DISTRIBUTION OF ELEMENTS IN SUBSOIL AND TOPSOIL

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1 DISTRIBUTION OF ELEMENTS IN SUBSOIL AND TOPSOIL W. De Vos 1, V. Gregorauskiene 2, K. Marsina 3, R. Salminen 4, I. Salpeteur 5, T. Tarvainen 4, P.J. O Connor 6, A. Demetriades 7, S. Pirc 8, M. J. Batista 9, M. Bidovec 10 with contributions by A. Bel-lan 11, M. Birke 12, N. Breward 13, B. De Vivo 14, M. Duris 15, J. Halamic 16, P. Klein 17, A. Lima 14, J. Locutura 11, J. Lis 18, A. Mazreku 19, R.T. Ottesen 20, A. Pasieczna 18, V. Petersell 21, S. Reeder 13, U. Siewers 12, I. Slaninka 3 1 Geological Survey of Belgium, Brussels, Belgium; 2 Geological Survey of Lithuania, Lithuania; 3 Geological Survey of Slovak Republic, Slovak Republic; 4 Geological Survey of Finland, Espoo, Finland; 5 Geological Survey of France, France; 6 Geological Survey of Ireland, Dublin, Ireland; 7 Institute of Geology and Mineral Exploration, Athens, Greece; 8 Geology Department, University of Ljubljana, Ljubljana, Slovenia; 9 Geological Survey of Portugal, Portugal; 10 Geological Survey of Slovenia, Ljubljana, Slovenia; 11 Geological Survey of Spain, Madrid, Spain; 12 Bundesanstalt für Geowissenschaften und Rohstoffe, Hannover, Germany; 13 British Geological Survey, Keyworth, Nottingham, United Kingdom; 14 Dipartimento di Scienze della Terra, Universita' di Napoli "Federico II", Naples, Italy; 15 Czech Geological Survey, Prague, Czech Republic; 16 Institute of Geology, Croatia, Zagreb, Croatia; 17 Geological Survey of Austria, Wien, Austria; 18 Polish Geological Institute, Warsaw, Poland; 19 Centre of Civil Geology, Tirana Albania; 20 Geological Survey of Norway, Trondheim, Norway; 21 Geological Survey of Estonia, Tallinn, Estonia. The FOREGS soil samples do not represent all soil types of Europe. According to the FOREGS Geochemical Mapping Field Manual (Salminen, Tarvainen et al. 1998), the sample should be taken from residual and sedentary (i.e., undisplaced) soil, which generally reflects the underlying lithology. Residual soil samples were collected from the small, second order drainage basins at suitable sites above their alluvial plain and base of slope, where alluvium and colluvium are respectively deposited. Residual soil may have been developed either on bedrock or on till, as is the case in glaciated terrains. Soil samples were mostly collected from forest soils. However, agriculture or pastureland sometimes constitutes the dominant land use type in the small catchments selected for sampling in southern Europe (Map 1). Photographs from each soil sampling site are available from the electronic version of the Atlas. The main purpose of the FOREGS geochemical survey is not to document the geochemistry of different soil types, but to discover the large pattern of geochemical signatures on the scale of the continent, and to investigate the different factors influencing this pattern, notably bedrock geology, climate and human influences. Detailed pedogenetic processes fall outside the scope of this investigation. From this point of view, soil geochemistry helps us to understand geology on the continental scale, and not the other way around. 21

2 U LAND USE Kilometers LAND USE Agriculture Pasture Forest: Deciduous Forest: Coniferous Forest: Mixed Wetland Non-cultivated Other Geochemical Baseline Mapping Canary Islands Map 1. Dominant land use of the soil sampling site. Land use

3 The distribution of most elements in soil shows a pattern related to geology and/or mineralisation. A major geochemical dichotomy appears on the various maps between soil developed on crystalline and Palaeozoic basement rocks, and those on unfolded cover rocks that are naturally impoverished in most trace elements (except for areas of intense agricultural activity). Palaeocene and Eocene climates caused strong argillic and ferralitic weathering of some basement rocks, with a change in mineralogy, and a modification of vertical and lateral distribution patterns of most major elements. On crystalline bedrock, there is also a systematic difference between the glacial till area encompassing Fennoscandia and Scotland, and the rest of Europe. Till is mostly quite fresh and chemically unweathered detritus, while in other areas the surficial deposits contain old and chemically strongly weathered material. Moreover, weathering of carbonates and silicates shows a different pattern. The anomaly pattern shown on the Na 2 O map (higher content over glacial till areas) can be found also for elements such as Ca, Sr, Ba, K and some others. Since these elements are also derived from other sources (carbonate in the case of Ca and Sr, shale in the case of K and Ba) high element values can be found in non-glacial southern Europe as well. The dilution of many element concentrations in the marginal area of the last glaciation is another geological process. The amount of weathered material in till increases towards the ice marginal area, where till contains a large amount of silica. The European map of the weathering index (Map 2) shows that weathering in Fennoscandia is generally less advanced than elsewhere in Europe. The weathering index is defined as: CIA = 100 x [Al 2 O 3 / (Al 2 O 3 + CaO* + Na 2 O + K 2 O)], where CaO* = Ca in silicate fraction (McClennan et al. 1990). A high CIA value indicates a long weathering history of the soil. It is noted that the underlying bedrock also affects the CIA index value, e.g., Alrich sedimentary rocks tend to have higher values, whereas Al-poor limestone always has low values (usually <40). In the section on soil, for the sake of uniformity, and as a necessary simplification in descriptions, the following definitions were adopted with reference to the coloured maps and histograms in Part 1 of the Geochemical Atlas of Europe (Salminen et al. 2005): Low values group the three lowest shades of blue in the colour scale, corresponding to the range from the minimum value up to the 25 th percentile, defined as very low and low background concentrations in Part 1 (Tarvainen et al. 2005, p.97), and High values group the three highest shades of red in the colour scale, corresponding to the range of values from the 75 th percentile up to the maximum, defined as high, very high and highly anomalous concentrations in Part 1 (Tarvainen et al. 2005, p.97). Correlation coefficients were calculated with Pearson s product-moment linear correlation method (Table available in electronic format on website after deletion of outliers and subsequent pairwise deletion of absent data. For a given element or determinand, outliers were defined here as values exceeding by a factor of 1.5 other nearby results, when all analytical results are ranked. They are generally visible on the histogram accompanying each map in Part 1 of the Geochemical Atlas. A maximum of four outliers were removed in this work for the calculation of linear correlation coefficients. A list of outliers is given separately for subsoil (Table 2) and topsoil (Table 3). Throughout the text the following notation is used for the correlation coefficients: Very strong correlation: > 0.8; Strong correlation: between 0.6 and 0.8; Good correlation: between 0.4 and 0.6, and Weak correlation: between 0.3 and 0.4. Because of the large number of samples, even the so-called weak correlations are significant at the 0.01 confidence level. The use of Pearson s correlation coefficients rather than Spearman s rank correlation, and the deletion of outliers, is linked to a factor analysis, performed on the dataset (Batista et al. 2006, Annex 5 in this volume). When interpreting correlation coefficients of major elements, especially Si, Al and Ca, it should be kept in mind that we work in a closed system where the sum total of all elements (or oxides in the case of majors) add up to approximately 100%. This leads to some form of autocorrelation, as observed in the negative correlation between Si and Ca, and the negative correlation Si-Al. This phenomenon also 23

4 contributes to the significance of the so-called weak correlations mentioned earlier. Despite the critical objections that can be made against using linear correlation coefficients in this data set covering very dissimilar regions, the coefficients were found to express general tendencies of element associations at the continental scale. These tendencies are similar for soil, stream sediment and floodplain sediment (together forming the solid sample media), and are, therefore, taken to be meaningful indicators of geochemical processes. For this reason, and despite the obvious shortcomings, correlations are mentioned throughout the text. The overall Table 2. Outliers of the subsoil data. Criterion: an outlier has a value exceeding by factor of 1.50 other nearby results when all analytical results are ranked. A maximum of four outliers were removed for the calculation of linear correlation coefficients. Sample Country Element Value Unit Next value Factor N36E03C1 Netherlands S mg kg -1 N32E05C2 Switzerland S mg kg N37E05C2 Germany S mg kg -1 N32E04C3 Switzerland S mg kg N26W03C1 Spain Hg 0.93 mg kg -1 N29W04C3 Spain Hg 0.90 mg kg N36W02C1 UK TOC % N32E04C2 France TOC 41.3 % N30E05C4 Italy Co 170 mg kg N40E04C4 Norway Nb 133 mg kg -1 N31E06C4 Italy Nb 88.4 mg kg -1 N31E01C5 France Nb 70.4 mg kg N30E05C4 Italy Ni mg kg -1 N26E14C2 Greece Ni mg kg -1 N27E12C1 Greece Ni mg kg N30E03C5 France Pb 938 mg kg -1 N26E14C4 Greece Pb 559 mg kg -1 N30E03C3 France Pb 531 mg kg N28W05C1 Portugal As 593 mg kg N26E12C4 Greece Cd 14.2 mg kg N41E03C5 Norway Mo 17.2 mg kg -1 N31E06C3 Italy Mo mg kg N30E03C5 France Sb 30.3 mg kg -1 N26E14C4 Greece Sb 16.9 mg kg -1 N29W04C3 Spain Sb 16.6 mg kg N32E06C3 Austria Te 1.63 mg kg N30E03C5 France Tl 21.3 mg kg N26E12C4 Greece P 2 O % N35E07C2 Germany P 2 O % N28E14C3 Greece Sn 106 mg kg -1 N28W05C4 Portugal Sn 103 mg kg -1 N28W04C4 Portugal Sn 86 mg kg N32E05C2 Switzerland Sr mg kg N30E03C5 France Zn mg kg -1 N30E03C3 France Zn 989 mg kg

5 Map 2. Weathering index of the FOREGS topsoil samples.

6 Table 3. Outliers of the topsoil data. Criterion: an outlier has a value exceeding by factor of 1.5 other nearby results, when all analytical results are ranked. A maximum of four outliers were removed for the calculation of linear correlation coefficients. Sample Country Element Value Unit Next value Factor N28W01T4 Spain S mg kg -1 N25E08T1 Italy S mg kg -1 N36W02T1 UK S mg kg -1 N32E04T3 Switzerland S mg kg N28E08T1 Italy Hg 1.35 mg kg -1 N33E11T2 Slovakia Hg 1.29 mg kg -1 N38W02T3 UK Hg 1.18 mg kg -1 N26W03T1 Spain Hg 1.04 mg kg N30E05T4 Italy Co 249 mg kg N37W03T4 UK Dy 44.9 mg kg -1 N27W04T1 Spain Dy 22.9 mg kg N37W03T4 UK Er 26.0 mg kg -1 N27W04T1 Spain Er 14.2 mg kg N37W03T4 UK Ho 9.16 mg kg -1 N27W04T1 Spain Ho 4.81 mg kg N37W03T4 UK Lu 3.21 mg kg -1 N27W04T1 Spain Lu 2.03 mg kg N37W03T4 UK Tm 4.03 mg kg -1 N27W04T1 Spain Tm 2.18 mg kg N37W03T4 UK Yb 25.0 mg kg -1 N27W04T1 Spain Yb 13.3 mg kg N37W03T4 UK Tb 7.01 mg kg N37W03T4 UK Y 267 mg kg N19W10T1 Spain Nb 134 mg kg -1 N40E04T4 Norway Nb 132 mg kg -1 N31E06T6 Italy Nb 110 mg kg N30E05T4 Italy Ni mg kg -1 N26E14T2 Greece Ni mg kg -1 N27E12T1 Greece Ni mg kg -1 N28E11T1 Albania Ni mg kg N26E14T4 Greece Pb 970 mg kg -1 N30E03T5 France Pb mg kg N37W03T4 UK U mg kg -1 N30E02T2 France U mg kg N29E01T3 Spain V 537 mg kg N26E12T4 Greece Cd 14.1 mg kg -1 N30E03T2 France Cd 7.11 mg kg -1 N30E04T4 France Cd 6.75 mg kg -1 N35E04T5 Germany Cd 5.38 mg kg N29E01T3 Spain Cu 256 mg kg -1 26

7 Table 3. Continued Sample Country Element Value Unit Next value f N30E05T1 Italy Cu 215 mg kg N29E01T3 Spain Mo 21.3 mg kg N29W01T4 Spain Te 0.93 mg kg N30E03T2 France Tl 24 mg kg N19W10T1 Spain TiO % N31E06T4 Italy TiO % N26E12T2 Greece Cr mg kg -1 N30E05T4 Italy Cr mg kg -1 N30E06T5 Italy Cr mg kg N41E05T3 Norway Sn 1060 mg kg N28W01T4 Spain Sr mg kg N30E03T5 France Zn mg kg -1 N30E04T4 France Zn mg kg N29W02T2 Spain Zn 991 mg kg -1 N30E03T3 France Zn 986 mg kg pattern will further be discussed in the factor analysis section by Batista et al (Annex 5 in this volume). The ratios topsoil/subsoil were calculated for all elements (Table 4), averaging all individual ratios between pairs of subsoil and topsoil samples, for the whole dataset including the outliers, provided both subsoil and topsoil were sampled at the same site. For calculation of the correlation coefficient between topsoil and subsoil, likewise, outliers were included (Table 4). The only purpose of this correlation coefficient is to evaluate how systematic an increasing (or decreasing) tendency is from subsoil to topsoil: a high coefficient means that enrichment (ratio >1) or leaching (ratio <1) in topsoil occurs in most of Europe, a low coefficient indicates that enrichment or leaching is more erratic or applies only to certain regions. Acknowledgements The Geochemical Atlas Agricultural Soils in northern Europe by Clemens Reimann et al. (2003) was often consulted for comparison. 27

8 Table 4. Ratios of topsoil/subsoil for all published elements. Element Ratio top/sub soil TOC Correlation REE Toxic / heavy metal Hg heavy metal Cd heavy metal Pb heavy metal P 2 O Sb S Sn heavy metal Mo Bi Cr heavy metal Zr Hf MnO Zn heavy metal Ag TiO As toxic SiO I Nb Ta Te W In Tl heavy metal Ba Cu heavy metal Rb V U Na 2 O Lu heavy REE (Y) Ga Yb heavy REE (Y) K 2 O ph Tm heavy REE (Y) Er heavy REE (Y) Al 2 O Th Fe 2 O Ho heavy REE (Y) Dy heavy REE (Y) 28

9 Table 4. Continued Element Ratio top/sub soil Correlation REE Toxic / heavy metal Co Ni heavy metal Y Y Tb heavy REE (Y) Cs La light REE (Ce) Be Ce light REE (Ce) Gd heavy REE (Y) Pr light REE (Ce) Nd light REE (Ce) Sc Sm light REE (Ce) Eu light REE (Ce) Sr MgO CaO

The Baltic Soil Survey

The Baltic Soil Survey C. Reimann, U. Siewers, T. Tarvainen, L. Bityukova, J. Erikson, A. Gilucis, V. Gregorauskiene, V. Lukashev, N. Matinian & A. Pasieczna PROJECT AIM: create a comparable database