Exploring microstructure and petrophysical properties of microporous volcanic rocks through 3D multiscale and super-resolution imaging.

Digital rock physics offers powerful perspectives to investigate Earth materials in 3D and non-destructively. However, it has been poorly applied to microporous volcanic rocks due to their challenging microstructures, although they are studied for numerous volcanological, geothermal and engineering applications. Their rapid origin, in fact, leads to complex textures, where pores are dispersed in fine, heterogeneous and lithified matrices. We propose a framework to optimize their investigation and face innovative 3D/4D imaging challenges. A 3D multiscale study of a tuff was performed through X-ray microtomography and image-based simulations, finding that accurate characterizations of microstructure and petrophysical properties require high-resolution scans (≤ 4 µm/px). However, high-resolution imaging of large samples may need long times and hard X-rays, covering small rock volumes. To deal with these limitations, we implemented 2D/3D convolutional neural network and generative adversarial network-based super-resolution approaches. They can improve the quality of low-resolution scans, learning mapping functions from low-resolution to high-resolution images. This is one of the first efforts to apply deep learning-based super-resolution to unconventional non-sedimentary digital rocks and real scans. Our findings suggest that these approaches, and mainly 2D U-Net and pix2pix networks trained on paired data, can strongly facilitate high-resolution imaging of large microporous (volcanic) rocks.

The pesticide fate tool for groundwater vulnerability assessment within the geospatial decision support system LandSupport.

The protection of groundwater resources from non-point-source pollutants, such as those coming from agricultural practices, is the focus of several European Directives, including the Water Framework Directive and the Pesticide Directive. Besides the environmental goals to be reached by the single EU member state, these directives clearly underline the role of experts in supporting planners and public authorities to fulfil these objectives. This work presents a new web-based, freely-available dynamical tool, named the pesticide fate tool, developed within the geospatial Decision Support system (DSS), LandSupport, for the assessment of groundwater vulnerability, specific for type of pollutant. The tool is based on the extended transfer function model, specifically expanded to consider the transport of reactive solutes, such as pesticides. The work describes the tool implementation for three case studies, with different spatial scales and pedo-climatic conditions: Valle Telesina, IT, Marchfeld, AT, and Zala County, HU. Principal inputs of the tool are: soil physical and hydrological properties, climate, groundwater table depth, type of crops and related pesticides. Results of the model are shown through the LandSupport GUI both as coloured maps, representing the relative concentration of pesticide at the arrival to the water table at the end of the simulation period, and as cumulative charts of the solute arrival at the depth of interest. The three case studies are shown as examples of application of the LandSupport DSS in supporting the Water and Pesticides directives, demonstrating that it represents a valuable instrument for public authorities, environmental planners, as well as agricultural extension services. For example, large differences are shown by soils in filtering the tetraconazole (99.9% vs 76%), a fungicide used in viticulture, or different percentage of arrival (0.32% and 0,01%) to the groundwater table are shown for two herbicides (Tribenuron and Florasulam) largely used to control annual dicotyledonous weeds.

A novel methodology for Groundwater Flooding Susceptibility assessment through Machine Learning techniques in a mixed-land use aquifer.

Many areas around the world are affected by Groundwater Level rising (GWLr). One of the most severe consequences of this phenomenon is Groundwater Flooding (GF), with serious impacts for the human and natural environment. In Europe, GF has recently received specific attention with Directive 2007/60/EC, which requires Member States to map GF hazard and propose measures for risk mitigation. In this paper a methodology has been developed for Groundwater Flooding Susceptibility (GFS) assessment, using for the first time Spatial Distribution Models. These Machine Learning techniques connect occurrence data to predisposing factors (PFs) to estimate their distributions. The implemented methodology employs aquifer type, depth of piezometric level, thickness and hydraulic conductivity of unsaturated zone, drainage density and land-use as PFs, and a GF observations inventory as occurrences. The algorithms adopted to perform the analysis are Generalized Boosting Model, Artificial Neural Network and Maximum Entropy. Ensemble Models are carried out to reduce the uncertainty associated with each algorithm and increase its reliability. GFS is mapped by choosing the ensemble model with the best predictivity performance and dividing occurrence probability values into five classes, from very low to very high susceptibility, using Natural Breaks classification. The methodology has been tested and statistically validated in an area of 14,3 km2 located in the Metropolitan City of Naples (Italy), affected by GWLr since 1990 and GF in buildings and agricultural soils since 2007. The results of modeling show that about 93% of the inventoried points fall in the high and very high GFS classes, and piezometric level depth, thickness of unsaturated zone and drainage density are the most influencing PFs, in accordance with field observations and the triggering mechanism of GF. The outcomes provide a first step in the assessment of GF hazard and a decision support tool to local authorities for GF risk management.

Author Correction: Global karst springs hydrograph dataset for research and management of the world's fastest-flowing groundwater.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Global karst springs hydrograph dataset for research and management of the world's fastest-flowing groundwater.

Karst aquifers provide drinking water for 10% of the world's population, support agriculture, groundwater-dependent activities, and ecosystems. These aquifers are characterised by complex groundwater-flow systems, hence, they are extremely vulnerable and protecting them requires an in-depth understanding of the systems. Poor data accessibility has limited advances in karst research and realistic representation of karst processes in large-scale hydrological studies. In this study, we present World Karst Spring hydrograph (WoKaS) database, a community-wide effort to improve data accessibility. WoKaS is the first global karst springs discharge database with over 400 spring observations collected from articles, hydrological databases and researchers. The dataset's coverage compares to the global distribution of carbonate rocks with some bias towards the latitudes of more developed countries. WoKaS database will ensure easy access to a large-sample of good quality datasets suitable for a wide range of applications: comparative studies, trend analysis and model evaluation. This database will largely contribute to research advancement in karst hydrology, supports karst groundwater management, and promotes international and interdisciplinary collaborations.

Effectiveness of a physical barrier for contaminant control in an unconfined coastal plain aquifer: the case study of the former industrial site of Bagnoli (Naples, southern Italy).

A vertical engineered barrier (VEB) coupled with a water treatment plant was surveyed in the framework of a vast remedial action at the brownfield site of the former ILVA of Bagnoli steel making facility located in western Naples, Italy. The VEB was put in place to minimize contaminant migration from the brownfield site toward the sea at the shorelines sites of Bagnoli and Coroglio. The efficiency of the VEB was monitored through 12 piezometers, 8 at the Bagnoli shoreline and 4 at the Coroglio shoreline. Concentrations of inorganic and organic pollutants were examined in upstream and downstream groundwater relative to the VEB. The mean levels of Al, As, Fe, and Mn largely exceeded the legal limits, 10-15-fold, whereas that of Hg was up to 3-fold the rules. The VEB decreased the outlet concentrations only at certain specific location of the barrier, four times for Al, 6-fold for Hg, and by 20% for Mn with means largely exceeding the rules. At the other sites, the downstream water showed marked increases of the pollutants up to 3-fold. Outstanding levels of the hydrocarbons > 12 were detected in the inlet water with means of some hundred times the limits at both sites. Likewise most of screened inorganic pollutants, the downstream water showed marked increases of the hydrocarbons up to ~113%. The treatment plant was very effective, with removal efficiencies >80% for As, Al, Fe, and Mn. The study evidenced the need to put alternative groundwater remedial actions.

Winter survival of microbial contaminants in soil: an in situ verification.

The aim of the research was to evaluate, at site scale, the influence of freezing and freeze/thaw cycles on the survival of faecal coliforms and faecal enterococci in soil, in a climate change perspective. Before the winter period and during grazing, viable cells of faecal coliforms and faecal enterococci were detected only in the first 10 cm below ground, while, after the winter period and before the new seasonal grazing, a lower number of viable cells of both faecal indicators was detected only in some of the investigated soil profiles, and within the first 5 cm. Taking into consideration the results of specific investigations, we hypothesise that the non-uniform spatial distribution of grass roots within the studied soil can play an important role in influencing this phenomenon, while several abiotic factors do not play any significant role. Taking into account the local trend in the increase of air temperature, a different distribution of microbial pollution over time is expected in spring waters, in future climate scenarios. The progressive increase in air temperature will cause a progressive decrease in freeze/thaw cycles at higher altitudes, minimising cold shocks on microbial cells, and causing spring water pollution also during winter.

Role of organic matter and clay fraction on migration of Escherichia coli cells through pyroclastic soils, southern Italy.

A comparative study on the adsorption of Escherichia coli cells to two different pyroclastic soils collected in southern Italy (carbonate Apennines) was performed in laboratory using surfactant-free solutions and solutions with the surfactants sodium dodecyl sulphate (SDS, anionic) and octylphenoxypolyethoxyethanol (Triton X-100, non-ionic). Both soils are rich in organic matter (up to 35%), but only one contains a clay fraction (2-5%). The experiments demonstrated that E. coli cells are significantly adsorbed to the clay fraction of the soil, while the organic matter content does not play a significant role. The pore size exclusion phenomenon is another factor to consider when analyzing the retention of E. coli cells within such soils. However, despite the existence of different factors that enhance bacterial cells retention, a high percent of E. coli cells is transported through soil media. The not absolute protection of such soils against microbial pollution is supported not only by the results of the column experiments at lab scale, but also by the findings of a field monitoring at site scale.