C. Sierra

Analysis of soil washing effectiveness to remediate a brownfield polluted with pyrite ashes.

Soil in a brownfield contaminated by pyrite ashes showed remarkably high concentrations of several toxic elements (Hg, Pb, Zn, Cu, Cd, and As). Initially, we assessed various physical, chemical and mineralogical properties of this soil. The data obtained, and particularly multivariate statistics of geochemical results, were useful to establish the predominant role of the soil organic matter fraction (6%) and iron oxyhydroxides in the binding of heavy metals and arsenic. In addition, we studied the viability of soil washing techniques to reduce the volume of contaminated soil. Therefore, to concentrate most of the contaminants in a smaller volume of soil, the grain-size fraction below 125 microm was treated by hydrocycloning techniques. The operational parameters were optimized by means of a factorial design, and the results were evaluated by attributive analysis. This novel approach is practical for the global simultaneous evaluation of washing effectiveness for several contaminants. A concentration factor higher than 2.2 was achieved in a separated fraction that contained less than 20% of the initial weight. These good yields were obtained for all the contaminants and with only one cycle of hydrocycloning. Hence full-scale soil washing is a plausible remediation technique for the study site.

Multivariate study of trace element distribution in the geological record of Roñanzas Peat Bog (Asturias, N. Spain). Paleoenvironmental evolution and human activities over the last 8000 cal yr BP

Trace element concentrations in the Roñanzas peat bog record reveal a contribution of natural processes but the influence of anthropogenic factors predominates in the last two millenniums, particularly aerosol deposition linked to mining and industrial activities in northern Spain. We observed that the Roñanzas record can be considered a preserved environment, suitable to search for local (<50 km), regional (50-150 km) and/or long-distance human activity fingerprinting, specifically that related to the deposition of heavy metals such as Pb, Zn and Hg. We also carried out a multivariate statistical study in order to clarify the geochemical behavior of trace and major elements. Our study design represents a novel approach to assign natural vs. human contributions in peatlands. Therefore, synergies obtained by the simultaneous study of multivariate statistics and enrichment factors allow robust conclusions about paleoenvironmental evolution and human activities. Anthropogenic influence has also been reported in similar records in other parts of Europe, thereby suggesting large-scale sources for atmospheric pollution. However, here we revealed remarkable particularities, such as the association of Cd, Zn and Pb, mainly linked to regional and local factors (mining and more recently the metallurgical industry), whereas we propose that the occurrence of Hg is associated with a combination of regional factors and global atmospheric pollution.

Comprehensive waste characterization and organic pollution co-occurrence in a Hg and As mining and metallurgy brownfield.

The abandonment of Hg-As mining and metallurgy sites, together with long-term weathering, can dramatically degrade the environment. In this work it is exemplified the complex legacy of contamination that afflicts Hg-As brownfields through the detailed study of a paradigmatic site. Firstly, an in-depth study of the former industrial process was performed to identify sources of different types of waste. Subsequently, the composition and reactivity of As- and Hg-rich wastes (calcines, As-rich soot, stupp, and flue dust) was analyzed by means of multielemental analysis, mineralogical characterization (X-ray diffraction, electronic, and optical microscopy, microbrobe), chemical speciation, and sequential extractions. As-rich soot in the form of arsenolite, a relatively mobile by-product of the pyrometallurgical process, and stupp, a residue originated in the former condensing system, were determined to be the main risk at the site. In addition, the screening of organic pollution was also aimed, as shown by the outcome of benzo(a) pyrene and other PAHs, and by the identification of unexpected Hg organo-compounds (phenylmercury propionate). The approach followed unravels evidence from waste from the mining and metallurgy industry that may be present in other similar sites, and identifies unexpected contaminants overlooked by conventional analyses.

Optimisation of magnetic separation: a case study for soil washing at a heavy metals polluted site.

Sandy loam soil polluted with heavy metals (As, Cu, Pb and Zn) from an ancient Mediterranean Pb mining and metallurgy site was treated by means of wet high-intensity magnetic separation to remove some of the pollutants therein. The treated fractions were chemically analysed and then subjected to magnetic characterisation, which determined the high-field specific (mass), magnetic susceptibility (κ) and the specific (mass) saturation magnetisation (σS), through isothermal remanent magnetisation (IRM) curves. From the specific values of κ and σS, a new expression to assess the performance of the magnetic separation operation was formulated and verified by comparison with the results obtained by traditional chemical analysis. The magnetic study provided valuable information for the exhaustive explanation of the operation, and the deduced mathematical expression was found to be appropriate to estimate the performance of the separation operation. From these results we determined that magnetic soil washing was effective for the treatment of the contaminated soil, concentrating the majority of the heavy metals and peaking its separation capacity at 60% of the maximum output voltage.

Nanofiltration of Acid Mine Drainage in an Abandoned Mercury Mining Area

In Asturias (north of Spain), mercury mining has been identified as a potential source of trace elements such as As, Sb, Pb, and Hg. In particular, at Los Rueldos mine site, some of these contaminants are dissolved in acidic mine drainage (AMD). Here we treated this leachate by means of nanofiltration to remove some of its pollutants. In order to improve our understanding of the geochemical factors involved in nanofiltration, we analyzed sediment geochemistry and the origin of acidic waters. In coherence with the observation of similar behaviors of As, Fe, and Al in the nanofiltration tests, a clear geochemical association between As, Sb, S, and Fe both in sediments and in the occurrence of AMD was detected. The FILMTEC™ NF-2540 membrane used in this study proved to be highly suitable for the treatment and concentration of the metallic and semimetallic contaminants in the acidic water, even at low pH and moderate pressures.

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.

Element enrichment factor calculation using grain-size distribution and functional data regression.

In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size. Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression, in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors, we concluded that the former regression technique has some advantages over the latter. First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions.

Origin, patterns and anthropogenic accumulation of potentially toxic elements (PTEs) in surface sediments of the Avilés estuary (Asturias, northern Spain).

Sediment quality has been assessed within the Avilés estuary, an important industrial area in the NW of Spain. The study started with a geochemical characterization of the superficial sediments that revealed some anomalous metal(oid)s concentrations in sensitive areas such as beaches or dunes. These data were studied by means of multivariate statistical techniques and enrichment factors calculation to evaluate the correlations and geochemical origin within the different elements. A novel approach using the combination of enrichment factors with a sequential application of factor analysis, clustering and kriging was essential to identify the possible sources of pollution. The collected information suggested that Cd (strongly correlated with Zn and Pb) was the potentially toxic element most widely distributed and problematic. Furthermore, particulate emissions from Zn metallurgy, as well as dust generated by the mineral loading and stockpile activities in the port were identified as the most important sources of pollution.

Nanoscale zero-valent iron-assisted soil washing for the removal of potentially toxic elements

The present study focuses on soil washing enhancement via soil pretreatment with nanoscale zero-valent iron (nZVI) for the remediation of potentially toxic elements. To this end, soil polluted with As, Cu, Hg, Pb and Sb was partitioned into various grain sizes (500-2000, 125-500 and <125 μm). The fractions were pretreated with nZVI and subsequently subjected, according to grain size, to Wet-High Intensity Magnetic Separation (WHIMS) or hydrocycloning. The results were compared with those obtained in the absence of nanoparticles. An exhaustive characterization of the magnetic signal of the nanoparticles was done. This provided valuable information regarding potentially toxic elements (PTEs) fate, and allowed a metallurgical accounting correction considering the dilution effects caused by nanoparticle addition. As a result, remarkable recovery yields were obtained for Cu, Pb and Sb, which concentrated with the nZVI in the magnetically separated fraction (WHIMS tests) and underflow (hydrocyclone tests). In contrast, Hg, concentrated in the non-magnetic fraction and overflow respectively, while the behavior of As was unaltered by the nZVI pretreatment. All things considered, the addition of nZVI enhanced the efficiency of soil washing, particularly for larger fractions (125-2000 μm). The proposed methodology lays the foundations for nanoparticle utilization in soil washing operations.

Particle size distribution fitting of surface detrital sediment using the Swrebec function

PurposeThe development of mathematical models to accurately represent the particle size distribution (PSD) of sediment has been addressed by different authors. Here, we introduce the three-parameter Swrebec function as a tool to fit the PSD of sediments. Moreover, we also assess the physical meaning of the undulation parameter (b) in the function.Materials and methodsWe performed PSD by means of laser diffraction spectroscopy. Then, sediments were classified and the statistical parameters (mean, skewness, sorting and kurtosis) calculated using GRADISTAT software, according to the Folk and Ward’s method. Subsequently, the Swrebec function (programmed in Matlab) was applied to the data and its goodness-of-fit were evaluated by means of the adjusted coefficient of determination (R2-Adj) and the root mean squared error (RMSE). The results obtained by Swrebec were also compared with other functions using the Ezyfit toolbox.Results and discussionThe Swrebec model provided excellent correlations and low RMSE when fitting all grain size data. Furthermore, a correlation between b and both the skewness and RMSE was established. This indicates that the greater the asymmetry of the function, and therefore the larger the presence of coarse-grained particles, the lower the performance of the function. It was also observed that a change in the behaviour of all trends seems to occur at a b value of ~4.5.ConclusionsResults suggest that the studied function could be a simple approach for modelling PSD, with potential applications in soil and sediment science, geochemistry, sedimentology and coastal research modelling.