Sequential extraction of heavy metals in soils from a copper mine: Distribution in geochemical fractions

Geoderma 230–231 (2014) 108–118

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Sequential extraction of heavy metals in soils from a copper mine: Distribution in geochemical fractions D. Arenas-Lago, M.L. Andrade ⁎, M. Lago-Vila, A. Rodríguez-Seijo, F.A. Vega Department of Plant and Soil Science, University of Vigo, As Lagoas, Marcosende, 36310, Vigo, Spain

a r t i c l e

i n f o

Article history: Received 24 May 2013 Received in revised form 5 February 2014 Accepted 9 April 2014 Available online xxxx Keywords: Minesoils Heavy metal Contamination TOF-SIMS FE-SEM

a b s t r a c t We evaluated heavy metal contamination (Cr, Cu, Mn, Ni, Pb, and Zn) in soils from the copper mine in Touro, NW Spain. Their total content and geochemical phase distribution were determined using sequential chemical extraction. Representative soils from the area were selected and the objectives were to analyse the total content of heavy metals and their distribution in the different geochemical soil phases, and to determine their association with the soil components using XRD, FE-SEM/EDS and TOF-SIMS. Results show that the Cu total contents (104–2924 mg kg−1) exceed the intervention limits. Most of the heavy metal content is in the residual fraction, Cr (82–95%), Cu (30–70%), Mn (60–96%), Ni (77–95%), Pb (47–68%) and Zn (85–97%). Fe-oxides play an important role in the fixation of metals, especially Cu. The amounts associated with soil organic matter and in the exchangeable form are very low (N10%). TOF-SIMS and FE-SEM/EDS techniques confirm the fractionation results, showing that only a very small part of the studied metals are sorbed, mainly in the iron oxides and interacting with several soil mineral components. FE-SEM/EDS combined with TOF-SIMS, sequential extraction assays and statistical analyses are an effective tool to check the affinity of the soil components for heavy metal cations. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Open cast mining produces a large amount of waste since significant quantities of material are removed. The environment is affected because the topography, soil, vegetation, hydrology, fauna, microclimate and landscape are altered or destroyed. In general, all of the stages of mining produce environmental impacts, resulting in the destruction of natural soils and the creation of new soils, known as Technosols (FAO, 2006). The environmental disturbances caused by human activities such as mining have a profound effect on the dynamics and functioning of ecosystems (Vitousek et al., 1997). The end of these activities may lead to the natural reconstitution of the ecosystem in the long term (Holl, 2002). However, in the case of major disturbances such as open cast mining, returning to the previously existing natural ecosystem is very difficult or even impossible (Escarré et al., 2011). Pyrite, the most abundant sulphide found in metallic minespoils, oxidises to sulphate, resulting in the formation of sulphuric acid. This frequently occurs, causing acidic mine drainage that may solubilise other sulphates and trace metals (Aguilar et al., 2004). As a result the physical, chemical and biological functions of the soil are damaged (Alvarenga et al., 2012; Novo et al., 2013). This happens in numerous areas affected by mining activities that produce waste, effluent and dust with high concentrations of metallic elements, causing adverse

⁎ Corresponding author. Tel.: +34 986812630; fax: +34 986812556. E-mail address: [email protected] (M.L. Andrade).

http://dx.doi.org/10.1016/j.geoderma.2014.04.011 0016-7061/© 2014 Elsevier B.V. All rights reserved.

effects on biological receptors and ecosystems (Wiegleb and Felinks, 2001). Technosols have severe chemical, physical and biological limitations. They lack the necessary levels of organic matter and nutrients for their optimum functioning. Also, Technosols usually have low cohesion and an unfavourable texture and structure (Asensio et al., 2013). High levels of heavy metals and acidic drainage due to the oxidation of sulphides are commonly found in many minesoils (Vega et al., 2005). It is known that amongst other intrinsic and external soil factors, the ecotoxicity and mobility of the heavy metals in soils mainly depend on their chemical speciation (Escarré et al., 2011). All of these characteristics make most of the Technosols an unsuitable environment for the growth of vegetation. They are therefore susceptible to erosion and environmental degradation, as well as acidification of the adjacent soils. The acidic drainage and the solubilisation of heavy metals can contaminate surface and underground waters by leaching (Mulligan et al., 2001). The Touro copper mine (Galicia, NW Spain) was operated between 1973 and 1988. The geological substrate is amphibolite, with significant amounts of metallic sulphides such as pyrite, pyrrhotite and chalcopyrite (Álvarez et al., 2010; Cerqueira et al., 2012; Vega et al., 2005). Today, the soils formed on the settling pond and on the minespoils have serious problems as a result of acidic drainage and extremely high concentration of heavy metals (Asensio et al., 2013) amongst others. Macías and Calvo (2009) indicated the reference levels for heavy metal contents in soils of the region where this study is carried out.

D. Arenas-Lago et al. / Geoderma 230–231 (2014) 108–118

Nevertheless, as the studied soils are in a mine area, Spanish legislation is more permissive and intervention values such as those from ICRCL (1987) must be carefully compared. Moreover, it is known that the total concentration is not sufficient in evaluating contamination by heavy metals, as it only provides limited information on their chemical behaviour and availability (Adamo et al., 2002). These elements are present in the soil in different fractions, which may strongly affect the way the element behaves in the soil, depending on its bioavailability, toxicity, chemical interactions and its mobility within the soil (Brummer, 1986). For this reason, using fractioning methods based on sequential extractions, we need to know how the heavy metals are distributed in the soil in order to study their availability and possible toxicity (Bacon and Davidson, 2008). The main problem that affects these techniques is that each of the extractants may affect the soil components in a different way and artefacts occur. They depend on different factors such as the choice and order of the extractants, the length of the process, the solid/liquid ratio, and the procedure of sample preparation and conservation (Filgueiras et al., 2002). Because of this problem, several authors have recommended using other techniques in order to identify how heavy metals are associated with the different soil components (Beesley and Marmiroli, 2011; Cerqueira et al., 2011, 2012; Moral et al., 2005) and thereby complete the information on the solid phases that directly retain heavy metals. Different direct characterisation methods were chosen, such as Field Emission Scanning Electron Microscopy (FE-SEM) with Energy-dispersive X-ray Spectroscopy (EDS), X-ray diffraction (XRD) and Time of Flight Secondary Ion Mass Spectrometry (TOFSIMS) for detecting and identifying heavy metals in the different geochemical soil phases (Cerqueira et al., 2011; Kierczak et al., 2008). Although they are semiquantitative methods, they provide detailed information about the nature of the soil components and possible interactions with heavy metals. As a result, a combination of both sequential extractions and direct methods can provide a large amount of real information about the solid phases that retain heavy metals. A number of studies have been published concerning soil pollution at the Touro mine (Álvarez et al., 2010, 2011; Buján, 2013; Cerqueira et al., 2012; Vega et al., 2005), although they do not examine in depth the distribution of heavy metals in the different geochemical soil phases using chemical, microscopic and spectroscopic techniques. For this reason, the aims of this study are (1) to characterise representative soils in the area focusing on the components and properties

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that have the greatest influence on the retention of heavy metals; (2) to determine the total soil content of Cr, Cu, Mn, Ni, Pb and Zn, (3) to evaluate their distribution in the geochemical soil phases using sequential chemical extraction, and (4) to determine how the metals interact with the geochemical soil phases using XRD, FE-SEM/EDS and TOFSIMS.

2. Material and methods 2.1. Selection and soil analysis This study was carried out at the Touro copper mine in North West Spain (8° 20′ 40″ W 42° 052′ 34″ N) (Fig. 1). Five different areas were selected and the soils were sampled in each area (T1, T2, T3, T4 and T5). T1, T2, and T3 are soils from different minespoils, T4 is in an area where fines from the settling pond have been carried by runoff, and T5 is from the settling pond. According to the FAO (2006), all of the soil samples are classified as Spolic Technosols. In each area, five soil samples were collected from the surface horizons (where possible, from the first 20 cm) using an Eijkelkamp sampler. They were then stored in polyethylene bags and taken to the laboratory. The soil samples from the same area were mixed, air dried, sifted through a 2 mm-sieve, and homogenized. All of the soil samples were previously analyzed and characterised in order to assess the influence of the soil components and properties on heavy metal retention, and to find out how they were distributed in the geochemical phases. The pH was determined according to Slattery et al. (1999). The soil: water and soil:KCl ratio was 1:2.5 for both measurements. The soil samples were also analysed for particle size distribution (Day, 1965). Total organic C was determined with a TOC analyzer-V CSH/CSN Shimadzu apparatus. This works by applying the principle of catalytic combustion oxidation. Detection was carried out by non-dispersive IR (according to UNE-EN 1484). Exchangeable acidity was determined after 1 M KCl extraction and titration to a phenolphthalein endpoint (Thomas, 1982). The effective cation exchange capacity (ECEC) and exchangeable cation content were determined according to Hendershot and Duquette (1986) method. Al, Ca, K, Mg and Na were extracted with 0.1 M BaCl2, and the concentrations were determined by Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) (PerkinElmer Optima 4300 DV, PerkinElmer, Waltham, MA, USA).

Fig. 1. Location of sample sites and representative soils.

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The Fe, Mn and Al oxide contents were determined using the dithionite–citrate method (Sherdrick and McKeague, 1975; U.S. Department of Agriculture, 1972). The samples were shaken with a solution of sodium hydrosulphite (0.5 g per gramme of soil) and sodium citrate (0.27 M), and the Fe, Al and Mn concentrations in the extract were determined by ICP-OES as above. Soil mineralogical analysis was performed in a Philips type powder diffractometer fitted with a Philips PW1710 control unit, Vertical Philips PW1820/00 goniometer and FR590 Enraf Nonius generator. The instrument was equipped with a graphite diffracted beam monochromator and copper radiation source [λ (Kα1) = 15,406 Å], operating at 40 kV and 30 mA. The X-Ray powder diffraction pattern (XRPD) was collected by measuring the scintillation response to Cu Kα radiation versus the 2θ value over a 2θ range of 2–65, with a step size of 0.02° and counting time of 4 s per step. The determination was done by the RIR procedure (Reference method Intensity/Radio) using the corundum as reference material (Chung, 1975). The identification and quantification of the crystalline phases were performed using the programme “Match!” Copyright © 2003–2012 Crystal Impact (Putz and Brandenburg, 2003). 2.2. Total heavy metal content The total heavy metal content was extracted with a mixture of HNO3 and HCl (1:3 v/v) in Teflon reactors under 10 bar, 180 °C and 35 min as operational conditions of the microwave oven. The concentration in the extracts was determined by ICP-OES as above. 2.3. Sequential heavy metal extraction experiments Previous studies have shown that soil oxides have a high affinity for heavy metal retention, although it was not possible to find any differences between the crystalline and amorphous oxides (Cerqueira et al., 2012; Covelo et al., 2007a,b). Shuman's (1979, 1985) procedure was selected, with the modifications suggested by Fabrizio de Iorio (2010) in the method of Chao and Zhou (1983), since they proposed the solution of amorphous iron oxides. The heavy metal content was determined by ICP-OES in all of the extractions associated with the soil fractions in order to identify how it was distributed amongst the soil components. Table 1 shows the steps and conditions of the sequential extraction

carried out. The results will help in determining the availability of the metal in the short or medium term. Apart from the metals in the soil solution, the exchangeable fraction (stage 1 of sequential extraction) contains the most mobile metals, and generally those that give rise to toxicity problems, as they can be easily released as ions (Roy et al., 2004) and therefore be in a available form. All of the soil components contribute to this fraction; for instance, the organic matter decisively influences the mobility of the metals, as it provides ligands to the soil solution that can form soluble complexes and therefore increase the mobility of metals mainly when conditions are highly acidic. Also, despite the fact that Fe and Mn oxides are relatively stable components, they may experience alterations and be transformed into mobile phases under certain conditions, such as high concentrations of Cu and strongly acidic environments, which may dissolve the Fe and Mn oxides and release the heavy metals associated with them, increasing their mobility and bioavailability (Yu et al., 2004). 2.4. Time Of Flight Secondary Ion Mass Spectrometry (TOF-SIMS) Time of flight secondary ion mass spectrometry (TOF-SIMS IV instrument from Ion-Tof GmbH) was used to investigate the elemental and molecular structure of the samples, and to better understand the chemical composition, location and relative abundance of the species present at the surface of the sample. The secondary ions collected and represented in the mass spectrum can be attributed to complete molecules, large fragments of molecules that have only lost functional groups. During the TOF-SIMS experiment the sample was bombarded with a pulsed bismuth ion beam. The secondary ions generated were extracted at a voltage of 10 kV, and their time of flight from the sample to the detector was measured in a reflection mass spectrometer. The analysis conditions for this study were 25 keV pulsed Bi3+ beam at 45° incidence, rastered over 304 × 304 μm2 at a squarepixel density of 256 × 256, and 50 accumulative scans in each analysed area. The operating pressure in the main chamber was 10–10 mbar. An Electron Flood Gun (low energy electrons) was used to compensate the surface charge build-up process during the experiment. Positive secondary ion mass spectra were acquired over a mass range from m/z = 0 to m/z = 1000. Negative ion TOF-SIMS spectra were not considered in this study.

Table 1 Operating conditions used in sequential extraction procedure. Stage

Fraction/Bound to

Reagents

Volume

Conditions

1

Exchangeable

Mg(NO3)2 1 M pH 7

25 mL

2

Organic matter

0.7 M NaClO pH 8.5

15 mL

3

Mn oxides

0.1 M NH2OH∙HCl pH 2

25 mL

4

Amorphous Fe oxides

0.25 M NH2OH·HCl + 0.25 M HCl

25 mL

5

Crystalline Fe oxides

0.2 M (NH4)2C2O4 + 0.1 M H2C2O4 + 0.1 M citricacid

25 mL

6

Residual fraction

HNO3–HCl (1:3)

9 mL

1. 6 g soil; 2 h agitation; centrifugation 6000 rpm (10 min); supernatant decantation. 2. Bidistilled water washing (15 mL–30 min); centrifugation 6000 rpm (10 min). 3. Washed added to supernatant. Remaining fraction: 1. Bath 100 °C (30 min); centrifugation 6000 rpm (10 min); supernatant decantation. 2. Bidistilled water washing (15 mL–30 min); centrifugation 6000 rpm (10 min). 3. Washed added to supernatant. 4. 0.5 mm sieving residue. Remaining fraction: 1. 30 min agitation; centrifugation 6000 rpm (10 min); supernatant decantation. 2. Bidistilled water washing (25 mL–3 min); centrifugation 6000 rpm (10 min). 3. Washed added to supernatant. Remaining fraction: 1. Bath 50 °C and agitation (30 min); centrifugation 6000 rpm (10 min). 2. Bidistilled water washing (25 mL–3 min); centrifugation 6000 rpm (10 min). 3. Washed added to supernatant. Remaining fraction: 1. Bath 100 °C (30 min); centrifugation 6000 rpm (10 min). 2. Supernatant decantation. 1. 0.2 g remaining fraction: digestion in microwave oven.

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The mass resolution (m/Δm) of the secondary ion peaks in the positive spectra was typically between 3600 and 6000. Before further analysis, + + + the positive spectra were calibrated using CH+ 3 , C2H3 , C3H5 , and C7H7 ions. To obtain two-dimensional imaging (chemical surface maps), polyatomic bismuth projectiles (Bi3+) were focused onto the surface in a rastered mode. The intensities detected for secondary ion signals were colour-coded according to a scale. The chemical maps produced by TOF-SIMS represent the ions that reached the detector, rather than the ions that were present on the surface. Although the intensities cannot be used to derive absolute surface concentrations, as each solid has its own ability to release ions, the chemical maps are very useful to indicate the relative surface abundance and how it changes depending on the time or sample treatment. TOF-SIMS analysis was performed using a TOF-SIMS IV from IONTOF GmbH of Münster, Germany at the Nanotechnology and Surface Analysis Service (CACTI, University of Vigo). Al, Si, Mn, Fe, Si, Al and C3H+ 5 (organic matter) were chosen as being representative of the main soil components, and Cr, Cu, Mn, Ni, Pb and Zn as contaminants (Cerqueira et al., 2011). 2.5. Field Emission Scanning Electron Microscopy (FE-SEM) The morphology, structural distribution and particle chemical composition of samples containing ultrafine particles and minerals (crystalline and/or amorphous) were investigated using a JEOL JSM6700 f plus FE-SEM with charge compensation for all applications in both conductive and non-conductive samples. The FE-SEM was equipped with an Energy Dispersive Spectrometer (EDS), and the mineral identification was made on the basis of morphology and grain composition using both secondary electron and back-scattered electron modes (Cerqueira et al., 2012). Samples were set on a standard aluminium slide with carbon adhesive, coating them with layers of carbon for 20 nm thick. EDS spectra were recorded in the FE-SEM image mode. 2.6. Statistical analysis The data obtained in the analytical determinations were analysed with the statistical program IBM-SPSS Statistics 19 (SPSS, Inc., Chicago,

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IL). The results shown for of the soil analyses are the average and the standard deviation of three determinations and they are expressed on a dry material basis. We applied the Kolmogorov–Smirnov test to check the normality of the data, and the Levene test for homogeneity of variances. 3. Results and discussion 3.1. Soil characteristics The characteristics of the soils that were studied revealed significant differences in terms of the components and properties that most influence the retention of heavy metals (Table 2). The pH in H2O varied between 4.38 (T1) and 3.63 (T2), while the pH in KCl varied between 3.88 (T1) and 3.17 (T2). These extremely acidic conditions favour the mobility and availability of heavy metals in the soil (Cerqueira et al., 2011; Vega et al., 2005). The total organic carbon content in T1 is 59.60 g kg−1 which indicates that the percentage of organic matter in T1 (11.92%) is very high with respect to the other soils, where it does not exceed 3%. It would be expected that except for T1, only a small part of the heavy metals are associated or fixed in the organic matter. However, care should be taken as organic matter plays an important role in Cu sorption (Vega et al., 2005, 2010), and tends to form insoluble complexes with Cu, as well as soluble complexes that can migrate throughout the profile (Schnitzer and Khan, 1978). The Fe, Al and Mn soil concentrations are shown in Table 2, expressed as oxide contents. The Mn oxide content is very low (b 0.32 mg kg−1), and the highest content in soil T1. The Al oxides vary between 22.81 mg kg−1 in T1 and 3.68 mg kg−1 in T2. The highest oxide content corresponds to Fe oxides in all of the soils, with values between 38.48 mg kg−1 (T5) and 139.63 mg kg−1 (T1). This last figure is not significantly different from its concentration in soil T2. In T3, T4 and T5 the amount of Fe oxides is relatively lower than in T2 and T1, mainly in T4 and T5. However, the content of Al and Mn oxides is higher in these three soils than in T2. The ECEC varies between 1.96 cmol (+) kg − 1 (T5) and 6.33 cmol(+) kg−1 (T4). The high content of exchangeable Al in all of the soils (from 1.45 cmol(+) kg−1 in T5 and 5.15 cmol(+) kg−1 in T4)

Table 2 Soil characteristics. Properties/Soil pHH2O pHKCl TOC FeOx MnOx AlOx ECEC Exchangeable

Sand Silt Clay Mineralogical analysis

Units

T1

T2 a

g kg−1 g kg−1

cmol(+) kg−1 Na+ K+ Ca2+ Mg2+ Al3+ %

Quartz Clinochlore Mg hornblende Fe Hydronium jarosite Albite Biotite Goethite Hematite Kaolinite Gibbsite

4.38 ± 0.03 3.88a ± 0.03 59.60a ± 0.13 139.63a ± 2.96 0.32a ± 0.01 22.81a ± 1.77 43.03e ± 4.16 1.89b ± 0.08 1.36d ± 0.15 1.79d ± 0.20 0.64e ± 0.05 37.35d ± 3.85 65.45b ± 0.54 21.59b ± 0.21 12.96b ± 0.12 48b ± 0.7 21a ± 0.3 15b ± 0.3 – 15c ± 0.2 – – – – –

T3 c

3.63 ± 0.01 3.17d ± 0.01 16.19bc ± 0.87 136.80a ± 13.68 0.03c ± 0.00 3.68c ± 0.66 123.64b ± 5.20 5.59a ± 1.92 9.66a ± 1.76 6.35b ± 0.64 5.49c ± 0.12 96.56b ± 2.48 71.83a ± 0.89 20.89b ± 0.23 7.28d ± 0.05 33d ± 0.4 9d ± 0.1 11c ± 0.2 – 23a ± 0.4 23a ± 0.4 – – – –

T4 b

4.24 ± 0.00 3.56b ± 0.01 12.94c ± 0.40 85.03b ± 8.10 0.19b ± 0.02 5.22c ± 0.59 187.97a ± 0.93 2.94b ± 0.34 4.98b ± 0.17 55.25a ± 1.66 17.10a ± 0.26 107.69a ± 2.73 63.31bc ± 0.95 25.09a ± 0.35 11.60c ± 0.18 22e ± 0.4 18b ± 0.6 38a ± 0.6 – 23a ± 0.5 – – – – –

T5 c

3.84 ± 0.01 3.39c ± 0.01 16.94b ± 0.50 44.42c ± 7.11 0.29a ± 0.06 7.73bc ± 1.48 95.10c ± 0.94 2.33b ± 0.22 3.10c ± 0.07 4.67c ± 0.08 7.76b ± 0.14 77.25c ± 0.60 62.23c ± 0.82 26.65a ± 0.42 11.12c ± 0.18 39c ± 0.5 14c ± 0.2 – – 20b ± 0.4 10c ± 0.1 – – 16a ± 0.2 –

3.78c ± 0.01 3.22d ± 0.01 14.74bc ± 0.53 38.48c ± 0.65 0.15b ± 0.01 10.66b ± 0.27 53.23d ± 0.70 1.46b ± 0.28 4.32bc ± 0.16 5.41bc ± 0.61 2.68d ± 0.03 39.36d ± 1.08 70.03a ± 0.89 14.38c ± 0.21 15.59a ± 0.36 51a ± 0.7 – – 5ª ± 0.2 – 12b ± 0.3 5ª ± 0.3 3ª ± 0.4 14b ± 0.4 10a ± 0.2

TOC: Total organic carbon, FeOx: Iron oxides, MnOx: Manganese oxides, AlOx: Aluminium oxides, ECEC: Effective cation exchange capacity, Mg hornblende Fe: magnesium hornblende ferrian. For each parameter, values followed by different letters differ significantly with P b 0.05.

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can decisively affect the mobility of heavy metals, since reactions involving exchangeable Al3+ on permanent charge sites in silicate clays are important in pH buffering (Bloom et al., 2005). The hydrolysis of Al3+ in solution produces H+ ions and the acidity increases. The soil with the highest percentage of clay is T5 (15.59%), which is from the settling pond, while the soil with the lowest percentage of clay is T2 (7.28%). Quartz is the main mineral found in all of the soils except for T3, which has a higher content of ferrian magnesiohornblende. Minespoil soils (T1, T2 and T3) also contain clinochlore, ferrian magnesiohornblende and albite. The runoff soil from washing fines (T4) and the settling pond (T5) are the only ones with kaolinite and gibbsite. T5 also contains jarosite, hematite and goethite. The results found are in agreement with those of Cerqueira et al. (2012), they found that these minerals are not present in other soils because the vegetation growing on them is older and influences the transformation of the minerals (Cerqueira et al., 2012). 3.2. Heavy metal concentrations in mine soils (mg kg−1) The total concentrations of Cr, Cu, Mn, Ni, Pb and Zn are shown in Table 3. They are detected in all the studied soils, and the highest concentration is of Mn in T2, T3, T4 and T5, and Cu in T1. The Cr content is higher than the Zn content in T1, T2 and T3, while the opposite occurs in T4 and T5. Ni and Pb have the lowest contents in all soils, not exceeding 51 and 25 mg kg−1, respectively. In summary, the metal concentration sequences for each soil are: T1: Cu N Mn N Cr N Zn N Ni N Pb; T2 and T3: Mn N Cu N Cr N Zn N Ni N Pb; T4 and T5: Mn N Cu N Zn N Cr N Ni N Pb. T1 has the highest concentration of Cu (2924 mg kg− 1), Mn (1194 mg kg−1), Cr (180.95 mg kg−1) and Pb (24.30 mg kg−1), and although it contains slightly more Pb than T4 there are no significant differences. T4 has the highest concentration of Ni (50.84 mg kg−1) and Zn (125.86 mg kg−1). It is important to note that all of the total Cu contents exceed the intervention limits defined in reference guides such as ICRCL (1987) and Macías and Calvo (2009). 3.3. Heavy metal distributions in mine soils (mg kg−1) As previously indicated, a sequential chemical extraction was carried out in order to identify the distribution of the heavy metals in the different geochemical soil fractions. Table 4 shows the content of Cr, Cu, Mn, Ni, Pb and Zn associated with the fractions of each soil. The majority of the Cr is in the residual soil fraction (between 82% of the total in T1 and 95% in T5). The exchangeable fraction, the concentration associated with the organic matter and with the Mn oxides, represent less than 0.04, 0.3 and 0.16% of the total content, respectively. These results indicate that Cr is strongly fixed in these soils and its mobility is limited. As previously stated, Cu is the most abundant metal in T1 and the second most abundant in all of the other soils (Table 4). The proportion of the total Cu associated with the residual soil fraction is also the highest, varying between 30 and 70%. Crystalline Fe oxides retain between 20% (T5) and 38% (T3) of the total Cu, and the amorphous oxides

less than 15% of the total (except in T1, 27%). T1 is the soil with the highest content of Cu associated with Fe oxides (more than 49% of the Cu is associated with both crystalline and amorphous Fe oxides). In the other soils, the amount of Cu found in these Fe fractions varies between 23% (T2) and 43% (T3). This fact shows the high affinity of Cu for these oxides, as previously indicated in Cerqueira et al. (2011) and Vega et al. (2010). Despite the low Mn oxide contents in the soils (Table 2), the proportion of Cu associated with this fraction ranges between 2% and 10% (except in T2, where it does not exceed 1%, Table 4). Covelo et al. (2007a,b) have also indicated the high affinity of Cu for these oxides regardless of their low content in different soils. The fraction of Cu associated with the organic matter is less than 1% of the total in the three tailing soils (T1, T2 and T3), while in T4 and T5 it is 5% and 2%, respectively. Although T1 is the soil with the highest organic matter content (Table 2), the Cu associated with this fraction is the lowest (Table 4). This fact is attributable to the soluble complexes that organic matter can form with Cu and they contribute more to the exchangeable fraction (F1) than to the organic matter fraction (F2). The exchangeable fraction of Cu in T4 represents more than 10% of the total content, while in T1, T2, T3 and T5 it ranges between 1% and 2% (Table 4). In absolute terms, Cu is the most available metal, and the one with the greatest mobility in these soils, together with Mn in T4 and T5 and Ni in T3. As previously indicated, Mn is the most abundant metal in the studied soils (except T1) (Table 4). The proportion associated with the residual fraction ranges between 60% (T4) and 95% (T2), while the proportion associated with the crystalline Fe oxides ranges from 2% (T2) to 16% (T4), and from 5% (T3) to 15% (T4) for the proportion that is linked to the amorphous Fe oxides (in T2 it is less than 1%.). The low stability of the Mn complexes with organic matter (KabataPendias, 2010) appears to be the reason why the amount of Mn associated with the organic matter fraction is lower than the amount associated with the exchangeable fraction of the soils (Table 4). These results indicate that Mn is the metal with the highest mobility and therefore, together with Cu, the one that is most available. Like the other metals, most of the Ni is associated with the residual fraction of the soils, ranging from 77% (T3) to 95% (T2). The fraction associated with crystalline Fe oxides is less than 11%, and the fraction linked with the amorphous Fe oxides is even lower (0.7% (T2 and T5) to 5% (T4)). The proportion of Ni related to Mn oxides, the organic matter and exchangeable fraction is lower than 1% of the total content (except in T3, where it is less than 2%, 1.5% and 6.5%, respectively, and the exchangeable fraction of T4, 2.6%; Table 4). These results are similar to those obtained by Kashem et al. (2007), who concluded that Ni is strongly retained in the residual fraction of contaminated soils. The amount of total Pb in the residual fraction varies from 47% (T4) to 68% (T1). The proportion bound to crystalline and amorphous Fe oxides in the soil fractions varies between 10% and 20% (Table 4). This comparatively high proportion is in line with the results from different studies that indicate that the high affinity of Pb for Fe oxides makes it possible for them to act as long-term sinks for Pb (Covelo et al., 2007a, b, 2008). Vega et al. (2010) also indicated that manganese oxides have a high retention capacity for Pb. In the current study, Pb is the metal that shows the greatest affinity for Mn oxides. After the sequential extraction procedure, the proportion related to Mn oxides was lower in

Table 3 Total concentrations of heavy metals in soils (mg kg−1). Metal/Soil

T1

Cr Cu Mn Ni Pb Zn

180.95a,c 2924.00a,a 1194.11a,b 31.55b,e 24.30a,f 109.33a,d

T2 ± ± ± ± ± ±

0.08 45.95 10.75 1.44 0.83 2.83

105.57c,c 382.35b,b 701.64c,a 20.39c,e 18.85b,e 79.18b,d

T3 ± ± ± ± ± ±

2.64 19.63 25.68 1.65 1.07 2.24

117.52b,c 531.75c,b 1014.19b,a 24.05c,e 13.57c,e 71.13c,d

T4 ± ± ± ± ± ±

2.25 20.38 7.33 2.44 2.54 2.36

102.22c,d 280.66d,b 620.41c,a 50.84a,e 24.00a,f 125.86a,c

T5 ± ± ± ± ± ±

1.34 5.69 10.59 0.31 3.24 1.41

78.10d,d 104.40e,b 409.86d,a 48.38a,e 11.75c.f 86.05c,c

± ± ± ± ± ±

2.99 5.18 12.91 4.41 1.50 4.24

In each column, values followed by the different italic letter differ significantly (P b 0.05); In each row, values followed by the different bold letter differ significantly (P b 0.05).

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Table 4 Heavy metals content in each fraction from each mine soil (mg kg−1). Soil

Fraction

Cr

T1

F1 F2 F3 F4 F5 F6 SF F1 F2 F3 F4 F5 F6 SF F1 F2 F3 F4 F5 F6 SF F1 F2 F3 F4 F5 F6 SF F1 F2 F3 F4 F5 F6 SF

0.03d 0.01d 0.22d 2.68c 29.70b 151.07a 183.71 0.04c 0.01c 0.09c 0.82c 7.39b 87.75a 96.10 0.01d 0.01d 0.13d 1.80c 10.95b 94.43a 107.33 0.03c 0.02c 0.16c 2.64c 9.73b 83.40a 95.98 0.01b 0.01b 0.08b 0.34b 3.40b 74.17a 78.00

T2

T3

T4

T5

Cu ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.01 0.00 0.04 0.28 1.95 3.44 3.96 0.01 0.00 0.00 0.08 1.85 1.77 2.56 0.00 0.00 0.01 0.06 0.59 9.65 9.67 0.00 0.00 0.01 0.26 0.91 8.66 8.72 0.00 0.00 0.01 0.01 0.42 5.39 5.41

58.26c 24.60c 108.07c 683.78b 580.25b 1093.43a 2548.39 7.62c 2.44c 3.84c 10.23c 97.10b 286.25a 407.48 10.60c 3.74c 11.49c 34.77b 224.06b 301.79a 586.45 25.99c 12.75d 25.06c 40.74b 72.87a 77.54a 254.96 1.22e 2.09e 6.83d 11.84c 20.07b 54.30a 96.35

Mn ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.67 0.39 5.15 54.53 25.40 180.92 190.73 0.25 0.16 0.21 1.20 6.25 31.47 32.11 0.35 0.44 0.31 1.83 20.00 25.16 32.20 0.33 0.18 1.04 2.29 9.94 6.17 11.97 0.08 0.07 0.21 0.97 3.18 3.40 4.77

Ni

8.28d 6.84d 24.43cd 85.79bc 159.21b 1104.90a 1389.45 2.34b 1.59b 1.14b 4.62b 13.42b 549.24a 572.34 26.04c 13.59c 26.73c 41.10b 43.84b 664.37a 815.66 22.38c 2.41c 14.54c 68.23b 74.04b 282.91a 464.51 13.22bc 1.53c 3.00bc 31.17b 30.01bc 376.24a 455.17

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.15 0.61 1.41 1.79 9.29 101.74 102.19 0.33 0.01 0.14 0.38 1.93 56.21 56.24 1.54 2.48 5.01 1.41 2.81 24.97 25.82 0.09 0.48 3.16 2.90 4.53 39.64 40.13 0.34 0.17 0.25 1.16 2.77 39.20 39.32

0.27c 0.08c 0.18c 0.42b 3.41c 28.70a 33.06 0.21c 0.07c 0.07c 0.14c 0.66b 19.67a 20.81 1.30b 0.31b 0.46b 1.05b 1.67b 16.30a 21.08 1.27bc 0.20c 0.23c 0.81c 3.31b 42.32a 48.14 0.24b 0.09b 0.10b 0.40b 1.88b 52.00a 54.71

Pb ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.01 0.02 0.42 0.03 0.19 0.76 0.79 0.00 0.02 0.01 0.03 0.17 0.71 0.73 0.02 0.07 0.05 0.01 0.07 3.07 3.07 0.01 0.01 0.02 0.05 0.29 4.35 4.36 0.01 0.00 0.01 0.06 0.13 0.94 0.95

0.13c ± 0.05c ± 0.12c ± 3.21b ± 3.55b ± 15.27a ± 22.34 ± 0.07c ± 0.03c ± 0.42c ± 1.93b ± 3.20b ± 11.00a ± 16.65 ± 0.10d ± 0.03d ± 1.08cd ± 1.74bc ± 2.97b ± 11.00a ± 16.92 ± 0.24d ± 0.39d ± 1.73c ± 4.15b ± 3.42b ± 8.82a ± 18.75 ± 0.14d ± 0.34d ± 1.90c ± 3.06b ± 1.38c ± 6.70a ± 13.51 ±

Zn 0.01 0.01 0.10 0.40 0.32 1.03 1.15 0.01 0.01 0.24 0.35 0.68 2.12 2.27 0.02 0.01 0.15 0.09 0.33 1.41 1.46 0.02 0.05 0.21 0.59 0.24 1.03 1.23 0.02 0.01 0.05 0.20 0.24 0.78 0.84

0.35c 0.67c 0.53c 0.59c 8.90b 97.75a 108.80 0.23d 0.32d 4.95b 1.10d 2.31c 71.75a 80.66 0.91b 0.37b 2.77b 3.55b 3.20b 59.89a 70.69 0.79bc 0.07c 2.20bc 1.53bc 4.76b 126.55a 135.99 0.22b 0.04b 1.32b 0.46b 1.50b 100.00a 103.54

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.03 0.02 012 0.08 0.91 3.18 3.31 0.01 0.05 1.06 0.54 0.49 1.06 1.67 0.03 0.12 0.49 0.56 0.10 6.64 6.68 0.04 0.04 0.27 0.36 0.77 8.11 8.16 0.03 0.00 0.33 0.02 0.28 9.17 9.18

F1: Exchangeable fraction. F2: Organic matter fraction. F3: Mn oxide fraction. F4: Amorphous Fe oxide fraction. F5: Crystalline Fe oxide fraction F6: Residual fraction. SF: Sum fractions. For each metal, in each soil values followed by different letters differ significantly with P b 0.05.

the minespoil soils (T1: 0.5%, T2: 5% and T3: 6%) than in T4 (9%) and T5 (7%). In line with previous results, the residual fraction of the soils contains the highest proportion of Zn (between 84%: T3 and 97%: T5) indicating that most of Zn is strongly retained in the soil. Therefore, the concentration in the other extracts related to Mn and crystalline and amorphous Fe oxides is lower than 6%, 8% and 1.5%, respectively. The exchangeable fraction and the fraction bound to organic matter are lower than 1.5%. In summary, the residual fraction of the studied soils contains the highest proportion of heavy metals. The fraction associated with crystalline Fe and amorphous Fe oxides is generally high, due to the large amount of Fe oxides in all of the soils, and their fixing capacity (Covelo et al., 2008). The contents of metals associated with the Mn oxide and organic matter fractions are relatively low due to the small quantity of these fractions in these soils, despite their high affinity and sorption capacity for metals (Table 4). Likewise, the total quantity of metals associated with the exchangeable fraction was also low due to the low exchange capacities of these soils. Although the highest heavy metal content is in fractions with limited mobility, care must be taken since the acidic conditions of the soils may cause them to be displaced to more mobile fractions, thereby increasing their mobility and availability. 3.4. TOF-SIMS Analyses of the distribution maps revealed a number of interesting aspects. As an example, we will see those from T1 (tailing) and T5 (settling pond). (Figs. 2 and 3) The intensity bar to the right of each image is indicative of the signal intensity of each ion. Black means no signal or,

therefore, points of minimal or zero presence of the ion. White indicates maximum signal strength and, therefore, points of maximum ion abundance. The bottom left of each image shows the ion corresponding to the intensity distribution map, and below it is the TC value, representing the number of total counts of each ion. The images are overlapped to determine the matching or non-matching rows in the ions distribution on the surface. When ions overlap this may or may not lead to areas in the last image in which only one colour can be seen. In the samples coloured in red, blue and green and in their overlaps, the different combinations of colours indicate the coincidences (or not) in the distribution of the ions on the different surfaces of the soil components. Overlapping the images makes it possible to see the areas where there is a secondary colour produced by the combination of red + green = yellow, red + blue = purple, and green + blue = cyan. The point where the ions coincide produces white areas. Fig. 2 shows the distribution of Al, C3H+ 5 , Si, Fe, and overlapping all the signals with 52Cr, 55Mn, 63Cu and 208Pb, 58Ni, 64Zn in soil T1. The bright yellow and orange colours on the distribution maps corresponding to the ions of C3H+ 5 , Al, Fe and Si confirm the high content in organic matter, Fe and Al oxides and Si minerals such as quartz, albite, ferrian magnesiohornblende and clinochlore (Fig. 2 and Table 2). The orange and bright yellow areas on the distribution map for 63Cu indicate that this is the metal with the highest concentration in this soil, validating the results discussed above. The maps show that 63Cu has the same distribution in terms of spatial intensity of iron (56Fe) and also show good concordance with its distribution. It was found that there was an overlap between the most intense signals of 63Cu and Fe (Fig. 2A). Similar results were obtained by overlapping the distribution maps for Fe (green) and Cu (blue), obtaining cyan signals that indicate their interaction. The distribution throughout the whole sample of violet signals also indicates the

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A

B

52 Fig. 2. T1 soil: A) TOF-SIMS images of Al, C3H+ Cr, 55Mn, 63Cu and 208Pb, 58Ni, 64Zn. B) TOF-SIMS images of Fe, Cu, Mn and Si and images of 5 , Si, Fe, and overlapping all the signals with overlapping of Fe, Si, Cu and Fe, Si, Mn showing the concordance.

interaction between Si and Cu (Fig. 2B). It is also clearly seen, the interaction between C3H+ 5 , Fe and Cu by comparing the corresponding images, the signals for each ion occupy the same positions on the maps. It is possible to deduce that the Cu is associated with organomineral complexes formed between the organic matter and Fe oxides (Fig. 2A).

In Fig. 2B, yellow signals are seen as a result of the overlap between Si (blue) and 55Mn (red), confirming that this metal is mainly associated with the residual fraction of soil T1. The distribution maps for 52Cr, 208Pb and 64Zn show dark colours with bright signals, indicating that their content in the soil is much less than of Cu, and that their distribution

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A

B

52 Fig. 3. T5 soil: A) TOF-SIMS images of Al, C3H+ Cr, 55Mn, 63Cu and 208Pb. B) TOF-SIMS images of Fe, Cr, Cu and Si and images of overlapping of Fe, 5 , Si, Fe, and overlapping all the signals with Cr, Cu and Si, Cr, Cu showing the concordance.

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on the soil component surfaces is more heterogeneous. However, the 58 Ni is distributed throughout the whole sample (Fig. 2A). The signals for these metals coincide with those of Si and Fe, in line with the data discussed above, indicating the higher proportion of these metals associated with Fe oxides and contained in the residual fraction (Table 4). Fig. 3 shows the distribution of Al, C3H+ 5 , Si, Fe, and overlapping all the signals with 52Cr, 55Mn, 63Cu and 208Pb in T5. In this soil, the intensity of the signal for the ions of C3H+ 5 is much lower than in T1. This once again confirms the data shown in Table 2, and also indicates the validity of this technique to evaluate the abundance of specific soil components and their interaction with metals. The Si signal (TOF-SIMS, Fig. 3A) is very intense due to the high content (≈77%, Table 2) of silicate minerals (quartz, kaolinite, and biotite) as indicated by the X-Ray diffraction analysis. The Cu and Mn distribution maps (Fig. 3A) are dark, which means that their concentration in these soils is lower than in others. The overlap of the two sequences of coloured maps shows that the 63Cu (blue) interacts both with the Si (red) (Fig. 3B-1), and Fe (red) (Fig. 3B-2) as there are purple areas over the whole map. However, because of the yellow signals resulting from the overlap the 52Cr (green) only interacts with the Si (red) (Fig. 3B-2). The sequential extraction procedure has also shown that

the Cu is associated to Fe oxides and in the residual fraction and the Cr mainly in the last one. The signals for 208Pb, like those of 63Cu, coincide with those of Fe and Si. In this soil, despite its low organic matter content (Table 2), the signals with the highest intensity of C3H+ 5 coincide with those of Fe, 63Cu and 208Pb, indicating that organomineral complexes are formed which interact with Pb and Cr (Fig. 3A). The results also indicate that image analysis by TOF-SIMS is a good method for complementing and verifying the sequential extraction results. 3.5. FE-SEM FE-SEM with EDS is used to investigate the morphology, structural distribution, and particle chemical composition of samples containing ultrafine particles and minerals (crystalline and/or amorphous). The occurrence of mineral and nanoparticles species is also investigated. The results for T1 and T5 (Figs. 4 and 5) are representative of all the soils studied, T1 shows that part of the Cu mainly interacts with albite, clinochlore and ferrian magnesiohornblende mixed with amorphous Fe oxides (Fig. 4A). Cu was also found on the surface of amorphous Fe oxides, and associated with schwertmannite (Fig. 4B and C). Although jarosite

A

B

C

D

Fig. 4. T1 soil: A) Crystalline minerals albite, clinochlore and ferrian magnesiohornblende, mixed with amorphous Fe oxides with sorbed Cu (points). B) Cu sorbed on amorphous Fe hydroxy compounds associated with schwertmannite. C) Amorphous Fe oxides containing Cu particles. D) Cu, Si and Fe distribution.

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Image analysis by TOF-SIMS and high-resolution microscopy studies is good methods for complementing and verifying the sequential extraction results. Combining them is an effective tool to check the affinity of the soil components for heavy metal cations. Acknowledgements This research was supported by Project EM2013/18 (Xunta de Galicia). F.A. Vega is hired under a Ramón y Cajal contract at the University of Vigo. D. Arenas-Lago is grateful to the Ministry of Science and Innovation and the University of Vigo for the FPI-MICINN. References

Fig. 5. T5 soil: A) Interaction of jarosite crystals with amorphous hidroxypolymers of Al–Cr–Cu–Pb (in white circles). B) Hematite and goethite EDS contain Pb, Cu and Cr.

was not detected in these soils, the fact that schwertmannite was found containing Cu indicates that the jarosite has probably been totally modified due to the higher pH, the better development of the soil and because of the effect of more abundant vegetation in comparison with the other soils. In line with the results obtained by TOF-SIMS, Cu is mainly associated with silicates and Fe oxides, as the distribution map for Cu clearly shows the association between the particles of Cu and Si and Fe (Fig. 4D). A special case is found in soil T5. This soil contains jarosite (Table 2, Fig. 5A), and this mineral retains part of the Pb, Cu and Cr in the form of amorphous hidroxypolymers of Al–Cr–Cu–Pb as the EDS spectra and the images have shown. Similar results are also shown in Cerqueira et al. (2012). The EDS spectra also indicates that nanocrystals of hematite containing Pb, Cr and Cu mixed with goethite are also found on soil T5, as shown in Fig. 5B. These results are in line with those found after the chemical and TOF-SIMS analyses.

4. Conclusions The highest heavy metal content is associated with the least available soil fractions, although acidic conditions may cause them to be rapidly displaced to more mobile positions. Promoting natural and artificial conditions that minimize the soil acidity is advisable. Crystalline and amorphous Fe oxides play an important role in the fixation of the studied metals, especially Cu. The amount of heavy metals associated with soil organic matter and in exchangeable form is very low.

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