M.T.D. Albuquerque

Using indicator kriging for the evaluation of arsenic potential contamination in an abandoned mining area (Portugal)

Mining and mineral-processing activities can modify the environment in a variety of ways. Sulfide mineralization is notorious for producing waters with high metal contents. Arsenic is commonly associated with sulfide mineralization and is considered to be toxic in the environment at low levels. The studied abandoned mining area is located in central Portugal and the resulting tailings and rejected materials were deposited and exposed to the air and water for the last 50 years. Sixteen water sample-points were collected. One of these was collected outside the mining influence, with the aim of obtaining a reference background. The risk assessment, concerning the proximity to abandoned mineralized deposits, needs the evaluation of intrinsic and specific vulnerabilities aiming the quantification of the anthropogenic activities. In this study, two indicator variables were constructed. The first one (I(1)), a specific vulnerability, considers the arsenic water supply standard value (0.05 mg/L), and the probability of it being exceeded is dependent on the geologic and hydrological characteristics of the studied area and also on the anthropogenic activities. The second one (I(2)), an intrinsic vulnerability, considers arsenic background limit as cut-off value, and depends only on the geologic and hydro-geological characteristics of the studied area. At Segura, the arsenic water content found during December 2006 (1.190 mg/L) was higher than the arsenic water content detected in October 2006 (0.636 mg/L) which could be associated to the arsenic released from Fe oxy-hydroxide. At Segura abandoned mining area, the iso-probability maps of October 2006 and December 2006, show strong anomalies associated with the water drainage from abandoned mining activities. Near the village, the probability of exceeding the arsenic background value is high but lower than the probability of exceeding the arsenic water supply value. The arsenic anomalies indicate a high probability for water arsenic contamination and those waters should not be used for human consumption.

Developing a new Bayesian Risk Index for risk evaluation of soil contamination

Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. In the herein research a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.

Spatial risk assessment related to abandoned mining activities: an environmental management tool

Mining activities and resulting wastes can be considered one of the most important sources of toxic metals and metalloids in the environment. To assess environmental risk in the surrounding areas of old abandoned W-Sn and Pb–Zn mines and resulting tailings and rejected materials, 333 samples were collected in stream sediments under the influence of abandoned mines. Samples were prepared and analyzed for Fe, Ba, P, Cu, Cr, Ag, B, Zn, Be, Y, Nb, Pb, Ni, V, Mn, Mo, As, W, Co, Cd, Sn and U. The inexistence of Portuguese legislation concerning parametric values for stream sediments led to the application of a quantitative index for progressive contamination on stream sediments, the Geoaccumulation Index (Igeo), as variables to create risk maps. A first exploratory multivariate statistical analysis, using the Principal Component Analysis (PCA), applied to the obtained Igeos, shows a first factor (F1) explaining the dependence of P and B (positive correlation with the axis) and the inverse correlation of these two elements with the cluster formed by Cr, Ni and V (negative correlation with the axis); the second factor (F2) explains Ni, Fe, Zn and As; Cd and U Igeos are not explained in the new factorial space and, therefore, are characterized individually. The variographic studies showed the existence of spatial structure for the new synthesis variables (F1, F2) as well as for Cd and U Igeos. The experimental point-support data was then interpolated using ordinary kriging within a narrow search window as shown in the fitted variogram models. The obtained maps show extremely high levels of pollution in Cd and W and strongly high levels of pollution in Cr, B, Ag, Zn and Pb. The accumulation of these elements in the studied stream sediments is higher on the abandoned mining areas and in their vicinity.

Combining raw and compositional data to determine the spatial patterns of Potentially Toxic Elements in soils

When considering complex scenarios involving several attributes, such as in environmental characterization, a clearer picture of reality can be achieved through the dimensional reduction of data. In this context, maps facilitate the visualization of spatial patterns of contaminant distribution and the identification of enriched areas. A set, of 15 Potentially Toxic Elements (PTEs) - (As, Ba, Cd, Co, Cr, Cu, Hg, Mo, Ni, Pb, Sb, Se, Tl, V, and Zn), was measured in soil, collected in Langreos municipality (80km2), Spain. Relative enrichment (RE) is introduced here to refer to the proportion of elements present in a given context. Indeed, a novel approach is provided for research into PTE fate. This method involves studying the variability of PTE proportions throughout the study area, thereby allowing the identification of dissemination trends. Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the elements analyzed make up the entirety of the soil. However, in geochemical studies the analyzed elements are just a fraction of the total soil composition. Therefore, considering compositional data is pivotal. The spatial characterization of PTEs considering raw and compositional data together allowed a broad discussion about, not only the PTEs concentrations distribution but also to reckon possible trends of relative enrichment (RE). Transformations to open closed data are widely used for this purpose. Spatial patterns have an indubitable interest. In this study, the Centered Log-ratio transformation (clr) was used, followed by its back-transformation, to build a set of compositional data that, combined with raw data, allowed to establish the sources of the PTEs and trends of spatial dissemination. Based on the obtained findings it was possible to conclude that the Langreo area is deeply affected by its industrial and mining legacy. City center is highly enriched in Pb and Hg and As shows enrichment in a northwesterly direction.

A Multivariate Geostatistical Methodology to Delineate Areas of Potential Interest for Future Sedimentary Gold Exploration

This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analysed for 22 elements. Gold (Au) was first predicted at all 376 locations using linear regression (

Spatial environmental risk evaluation of potential toxic elements in stream sediments

R^{2}=0.798

Geostatistical Estimation of Forest Cover Areas Using Remote Sensing Data

R2=0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold’s paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The 100 classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization.

Potential toxic elements in stream sediments, soils and waters in an abandoned radium mine (central Portugal)

1 Received on 07.06.2016accepted for publication on 24.05.2017. 2 Instituto Politécnico de Castelo Branco,Escola Superior Agrária,IPCB/ESA, Portugal. E-mail:<susanamestre@aflobei.pt>. 3 Centro de Estudos de Recursos Naturais, Ambiente e Sociedade,Instituto Politécnico de Castelo Branco,Escola Superior Agrária,CERNAS/IPCB/ESA, Portugal. E-mail: <crisalegria@ipcb.pt>. 4 Instituto Politécnico de Castelo Branco,Escola Superior de Tecnologia,IPCB/EST, Portugal. E-mail: <teresal@ipcb.pt>. 5 BioMedware, United States of America. E-mail: <Pierre.Goovaerts@biomedware.com>. *Corresponding author.