Abstract

This work was carried out as the Italian contribution to the EuroGeoSurveys GEMAS project, the objective of which was the geochemical characterisation of agricultural and grazing land soil in Europe. Soil samples were collected at an average sampling density of 1 site per 2500km2. Two different sample types were collected in Italy: (1) 121 samples of agricultural soil (Ap) on regularly ploughed land to a depth of 20cm, and (2) 121 samples of grazing land soil (Gr) (land under permanent grass cover) to a depth of 10cm. All soil samples were air dried, sieved to <2mm, homogenised and finally split into 10 sub-samples. Both sample types (Ap and Gr) were analysed after an aqua regia (AR) extraction by a combination of ICP-AES and ICP-MS for 53 elements. In addition, other parameters were determined: pH, TOC, total carbon and total sulphur, LOI, CEC, Sr-isotopes, Pb-isotopes, and MIR-spectra. In this paper, the results of only aqua regia extraction on agricultural soil samples (Ap) and Pb-isotope determinations are presented and discussed.By means of a GIS software, georeferenced data of the Italian territory were used to produce the geochemical maps of all analysed parameters. This work shows that the spatial distribution of chemical elements is governed mostly by geogenic factors, although the concentration levels of many of the investigated elements are well above the corresponding statutory Italian intervention limits. The anomalies of Be and Sn in some areas are absolutely geogenic, as well as the anomalous values of Cr, Co and Ni that characterise the north-western regions. In other areas, the work has instead highlighted high element concentrations related to anthropic activities. This is the case of high Pb concentration near the urbanised and industrialised cities of Roma and Napoli. Lead isotope investigation has corroborated the hypothesis that road traffic is the main source of this contamination.Finally, the continental-scale geochemical maps of Italy show that the element distribution patterns identify clearly the presence of the major geolithological structures and lineaments, even using such a very low sampling density grid.

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