I.M.H.R. Antunes

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.

The mineralized veins and the impact of old mine workings on the environment at Segura, central Portugal

Abstract At Segura, granitic pegmatite veins with cassiterite and lepidolite, hydrothermal Sn–W quartz veins and Ba–Pb–Zn quartz veins intruded the Cambrian schist–metagraywacke complex and Hercynian granites. Cassiterite from Sn–W quartz veins is richer in Ti and poorer in Nb and Nb+Ta than cassiterite from granitic pegmatite. Wolframite from Sn–W quartz veins is enriched in ferberite component. The Sn–W quartz veins contain pyrrhotite, arsenopyrite, sphalerite, chalcopyrite, stannite, matildite and schapbachite and the Ba–Pb–Zn quartz veins have cobaltite, pyrite, sphalerite, chalcopyrite, galena and barite, which were analyzed by electron microprobe. The presently abandoned mining area was exploited for Sn, W, Ba and Pb until 1953. Stream sediments and soils have higher concentrations of metals than parent granites and schists. Sn, W, B, As and Cu anomalies found in stream sediments and soils are associated with Sn–W quartz veins, while Ba, Pb and Zn anomalies in stream sediments and soils are related to Ba–Pb–Zn quartz veins. Sn, W, B, As, Cu, Ba, Pb and Zn anomalies in stream sediments and soils are also related to the respective old mining activities, which increased the mobility of trace metals from mineralized veins to soils, stream sediments and waters. Stream sediments and soils are sinks of trace elements, which depend on their contents in mineralized veins and weathering processes, but Sn, W and B depend mainly on a mechanic process. Soils must not be used for agriculture and human residence due to their Sn, B, As and Ba contents. Waters associated with mineralized veins were analyzed by flame atomic absorption spectroscopy (FAAS) and ICP-AES have high As, Fe and Mn and should not be used for human consumption and agriculture activities. The highest As values in waters were all related to Sn–W quartz veins and the highest Fe and Mn values were associated with the Ba–Pb–Zn quartz veins. No significant acid drainage was found associated with the old mine workings.

Potential risk assessment in stream sediments, soils and waters after remediation in an abandoned W>Sn mine (NE Portugal)

The mining complex of Murçós belongs to the Terras de Cavaleiros Geopark, located in Trás-os-Montes region, northeast Portugal. A stockwork of NW-SE-trending W>Sn quartz veins intruded Silurian metamorphic rocks and a Variscan biotite granite. The mineralized veins contain mainly quartz, cassiterite, wolframite, scheelite, arsenopyrite, pyrite, sphalerite, chalcopyrite, galena, rare pyrrhotite, stannite, native bismuth and also later bismuthinite, matildite, joseite, roosveltite, anglesite, scorodite, zavaritskite and covellite. The exploitation produced 335t of a concentrate with 70% of W and 150t of another concentrate with 70% of Sn between 1948 and 1976. The exploitation took place mainly in four open pit mines as well as underground. Three lakes were left in the area. Remediation processes of confination and control of tailings and rejected materials and phytoremediation with macrophytes from three lakes were carried out between 2005 and 2007. Stream sediments, soils and water samples were collected in 2008 and 2009, after the remediation process. Most stream sediments showed deficiency or minimum enrichment for metals. The sequential enrichment factor in stream sediments W>Bi>As>U>Cd>Sn=Ag>Cu>Sb>Pb>Be>Zn is mainly associated with the W>Sn mineralizations. Stream sediments receiving drainage of a mine dump were found to be significantly to extremely enriched with W, while stream sediments and soils were found to be contaminated with As. Two soil samples collected around mine dumps and an open pit lake were also found to be contaminated with U. The waters from the Murçós W>Sn mine area were acidic to neutral. After the remediation, the surface waters were contaminated with F(-), Al, As, Mn and Ni and must not be used for human consumption, while open pit lake waters must also not be used for agriculture because of contamination with F(-), Al, Mn and Ni. In most waters, the As occurred as As (III), which is toxic and is easily mobilized in the drainage system. The remediation promoted a decrease in metals and As concentrations of soils and waters, however the applied processes were not enough to rehabilitate the area.

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.

Metal and metalloid leaching from tailings into streamwater and sediments in the old Ag–Pb–Zn Terramonte mine, northern Portugal

Tailings deposited over the Castanheira, a stream which flows through the old Ag–Pb–Zn Terramonte mine area, showed a great potential environmental risk due to sulphide weathering, facilitated by the tailings–water interaction. The high concentrations of Al, Fe, Pb and Zn in the tailings are associated with the exchangeable, reducible and sulphide fractions and suggest sphalerite and pyrite occurrences. Oxidation of pyrite is responsible for the low pH values (3.38–4.89) of the tailings. The water from the Castanheira stream is not suitable for human consumption due to high concentrations of SO42−, Mn, Al, Cd, Ni, and Pb. The lowest concentrations of metals and metalloids were detected in downstream stretches of the Castanheira. However, As, Fe and Zn in deeper sediments tend to increase downstream. Significant concentrations of trivalent forms of arsenic were detected in water samples. In downstream stretches of the Castanheira, some free ions (Fe2+, Mn2+ and Zn2+) also predominate and the water is saturated with ferrihydrite, goethite, hematite, lepidocrosite and magnetite.

A Spatial statistical Approach for Sedimentary Gold Exploration: A Portuguese Case Study

This paper describes the mapping of gold content in the surroundings of abandoned gold mines located in central Portugal. In 1988, 376 samples were collected and analyzed for 22 elements. Gold (Au) was measured only inside the gold mines and its value was predicted at other locations using linear regression (\(\mathrm{{R}}^{2}=0.46\)) and four metals (Fe, As, Mn 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 Gaussian simulation. Each simulated map then underwent a local cluster analysis to identify areas of significantly low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated likelihood. The distribution of the hot-spots and cold-spots shows a clear enrichment in Au along the Erges River.

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 (

Contaminated water, stream sediments and soils close to the abandoned Pinhal do Souto uranium mine, central Portugal

R^{2}=0.798