Soil pollution by oxidation of tailings from toxic spill of a pyrite mine

Long-term effects of submergence and wetland vegetation on metals in a 90-year old abandoned Pb-Zn mine tailings pond

A Pb-Zn tailings pond, abandoned for approximately 90 years, has been naturally colonized by Glyceria fluitans and is an excellent example of long-term metal retention in tailings ponds under various water cover and vegetation conditions. Shallow/intermittently flooded areas (dry zone) were unvegetated and low in organic matter (OM) content. Permanently flooded areas were either unvegetated with low OM, contained dead vegetation and high OM, or living plants and high OM. It was expected that either water cover or high OM would result in enhanced reducing conditions and lower metal mobility, but live plants would increase metal mobility due to root radial oxygen loss. The flooded low OM tailings showed higher As and Fe mobility compared with dry low OM tailings. In the permanently flooded areas without live vegetation, the high OM content decreased Zn mobility and caused extremely high concentrations of acid-volatile sulfides (AVS). In areas with high OM, living plants significantly increased Zn mobility and decreased concentrations of AVS, indicating root induced sediment oxidation or decreased sulfate-reduction. This is the first study reporting the ability of wetland plants to affect the metal mobility and AVS in long-term (decades), unmanaged tailings ponds.

Evolution of biological effects of Aznalcóllar mining spill in the Algerian mouse (Mus spretus) using biochemical biomarkers

The status of Guadiamar stream, polluted in 1998 by metals spilled from a pyrite tailings dam, was monitored from 1999 to 2001 to assess possible biological effects in terrestrial ecosystems of Doñana National Park (DNP) (Andalusia, SW Spain). The Algerian mouse (Mus spretus) was used as bioindicator at different Guadiamar and Doñana sites. Eleven biochemical parameters, including the activities of antioxidative and biotransforming enzymes and oxidative damages to biomolecules, were assayed in liver as biomarkers responsive to metals and organic pollutants. In 2001, metals were also determined in kidney and their possible correlation with biomarker responses was studied. Contents of Pb, Cd and As significantly correlated with several antioxidative enzymes. Biomarkers responsive to oxidative stress indicate the presence of transition metals in the high and medium Guadiamar course, and their response diminished with the distance to the collapsed dam. The high ethoxyresorufin-O-deethylase (EROD) activity of mice from the medium and low Guadiamar course point to organic pollutants, such as the pesticides used in intensive crops grown in areas nearby Doñana. The increasing responses of several biomarkers at reference sites may suggest a progressive pollution of key Doñana ecosystems.

Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy

The possibility to adapt chemometrics approaches for the quantitative estimation of heavy metals in soils polluted by a mining accident was explored. In April 1998, the dam of a mine tailings pond in Aznalcóllar (Spain) collapsed and flooded an area of more than 4000 ha with pyritic sludge contaminated with high concentrations of heavy metals. Six months after the end of the first remediation campaign, soil samples were collected for chemical analysis and measurement of visible to near-infrared reflectance (0.35-2.4 microm). Concentrations for As, Cd, Cu, Fe, Hg, Pb, S, Sb, and Zn were well above background values. Prediction of heavy metals was achieved by stepwise multiple linear regression analysis (MLR) and an artificial neural network (ANN) approach. It was possible to predict six out of nine elements with high accuracy. Best R2 between predicted and chemically analyzed concentrations were As, 0.84; Fe, 0.72; Hg, 0.96; Pb, 0.95; S, 0.87; and Sb, 0.93. Results for Cd (0.51), Cu (0.43), and Zn (0.24) were not significant. MLR and ANN both achieved similar results. Correlation analysis revealed that most wavelengths important for prediction could be attributed to absorptions features of iron and iron oxides. These results indicate that it is feasible to predict heavy metals in soils contaminated by mining residuals using the rapid and cost-effective reflectance spectroscopy.