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The focus of this study was to comprehend the spatial distribution, source apportionment, evaluation of natural background level (NBL), and mobilization mechanisms of arsenic (As) and manganese (Mn) in the Ghaghara river basin, located in the middle Gangetic plain (MGP) of India. A combination of analytical tools was employed, including multivariate statistical analysis (MSA), correlation analysis, and receptor models such as positive matrix factorization (PMF) and principal component analysis-multi linear regression (PCA-MLR). Taking into consideration the compositional constraints of the geochemical data, compositional data analysis (CoDA) methods were applied on the raw data prior to the correlation analysis and MSA. The results of MSA, correlation analysis and receptor models showed that major ion chemistry and As, Mn enrichment in groundwater were largely controlled by carbonate weathering, Fe and Mn oxyhydroxides dissolution. Anthropogenic activities, such as the infiltration of dissolved organic matter-rich water from local surface water bodies used as dumping sites and the infiltration of fertilizer-rich water from agricultural lands, were found to notably impact the groundwater geochemistry and release of As in the research area. The lower NBL of As (5.92 μg/L) compared to its mean concentrations (17.9 μg/L) in the study area also indicated that the rate of As release in groundwater through natural processes was comparatively low but various anthropogenic activities operating in the study region possibly acted as a trigger for the mobility of As from the mineral phases of the subsurface sediments. This study also highlighted the significance of applying CoDA techniques on geochemical data prior to statistical analysis, and the importance of receptor models, to better understand the nature and contribution of various natural and anthropogenic processes governing the hydrochemistry of groundwater in the alluvial aquifers. • Compositional data analysis (CoDa) methods were applied in the statistical analysis. • The PMF receptor model was preferred over PCA-MLR for source identification. • The natural background level of As was found lower than WHO acceptable limit. • Reductive dissolution of Fe-Mn oxyhydroxides caused released of As in Ghaghara Basin. [ABSTRACT FROM AUTHOR]

Regional mineral exploration is based on geochemical data of which the nature is compositional and frequently involves a large number of components. Consequently, it mostly needs multivariate dimension reduction methods such as principal component analysis (PCA) and its various robust versions. The application of such methods, defined for real random variables, require the data to be represented in coordinates supported in the real space. However, a common problem in exploration geochemistry is to select the appropriate representation. Using centered (clr) and isometric (ilr) logratio coordinates to discriminate anomalous zones for orogeny gold exploration throughout Sweden revealed that there is, as expected, no difference between the two representation methods. The main difference affects the interpretation of the coordinates used. This is observed for regional scale exploration, while it is also needed to study different ways of representing geochemical data in local scale. [Display omitted] • Geochemical data possess a compositional nature, necessitating meaningful representation • Among compositional tools, clr and olr coordinates are used for representation • Extensive till geochemical data from across Sweden were employed for comparison • Both models exhibited strikingly similar results in terms of anomaly separation • The ilr-based model enhanced accuracy in delineating anomalous zones [ABSTRACT FROM AUTHOR]

Rare earth elements (REE), classified as critical minerals which are crucial for clean energy technologies, face soaring demand. While economic deposits are found in limited geologic environments including carbonatites and ion-adsorption clays, unconventional, secondary sources such as those from sedimentary basins could hold potential to meet this increased demand. Coal and its associated combustion by-products, phosphorites, oil sands tailings, and formation waters have all garnered interest for REE recovery, yet they remain significantly underexplored. Accordingly, new tools for data analysis and optimization such as machine learning can assist in mineral prospectivity, with these tools being subject to rapid proliferation in the Earth sciences. This work leverages compositional data analysis principles and machine learning to probe geochemical relationships and predict REE abundances in sedimentary lithologies using unsupervised (correlation, principal component, and cluster analysis) and supervised (regression, support vector machine, random forest, and boosting) machine learning models. These three unsupervised models display similar results, with REE typically being associated with incompatible elements (e.g., Th, Nb, and Hf). Gradient boosting, Adaboost, and Random Forest had the highest performance for predicting REE concentrations, with Th and P commonly being the most important predictor variables. Identifying geochemical indicators of REE enrichment that may be used to assist in discovering potentially exploitable REE resources based on existing data, as well as increasing the understanding of metal behaviour in sedimentary systems, is a step forward in understanding novel secondary and unconventional REE sources. Although REE concentrations from these sources are generally lower than primary ore deposits, the amount of available feedstock, potentially simpler, cheaper, and less environmentally taxing extraction processes, and the added benefit of remediating waste streams and contributing to the circular economy make these sources alluring. • Secondary sources from sedimentary basins may meet high rare earth element demand. • CoDA principles and machine learning identified geochemical relationships. • Unsupervised ML algorithms found REE are associated with Th, Nb, and P. • Random Forest and Boosting had the best accuracy for predicting REE abundances. • These models may fit into broader frameworks for identifying REE recovery targets. [ABSTRACT FROM AUTHOR]

When conducting Mineral Potential Mapping (MPM) using multiple sources of data such as geology, geochemistry, and geophysics, it often encounters the challenges of complex and diverse data distributions within these datasets. Enhancing the capability to extract nonlinear data features and further uncover metallogenic information is a crucial research objective. This study utilizes the C-A multifractal approach to extract anomalous information related to metallogenic elements, employs compositional data analysis methods for quantitatively extracting geochemical associations of mineralization, and utilizes GIS spatial analysis techniques to quantitatively extract predictive indicators from various data sources, including geology, geophysics, and remote sensing, to construct an MPM prediction dataset. Building upon the foundation of AutoEncoder (AE), this study introduces a discriminator and employs the AEGAN (Auto Encoder Generative Adversarial Network) algorithm, which combines AutoEncoder and Generative Adversarial Network (GAN), for metallogenic prospectivity prediction in the Lhasa region. Compared to AE algorithms, AEGAN combines the strengths of AE and GAN, significantly improving the model's ability to reconstruct input data through the interaction between the generator and discriminator. Additionally, this study designs comparative experiments with AE, and the results demonstrate that the AEGAN model can more accurately identify the correlation between high anomaly areas and polymetallic deposits, providing a more precise delineation of anomalous extents. The Area Under the Receiver Operating Characteristic Curve (AUC) further validates the superior performance of the AEGAN model. These findings indicate that the AEGAN model exhibits outstanding capabilities in learning the internal connections and features among multiple data sources, holding significant potential for practical applications in mineral exploration. • Metallogenic geochemical association is extracted by compositional data analysis. • A regional quantitative mineral prediction model is constructed. • Auto Encoder Generative Adversarial Network is used for MPM. [ABSTRACT FROM AUTHOR]

The importance of soil as a critical environmental medium cannot be overstated, as it provides various ecosystem services while supporting agricultural activities. Regrettably, soil contamination can impair the utility of these services and productivity in agriculture, and may even create several health hazards. Therefore, this study aimed to envisage the levels of heavy metals (HM) contamination in agricultural soils and identify the possible sources of contamination. A total of 54 soil samples from the designated area were obtained and subjected to analysis for the presence of nine HM, i.e., Ni, Cd, Cr, Co, Fe, Mn, Cu, Zn, and Pb, using atomic absorption spectrometry. This study used several single and multi-parameter indices for pollution and ecological risk evaluation. Compositional data analysis (CoDa) was used to assess the associations and potential sources of the HM. The findings revealed that Fe demonstrated the highest concentration among the studied HM, while Cd exhibited the lowest. The ecological risks were primarily attributed to Mn and Cd, with a considerable (40%) number of samples indicating high ecological risks. Based on contamination indices, Cd was identified as the study area's primary concern for soil pollution. Nevertheless, the PLI and modified degree of contamination (mCdeg) values exhibited a minimal inclusive effect of HM on soil. Furthermore, it was determined that the source of origin for all HM except Cd and Pb was geogenic. The soils in the area are under lesser human influence and mostly suggest a natural state. However, the contamination risk posed by Cd and the ecological risk posed by Cd and Mn raise concerns. A comprehensive evaluation of the contamination status and the risks was obtained using multiple indices. This study highlights the need for monitoring and managing HM contamination and mitigating the potential risks from Cd and Mn contamination in the area. • Compositional data analysis was used to assess associations and potential sources. • Fe demonstrated the highest concentration in soil, while Cd exhibited the lowest. • 40 % of samples in this study showed high ecological risks due Cd and Mn. • Based on contamination indices, Cd identified as primary concern for soil pollution. [ABSTRACT FROM AUTHOR]

Particulate air pollution is a significant environmental concern due to its potential effects on the environment and human health. Meteorological variables can affect greatly on PM accumulation, also establishing a possible obstacle to the achievement of air quality standards. The objective of the study is to evaluate the effect of meteorological variables such as temperature, relative humidity and daily precipitation on coarse and fine size fraction of PM with and without considering PM 10 exceedances conditions using a multiple compositional linear model. The study was conducted in an environmental traffic site placed in a low emission zone (LEZ) of Milan, which is located in the Po Valley (Northern Italy), during the cold season. The results indicated that an increasing in temperature of about 4.7 °C determined a decrease in fine size fraction of PM of about 6.2 % and 24.1 % with and without exceedances, respectively. Whereas an increase in relative humidity of about 19 % determined an increase in fine size fraction of PM of about 10.5 %. In addition, the analysis showed that the PM 10 measured when there were PM 10 exceedances was compositionally different from that measured when there were not PM 10 exceedances as it was richer in fine particles of about 17.2 %. The study showed that PM 10 exceedances were mainly caused by an increase in the fine size fraction of PM rather than the coarse one. Furthermore, research results indicated that changes in temperature and relative humidity could affect the particle size fractions of PM. A decrease in temperature and an increase in relative humidity led to an increase in the PM fine size fraction. The study provides insights into the underlying mechanisms affecting both fine and coarse size PM fractions. • Meteorological variables affect PM fine and coarse size fractions. • A multiple compositional linear model was used. • The differential effect of PM 10 exceedance conditions was considered. • Temperature and humidity significantly impact PM coarse and fine size fractions. • PM 10 with exceedance is richer of fine PM particles compared to PM 10 without exceedance. [ABSTRACT FROM AUTHOR]

Abstract The Sarno River Basin (south-west Italy), nestled between the Somma–Vesuvius volcanic complex and the limestone formations of the Campania–Apennine Chain, is one of the most polluted river basins in Europe due to widespread industrialization and intensive agriculture. Water from the Sarno River, which is heavily contaminated by the discharge of human and industrial wastes, is partially used for irrigation on the agricultural fields surrounding it. We apply compositional data analysis to 319 soil samples, collected during two field campaigns along the river course and throughout the basin, to determine the concentration and possible origin (anthropogenic and/or geogenic) of the elemental anomalies, including potentially toxic elements (PTEs). The concentrations of 53 elements were determined using ICP-MS and, subsequently, log-transformed. Using hierarchical clustering, clr-biplot and a principal factor analysis, the variability and the correlations between a subset of extracted variables (26 elements) were identified. Factor score interpolated maps were then generated using both lognormal data (NDR) and clr-transformed data to better visualize the distribution and potential sources of the patterns in the Sarno Basin. The underlying geology substrata appear to be associated with raised levels of Na, K, P, Rb, Ba, V, Co, B, Zr, and Li, due to the presence of pyroclastic rocks from Mt. Somma–Vesuvius. Similarly, elevated Pb, Zn, Cd, and Hg concentrations are most likely related to the geological and anthropogenic sources, the underlying volcanic rocks, and contamination from fossil fuel combustion associated with nearby urban centers. Interpolated factor score maps and the clr-biplot show a clear correlation between Ni and Cr in samples taken along the Sarno River, and Ca and Mg near the Solofra district. After considering nearby anthropogenic sources, the Ni and Cr are PTEs most likely originating from the Solofra tannery industry, while Ca and Mg correlate to the underlying limestone-rich soils of the area. This study shows the applicability of the log-ratio transformations to these studies, as they clearly show relationships and dependencies between elements which can be lost when univariate and classical multivariate analyses are employed on raw and lognormal data. Highlights • Cluster based clr-data unveils interrelationships between groups of elements related with their possible sources. • Clr-biplot displays groups of variables related to the lithological background and main human activities. • Factor scores maps enhance the dominant underlying lithology and anthropogenic element associations. • Clr-data in factor analysis highlights the release of Ni and Cr from Solofra tannery industries in the Sarno River. [ABSTRACT FROM AUTHOR]

Abstract Unsupervised and supervised machine learning techniques are used to classify and predict the alteration facies of the Rosemont skarn deposit. The deposit comprises three major geological domains informally named the Lower Plate, Upper Plate, and West Block. Over 1 billion tons of Cu-Mo-Ag mineralization are primarily hosted within the Lower Plate and West Block, concealed under poorly mineralized rocks of the Upper Plate. A training dataset comprising 882 drill core samples analyzed for X-ray powder diffraction (XRD) mineralogy and multi-element geochemistry is used to improve the classification and recognition of the skarn alteration. The XRD-mineralogy is used for unsupervised learning in which hierarchical and K -means cluster analysis is applied to devise a classification of the skarn alteration using a compositional data analysis approach. The complex alteration at the Rosemont deposit is simplified into garnet-pyroxene-wollastonite-vesuvianite (GrtPxWoVes) skarn, serpentine-amphibole (SrpAm) skarn, epidote (Ep) skarn, and least altered (LAlt) rock. These four classes are coded into a categorical variable named Alteration Facies. The supervised learning uses the multi-element geochemical data to predict Alteration Facies. Several predictive models are fitted using different machine learning algorithms including support vector machines with linear (SVM-linear) and radial (SVM-radial) kernels, linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), classification and regression trees (CART), and random forests (RF). Each predictive model is fitted onto three predictor spaces including raw geochemical data, isometric log-ratios (ilr), and centered log-ratios (clr) to evaluate the effect of compositional representations on the performance of the predictive models. The analysis shows that the RF algorithm yields predictive models that outperform all other algorithms using any of the predictor spaces. The RF test set prediction accuracy after 10-fold cross-validation is 79%; which is significantly higher than an accuracy of 45% obtained from visual core logging classification of the skarn alteration. Hypothesis testing, using multilevel linear modeling, suggests that there are no statistically significant differences in the quality of the predictive models fitted onto the raw, ilr, and clr geochemical data. Therefore, compositional representations of the geochemical data do not improve the performance of the predictive models. The best RF model is used to predict Alteration Facies on 33,000 drill core samples in which multi-element geochemistry is available but the skarn alteration is uncertain due to the lack of XRD-mineralogy. The predicted alteration shows that the Ep skarn facies occurs above the economic mineralization within Mesozoic siliciclastic and volcanic rocks of the Upper plate. The GrtPxWoVes skarn and SrpAm skarn occur within Paleozoic carbonate rocks of the Lower Plate and West Block. Accordingly, the Ep skarn facies can be used to target blind Cu-Mo-Ag rich porphyry-skarn mineralization in areas where the Paleozoic carbonate rocks are concealed under the Mesozoic siliciclastic and volcanic rocks. Highlights • Supervised and unsupervised machine learning techniques are applied to classify and predict skarn metasomatic alteration. • The quality of the predictive models fitted onto raw geochemical data and compositional representations is similar. • Machine learning is more accurate at detecting skarn alteration than visual core logging. • Random forest is the best performing algorithm to predict metasomatic alteration. [ABSTRACT FROM AUTHOR]

The Piper diagram has been a staple for the analysis of water chemistry data since its introduction in 1944. It was conceived to be a method for water classification, determination of potential water mixing between end-members, and to aid in the identification of chemical reactions controlling a sample set. This study uses the information gleaned over the years since the release of the Piper diagram and proposes an alternative to it, capturing the strengths of the original diagram while adding new ideas to increase its robustness. The new method uses compositional data analysis to create 4 isometric log-ratio coordinates for the 6 major chemical species analyzed in the Piper diagram and transforms the data to a 4-field bi-plot, the ilr-ion plot. This ilr-ion plot conveys all of the information in the Piper diagram (water mixing, water types, and chemical reactions) while also visualizing additional data, the ability to examine Ca 2+ /Mg 2+ versus Cl-/SO 4 2− . The Piper and the ilr-ion plot were also compared using multiple synthetic and real datasets in order to illustrate the caveats and the advantages of using either diagram to analyze water chemistry data. Although there are challenges with using the ilr-ion plot (e.g., missing or zero values zeros in the dataset must be imputed by positive real numbers), it appears that the use of compositional data analysis coupled with the ilr-ion plot provides a more in-depth and complete analysis of water quality data compared to the original Piper diagram. [ABSTRACT FROM AUTHOR]

The data represent the first available data set on platinum group element (PGE) levels in Berlin topsoil. In 2013, over 100 topsoil samples (0–20 cm) were collected in the inner city of Berlin. The PGEs in topsoil samples were analysed by ICP-MS after a trace-matrix separation. The results were compared to analyses of PGEs in Berlin topsoil collected at the same sample sites in 1992. The data show an increased accumulation of PGE concentrations in Berlin topsoil during this two-decade interval as a result of gradual increase in the use of vehicles with catalytic converters. A comparison of PGE median concentrations show an increase by a factor of 1.5 for Pt, 3.0 for Pd, 4.3 for Rh, and 3.1 for Au, over the 20-year period. The single element distribution maps of PGEs from 2013 confirm a clear correlation with traffic density. The PGE ratios (Pt/Pd, Pt/Rh, Pd/Rh) were consistent with known catalytic converter compositions, and indicate catalytic exhausts as the main source of these elements. A comparison of the maps of measured raw PGE data with the clr-transformed maps reveals identical distribution patterns (including PGE anomalies). Principal component analysis (PCA) results obtained from the classical statistical approach (standardised) are quite similar to those from PCA carried out using the compositional data analysis (CoDA) approach. Grouping of the PGEs, based on hierarchical cluster analysis, provides a complementary treatment and confirms the PCA results, indicating that the PGE concentrations in Berlin topsoil from 2013 are mainly caused by vehicle emissions. [ABSTRACT FROM AUTHOR]

Modelling of topsoil geochemical data using discriminant function analysis, along with a set of corresponding geochemical maps, portray a legacy of environmental contamination in the city of Sisak. Based on centred log-ratio (clr) transformed compositional data from topsoil samples collected on Kupa-Odra and Sava River floodplain sediments and loess, underlying the Sisak urban area, and regrouped according to their land use characteristics (urban, industrial and agricultural areas), a few element clusters emerged accounting for discrimination between the defined groups. The most prominent feature of the lithological model, as suggested by the first discriminant function, is the carbonate group (Mg, Ca, Sr) versus anthropogenic sourced elements/transition metals (e.g., Sn, Zn, Ba, Mn, Pb, U, Ag, Cr, V) dichotomy separating soil developed on floodplain sediments of Sava and Kupa-Odra Rivers from those evolved on loess. The major attribute of the land use model, according to the main discriminant function, is differentiation between urban/industrial and rural (agricultural) samples based on contrast provided by the carbonate component and other anthropogenic sourced element clusters (predominantly alkaline and concentrated over the loess substrate), in contrast to fertiliser trace and transition element clusters (predominantly acid and disseminated over Kupa-Odra and Sava River floodplain sediments). In both cases, the second discriminant function provides additional information on the secondary level, i.e., in the lithology model the Kupa-Odra sediment group is distinguished from other lithological groupings owing to its predominantly transition element signature. In the land use model, urban soil samples are separated from industrial ones based on the characteristic Na-Sr-Ca-pH(KCl) element association (urban waste) against a typical ‘industrial’ element signature (Zn-Cd-Mn-Pb-Mo). [ABSTRACT FROM AUTHOR]

There have been recent breakthroughs in exploration for tin (Sn) and silver (Ag) mineral resources in the Southern Great Xing'an Range, making this region a world-class Sn and Ag metallogenic area. However, thick surface coverage and hidden deposits make exploration difficult, and conventional geological and geochemical prospecting methods have severely limited efficacy. In order to further extract geological and geochemical information on the basis of the existing research, to serve the regional mineral exploration, the knowledge-driven CoDA method is used to identify geochemical associations for inferring matallogenic information. Meanwhile, the fractal C-A model is used to determine geochemical anomalies, the catchment basin geochemical method is used to delineate anomaly boundary. Especially, the data-driven CoDA method is employed for comparison analysis with the knowledge-driven one. As a result, seven groups of compositional balances are proposed and verified, and geological bodies closely related to Sn and Ag mineralization are identified. These occurred in Dashizhai Formation strata, being calcareous rocks and granites, especially A-type granite, which is most closely related to mineralization. Meanwhile, Sn and Ag mineralization areas were identified and found to be consistent with known deposits. Finally, this paper proposes a novel model of regional porphyry Sn–Cu metallogenic systems and delineates exploration targets for Ag–Zn–Pb–Cu deposits and Sn–polymetallic deposits based on the analysis of catchment basins. • Meaningful geochemical associations are gained from knowledge- driven CoDA. • Using geochemical associations to infer ore-bearing strata, granite, calcareous rocks and mineralization • Catchment basin division for geochemistry is useful for regional metallogenic prediction. • Regional metallogenic model is proposed. [ABSTRACT FROM AUTHOR]

The Rosemont skarn deposit contains resources exceeding 1 billion tons of Cu-Mo-Ag mineralization. The deposit comprises three major structural-stratigraphic domains informally named Lower Plate, Upper Plate, and West Block. The Lower Plate is composed dominantly of Paleozoic chemical sedimentary rocks forming an east dipping homoclinal sequence. The Upper Plate overlies the Lower Plate and is composed dominantly of Mesozoic and Cenozoic siliciclastic sedimentary rocks. The West Block is dominated by Precambrian granitoids structurally interleaved with panels of Paleozoic chemical and siliciclastic sedimentary rocks. Most of the economic mineralization is hosted in the Lower Plate. Lithological variability, structural complications of the stratigraphy, and calc-silicate metasomatism complicates the visual identification of lithologies resulting in uncertain stratigraphic domains based on drill core observations. To circumvent this problem, a deposit-scale lithogeochemical and chemostratigraphic model was developed using multivariate statistics following a compositional data analysis approach to respect the relative scale and multivariate nature of geochemical compositions. Accordingly, hierarchical cluster analysis of variables using the variation matrix as a measure of similarity, and principal component analysis on centered logratio coefficients (clr) were used to explore relationships between variables and to reduce dimensionality. Simplicial projections including centered tetrahedral and ternary diagrams were used to develop a lithogeochemical classification for the sedimentary rocks of the Rosemont deposit. A ternary diagram, or 3-part simplex with vertices Ca, Mg, and a composite variable given by the geometric mean of Cr, Ni, Co, V, P, Hf, Zr, Th, Ti, Al, Nb, Sc, Ta, Y, Ce, and La contains a rich data structure for lithogeochemical classification. The lithological attributes of the Rosemont deposit can be subdivided into 7 lithogeochemical classes evident on the ternary diagram. Limestone, dolostone, and siliciclastic-crystalline classes are key end members. The remaining 4 classes represent mixed chemical-siliciclastic lithogeochemical attributes discriminating complex lithological variations induced by the incorporation of siliciclastic component in chemical sedimentary rocks. The lithogeochemical classification derived from the ternary diagram is supported by K -means clustering applied to two balances representing isometric logratio (ilr) coordinates of the 3-part simplex. The geospatial distribution of the lithogeochemical classes allows the simplification of the stratigraphy of the Lower Plate into three relevant chemostratigraphic units, namely a Lower Limestone Unit and an Upper Dolostone Unit separated by a Mixed Unit of chemical-siliciclastic sedimentary rocks. It is noteworthy that ore grades are controlled by the relative proportion of chemical to siliciclastic component of the mineralized rocks. Higher metal grades are characteristic of relatively pure chemical sedimentary rocks in the Lower Limestone and Upper Dolostone Units; whereas low metal grades characterize the Mixed Unit with a large proportion of siliciclastic component. As a corollary, the lithogeochemical classification can be used as a skarn fertility tool to predict the economic potential of chemical-siliciclastic sedimentary sequences in geological environments permissive of skarn mineralization. [ABSTRACT FROM AUTHOR]

Over 4000 agricultural and grazing land soil samples were collected for the “Geochemical Mapping of Agricultural and Grazing Land Soil of Europe” (GEMAS) project carried out by the EuroGeoSurveys Geochemistry Expert Group. The samples were collected in 33 European countries, covering 5.6 million km 2 at a density of 1 sample site per 2500 km 2 . All samples were analysed by ICP-MS following an aqua regia extraction. The European median Cd concentration is 0.182 mg/kg in agricultural soil and 0.197 mg/kg in grazing land soil (including eastern Ukraine). The Cd map demonstrates the existence of two different geochemical background regimes in northern and southern Europe, separated by the southern limit of the Quaternary glaciation. Cadmium shows two times higher background concentrations in the older and more weathered southern European soil than in northern European soil. The spatial distribution patterns of Cd in the collected soil samples are mainly governed by geology (parent material and mineralisation), as well as weathering, soil formation and climate since the last glaciation period. Locally, in several areas, the natural anomaly pattern is overprinted by anthropogenic emissions from former mining, ore processing and related metal industries. Some Cd anomalies can be attributed to urbanisation and the use of fertilisers. A comparison of the raw data Cd concentration map with its clr-transformed counterpart and selected single element ratio maps demonstrates that substantial additional information about sources and processes governing the distribution of Cd in agricultural soil at the European scale can be obtained. Results of a PCA, carried out following the classical approach (standardised) versus a PCA based on the statistically acceptable approach, using clr-transformed data, are quite comparable. [ABSTRACT FROM AUTHOR]

In this study, 500 bottom soils and 7300 topsoils samples were collected to determine the major elements concentrations and to evaluate the correspondence of soil chemical contents and the local geology and to better identify background values. The <2 mm fraction of each sample was analysed for a total of 52 elements combining ICP-MS and ICP-ES techniques. This study focuses on the concentration and distribution of major elements: Al, Ca, Fe, K, Mg, Mn, Na, P and Ti. A combined methodological approach, which involved compositional data analysis, multifractal data interpolation, as well as enrichment factor analysis, was applied to the geochemical dataset. Results indicate that the compositional approach in the multivariate data analysis allows to better interpret the behaviour of elements in natural environment and in geochemical processes as compared to raw data and to improve the quality of related maps. The study has shown that the soils of the Campania region generally contain higher concentrations of investigated major elements than those assessed for European and Italian agricultural soils. In Campania, both bottom soils and topsoils contain higher concentrations of Al, K, Na, P and Ti in volcanic areas. Limestone and dolostone are responsible for higher Ca and Mg contents, while soils formed on siliciclastic deposits generally contain higher levels of Fe and Mn. P and Na concentrations are higher in volcanic soils, but the contribution of fertilizers, which is evident in some areas, should not be underestimated. Weathering processes play an essential role in the distribution of elements in soils, and this appears very evident mostly in volcanic areas. The observations are consistent with the results of the enrichment factors analysis. • The paper describes the major elements distribution in soils of Campania. • Compositional data analysis together with geochemical mapping is an effective approach. • The chemical composition of soils generally reflects the underlying bedrock chemistry. • At some sites, the concentrations of some elements are higher due to the use of fertilizers. • A key role in the distribution of the elements is played by their geochemical behaviour in the surface environment. [ABSTRACT FROM AUTHOR]

The mining industry, in most cases, targets a specific valuable commodity that is present in small quantities within large volumes of extracted material. After milling and processing, most of the extracted material and the effluents are stored as waste (tailings) in impoundments, such as dams or waste dumps, or are backfilled into underground mines. In time, tailing materials may become an issue of environmental and health concern due to the hazardous elements, ions, and oxides contained within the waste material. In addition, handling and storage of such waste in dams may pose the risk of dam failure with catastrophic consequences to nature and nearby communities. On the other hand, tailings may offer potential as secondary sources of critical elements (CEs), including rare earth elements (REEs), which may have been overlooked during primary production and processing. Therefore, treating mine tailings as a resource has economic and environmental benefits by reducing the waste from new and historical mine sites through remining. One of the critical steps for taking advantage of these benefits is to spatially quantify the resources and the pollutants, which require the application of adequate data analysis and modeling methods, often to compositional geochemical data. Utilizing adequate methods is especially important for correctly quantifying resource potential, as the quantities will often be at low concentrations. This work presents quantification of resource potential (Au, Ag, Cu, Zn, Pb) and elements of environmental concern (Hg and As) from the tailings of a historic mine site, Katherine Mine, AZ, USA. Data reported by the U.S. Bureau of Mines (USBM) after extensive field campaigns in the 1990s, including sampling from tailing impoundment and surrounding areas for geochemical characterization and geophysical surveys, were used. First, compositional data (CoDa) analysis was employed to explore associations of sampling locations, geochemical parts, and the clustering of samples. Next, sequential Gaussian simulation (SGSIM) was applied to samples that showed a genetic link to tailing material after isometric log-ratio transformation (ilr) and mix/max autocorrelation factor (MAF) transformation for spatial modeling and uncertainty evaluation. Geostatistical results revealed spatial variability of concentrations within the tailing area. Uncertainty evaluation based on realizations indicated that Cu (14.27–20.01 t), Zn (44.23–76.23 t), and Pb (22.56–38.28 t) are the most abundant elements within a 5 %–95 % interval, followed by Ag and Au (~5.3 and 0.18 t, at 50th percentile), respectively. Of the elements of health concern, As was found to be ~4.8 t (50th percentile) in the tailing area. The work also showed that ~0.51 t As, 0.005 t Hg, 0.020 t of Au, and 0.62 t of Ag were carried to Lake Mohave by an ephemeral stream called Katherine Wash, which transects the tailings. • Tailing impoundment of Katherine Mine, USA, was studied. • Quantities of Au, Ag, Cu, Zn, Pb, Hg and As were modeled spatially. • Compositional data analysis was used to explore associations and clusters. • Geostatistical methods applicable to compositional data were employed for quantification. • In-place tonnage and the quantities washed to Lake Mohave were determined. [ABSTRACT FROM AUTHOR]

The management of groundwater resources has become a matter of public interest during recent years. It is a difficult and sensitive topic for all involved subjects, scientists, state agencies, public and private water supply companies and citizens. Therefore, tools for collection, storage, evaluation and public visualization of groundwater-relevant information have to be constructed and implemented. The development of network-based technologies, and the support of the Geographical Information Systems, would help the dissemination of the knowledge about groundwater chemistry, as well as the integration of the geochemical data with other type of information (geological, hydrogeological, soil use, etc.). However, dedicated repositories often contain fragmented numerical and categorical information from different sources obtained for dissimilar aims and divergent interests, thus compromising a holistic management of the resource. Given the complicated nature of the information related to groundwater and geochemical behavior, statistical methods describing and evaluating data patterns in complex regional databases have to be developed in a dedicated way. An application example is presented for the Geobasi–Tuscany (central Italy) project, a repository where all the information about the geochemistry of natural media, including groundwater, collected in the regional territory is stored. Robust methods, not sensible to anomalous cases, have been associated to the compositional approach, leading to the identification of the baseline compositional facies, related to frequent and spatially diffused geochemical phenomena. The facies have been discriminated from the other ones, related to specific environmental conditions, affecting limited portions of the territory. The methodology can be easily integrated with those based on the classical ternary or square diagrams for the identification of the hydrochemical facies. The proposed robust approach, compositional and, therefore, multivariate, appears to be an efficient way to understand how the natural and not natural processes are working on a regional scale. [ABSTRACT FROM AUTHOR]

Like the statistical analysis of compositional data in general, spatial analysis of compositional data requires specific tools. A historical overview of their development is presented in three steps: (a) the recognition of the problem, known as spurious spatial covariance, (b) first attempts to use the logratio approach, and (c) the application of the principle of working in coordinates using isometric logratio representations. Also mentioned are the use of matrix-valued variation-variograms as a tool to model crossvariograms, and the simplicial approach to indicator kriging, that solves inconsistencies in the standard approach to indicator kriging. [ABSTRACT FROM AUTHOR]

The requirements for quantitative data in geological surveillance are ever increasing; traditional geological maps and 3D models are evolving into quantitative conceptual models based on a broad range of analytical measurements of surface and subsurface properties. The British Geological Survey's Geochemical Baseline Survey of the Environment (G-BASE) project provides one such source of data: national coverage of stream sediment, stream water and soil geochemistry. While this geochemical data is of obvious benefit to our understanding of the present day surface environment, the rich geological diversity of south west England provides an ideal setting in which to showcase the potential of such data to enhance our understanding of the underlying bedrock. In this paper we use compositional data analysis to extract lithostratigraphic information from G-BASE stream sediment data. We find that variations in G-BASE stream sediment geochemistry correspond very closely to mapped variations in bedrock geology. Geochemical variations between the 16 lithostratigraphic domains into which we classify the region provide insight into the distinct geological histories of the lithologies within each domain, particularly in relation to depositional environments and sediment provenance. [ABSTRACT FROM AUTHOR]

Single component geochemical maps are the most basic representation of spatial elemental distributions and commonly used in environmental and exploration geochemistry. However, the compositional nature of geochemical data imposes several limitations on how the data should be presented. The problems relate to the constant sum problem (closure), and the inherently multivariate relative information conveyed by compositional data. Well known is, for instance, the tendency of all heavy metals to show lower values in soils with significant contributions of diluting elements (e.g., the quartz dilution effect); or the contrary effect, apparent enrichment in many elements due to removal of potassium during weathering. The validity of classical single component maps is thus investigated, and reasonable alternatives that honour the compositional character of geochemical concentrations are presented. The first recommended such method relies on knowledge-driven log-ratios, chosen to highlight certain geochemical relations or to filter known artefacts (e.g. dilution with SiO 2 or volatiles). This is similar to the classical normalisation approach to a single element. The second approach uses the (so called) log-contrasts, that employ suitable statistical methods (such as classification techniques, regression analysis, principal component analysis, and clustering of variables) to extract potentially interesting geochemical summaries. The caution from this work is that if a compositional approach is not used, it becomes difficult to guarantee that any identified pattern, trend or anomaly is not an artefact of the constant sum constraint. In summary the authors recommend a chain of enquiry that involves searching for the appropriate statistical method that can answer the required geological or geochemical question whilst maintaining the integrity of the compositional nature of the data. The required log-ratio transformations should be applied followed by the chosen statistical method. Interpreting the results may require a closer working relationship between statisticians, data analysts and geochemists. [ABSTRACT FROM AUTHOR]

Soil geochemistry is often investigated by considering a large number of variables, including major, minor and trace elements. Some of the variables are usually highly correlated due to coherent geochemical behaviour, but the effect of anthropic factors tends to increase data variability, sometimes obscuring natural relationships governing their distributions. In this framework it may be difficult to identify geochemical features linked to natural phenomena as well as to separate geogenic anomaly from the anthropogenic ones. Consequently the identification of background/baseline values may be seriously compromised. However, knowledge about these reference terms is fundamental to manage and protect natural resources on different scales. Moreover, adequate estimations of background/baseline values are possible only if a sufficient number of chemical analyses are stored in complex repositories. In this contribution the multi-element data archive of the Campania Region (Southern Italy) was explored from the CoDA (Compositional Data Analysis) multivariate perspective to characterise its structure. The archive contains abundance data of Al, As, B, Ba, Ca, Co, Cr, Cu, Fe, K, La, Mg, Mn, Mo, Na, Ni, P, Pb, Sr, Th, Ti, V and Zn (mg/kg) determined in 3535 new topsoils as well as information on coordinates, geology and land cover. Under CoDA the proportionality features of abundance data are fully taken into account enhancing their relative multivariate behaviour in the correct sample space. Results indicate that the structure of the whole matrix appears to be constituted by a core that geographically is mainly given by topsoils developed on volcanic materials and several outlier compositions whose origin is different. Anomalous compositions can originate from the robust barycentre all around when the following conditions are present: 1) high Na–K volcanic products, 2) limestones and dolostones with their terrigenous component, 3) flysch deposits or 4) fertiliser contribution. The (1 × D ) robust barycentre of the whole dataset together with the variation array of the core represents the most frequent (1 × D ) multi-element vector as well as the proportionality relationships among its components. It might be considered a compositional baseline. [ABSTRACT FROM AUTHOR]

Soil geochemical anomalies can be used to identify pathfinders in exploration for ore deposits. In this study, compositional data analysis is used with multivariate statistical methods to analyse soil geochemical data collected from the Coles Hill uranium deposit, Virginia, USA, to identify pathfinders associated with this deposit. Elemental compositions and relationships were compared between the collected Coles Hill soil and reference soil samples extracted from a regional subset of a national-scale geochemical survey. Results show that pathfinders for the Coles Hill deposit include light rare earth elements (La and Ce), which, when normalised by their Al content, are correlated with U/Al, and elevated Th/Al values, which are not correlated with U/Al, supporting decoupling of U from Th during soil generation. These results can be used in genetic and weathering models of the Coles Hill deposit, and can also be applied to future prospecting for similar U deposits in the eastern United States, and in regions with similar geological/climatic conditions. [ABSTRACT FROM AUTHOR]

Abstract: Groundwaters, like other natural waters, are solutions of a variety of substances in the solvent water. Human impact upon groundwater systems has created many environmental problems so that hydrogeochemical studies related to pollution have become very important. In this context high abundance of nitrogen species, particularly nitrates, can cause adverse health effects, their main sources being fertilizer, animal or human waste, natural soil organic matter, nitrogen fixation and rain. Graphical representations of hydrogeochemical data (for example molar ratio diagrams, stochiometric diagrams, triangular diagrams, mixing diagrams) pose considerable problems if statistical evaluations have to be performed. In fact, since hydrochemical data are compositional (proportional data), their sample space is the simplex, a constrained space where the application of the standard geometrical approach may provide misleading information. In this paper classical binary diagrams used to investigate the whole chemistry of water as well as the behaviour of nitrogen species were substituted by new equivalent graphs coherent with the properties of compositional data, thus opening new perspectives in the evaluation of geochemical processes affecting water resources. Moreover, since nitrogen species are often affected by the presence of numerous data below the detection limit, their role was investigated by considering different imputation methods, and evaluating their performance in the sub-composition NH4 +, NO2 −, NO3 −. The first phase of the analysis did not consider the presence of data below the detection limit but subsequently, step by step, the possibility to recuperate this type of information was explored. The opportunity to recover information about data below the detection limit from the variance–covariance structure of the whole composition (Ca2+, Mg2+, K+, Na+, HCO3 −, Cl−and SO4 2− and available nitrogen data) was also investigated by considering a multivariate framework. The approaches, coherent with compositional data analysis theory, pointed out interesting aspects of 1) the relationships in the sub-composition of NH4 +, NO2 −, and NO3 −, giving us a different perception of the background level of these species as well as of their source; 2) the effects on the variance–covariance structure of the whole composition (main anions and cations and nitrogen species); and 3) the loss of information about the covariance between chemical variables (coherence of geochemical behaviour), which is able to affect the understanding of geochemical systems. [Copyright &y& Elsevier]

Abstract: Present soil P cycling models and the related ecosystem services such as P retention deny the special properties of compositional data resulting from closure and leading to methodological inconsistencies and pathological behavior. Our objective was to elaborate and interpret a hierarchy of supervised balances between components of the soil P cycle computed as isometric log ratios (ilr) to avoid biases when assessing P risk in managed terrestrial ecosystems. Fourty-one acid sandy soils, 56 acid to neutral loamy to clayey soils and 41 acid organic soils (10 fibric or hemic peat and 31 sapric ‘moorsh’ or ‘muck’ materials) were collected in Québec, Canada, analyzed for resin-P, NaHCO3-P, NaOH-P and residual P fractions, arranged into a hierarchy of sound balances and classified for the risk of P leaching. Pathological behavior was shown by spurious correlations between P fractions varying in magnitude, sign and significance depending on what was considered as the “whole” (e.g. total P or soil dry matter). Discriminant analysis across unbiased balances showed that the balances related to soil genesis dominated P biogeochemistry. Soils at low or high P risk for P leaching showed contrasting degrees of Po and Pi loads. Low- and high-P risk loamy and clayey soils differed significantly for balances relating labile to slowly available Pi, as well as those relating Pi to Po. Low- and high-P risk sandy soils were contrasted by the balance relating oxalate-extractable Al and Fe. Peat and ‘moorsh’ materials differed in most ilrs. As soils loose ecosystem services provided by soil P geochemical and biological pools in terms of P-fractions balances, terrestrial ecosystems become less sustainable. [Copyright &y& Elsevier]

Abstract: Compositional data analysis (CoDa) of sedimentary basin brines can help reveal the hydrogeochemical history of hydrocarbon producing formations. CoDa uses log-ratio transformations to convert compositional data into Euclidean space, a necessary assumption for many mathematical models and basic data interpretation. This paper demonstrates the novel approach of pairing CoDa with thermodynamic geochemical models for bivariate and multivariate subcompositions for a suite of brines within three producing formations from the Permian Basin, USA. Subcompositional analysis of basin brines using [Na, Cl, H2O] and [Ca, SO4, H2O] with corresponding equilibration models, plotted using isometric log-ratio (ilr) transformations, shows reaction with anhydrite and halite at depth. The [Na, Cl, Br] subcomposition often can discriminate between seawater evaporation and halite dissolution; the sparse Br data suggest that both processes occur. Principal component analysis (PCA) of centered log-ratio (clr) data is useful for visualizing combinations of processes that are not readily apparent in the two-solute models. PCA of the Permian Basin brines reveals distinct groups of data, including samples likely affected by water flooding, and others influenced by a combination of Late Permian seawater evaporation or halite dissolution coupled with Ca+Mg ion exchange for Na on clays. These relationships support a hydrogeologic model in which meteoric inflow occurs in a heterogeneous manner much more complicated than the previously suggested concept of west-to-east piston style flow. [Copyright &y& Elsevier]

Abstract: Geochemical data are typically reported as compositions, in the form of some proportions such as weight percents, parts per million, etc., subject to a constant sum (e.g. 100%, 1,000,000ppm). This latter implies that such data are “closed”; that is, for a composition of D-components, only D −1 components are required. The statistical analysis of compositional data has been a major issue for more than 100years. The problem of spurious correlation, introduced by Karl Pearson in 1897, affects all data measuring parts of some whole, which are by definition, constrained; and such type of measurements are present in all fields of geochemical research. The use of the log-ratio transform was introduced by John Aitchison to overcome these constraints by opening the data into the real number space, within which standard statistical methods can be applied. However, many statisticians and users of statistics in the field of geochemistry are unaware of the problems affecting compositional data, as well as solutions that overcome these problems. A look into the ISI Web of Science and Scopus databases shows that most papers where compositional data are the core of a geochemical research continue to ignore methods to correctly manage constrained data. A key question is how we can demonstrate that the interpretation of the behaviour of chemical species in natural environment and in geochemical processes is improved when the compositional constraint of geochemical data is taken into account through the use of new methods. In order to achieve this aim, this special issue of the Journal of Geochemical Exploration focuses on the correct statistical analysis of compositional data. Applications in exploration, monitoring and environments by considering several geological matrices are presented and discussed illustrating that several paths can be followed to understand how geochemical processes work. [Copyright &y& Elsevier]

Establishing the natural background levels of chemical elements is very often extremely complicated. This is even more true especially for the more anthropized areas, where the concentration of an element in the environmental matrices is conditioned not only by the natural (geogenic) context but also by anthropic activities. In this study, the natural background levels (NBLs) of several chemical elements in the stream sediments of the High Agri River basin have been calculated using a combination of multivariate statistical analysis (MSA) and machine learning techniques in the frame of compositional data analysis (CoDA). Specifically, data clustering and robust principal component analysis (rPCA) have allowed us to recognize and isolate three different data populations, belonging to three different geological domains. A first population of data is clearly related to calcareous lithologies, while the second one is associated to siliciclastic lithologies. A third one is of mixed origin, partly linked to the volcanoclastic nature of the sediments and due to the presence of co-precipitation phenomena. The three data populations have been later separated into three different databases and, whereas needed, the outliers have been eliminated. Based on the obtained results, the NBLs have been calculated using the US-EPA's (United States Environmental Protection Agency) ProUCL software. The results derived from this new approach have been later compared with those obtained using the spectral analysis (S-A) method to evaluate its advantages and versatility. This approach allowed calculating more reliable natural background values. [Display omitted] • An approach combining various geochemical data processing techniques is proposed. • Multivariate statistical analysis, clustering techniques and compositional data were used. • Data clustering, rPCA and ProUCL software were used to calculate background. [ABSTRACT FROM AUTHOR]

Stream sediment geochemical prospecting is historically acknowledged as a reliable tool to investigate the influence of geology and to assess the presence of natural and/or anthropogenic anomalies within river catchment basins at different scales. Several methods have been developed during decades to seek for geochemical anomalies especially those considered with a high potential for mineral resources. In the last decades, the Sample Catchment Basin (SCB) method, based on the use of dilution-corrected residuals (DCRs), has been developed, improved, and extensively used in mineral exploration, mostly at regional scale. In this paper the method was applied to a small catchment basin in southern Italy (Mingardo River) with the purposes of testing its reliability and improving the interpretation of geochemical patterns. The SCBs method was performed by using as input both unprocessed (raw) and centered log-ratio (clr) transformed data, and the obtained DCRs were classified through the concentration-area (C-A) method. After verifying the inadequacy of raw data, Principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA) on PCA loadings were used to address and improve the interpretation processes in a multivariate perspective. The method has proved to be effective in identifying anomalies associated with lithological layers that are often not reported on the medium scale and which, however, have the ability to condition the geochemical patterns of the basin to which they belong. The geochemical footprint of anthropic presence (road, villages, etc.) was also detected although its impact on the study area is limited. • A first attempt to couple different data elaboration techniques using stream sediments • Sample catchment basins were outlined considering geomorphological features. • The dilution effect was modeled and corrected to enhance geochemical signals. • The log-ratio transformation improved discriminating and bounding the sources. • Multivariate analyses distinguished among natural and anthropogenic sources. [ABSTRACT FROM AUTHOR]

The Tethyan Himalaya is the fourth metallogenic belt discovered to date within the Tibetan Plateau and contains chromite, gold and Sb–polymetallic mineralization. The belt records a series of complex tectonic–magmatic events that include the break-up, subduction, collision, detachment, and obduction of the Eastern Gondwana continent, all of which generated ideal conditions for ore formation. However, this area remains under-explored, with only limited knowledge of the deposit types present in this area and their relationship to tectonic and magmatic events. This has hampered mineral exploration in the region. This study uses previous research and mineral exploration in this region, combined with knowledge of mineralization in adjacent regions, to suggest the presence of three main deposit types (ophiolitic chromite, orogenic Au, and hydrothermal vein Sb–polymetallic deposits), and highlights the potential for this area to host as yet undiscovered island arc-type porphyry Cu–Au and leucogranite-related Sn–(Be) mineralization. This study summaries the relationship between the tectonic evolution and magmatism within the region and the mineralization, establishing a temporal and spatial framework for mineralization within the eastern Tethys Himalayan metallogenic belt. Improved knowledge of the geochemical signatures of mineralization, obtained using compositional data analysis (CoDA) combined with geological information, enabled the identification of nine compositional balances, including the presence of sedimentary units, accretionary wedge structures, leucogranites, mafic–ultramafic rocks, chromite mineralization, Au mineralization, and Sb–polymetallic mineralization. This study establishes a predictive mineral exploration model for the eastern Tethys Himalayan metallogenic belt and combines this model with geological and geochemical data to identify areas for future exploration. These targets include a 50-km-long ophiolitic belt in the Nedong area that is highly prospective for chromite mineralization; a positive Au–Cu–Mo geochemical anomaly in the Qonggyai–Qusum area that is prospective for orogenic Au mineralization; and positive Au–Cu–Mo geochemical anomalies in the Nianzha, Sangri, and Gyaca areas that are prospective for exploration for Xiongcun-type island arc-associated porphyry Cu–Au mineralization, as well as deep-seated extensions to known Sb–polymetallic mineralization and the inner and outer contacts of the Lhozhag and Cuonadong leucogranite, all of which are prospective for exploration for Sn–(Be) mineralization. • The time–space framework of regional mineralization was established. • Nine knowledge-driven compositional balances obtained from CoDA • The metallogenic prediction model was established to predict mineralization. • Eleven prospective areas with different ore deposit type were proposed. [ABSTRACT FROM AUTHOR]

Elba Island was a hotspot of iron mining and smelting in Italy since Etruscan times (6th century BCE). Whereas the environmental burden of modern (base) metal mining in Tuscany is well studied, the impact of both ancient iron mining and smelting on soils in the region is poorly understood. Therefore, we took soil samples from an ancient smelting site and adjacent areas to evaluate the release of trace metal(loid)s from smelting. Additionally, we evaluated 'metallurgical activity markers' on the site, i.e. the chemical signature of the production process. The evaluation is based on the soils' element composition, total and pyrogenic carbon contents, pH, and magnetic susceptibility. Statistical analysis include clustering, principal component analysis, and inference tests. Our results indicate that (i) Fe, As, Cu, Ca, total organic, and pyrogenic carbon contents and pH are increased on the smelting site. (ii) This increase corresponds to relatively high contents in these parameters in slag, ore, charcoal, and ash compared to values from background soils. (iii) Metal(loid) contents partly exceed guideline values, but appear negligible compared to (modern) mining – mostly hematite was mined in antiquity, whereas limonite (associated with galena) was only extracted in modern times. • Iron smelting on an ancient site clearly changed soil element composition and pH. • The alteration matches the composition of ferrous slag or ore, charcoal and fuel ash. • Trace metal(loid) contents partly exceed guideline levels, but only negligibly. • Compared to (ancient) base metal processing, values are of secondary importance. • Deposits showing high contents of trace metal(loid)s were only mined in modern times. [ABSTRACT FROM AUTHOR]