Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring

The soil science community is facing a growing demand of regional, continental, and worldwide databases in order to monitor the status of the soil. However, the availability of such data is very scarce. Cost-effective tools to measure soil properties for large areas (e.g., Europe) are required. Soil spectroscopy has shown to be a fast, cost-effective, environmental- friendly, nondestructive, reproducible, and repeatable analytical technique. The main aim of this paper is to describe the state of the art of soil spectroscopy as well as its potential to facilitating soil monitoring. The factors constraining the application of soil spectroscopy as an alternative to traditional laboratory analyses, together with the limits of the technique, are addressed. The paper also highlights that the widespread use of spectroscopy to monitor the status of the soil should be encouraged by (1) the creation of a standard for the collection of laboratory soil spectra, to promote the sharing of spectral libraries, and (2) the scanning of existing soil archives, reducing the need for costly sampling campaigns. Finally, routine soil analysis using soil spectroscopy would be beneficial for the end users by a reduction in analytical costs, and an increased comparability of results between laboratories. This ambitious project will materialize only through (1) the establishment of local and regional partnerships among existent institutions able to generate the necessary technical competence, and (2) the support of international organizations. The Food and Agriculture Organization (FAO) of United Nations and the Joint Research Centre of the European Commission are well placed to promote the use of laboratory and field spectrometers for monitoring the state of soils

GEMAS: Establishing geochemical background and threshold for 53 chemical elements in European agricultural soil

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GEMAS: Indium in agricultural and grazing land soil of Europe - Its source and geochemical distribution patterns

Indium is a very rare element, which is usually not reported in geochemical data sets. It is classified as a critical metal, with important applications in the electronics industry, especially in the production of solar panels and liquid-crystal displays (LCDs).Over 4000 samples of agricultural and grazing land soil have been collected for the "Geochemical Mapping of Agricultural and Grazing Land Soil of Europe" (GEMAS) project, carried out by the EuroGeoSurveys Geochemistry Expert Group. Indium concentrations in soil have been analysed using aqua regia extraction followed by ICP-MS. Median values of In for both land use types are nearly identical, 0.0176. mg/kg for agricultural soil and 0.0177. mg/kg for grazing land soil.The spatial distribution patterns of In in European soil are mainly controlled by geology and the presence of Zn and Sn mineralisation. The preference of In to accumulate in the fine-grained fraction of soil with high clay content dominates the major anomaly patterns on the geochemical maps. In the Mediterranean region, secondary In enrichment is visible in karst areas. A notable feature of the In spatial distribution is the large difference between northern and southern Europe, with median values of 0.012 and 0.021. mg. In/kg, respectively, suggesting that, in addition to lithology, weathering and climate are important factors influencing In soil enrichment over time. \ua9 2015 Elsevier B.V

GEMAS: Cobalt, Cr, Cu and Ni distribution in agricultural and grazing land soil of Europe

In the framework of the GEMAS project, 2211 samples of agricultural soil (Ap, 0–20 cm, regularly ploughed fields), and 2118 samples fromland under permanent grass cover (Gr, 0–10 cm, grazing land soil)were collected across almost the whole European continent, at a density of 1 sample site/2500 km2, in accordance with a common sampling protocol. Among many other elements, the concentrations of Co, Cr, Cu and Ni in European soil were determined by ICP-MS after a hot aqua extraction, and WD-XRFS analytical methods, and their spatial distribution patterns generated by means of a GIS software. The presence of mafic and ultramafic rocks, ophiolite complexes and mineralisation, is widespread across the European continent, and seems to explain most of the variability of the elements studied in this paper. A large belt, north of the last glaciation maximum limit, is generally dominated by lower concentrations compared with central European and Mediterranean areas and to some areas in Northern Europe where higher Co, Cr, Cu and Ni values also occur. The application of the guideline value set for Cu and Ni by the EU Directive 86/278/EEC to the Ap soil samples of the GEMAS data set highlighted that at the continental scale the use of a unique reference interval is a tool of limited effectiveness; the lithological variation, occurring across a whole continent, generates changes in the geochemistry of soil, which cannot be accommodated by using a single reference interval even if it is very wide. The GEMAS data set should form the sound basis to set at the European scale the geochemical background reference intervals, at least, for regions sharing common lithological settings and a common geological history

Prediction of the concentration of chemical elements extracted by aqua regia in agricultural and grazing European soils using diffuse reflectance mid-infrared spectroscopy

The aim of this study was to develop partial least squares (PLS) models to predict the concentrations of 45 elements in soils extracted by the aqua regia (AR) method using diffuse reflectance Fourier Transform mid-infrared (MIR; 4000–500 cm1) spectroscopy. A total of 4130 soils from the GEMAS European soil sampling program (geochemical mapping of agricultural soils and grazing land of Europe) were selected. From the full soil set, 1000 samples were randomly selected to develop PLS models. Cross-validation was used for model training and the remaining 3130 samples used for model testing. According to the ratio of standard deviation to root mean square error (RPD) of the predictions, the elements were allocated into two main groups; Group 1 (successful calibrations, 30 elements), including those elements with RPDP1.5 (the coefficient of determination, R2, also provided): Ca (3.3, 0.91), Mg (2.5, 0.84), Al (2.4, 0.83), Fe (2.2, 0.79), Ga (2.1, 0.78), Co (2.1, 0.77), Ni (2.0, 0.77), Sc (2.1, 0.76), Ti (2.0, 0.75), Li (1.9, 0.73), Sr (1.9, 0.72), K (1.8, 0.70), Cr (1.8, 0.70), Th (1.8, 0.69), Be (1.7, 0.66), S (1.7, 0.66), B (1.6, 0.63), Rb (1.6, 0.62), V (1.6, 0.62), Y (1.6, 0.61), Zn (1.6, 0.60), Zr (1.6, 0.59), Nb (1.5, 0.58), Ce (1.5, 0.58), Cs (1.5, 0.58), Na (1.5, 0.57), In (1.5, 0.57), Bi (1.5, 0.56), Cu (1.5, 0.55), and Mn (1.5, 0.54); and Group 2 for 15 elements with RPD values lower than 1.5: As (1.4, 0.52), Ba (1.4, 0.52), La (1.4, 0.52), Tl (1.4, 0.51), P (1.4, 0.46), U (1.4, 0.45), Sb (1.3, 0.46), Mo (1.3, 0.43), Pb (1.3, 0.42), Se (1.3, 0.40), Cd (1.3, 0.40), Sn (1.3, 0.38), Hg (1.2, 0.33), Ag (1.2, 0.32) and W (1.1, 0.19). The success of the PLS models was found to be dependent on their relationships (directly or indirectly) with MIR-active soil components