide risk assessment in seismically active areas.

The subsurface urban heat island in Milan (Italy) - A modeling approach covering present and future thermal effects on groundwater regimes.

Knowledge on the intensity and extension of current subsurface urban heat islands (SUHI) is not only based on the availability of spatiotemporal high-resolution and long-term groundwater monitoring data but also in-depth investigations on the role of single natural and anthropogenic factors. A holistic city-scale 3D FEM model is presented to introduce possible thermal management applications in the Milan metropolitan area such as: (1) understanding the hydro-thermal regime of the urban aquifer disentangling the thermal contribution of natural and anthropogenic heat sources, (2) quantifying the geothermal potential and (3) investigating the effects of urbanization and climate change scenarios. Focusing on the most relevant heat sources (boundaries) and transport mechanisms (parameters), this work deals with (I) the reconstruction of large-scale aquifer heterogeneities to consider the advective dominated heat transport, (II) the accurate definition of the upper thermal boundary by a coupled analytical solution and (III) the integration of natural and human-related fluid/heat sources as transient boundary conditions. The model was calibrated against 15 groundwater head and temperature time series and validated in space and time by temperature profiles at 40 additional observation wells. Thus, a fluid and heat budget analysis revealed the most relevant natural and anthropogenic sources at the city-scale. The heat flow from buildings, surface infrastructures and tunnels contribute to 85% of the net annual heat accumulation in the subsurface which totals to 1.4 PJ/y. The results of the simulations were used to evaluate the geothermal potential of the shallow aquifer and to localize promising and critical areas that should be further investigated for an effective thermal management. Finally, it was demonstrated that possible future climate change and city expansion scenarios could lead to the highest thermal energy increment in the subsurface compared to shallow geothermics development which, for this reason, should be highly supported.

Hydro-stratigraphic datasets for the reconstruction of a large scale 3D FEM numerical model in the Milan metropolitan area (northern Italy).

One of the objectives of groundwater numerical modeling is to accurately reproduce the flow velocity field and the flow and transport pathways. In this article the hydro-stratigraphic dataset, used in the co-submitted article "Modeling the interference of underground structures with groundwater flow and remedial solutions in Milan" (De Caro et al., 2020) [1], is presented. The work aims to reconstruct the spatial variability of the hydraulic parameters in the shallow aquifers of the Milan City area (northern Italy) and to integrate them in a groundwater flow 3D finite element method (FEM) numerical model. This objective is achieved by converting qualitative borehole logs stratigraphic information into hydrogeological parameters (e.g. hydraulic conductivity and porosity) and by interpolating these parameters over the finite element mesh nodes by means of 3D kriging techniques. The modeling domain and the mesh nodes, the boundary surfaces between the aquifers as well as some of the piezometric data used to calibrate the model are presented to make the numerical experiment reproducible.

Multi-element compound specific stable isotope analysis of chlorinated aliphatic contaminants derived from chlorinated pitches.

Tetrachloroethene and trichloroethene are typical by-products of the industrial production of chloromethanes. These by-products are known as "chlorinated pitches" and were often dumped in un-contained waste disposal sites causing groundwater contaminations. Previous research showed that a strongly depleted stable carbon isotope signature characterizes chlorinated compounds associated with chlorinated pitches whereas manufactured commercial compounds have more enriched carbon isotope ratios. The findings were restricted to a single case study and one element (i.e. carbon). This paper presents a multi-element Compound-Specific Stable Isotope Analysis (CSIA, including carbon, chlorine and hydrogen) of chlorinated aliphatic contaminants originated from chlorinated pitches at two sites with different hydrogeology and different producers of chloromethanes. The results show strongly depleted carbon signatures at both sites whereas the chlorine and the hydrogen signatures are comparable to those presented in the literature for manufactured commercial compounds. Multi-element CSIA allowed the identification of sources and site-specific processes affecting chloroethene transformation in groundwater as a result of emergency remediation measures. CSIA turned out to be an effective forensic tool to address the liability for the contamination, leading to a conviction for the crimes of unintentional aggravated public water supply poisoning and environmental disaster.

Borehole flowmeter logging for the accurate design and analysis of tracer tests.

Tracer tests often give ambiguous interpretations that may be due to the erroneous location of sampling points and/or the lack of flow rate measurements through the sampler. To obtain more reliable tracer test results, we propose a methodology that optimizes the design and analysis of tracer tests in a cross borehole mode by using vertical borehole flow rate measurements. Experiments using this approach, herein defined as the Bh-flow tracer test, have been performed by implementing three sequential steps: (1) single-hole flowmeter test, (2) cross-hole flowmeter test, and (3) tracer test. At the experimental site, core logging, pumping tests, and static water-level measurements were previously carried out to determine stratigraphy, fracture characteristics, and bulk hydraulic conductivity. Single-hole flowmeter testing makes it possible to detect the presence of vertical flows as well as inflow and outflow zones, whereas cross-hole flowmeter testing detects the presence of connections along sets of flow conduits or discontinuities intercepted by boreholes. Finally, the specific pathways and rates of groundwater flow through selected flowpaths are determined by tracer testing. We conclude that the combined use of single and cross-borehole flowmeter tests is fundamental to the formulation of the tracer test strategy and interpretation of the tracer test results.

Characterization of fine resolution field spectrometers using solar Fraunhofer lines and atmospheric absorption features.

The accurate spectral characterization of high-resolution spectrometers is required for correctly computing, interpreting, and comparing radiance and reflectance spectra acquired at different times or by different instruments. In this paper, we describe an algorithm for the spectral characterization of field spectrometer data using sharp atmospheric or solar absorption features present in the measured data. The algorithm retrieves systematic shifts in channel position and actual full width at half-maximum (FWHM) of the instrument by comparing data acquired during standard field spectroscopy measurement operations with a reference irradiance spectrum modeled with the MODTRAN4 radiative transfer code. Measurements from four different field spectrometers with spectral resolutions ranging from 0.05 to 3.5nm are processed and the results validated against laboratory calibration. An accurate retrieval of channel position and FWHM has been achieved, with an average error smaller than the instrument spectral sampling interval.

Scoring CFU-GM colonies in vitro by data fusion: a first account.

OBJECTIVE: In vitro models of hematopoiesis used in investigative hematopathology and in safety studies on candidate drugs, involve clonogenic assays on colony-forming unit granulocyte macrophage (CFU-GM). These assays require live and unstained colonies to be counted. Most laboratories still rely on visual scoring, which is time-consuming and error-prone. As a consequence, automated scoring is highly desired. An algorithm that recognizes and scores CFU-GM colonies by data fusion has been developed. Some preliminary results are presented in this article. METHODS: CFU-GM assays were carried out on hematopoietic progenitors (human umbilical cord blood cells) grown in methylcellulose. Colony images were acquired by a digital camera and stored. RESULTS: The classifier was designed to process images of layers sampled from a three-dimensional (3D) domain and forming a stack. Structure and texture information was extracted from each image. Classifier training was based on a 3D colony model applied to the image stack. The number of scored colonies (assigned class) was required to match the count supplied by the human expert (class of belonging). The trained classifier was validated on one more stack and then applied to a stack with overlapping colonies. Scoring in distortion- and caustic-affected border areas was also successfully demonstrated. Because of hardware limitations, compact colonies in some cases were missed. CONCLUSIONS: The industry's scoring methods all rely on structure alone and process 2D data. Instead, the classifier here fuses data from a whole stack and is capable, in principle, of high-throughput screening.

Classifying structural alterations of the cytoskeleton by spectrum enhancement and descriptor fusion.

A classifier capable of ranking structural alterations of the cytoskeleton is developed. Images of cytoskeletal microtubules obtained from the epifluorescence microscopy of primary culture rat hepatocytes are analyzed. Morphological descriptors are extracted by contour and mass fractal analysis, direct methods, and spectrum enhancement. All methods are designed and tuned to make the extracted morphological descriptors insensitive to absolute fluorescence intensities. Spectrum enhancement is a nonlinear filter that involves spatial differentiation of the gray-scale image followed by conversion of power spectral density to the logarithmic scale and averaging over arcs in the reciprocal domain. Enhanced spectra exhibit local maxima that correspond to the structured microtubule bundles of a normal cytoskeleton. Descriptor fusion for classification is achieved by means of multivariate analysis. The classifier is trained by image sets representing normal ("negative control") microtubules and those altered by exposure to a fungicide at the highest dose of the experiment design. Some sensitivity and validation tests, including discriminant functions analysis, are applied to the classifier. The latter is applied to recognize images of microtubules not used in the training stage and comes from treatments at lower concentrations and shorter times. As a result, structural alterations are ranked and structural recovery after treatment is quantified. The method has potential use in quantitative, morphology-based tests on the cytoskeleton treated either by anticancer drugs or by cytotoxic agents.

Microscopy and quantitative morphology of aluminum silicate nanoparticles grown on organic templates.

Biomimetic synthesis of ceramic materials is increasing in popularity because it offers many advantages. In this work, aluminum silicate nanoparticles were obtained on a self-assembled organic multilayer at near room temperature (30 < or = T < or = 50 degrees C) and at atmospheric pressure. Morphological and microanalytical characterization was carried out by means of transmission electron microscopy and subsequent image analysis. The roles of some process parameters such as template type, reactant concentration, [Al]:[Si] molar ratio, number of initiation steps (IS) of mineralization, and reaction time (rt) were assessed by comparing images, diffraction patterns, and EDX spectra. Generally, the Si-rich phase exhibited higher crystallinity, whereas the Al-rich phase was mostly amorphous. Crystal structure resulted with rt > or = 4 days for template-grown materials. Images of materials obtained at T = 50 degrees C, rt = 3 days, and 1 < or = IS < or = 4 were further analyzed by "spectrum enhancement," an algorithm based on the Fourier transform. Morphological indicators were extracted from suitably processed power spectral densities, a correlation matrix was formed, and multivariate statistics was carried out. Visual differences in nanoaggregate morphology were quantitatively translated. Materials were ranked by the spatial uniformity of nanoparticle distribution: the most uniform aggregates were those grown on templates by IS > or = 2. Univariate statistics validated the conclusion: the particles of those same materials had a narrower size distribution and sharper edges. This last property has been ascribed to crystalline structure, independently demonstrated by diffraction patterns.

Multivariate analysis and classification of two-dimensional angular optical scattering patterns from aggregates.

Two-dimensional light-scattering patterns from aggregates have undergone feature extraction followed by multivariate statistical analysis. The aggregates are comprised of primary particles of varying shape and size. Morphological descriptors (features) were extracted by a nonlinear filtering algorithm (spectrum enhancement) and then processed by principal component analysis and discriminant function analysis. The analysis was performed on two data sets, one in which the aggregates had a fixed primary particle size but varied in overall dimension and another in which the aggregate size was fixed but the primary particle size varied. Classification of the samples was performed adequately, providing some distinction among the limited classes that were analyzed.