As and S speciation in a submarine sulfide mine tailings deposit and its environmental significance: The study case of Portmán Bay (SE Spain).

The dumping of an estimated amount of 57 million tons of hazardous sulfide mine waste from 1957 to 1990 into Portmán's Bay (SE Spain) caused one of the most severe cases of persistent anthropogenic impact in Europe's costal and marine environments. The resulting mine tailings deposit completely infilled Portmán's Bay and extended seawards on the continental shelf, bearing high levels of metals and As. The present work, where Synchrotron XAS, XRF core scanner and other data are combined, reveals the simultaneous presence of arsenopyrite (FeAsS), scorodite (FeAsO4·2H2O), orpiment (As2S3) and realgar (AsS) in the submarine extension of the mine tailings deposit. In addition to arsenopyrite weathering and scorodite formation, the, the presence of realgar and orpiment is discussed, considering both potential sourcing from the exploited ores and in situ precipitation from a combination of inorganic and biologically mediated geochemical processes. Whereas the formation of scorodite relates to the oxidation of arsenopyrite, we hypothesize that the presence of orpiment and realgar is associated to scorodite dissolution and subsequent precipitation of these two minerals within the mine tailings deposit under moderately reducing conditions. The occurrence of organic debris and reduced organic sulfur compounds evidences the activity of sulfate-reducing bacteria (SRB) and provides a plausible explanation to the reactions leading to the formation of authigenic realgar and orpiment. The precipitation of these two minerals in the mine tailings, according to our hypothesis, has important consequences for As mobility since this process would reduce the release of As into the surrounding environment. Our work provides for the first time valuable hints on As speciation in a massive submarine sulfide mine tailings deposit, which is highly relevant for similar situations worldwide.

Calibrating high resolution XRF core scanner data to obtain absolute metal concentrations in highly polluted marine deposits after two case studies off Portmán Bay and Barcelona, Spain.

X-ray fluorescence core scanners (XRF-CS) allow rapid, non-destructive, continuous and high-resolution analyses of the elemental composition of sediment cores, providing large sets of semi-quantitative data. These data can be converted to quantitative data through the linear regression approach using a relatively small number of discrete samples analyzed by techniques providing absolute concentrations. However, a precise characterization of the errors associated with the linear function is required to evaluate the quality of the calibrated element concentrations. Here we present a calibration of high-resolution XRF-CS for six metals (Ti, Mn, Fe, Zn, Pb and As) measured in heavily contaminated marine deposits so that absolute concentrations are obtained. In order to determine the best linear function for conversion of XRF data, we have tested three regression methods: the ordinary least-squares (OLS), which does not consider the standard error in any variable (x and y), the weighted ordinary least-squares (WOLS), which considers the weighted standard error of the vertical variable (y), and the weighted least-squares (WLS), which incorporates the standard error in both x and y variables. We demonstrate that the calibration method presented in this study significantly increases the correlation coefficient, higher than r2 = 0.94, and reduces both the data deviation and the errors of the linear function for the three regression methods. Nonetheless, the WLS appears as the best regression method to minimize errors in the calibrated element concentrations. Our results open the door to use calibrated XRF-CS data to evaluate marine sediment pollution according to the levels of the strictest sediment quality guidelines (SQG) with errors lower than 0.4%-2% for Fe, 1%-7% for Zn, 3-14% for Pb and 5%-16% for Mn. They highlight the robustness of the calibration procedure here presented for accurate and precise quantification of element concentrations from XRF-CS semi-quantitative data.