A revised landslide inventory of the Campania region (Italy).

Landslides represent a severe geohazard in many countries. The availability of inventories depicting the spatial and temporal distribution of landslides is crucial for assessing landslide susceptibility and risk for territorial planning or investigating landscape evolution. Nevertheless, these inventories are usually affected by limitations due to their nonpublic availability and inhomogeneities in characterization and mapping. Such problems are fully recognizable by the analysis of the multiple landslide inventories of the Campania region, which is one of the Italian regions with the highest exposure to landslide hazard and risk. On this basis, a revised Landslide Inventory of the Campania region (LaICa), resulting from the processing of multiple existing landslide inventories, has been reconstructed. It aims to (i) provide a new geodatabase that is able to overcome issues derived from the coexistence of multiple inventories and (ii) provide a methodological paradigm able to support the reorganization of existing official inventories. The implication of LaICa, with its 83,284 records, will possibly improve the assessment of landslide susceptibility and then reassess the related risk.

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.

Pedosedimentary and microbial investigation of a karst sequence record.

A 3-m thick sediment sequence, found in a limestone mine located in the south of Italy at a depth of ca. 25-30 m from the current ground level, was investigated. Samples from 5 layers were analysed by X-ray diffraction, elemental analysis, Inductively Coupled Plasma Mass Spectrometry and micromorphology. Microbial DNA was analysed by 16S rRNA gene metabarcoding. The main mineral compounds found in the 5 layers were calcite (70-80%) and clay minerals in layers #1 and #5, goethite (75%) and hematite in layer #2, manganese (66%) and iron oxides in layer #3, and almost exclusively goethite in layer #4. Micromorphology data allowed to shed light in understanding whether these sediments formed by subsequent weathering of carbonates and silicates or by migration of soil sediments from the surface, or also by the accumulation of shallow marine sediments occurring between the middle Pliocene and the lower Pleistocene, when the extreme western sector of this area underwent strong subsidence. From the microbiological point of view, upon the 16S rRNA gene analysis, these 5 layers appear to cluster in three groups. Overall, such a distribution suggests that, both in the top (#1) and in bottom layers (#4 and #5), different communities would have undergone in situ reproduction and colonization exploiting metabolically the substrate, whereas the two mid layers would have received bacterial convection by passive transport of percolating waters. At the same time, micromorphological data show that each layer preserved its distinct features to be related to the environmental condition at the time of deposition. The chemical, mineralogical and micromorphological features of the layers and the known physiology of the microbial taxa thereby encountered highlight the possible role of the latter in elucidating the occurrence of certain mineral species as well as the biogeochemistry of elements like Mn and Fe in sediment layers.

Monitoring metal pollution in soils using portable-XRF and conventional laboratory-based techniques: Evaluation of the performance and limitations according to metal properties and sources.

Large variability in the spatial distribution and content of metals is generally recognised in anthropogenically-polluted soils, hence, a detailed site investigation implying the collection and analysis of a large number of soil samples is often necessary. To this regard, the selection of a rapid, cost-effective and accurate analytical technique to assess the concentration of metals in soil is of paramount importance. The overall objective of this work was to evaluate the possibility of assessing the aqua regia-extractable (AR) content of metals in soil from the multi-element profile of the soil obtained by a portable X-ray fluorescence analyser (pXRF). To this objective, we attempted: (i) to establish, by simple linear regressions, the relations occurring between the metal contents measured by pXRF and AR in laboratory setting on air-dried and 2 mm-sieved soil samples from two case studies (A-agricultural and B-industrial sites); (ii) to define metal-based linear models predicting metal AR contents from pXRF measurements; (iii) to assess the influence of metal properties and sources on relations found between the two analytical methods. Very satisfying correlations (R2 > 0.90) were observed between the AR and pXRF contents of Ca, Cu, Cr, Ni, Pb and Zn in the site A, and of Cd, Cu, Pb and Zn in the site B. For the majority of metals, lower AR than pXRF contents were measured, as result of the AR incomplete dissolution of metal-bearing silicates. This was not observed when metals - of anthropogenic origin - occurred in soil in very high concentrations (i.e., Cr for A and Pb for B). In both sites, the comparison among different regression parameters revealed a strong metal-dependence. Moreover, for most of the metals, the parameters of each metal-regression line significantly differed between the two case studies, indicating site-dependence of regression fits.

A Combined Approach of Sensor Data Fusion and Multivariate Geostatistics for Delineation of Homogeneous Zones in an Agricultural Field.

To assess spatial variability at the very fine scale required by Precision Agriculture, different proximal and remote sensors have been used. They provide large amounts and different types of data which need to be combined. An integrated approach, using multivariate geostatistical data-fusion techniques and multi-source geophysical sensor data to determine simple summary scale-dependent indices, is described here. These indices can be used to delineate management zones to be submitted to differential management. Such a data fusion approach with geophysical sensors was applied in a soil of an agronomic field cropped with tomato. The synthetic regionalized factors determined, contributed to split the 3D edaphic environment into two main horizontal structures with different hydraulic properties and to disclose two main horizons in the 0-1.0-m depth with a discontinuity probably occurring between 0.40 m and 0.70 m. Comparing this partition with the soil properties measured with a shallow sampling, it was possible to verify the coherence in the topsoil between the dielectric properties and other properties more directly related to agronomic management. These results confirm the advantages of using proximal sensing as a preliminary step in the application of site-specific management. Combining disparate spatial data (data fusion) is not at all a naive problem and novel and powerful methods need to be developed.