Clemens Reimann

Leaves from four different plant species (birch, willow, juniper, and heather) together with samples of the soil O and C horizons were collected at 44-46 sites along a south-to-north transect extending inland for 200km from the southern tip of Norway. The transect covers one of the steepest vegetation gradients on Earth, crossing six vegetation zones. Juniper and heather are evergreen, and preferably exclude potentially toxic elements to avoid their accumulation in assimilating tissues, birch and willow shed their leaves in autumn together with the load of potentially toxic elements, and thus can tolerate the uptake of such elements. The plant leaves show the highest concentrations for B, Ca, K, Mg, Mn, P, Rb and S. In the soil O-horizon Ag, Au, As, Bi, Cu, Ge, Hg, In, Pb, Sb, Se, Sn, Te and W are enriched with respect to the C-horizon, whilst Mn and Rb are depleted. Cadmium, Sr and Zn are enriched in willow and Cs, Na and Tl in heather. In terms of concentration gradients from the coast inland, two different patterns are detected: 1) short range with an almost exponential decrease of concentrations from the coast, which appears to be typical for seaspray-related element input, and 2) long range with an almost linear decrease of concentrations with distance from the coast. These patterns differ among the four species, even for one and the same element. Inter-element correlation is different from material to material. Along the transect each of the different plants at the same site individually adapts to the available element combination. High linear correlations in the plants occur between the lanthanides (La, Ce, Y), and interestingly, between P and Ti. The plant/soil system appears highly non-linear and self-regulated.

S-concentrations were determined in 9 different sample materials (precipitation (rain and snow), vegetation, O-, E-, B- and C- horizon pf podzols, streams water and ground water) collected in eight small catchments (10-30 km2) at different distances from major SO2 point-source emitters on the Kola Peninsula, Russia. Comparison of the results from these materials, representing different compartments of the ecosystems under varying natural conditions leads to a better understanding of sources, cycling and fate of S in the Arctic environment. More than 300,000 t of SO2 emitted annually from the Kola smelters affect the air quality over a large area. Arctic climatic conditions (cold and dry) and the remote location of the emitters results in considerably lower S-deposition values than those observed in central Europe. The pathways of atmospheric S-deposition in the terrestrial environment vary significantly from summer to winter because different compartments of the ecosystems, with a different capability to accumulate S, are active. The actual S-flux is altered by every component of the ecosystem. When estimating the total S-deposition this effect must be considered.

During 1996/97, c. 1500 samples of agricultural soils from ten northern European countries (western Belarus, Estonia, Finland, northern Germany, Latvia, Lithuania, Norway, Poland, northwestern Russia and Sweden) were collected from the Ap and B/C-horizons at 750 sites. The sample sites were evenly spread over a 1 800 000 km2 area, giving an average sample density of one site per 2500 km2. The <2 mm fractions (Poland: <1 mm) of all samples were analysed for up to 62 chemical elements following ammonium acetate, aqua regia and HF extractions, and for total element concentrations by X-ray fluorescence spectrometry. Electrical conductivity, pH (water extraction) and loss on ignition (1030°C) were also determined. Each method was applied to all the samples in one laboratory only. The analytical results were evaluated and mapped using exploratory data analysis techniques. Even at this low sample density, regional-scale geochemical patterns emerge for all elements. These patterns show the influence of factors such as geology, agriculture, pollution, topography, marine aerosols and climate, and their relative importance for the observed element concentrations in the soils. Low-density geochemical mapping of agricultural soils is a viable tool to study the geochemical processes that determine the element distribution in soils at a sub-continental scale.

In April 1996 snowpack samples were collected from the surroundings of the ore roasting and dressing plant at Zapoljarnij and the nickel smelters at Nikel and Monchegorsk, Kola Peninsula, NW Russia. In the laboratory, filter residues of snowpack samples (fraction>0.45μm) from 15 localities (close to the nickel processing centres) were chemically for precious metals (Rh, Pt, Pd, Au) and Te

Page 1. Statistical Data Analysis Explained Statistical Data Analysis Explained: Applied Environmental Statistics with R. C. Reimann, P. Filzmoser, RG Garrett, R. Dutter © 2008 John Wiley & Sons, Ltd. ISBN: 978-0-470-98581-6 Page 2. Statistical Data Analysis Explained ...

Analysis of soil C and O horizon samples in a recent regional geochemical survey of Nord-Trøndelag, central Norway (752 sample sites covering 25,000 km2), identified a strong enrichment of several potentially toxic elements (PTEs) in the O horizon. Of 53 elements analysed in both materials, Cd concentrations are, on average, 17 times higher in the O horizon than in the C horizon and other PTEs such as Ag (11-fold), Hg (10-fold), Sb (8-fold), Pb (4-fold) and Sn (2-fold) are all strongly enriched relative to the C horizon. Geochemical maps of the survey area do not reflect an impact from local or distant anthropogenic contamination sources in the data for O horizon soil samples. The higher concentrations of PTEs in the O horizon are the result of the interaction of the underlying geology, the vegetation zone and type, and climatic effects. Based on the general accordance with existing data from earlier surveys in other parts of northern Europe, the presence of a location-independent, superordinate natural trend towards enrichment of these elements in the O horizon relative to the C horizon soil is indicated. The results imply that the O and C horizons of soils are different geochemical entities and that their respective compositions are controlled by different processes. Local mineral soil analyses (or published data for the chemical composition of the average continental crust) cannot be used to provide a geochemical background for surface soil. At the regional scale used here surface soil chemistry is still dominated by natural sources and processes.

Forty-five soil samples from the O-and C-horizons were taken in a 12,000 km 2 area on the borders of Finland, Norway and Russia. The nickel smelter at Nikel, the ore roasting plant at Zapoljarnij, both in Russia, and the iron ore mine and processing plant at Kirkenes in ...

... Date de parution : 03-1998 Langue : ANGLAIS Env. 396p. 20x27.5 Hardback Épuisé. Commentaire de Chemical elements in the environment factsheets for the... : ... How low a detection limit do I need to attain if I want to analyse for an element in soils, sediments, water or plants? ...

The environmental chemistry of Li has received attention because Li has been shown to have numerous and important implications for human health and agriculture and the stable isotope composition of lithium is a powerful geochemical tool that provides quantitative information about Earth processes such as sediment recycling, global chemical weathering and its role in the carbon cycle, hydrothermal alteration, and groundwater evolution. However, the role of bedrock sources, weathering and climate changes in the repartition of Li at the continental scale have been scarcely investigated. Agricultural soil (Ap-horizon, 0–20 cm) and grazing land soil (Gr-horizon, 0–10 cm) samples were collected from a large part of Europe (33 countries, 5.6 million km2) as a part of the GEMAS (GEochemical Mapping of Agricultural and grazing land Soil) soil mapping project. GEMAS soil data have been used to provide a general view of element mobility and source rocks at the continental scale, either by reference to average crustal abundances or to normalized patterns of element mobility during weathering processes. The survey area includes a diverse group of soil parent materials with varying geological history, a wide range of climate zones, and landscapes. The concentrations of Li in European soil were determined by ICP-MS after a hot aqua regia extraction, and their spatial distribution patterns generated by means of a GIS software. Due to the partial nature of the aqua regia extraction, the mean concentration of Li in the European agricultural soil (ca 11.4 mg/kg in Ap and Gr soils) is about four times lower than in the Earth's upper continental crust (41 mg/kg). The combined plot histogram - density trace one- dimensional scattergram - boxplot of the aqua regia data displays the univariate data distribution of Li. The one-dimensional scattergram and boxplot highlight the existence of many outliers at the lower end of the Li distribution and very few at the upper end. Though the density trace, histogram and boxplot suggest a slight skew, the data distributions are still rather symmetrical in the log-scale. The median values of the Ap and Gr samples do overlap, demonstrating they are not statistically different at the 5% significance level. The maps of Li in the aqua regia extraction show a distinct difference between northern Europe with predominantly low concentrations (median 6.4 mg/kg Li) and southern Europe with significant higher values (median 15 mg/kg Li). The maximum extent of the last glaciation is visible as a discrete concentration break on the maps. The principal Li anomalies occur spatially associated with the granitic rocks, Li-pegmatites and their weathering products throughout Europe, e.g. in the Central Sweden (Scandinavian Clay Belt) and in the western part of the Alpine Region (higher Li concentrations). Even the new Li-deposit near Wolfsberg, Austria is marked by a clear anomaly. Finally, high values occurring over limestone areas in southern Europe are due to secondary Li enrichment attributable to climatic conditions.

For both, silver (Ag) and mercury (Hg), the median concentrations in an aqua regia extraction of minerogenic top- and subsoil from continental scale geochemical surveys (Australia, China, Europe) are around 0.02 mg/kg. When the soil O horizon is collected as topsoil sample, the concentration of again both elements is higher by about a factor of 10 (range 7-30), with median concentrations around 0.2 mg/kg Ag and Hg. Geochemical maps of top- and subsoil at different scales for both elements display regional patterns which reflect mainly geology, climate and topography. Anthropogenic sources like mines, power plants, or major cities visually occur only as local anomalies. For Ag in organogenic topsoil the maximum possible input due to diffuse contamination is estimated to be in the 0.02 mg/kg range, about 10% of the median concentration in the soil O horizon. For Hg this value is slightly higher at 0.03 mg/kg. In the soil O horizon Hg concentrations show less variability than in the C horizon. Substantial Hg soil contamination should lead to noticeably increased Hg/Ag ratios.

Magnetic measurements are routinely used in geophysics and environmental sciences to obtain detailed information about concentrations and quality of iron minerals. Here, magnetic properties of 38 terrestrial moss samples (Hylocomium splendens) from a ~120km south-north transect through Oslo are studied to gain better insight into the nature and origin of their Fe fraction. The concentration-dependent quantities, magnetic susceptibility k, and isothermal remanent magnetization IRM(700mT) after weight normalization have significantly higher values in urban regions, and parallel the previously found concentration signals of 16 out of 29 chemical elements (Ag, Al, Au, Bi, Cd, Co, Cr, Cu, Fe, Mo, Ni, Pb, Pt, Sb, Ti, and Zn). Because there is no evidence that Hylocomium splendens produces biogenic magnetic remanence carriers, the increase in IRM is attributed to adsorption of dust containing iron oxide minerals. This agrees with previous observations that Ti concentrations, related to local mineral dust, have a peak in Oslo, and at sites close to known dust sources. Scanning electron microscopy images also showed an increased density of minerogenic particles on the moss surfaces in the urban samples, which qualitatively supports the dust based interpretation. The concentration-independent ratios k/Fe and IRM(700mT)/Fe also have extreme values in the urban parts of the transect. This indicates that more of the total iron occurs in magnetically ordered form and in remanence carriers, interpreted as adsorbed dust. In addition, purely magnetic ratios displayed differences in urban and rural areas, indicating that their magnetic dust particles are inherently of different types. Therefore, it is likely that anthropogenic dust sources contribute considerably to the magnetic signal. Urban dust enhancement is not exclusively due to increased erosion, leading to higher loads of geogenic dust in the atmosphere, but also to specific anthropogenic sources from combustion, corrosion, or other synthetic emitters.

Preface. Acknowledgements. About the Authors. 1. Introduction. 1.1 The Kola Ecogeochemistry Project. 2. Preparing the Data for Use in R and DAS+R. 2.1 Required data format for import into R and DAS+R. 2.2 The detection limit problem. 2.3 Missing Values. 2.4 Some "typical" problems encountered when editing a laboratory data report file to a DAS+R file. 2.5 Appending and linking data files. 2.6 Requirements for a geochemical database. 2.7 Summary. 3. Graphics to Display the Data Distribution. 3.1 The one-dimensional scatterplot. 3.2 The histogram. 3.3 The density trace. 3.4 Plots of the distribution function. 3.5 Boxplots. 3.6 Combination of histogram, density trace, one-dimensional scatterplot, boxplot, and ECDF-plot. 3.7 Combination of histogram, boxplot or box-and-whisker plot, ECDF-plot, and CP-plot. 3.8 Summary. 4. Statistical Distribution Measures. 4.1 Central value. 4.2 Measures of spread. 4.3 Quartiles, quantiles and percentiles. 4.4 Skewness. 4.5 Kurtosis. 4.6 Summary table of statistical distribution measures. 4.7 Summary. 5. Mapping Spatial Data. 5.1 Map coordinate systems (map projection). 5.2 Map scale. 5.3 Choice of the base map for geochemical mapping 5.4 Mapping geochemical data with proportional dots. 5.5 Mapping geochemical data using classes. 5.6 Surface maps constructed with smoothing techniques. 5.7 Surface maps constructed with kriging. 5.8 Colour maps. 5.9 Some common mistakes in geochemical mapping. 5.10 Summary. 6. Further Graphics for Exploratory Data Analysis. 6.1 Scatterplots (xy-plots). 6.2 Linear regression lines. 6.3 Time trends. 6.4 Spatial trends. 6.5 Spatial distance plot. 6.6 Spiderplots (normalized multi-element diagrams). 6.7 Scatterplot matrix. 6.8 Ternary plots. 6.9 Summary. 7. Defining Background and Threshold, Identification of Data Outliers and Element Sources. 7.1 Statistical methods to identify extreme values and data outliers. 7.2 Detecting outliers and extreme values in the ECDF- or CP-plot. 7.3 Including the spatial distribution in the definition of background. 7.4 Methods to distinguish geogenic from anthropogenic element sources. 7.5 Summary. 8. Comparing Data in Tables and Graphics. 8.1 Comparing data in tables. 8.2 Graphical comparison of the data distributions of several data sets. 8.3 Comparing the spatial data structure. 8.4 Subset creation - a mighty tool in graphical data analysis. 8.5 Data subsets in scatterplots. 8.6 Data subsets in time and spatial trend diagrams. 8.7 Data subsets in ternary plots. 8.8 Data subsets in the scatterplot matrix. 8.9 Data subsets in maps. 8.10 Summary. 9. Comparing Data Using Statistical Tests. 9.1 Tests for distribution (Kolmogorov-Smirnov and Shapiro-Wilk tests). 9.2 The one-sample t-test (test for the central value). 9.3 Wilcoxon signed-rank test. 9.4 Comparing two central values of the distributions of independent data groups. 9.5 Comparing two central values of matched pairs of data. 9.6 Comparing the variance of two test. 9.7 Comparing several central values. 9.8 Comparing the variance of several data groups. 9.9 Comparing several central values of dependent groups. 9.10 Summary. 10. Improving Data Behaviour for Statistical Analysis: Ranking and Transformations. 10.1 Ranking/sorting. 10.2 Non-linear transformations. 10.3 Linear transformations. 10.4 Preparing a data set for multivariate data analysis. 10.5 Transformations for closed number systems. 10.6 Summary. 11. Correlation. 11.1 Pearson correlation. 11.2 Spearman rank correlation. 11.3 Kendall-tau correlation. 11.4 Robust correlation coefficients. 11.5 When is a correlation coefficient significant? 11.6 Working with many variables. 11.7 Correlation analysis and inhomogeneous data. 11.8 Correlation results following addictive logratio or central logratio transformations. 11.9 Summary. 12. Multivariate Graphics . 12.1 Profiles. 12.2 Stars. 12.3 Segments. 12.4 Boxes. 12.5 Castles and trees. 12.6 Parallel coordinates plot. 12.7 Summary. 13. Multivariate Outlier Detection. 13.1 Univariate versus multivariate outlier detection. 13.2 Robust versus non-robust outlier detection. 13.3 The chi-square plot. 13.4 Automated multivariate outlier detection and visualization. 13.5 Other graphical approaches for identifying outliers and groups. 13.6 Summary. 14. Principal Component Analysis (PCA) and Factor Analysis (FA). 14.1 Conditioning the data for PCA and FA. 14.2 Principal component analysis (PCA). 14.3 Factor Analysis. 14.4 Summary. 15. Cluster Analysis. 15.1 Possible data problems in the context of cluster analysis. 15.2 Distance measures. 15.3 Clustering samples. 15.4 Clustering variables. 15.5 Evaluation of cluster validity. 15.6 Selection of variables for cluster analysis. 15.7 Summary. 16. Regression Analysis (RA). 16.1 Data requirements for regression analysis. 16.2 Multiple regression. 16.3 Classical least squares (LS) regression. 16.4 Robust regression. 16.5 Model selection in regression analysis. 16.6 Other regression methods. 16.7 Summary. 17. Discriminant…

Environmental contextTellurium, a chemical element increasingly being used in new technologies, is an emerging contaminant. Our understanding of tellurium’s environmental behaviour, however, is poor, with critical knowledge gaps such as its distribution in the various environmental compartments and the environmental fluxes associated with mining, usage and disposal. Significant progress in these areas requires the development of robust analytical methods that are sufficiently sensitive to provide data at environmentally relevant concentrations. Tellurium has recently become a ‘technology-critical element’ increasingly used in new applications. Thus, potential environmental impacts need to be evaluated. This, in turn, requires knowledge of its typical concentrations in the environment along with better understanding of the chemical processes governing its environmental behaviour. We evaluate the current situation of our understanding of tellurium in the environment and identify the a...