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Journal of Geochemical Exploration 119–120 (2012) 32–43

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Heavy metals fractionation and multivariate statistical techniques to evaluate the environmental risk in soils of Huelva Township (SW Iberian Peninsula) Marco Tulio Guillén a, Joaquín Delgado a, Stefano Albanese b, José Miguel Nieto a,⁎, Annamaria Lima b, Benedetto De Vivo b a b

Department of Geology, University of Huelva, 21071 Huelva, Spain Dipartimento di Scienze della Terra, Università di Napoli Federico ll, Via Mezzocannone 8, 80138 Napoli, Italy

a r t i c l e

i n f o

Article history: Received 21 September 2011 Accepted 6 June 2012 Available online 15 June 2012 Keywords: Multivariate statistical analysis BCR-sequential extraction Ecological risk indexes Iberian Pyrite Belt Huelva city

a b s t r a c t The city of Huelva and surrounding areas are affected by several sources of pollution such as acid mine drainage, industrial complexes, urban wastes and agriculture activities that could pose an important environmental risk. For this reason, the modified BCR (three steps) sequential extraction method was applied to evaluate the mobility and bioavailability of the trace elements in 25 representative samples of the study area. The operational scheme of the BCR was classified into three steps: water/acid soluble fraction, reducible and oxidisable fraction. The mobility sequence based on the sum of three first phases was: Cu (82.01%) > Zn (71.14%) > Cd (68.35%) > Ni (50.44%) > Pb (36.39%) > Cr (29.22%) > As (18.82%). Among metals, Cd poses a serious threat to human health and the environment due to the calculated high percentage of mobility. Additionally, multivariate statistical techniques (principal components and cluster analyses) were applied to the chemical results to evaluate the degree of metallic pollution and the levels of association between the variables (metal-metalloids) at the different steps of sequential extraction and to recognise possible sources of potential contamination. The PCA suggests that the study area is influenced by four sources of anthropogenic contributions: acid mine drainage, industrial activities, traffic, and agriculture, aside from the natural sources characteristic of the zone. Calculated environmental risk index reveal a considerable-high ecological risk in the saltmarshes of the Huelva estuary probably related to acid mine drainage and the industrial complexes located in these areas, while in the north sector of Huelva the metallic content is more closer to the natural sources values. The results obtained suggest the need for corrective remediation measures due to the higher accumulation of potentially dangerous metals, which in most cases exceed the limits established by certain legislation. © 2012 Elsevier B.V. All rights reserved.

1. Introduction In recent decades, environmental pollution problems have been a growing interest in the scientific community due to the potentially harmful substances emitted from anthropogenic activity, which can pose a serious hazard to the environment and to the human health. Among the environmental matrices, soils are more easily affected by the negative effect of anthropogenic activities due to their close relationship with the atmosphere and meteoric waters. The toxicity and mobility of heavy metals in soils depend not only on their concentrations, but also on both their associations, and chemical properties, and on some surrounding environmental conditions such as pH, redox potential, and biological action of the roots and the formation of chelates (Thomson and Frederick, 2002). It has also proven that the contents of clays and of organic matter play important roles on the

⁎ Corresponding author. Tel.: + 34 959 219824. E-mail address: [email protected] (J.M. Nieto). 0375-6742/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.gexplo.2012.06.009

behaviour of metals (Otero et al., 1998). For these reasons it is important to recognise the speciation of metals in different fractions of soil to determine their degree of mobility, availability and persistence in the environment. As a matter of fact, soil contamination due to heavy metals and metalloids such as As, Cd, Cr, Cu, Ni, Pb and Zn, represents the source of a severe potential hazard for the ecosystem equilibrium and the health of living beings (Nagajyoti et al., 2010). Dating back to the 1960's, one of the largest industrial complexes in Spain (Punta del Sebo industrial complex) was built around the city of Huelva (SW Iberian Peninsula). A wide range of industrial products related to the fertiliser and copper smelting can be found in this area affecting the quality of the soils, which could pose a serious risk to the health of resident population and to the environment. A previous study detected high amounts of trace elements in the soils of this area and linked their origin to the industrial activity (Guillén et al., 2011). In addition to the industrial activities, other anthropogenic potential sources of toxic elements (acid mine drainage — AMD, urban wastes, and agriculture activities) are seen in the study area (Barba-Brioso et al., 2010). However, to efficiently evaluate the environmental impact

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of heavy metals accumulated in these soils the chemical state in which the elements are present (easily exchangeable ions, metal carbonates, oxides, sulphides, organometallic compounds, ions in crystal lattices of minerals, etc.) needs to be studied (Pérez et al., 2008; Yu et al., 2010). There are several analytical techniques to assess the content and behaviours of some metals in soils/sediments (Rauret et al., 2000; Sahuquillo, et al., 1999, 2003). The sequential extraction method should provide quantitative information on the distribution of the various elements in soils/sediments, and implications for metal mobility and bioavailability potential (Sundaray et al., 2011). Determination of bioavailability has been mostly based on metals concentration in the exchangeable/carbonate soil fraction (Karbassi and Shankar, 2005; Vanek et al., 2005). In recent years, the sequential extraction methods have become the most effective tools to assess the risks generated by metal contamination in soils and sediments. These techniques have been widely applied in environmental geochemical studies aiming to characterise surface sediments (river, lakes and estuaries) and soils (Janoš et al., 2010; Madrid et al., 2007). Furthermore, studies on toxic metal fractionation (Cu, Pb, Fe, Mn and Sn) in urban soils (Hursthouse et al., 2004; Madrid et al., 2002, 2004) have discriminated natural and anthropogenic sources. For these reasons, the objectives of this study were to investigate the mobility of the most harmful elements (As, Cd, Cu, Cr, Hg, Ni, Pb and Zn) in the soils of Huelva municipality, by applying a modified European Community Bureau of Reference, (BCR) sequential extraction scheme, and to determine the mineral reactivity occurring in the different fractions of the soils. Additionally the application of statistical techniques besides ecological indexes, have allowed us to establish monitoring strategies to support future action/remediation plans on the study area. 2. Materials and methods 2.1. Area of study The Huelva municipality is situated in the southwest of the Iberian Peninsula (Fig. 1). The soils in the area of study are mainly affected by the production activity in two important industrial complexes which could release a number of contaminants into the surrounding environment (e.g. inorganic acids, fossil fuel combustion residues, detergents, metallurgic product residues, animal food and fertilizers among others). Furthermore, contaminants released to the environment as atmospheric emissions, can affect surface soils as a result of atmospheric fall out. Besides industries, the intense agriculture activity and urban waste management are other potential sources of pollutants. On the other hand, Huelva estuary, where the city of Huelva is located, has been historically affected by acid mine drainage (AMD) generated in the inner zones of the basin, so it is considered one of the most contaminated estuaries in the world (Nieto et al., 2007; Sarmiento et al., 2009). Geologically, the study area is characterised by the presence of recent Holocene sediments overlying a siliciclastic Tertiary succession whose ages range from Miocene to Pliocene (Fig. 1). The more recent Holocene sediments are mainly constituted by clay and sand as is typical of sediments in estuarine systems; the Tertiary succession, deposited in marine and continental environments (Civis et al., 1987) consists of a basal gray-blue marlstone corresponding to the Gibraleon Clay Formation (GCF) and of a upper fine sands and gray-yellow silt corresponding to the Huelva Formation (López-Gónzalez et al., 2006). 2.2. Sample collection and pre-treatment During the fall of 2007, 25 from a total of 150 soil samples were determined using a modified BCR‐sequential extraction procedure. Soil samples were obtained from industrial sites, urban and periurban areas according to Guillén et al. (2011). The operation included parks, open spaces, mud flats, farmlands and industrial areas. From the 25

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soil samples studied in this work, 2 were recollected in agricultural zones, 6 from saltmarshes areas, 8 from sites close to industrial activities and the rest were sampled in urban areas. Due to the heterogeneous nature of the samples in this work, we do not discriminate whether or not they are sediments or soils (Fernández-Caliani, 2012). The location of each sampling point (Fig. 1) was chosen to be representative of areas affected by industrial activity or/and influenced by the chemistry of some effluent of the Odiel and Tinto rivers, which carrying an important heavy metals load (Sarmiento et al., 2009). Approximately 3 kg of soil were collected between 0 and 15 cm depth, by combining five individual specimens collected at the centre and vertices of a 2 m wide cross. The samples were stored in polyethylene bags following internationally adopted methods (Salminen et al., 1998). In the laboratory, the samples were ground, oven-dried (40 °C) until completely dry, homogenised, sieved (b2 mm), and stored in polyethylene containers. Because of the strong association of trace elements with fine-grained soil components, we used the b63 μm soil fraction for the sequential extraction and total acid digestion methods (Cuong and Obbard, 2006). Based on previous results (Guillén et al., 2011) 25 samples from this study were selected to evaluate mobility and availability of trace elements in soil by a BCR‐sequential extraction combining with statistical analyses. Additionally, the metal concentrations obtained by Guillén et al. (2011) were used for mapping the distribution of the potential ecological risk in the study area. 2.3. Reagents Double-deionised water (18.2 μΩ) was used for preparing the solutions and to clean the instrumental. Analytical grade acetic acid (Qemical©), hydroxylamine hydrochloride (Merck©), hydrogen peroxide (Panreac©) and ammonium acetate (Panreac©) were used in the sequential extraction procedure. Suprapure hydrochloric and nitric acids (Merck©) were used for the sequential extraction and to extract the chemical elements for measure the pseudo-total content. All glassware and plastic material used were first treated with a 10% (v/v) suprapure nitric acid solution for 24 h and rinsed with distilled water before use. 2.4. Procedures 2.4.1. Chemical analysis and quality control Chemical analyses of soil samples (Table 1) were carried out, at Acme Analytical Laboratories Ltd (Vancouver, Canada) accredited under ISO 9002, by ICP-MS and ICP-AES. Using Acme's Group 1F-MS package (ultratrace aqua regia digestion) a total of seven elements (As, Cd, Cr, Co, Ni, Pb and Zn) were reported for a 15 g sample analysed by ICP-emission spectrometry following an aqua regia digestion. To ensure the reproducibility of the results, the analysis sequence consisted of calibration of standards, blind standard solutions analysis as an unknown (quality control solutions), method blanks and one certified reference (STD-SD7). In addition, a total of 13 replicates were used. Accuracy was calculated on Acme's in-house reference material, (STD-SD7). The results obtained for extractable concentrations in the sequential extraction procedure (SEP) were compared with indicative or certified values, following the procedures for the standard reference material (BCR-701). They showed that certified (or indicative) and obtained values were not significantly different. The recovery rates (Eq. (1)) for heavy metals in the standard reference material were between 70 and 104% (Table 2). % Recovery ¼ ðF1 þ F2 þ F3 þ R=Pseudo−total concentrationÞx100 ð1Þ Where: Pseudo-total concentration refers to the values obtained by Acme Laboratories and F1, F2, F3 and R (residual fraction)

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M.T. Guillén et al. / Journal of Geochemical Exploration 119–120 (2012) 32–43 145000

150000

155000

4140000

4140000

Iberian Peninsula Tinto-Odiel Basin

7

117

HUELVA

16

"Tejar-Colmenilla"

19

15 27

25

44 4135000

121

"Tartessos"

"Fórtiz"

4135000

124

43

219 Seamounts

69

190 Old mineral port

HUELVA

144

e

r

91

R

iv

97

in

"Punta del Sebo" Industrial complex

134

Legend Sampling Points Background Profiles

107

Phosphogypsum Stack Industrial Complexes Towns Seamounts

4125000

135

136

PALOS DE LA FRONTERA

to

90 146 145

163

4130000

160

149

4125000

182"El Rincón"

184

T

4130000

Huelva Port

r ive el R Odi

77 76

Recreational areas PUNTA UMBRIA

Saltmarshes (Quaternary) Blue Marlstones (Tertiary)

Km 0 145000

1

Geology

2 150000

155000

Fig. 1. Location of the 25 sites in Huelva Township and geology of study area.

correspond to the concentrations obtained in the each steps of sequential extraction procedure. 2.4.2. Sequential extraction procedure (SEP) The SEP, based on an improved version of the initial three-step BCR scheme (Quevauviller et al., 1989), was applied to evaluate the metal fractionation in the soils of the Huelva municipality. The procedure is summarised below and full details are reported elsewhere (Rauret et al., 1999; Sahuquillo et al., 1999).

Extracts obtained from each phase were analyzed by optical emission spectroscopy (model IVON Jovin ULTIMA ll) in the Central Service I + D of Huelva University. The summary of the method steps are: Step 1. (Water/acid soluble and exchangeable fraction/carbonate included, F1): 20 ml of HC2H3O2 0.11 M solutions was added to 0.5 g of accurately weighed sample in 50 ml polyethylene centrifuge tubes, and shaken for 16 h at room temperature. The

M.T. Guillén et al. / Journal of Geochemical Exploration 119–120 (2012) 32–43 Table 1 Pseudo-total concentrations of the main metals and metalloids (presented in mg·kg–1) in soils of the Huelva Township as well as the background values and statistical parameters obtained by Guillén et al. (2011) from the Huelva municipal area. Sample

As

Cd

Cu

Cr

Cu

Ni

Pb

Zn

7 43 69 76 77 90 91 97 107 117 121 124 134 135 136 144 145 146 149 160 163 182 184 190 219 Min Max Mean Median St dev Kurtosis Skewness Bkg

8.90 10.6 211 2066 212 504 151 701 417 242 276 218 200 294 370 17 126 109 201 162 69.2 67.6 22.6 64.8 74.2 8.90 2066 272 200 401 0.41 1.09 8.45 ± 1.15

0.42 0.27 0.74 1.07 0.28 0.97 1.96 1.07 1.18 3.46 0.16 1.21 15.9 3.60 18.4 0.52 2.37 4.74 1.26 1.67 1.18 2.32 0.30 0.85 0.20 0.16 18.4 2.64 1.18 4.44 8.91 3.14 0.13 ± 0.02

5.40 8.60 94.5 110 18.6 14.9 39.3 13.2 124 22.6 8.00 19.1 18.0 18.1 38.7 5.40 8.60 17.4 17.2 12.8 37.7 45.1 13.1 15.8 5.40 5.40 124 29.3 17.4 32.4 2.16 1.83 9.72 ± 1.39

24.2 40.0 19.8 6.5 33.1 51.1 42.7 86.9 51.0 32.7 43.4 25.5 61.4 14.5 112 31.8 16.3 49.0 37.8 22.7 49.4 55.2 40.3 35.6 18.2 6.50 112 40.0 37.8 22.5 7.87 2.49 45.2 ± 10.2

1945 386 1284 269 734 996 1180 1011 1807 628 764 4225 10,000 710 10,000 2003 1053 1890 748 967 980 1288 623 601 599 269 10,000 1868 996 2521 23.2 4.76 17.6 ± 2.76

11.3 20.1 29.6 8.50 26.4 26.8 40.7 25.6 48.7 23.7 18.1 17.7 12.5 6.00 37.7 22.4 7.50 19.9 24.0 10.6 12.7 18.1 16.9 16.8 10.9 6.00 48.7 20.5 18.1 10.3 6.41 1.79 24.2 ± 3.69

88.4 68.6 168 5469 306 490 190 651 594 176 297 229 611 684 1225 83.6 403 282 188 261 254 487 97.0 295 144 68.6 5469 550 282 1036 5.98 2.10 26.8 ± 10.6

216 74.6 1553 414 522 735 1101 983 3513 1857 321 708 1528 1346 4011 1049 583 1306 545 605 2242 4707 497 828 246 74.6 4707 1259 828 1176 11.1 3.23 47.2 ± 3.25

Bkg: Background values established by Guillén et al. (2011).

extracts were then separated from the residue by centrifuging for 20 min at 3000 rpm, decanted into polyethylene containers and stored at 4 °C for analysis. The residues were washed with 10 ml de-ionised water then shaken for 15 min and centrifuged. Step 2. (Reducible fraction, F2): 20 mL of 0.1 M NH2OHHCl (adjusted to pH of around 2 by adding HNO3) were added to residues from Step 1. The extraction was performed as described in Step 1. Step 3. (Oxidisable fraction, F3): 10 ml of 8.8 M H2O2 (pH 2.0–3.0) was added drop-by-drop to the residues from Step 2. The tubes were covered and the contents were digested for 1 h at room temperature and 1 h at 85 °C in a water bath. Volume was reduced to around 2–3 ml by evaporation. Step 3 was performed twice. After repeating, 25 ml of 1 M NH4C2H3O2 (adjusted to pH around 2 by adding HNO3) was added to the cool residues, which were separated and rinsed as described in Step 1.

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Step 4. (Residual fraction, R): The residue from Step 3 was treated using the procedure to determine the pseudo-total trace elements content by aqua regia digestion (10 ml of a mixture of 12 M HCl and 15.8 M HNO3 in a 3:1 ratio) in Teflon reactors. Reactors were maintained for 20 h in a fume hood and then simmered on a hot plate for 1 h at 100 °C. The sum of the first three steps of the sequential extraction corresponds to the total content related to the potentially mobile fraction, considered to be the most important factor for the assessment of bioavailability and of the potential ecological risk (Pérez et al., 2008). The first fraction contains those metals weakly bound which are readily soluble in water or a slightly acidic medium. The ease with which metals are free from this fraction, provide an idea of the high potential risk which is associated to it. The second and third fraction, associated with oxides and hydroxides of Fe and Mn, and sulfides and organic matter, respectively, are susceptible to releasing those metals present in its structure depending on variations that occur with changes potential or pH. 2.4.3. Statistical analysis Univariate and bivariate statistical analysis was applied to the compositional data to estimate basic statistical parameters and to find the correlation coefficients between the different metals and metalloids. Additionally, a multivariate statistical analysis of the principal components (PCA) and a cluster analysis (CA) were performed to identify the factors that could explain the correlation model between the data variables (Idris, 2008; Sielaff and Einax, 2007). The PCA was performed using a Spearman correlation matrix (significance level 0.05) to identify the possible sources of metal contamination in the study area and to evaluate the degree of association between the variables (metal-metalloids). The PCA technique allows simplification of data complexity by reducing the number of variables orthogonal factors, thus facilitating the visualization of meaningful correlations (Jolliffe, 2002; Kaiser, 1958). The values of the factor matrix can be improved by using the Varimax rotation method, which maximises factor variance (Kaiser, 1958), since it is an orthogonal rotation that minimises the number of variables that have high loadings on each factor by giving to those variables maxim weight the factor and minimum weight to the variables less correlated to the axis. Thereby simplifying the transformed data matrix and facilitate the interpretation. Cluster Analysis (CA) was also used to find homogeneous groups of samples based on their geochemical compositions. The Ward method was applied and the Euclidean distance was used for the regrouping of samples and to identify distribution model of the metal content in the soils. The variables with reduced distance are more similar than those with longer distances and therefore could be grouped within the same cluster (Césari, 2007). The results obtained can be represented in a dendogram, which shows the levels of similarity between the different variables. This method is very efficient and produces high stable groups' structures (Zupan et al., 2000).

Table 2 Quality control of data using: (1) measured, certified, and indicative values for extractable amounts in certified reference material BCR-701; and (2) comparative results (% recovery) calculated using Eq. (1) (%Recovery = (F1 + F2 + F3 + R/Pseudo-total concentration) × 100). Steps F1

Determined Certified F2 Determined Certified F3 Determined Certified R Determined Certified Recovery% (Eq. (1))

As

Cd

Co

Cr

Cu

Ni

Pb

Zn

– – – – – – – – 86

7.05 ± 0.09* 7.34 ± 0.35 3.21 ± 0.023.77 ± 0.28 0.09 ± 0.07 0.27 ± 0.06 0.05 ± 0.01 0.125 ± 0.075 73

– – – – – – – – 72

2.10 ± 0.15 2.26 ± 0.16 47.0 ± 0.9 45.7 ± 2.0 129 ± 13 143 ± 7 52.3 ± 7.7 62.5 ± 7.4 65

48.0 ± 1.5 49.3 ± 1.7 138 ± 3 124 ± 3 46.9 ± 4.9 55.2 ± 4.0 32.6 ± 3.8 38.5 ± 11.2 86

14.1 ± 0.1 15.4 ± 0.9 27.5 ± 1.1 26.6 ± 1.3 13.8 ± 3.7 15.3 ± 0.9 31.3 ± 0.8 41.4 ± 4.0 70

2.55 ± 0.01 3.18 ± 0.21 121 ± 3 126 ± 3 9.5 ± 1.8 9.3 ± 2.0 12.0 ± 0.7 11.0 ± 5.2 104

184 ± 7 205 ± 6 98 ± 6 114 ± 5 43.3 ± 13.3 45.7 ± 4.0 69.6 ± 0.1 94.6 ± 12.2 81

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PCA and CA are complementary techniques; both compress a large amount of data into more manageable groups and increase significance. The difference is that CA is considered more efficient in producing structures and groups clearly, and is relatively more stable. The XLSTAT 2009 software package has been used for the data processing. 2.4.4. Assessment of potential ecological risk Ecological risk assessments must comply with some conditions that are accessible to prediction and measurement, besides being susceptible to the dangers of hazardous waste sites that pose a threat to human health and the environment (Hakånson, 1980). A significant number of indicators designed to approximate the quality of soils can be found in literature (Caeiro et al., 2005). In our case, assessment of soil contamination level is performed by the quantification of the Pollution Index (PI) (Chen et al., 2005) known as contamination factor (Cif) and by the Contamination Degree (Cd) (Hakånson, 1980). For each soil sample and each heavy metal the Cif has been calculated as the ratio between the metal concentrations with its background values as established for the study area by Guillén et al. (2011): PI ¼ Ci f ¼ Cheavy metal =Cbackground Cd ¼ SCi f Where: Cif (Contamination Factor) is the ratio between the concentrations of each metal in the sediments and the reference background value (Table 1); and Cd is the Contamination Degree calculated as the sum of the Cfi of each of considered metals. According to the literature, the variation in Cd can be defined as: low degree of contamination; Cd b n n b Cd b 2n moderate degree of contamination; 2n b Cd b 3n high degree of contamination; Cd > 3n very high degree of contamination. Where n is the number of contaminants involved in the Cd determination. Sequential extraction investigations can be also used to estimate the potential risk of waste-soil-sediment based on relative comparison between extracted fractions. The distribution of metals in the different phases of the modified BCR procedure offers an indication of their availability, which in turn enables a risk assessment study for the pollutants present in an aquatic environment (Chen et al., 2010). In this study, the soils were classified according to a Risk Assessment Code (RAC) (Perin et al., 1985). The RAC measures risk by using the percentage of metal associated with the soil/sediment in the exchangeable and carbonate fractions: there is no risk when the F1 BCR fraction is lower than 1%; low risk for a range of 1–10%; medium risk for a range of 11–30%; high risk from 31 to 50%; and very high risk for F1 percentages over 50%. 3. Results and discussion 3.1. Statistical analysis 3.1.1. Univariate analysis, heavy metal concentration The results of statistical analysis (range, mean, median, standard deviation, kurtosis and asymmetry) for As, Cd, Cr, Cu, Ni, Pb and Zn in the 25 selected soil samples from the city of Huelva after acid digestion (pseudo-total concentration) have been summarised in Table 1. The highest average values correspond to As, Cu, Pb and Zn (272 mg·kg− 1, 1868 mg·kg− 1, 550 mg·kg− 1 and 1259 mg·kg− 1, respectively). The distribution of mean values and the extreme ranges of variation suggest an abnormal distribution of the chemical data. This is confirmed by the standard deviation and it is also corroborated by the values of kurtosis and asymmetry.

More details and a wide discussion on the spatial distribution of the main pollutant in the study area can be found in Guillén et al. (2011). 3.1.2. Bivariate analysis, correlation coefficients Based on the Spearman correlation coefficients (alpha 0.05) some elements showed a strong correlations: Bi-Pb (0.908), As-Pb (0.825), Bi-As (0.785), Zn-Cd (0.750), Cr-Ni (0.693), Zn-Cu (0.631); other correlations were moderate: Cd-Pb (0.593), Cd-Cu (0.553), Zn-Bi (0.545), ZnPb(0.535) and Cr-Cu (0.510). This suggests a common origin for these elements probably the result of contributions from several anthropogenic activities. In addition, the Bi was included in this work because it is a good indicator of As as evidenced by their common association to metal sulfides as explained in Section 3.1. Additionally, it presents similar behaviour to Pb during smelting processes which volatilize at high temperatures. Furthermore, the presence of trace elements in the study area could also be due to atmospheric transport as solid particles by the wind. According to Querol et al. (2002) these particles are mainly derived from the activities related to the fertiliser production plants and the copper foundries located in the Punta del Sebo industrial complex. Another possible explanation for the high correlations between trace elements is that the elements are concentrated mainly in the infill sediment of the estuarine areas where the main source of trace elements are associated to AMD pollution generated in inner areas of the Tinto-Odiel basin (Sarmiento et al., 2009). Other anthropogenic inputs such as fossil fuel combustion or agrochemical compounds (derived from the intense agriculture activities) could explain the concentrations of trace elements such as Pb and As, respectively. PCA and CA have been applied to understand the relationships among the trace elements responsible for pollution in the Huelva municipality. 3.1.3. Principal component analysis (PCA) The data matrix used for PCA analysis took into account 150 samples, considering the total samples available in the study area (see Guillén et al., 2011) with the idea of facilitate the statistical interpretation of results. Three principal components of PCA were extracted according to the Kaiser criterion explaining up to 85% of total variance. The loading plot of the variables (trace elements, in this case) after a Varimax rotation methods (Fig. 2A) shows the existing correlations on a bi-axial (D1–D2) plane for three groups of elements associated to three factors. Factor 1 (D1) explains the 35% of the total variance and presents high loading values for As, Pb and Bi (Group-I); Group-II includes elements such as Cr and Ni associated to the Factor 2 (D2) explaining the 24% of the total variance; Factor 3 (D3, represented as a projection in Fig. 2A) groups elements including Cu, Cd and Zn (Group III) explaining the 27% of the total variance. Thus, we can suppose that D1 and D3 represent elements with different origins (groups I and III) both related to human activities and they could be named “anthropogenic inorganic factors”. Similar results have been obtained by Tokalioglu and Kartali (2006), Tokalioglu et al. (2010) with the same technique in industrial urban waste soil and soil samples from a Turkey farm. Due to the high and multiple sources of pollution in the study area, it is difficult to differentiate the direct origin of the anthropogenic factor from the PCA. However, the Group II association has widely been described in the literature (Delgado et al., 2010; López-Gónzalez et al., 2006) as result of natural sources associated to the bedrock in the SW Iberian Peninsula. Fig. 2B shows the variables vs. observations axial plane, allowing recognition of areas characterised by the geochemical associations described above. In this sense, sample 76 shows significantly higher concentrations of Pb and As, probably related to activities in the ancient port of Tharsis where the ore arrived from the Iberian Pyrite Belt, (IPB). Other sources of pollution such as vehicle fossil fuels or power plants (Pb) and fertilizers (As) are not discarded. The sample 91 is an anthropized soil, characterised by elevated concentrations of Cu and Zn and moderate concentrations for all trace elements. Due to it sampling site, the concentrations are probably related

M.T. Guillén et al. / Journal of Geochemical Exploration 119–120 (2012) 32–43

Variables after Varimax rotation

axis D2 (24%)

1

A

3.1.4. Cluster analysis (CA) The CA allowed for determining similarities between the trace elements and to identify homogeneous groups that are mutually correlated within a data matrix and the dendogram (Fig. 3A) shows different stable clusters/associations for the analysed variables. The first association includes Cr and Ni (Cluster-A), and it clearly represents the previously described contribution of multiple sources in the study area. The As-Pb-Bi association form the Cluster B and confirms the assumption that these elements probably are related to the sulphuric mineral treatments in the Punta del Sebo industrial complex. The last group (Cluster-C in Fig. 3A) includes elements such as Cu, Cd and Zn which are metals widely described as being related to the massive sulphides of the IPB (Delgado et al., 2009; Fernández-Caliani et al., 1997; Olías et al., 2006), and consequently to the AMD discharges in the Huelva estuary. This fact supports the original assumption that the metals included in the Cluster B and C are generally related with different activities of anthropogenic origin. Furthermore, the dendogram of the observations (Fig. 3B) can help to group the samples with similar characteristics according to their spatial distribution and permits distinction of four groups:

Ni

0.8

Cr G2

0.6 0.4

G1 As

Cu Zn

0.2

G3

Bi Pb

Cd

0 1

Cd

axis D3 (27%)

0.8

Cu Zn G3

0.6

Pb

0.4

G1 Bi

Cr 0.2

As

Ni G2

0 0.5

0

1

0

0.5

1

axis D1 (35%)

Variables and observations (D1-D2: 59%) 4

B 107 Ni 136

2

97

91

axis D2 (24%)

Cr Cu

Zn

90 As

146

0

117 121

37

Bi

Cluster-1 (CLR-1 in Fig. 3B) correspond to samples 134 and 136 associated with industrial complex Punta del Sebo, (which produces inorganic acids, fertilizers and metallurgical products), as an explanation for the highest concentration of trace elements in this group; Cluster-2 (CLR-2 in Fig. 3B) is characterised by groups of samples with high concentrations in most of the trace elements (samples 90, 91, 97 and 107), probably related to the atmospheric deposition because they are located near to important industrial areas; Cluster-3 groups the samples 182, 7, 184, 219, 190, 135, 145 and 160 close to saltmarshes zones and located generally near industrial

Pb

124

Cd

163

12

182

A

135

10

76

-4

-2

0

2

49

10

Dissimilarity

-2

8 6 4

axis D1 (35%) Cluster-C

2 Fig. 2. A) Contribution of each chemical element to the PC loading obtained by the principal component analysis showing both D1-D2 and D1-D3 axial-planes. B) Principal component score plot of sampling sites from Huelva City.

0

Cr

Cluster-B

Ni

As

Bi

Pb

Cu

Cd

Zn

30

B 25

Dissimilarity

20 15 10 5

CLUSTER-1 CLUSTER-2

CLUSTER-3

77 121

91 43 149

146 69 144

163 124

160 117

190

135 145

184 219

76 107 90 97 182 7

0 134 136

to the metallurgical treatment plant located nearby in the Punta del Sebo industrial complex (Elbaz-Poulichet et al., 1999). Extreme concentrations of As, Cd, Pb, Cu and Zn and the highest values of Cr and Ni have been obtained for the samples 134, 135 and 136 located next to the metallurgical plant, which explains their high degree of metallic contamination. Samples of 90, 97 and 107 located in the vicinity of industrial complex, but in the sediments of the marshes for the Quaternary period (Fig. 1) have show high contents of As, Cu, Pb and Zn. These values are not only related with the industrial activities, but are also hypothesized or to be related to the metal discharge from acid leachates of IPB in the estuarine areas (Borrego et al., 2002). Samples 117, 121, 124, 146, 163 and 182 (Fig. 2B), located in the Huelva estuary floodplain area around industrial sites are characterised by moderate-high concentrations of trace elements (including Cr and Ni), and they were probably affected by multiple pollution sources such as AMD, industrial wastes and atmospheric emissions (Querol et al., 2002). The remaining samples form a more compact group (Fig. 2B) and have less extreme trace elements levels.

Cluster-A

CLUSTER-4

Fig. 3. A) Dendogram of selected metals in soil samples using complete linkage method. B) Dendogram of the hierarchical cluster analysis for trace elements concentration in the soils samples from Huelva Township.

38

M.T. Guillén et al. / Journal of Geochemical Exploration 119–120 (2012) 32–43

complexes. This group most likely represents sites affected by various metal sources related to AMD and mining activity in IPB, aerial dispersion of contaminants from the treatment plants, and wastes resulting from several anthropogenic activities; Cluster-4 contains the samples 117, 163, 124, 146, 69, 144, 91, 43, 149, 77 and 121, which showed the lower metal contents. However, similar to Cluster-3, these samples (excluding t the sample 43, discussed below) are located in the vicinity of the industrial areas and are, also probably affected by multiple sources of metals. Finally, sample 76 is

enriched in As and Pb probably due to the ancient mineral port of Tharsis, when a large amount of waste increased the concentrations of trace elements in the soil significantly, as mentioned before. 3.2. Metal fractionation, sequential extraction data In order to determine the reactivity of the mineral phases susceptible of incorporating metals and metalloids, the leachates of each step from the SEP of the soils were analysed, and are shown in Fig. 4 as percentages of As, Cd, Co, Cr, Cu, Ni, Pb, and Zn.

A 7 43 69 76 77 90

As

7 43 69 76 77 90

91 97 107 117 121 124 134

91 97 107 117 121 124 134

135 136 144 145 146 149

135 136 144 145 146 149

160 163 182 184 190 219

160 163 182 184 190 219 0%

20%

40%

60%

80%

7 43 69 76 77 90

100%

Co

0%

91 97 107 117 121 124 134

135 136 144 145 146 149

135 136 144 145 146 149

160 163 182 184 190 219

160 163 182 184 190 219 20%

40%

60%

80%

100%

20%

40%

60%

80%

7 43 69 76 77 90

91 97 107 117 121 124 134

0%

Cd

100%

Cr

0%

20%

40%

60%

80%

100%

SEQUENTIAL EXTRACTION PHASES F1 = Exchangeable water/acid condition Carbonates

F3 = Oxidisable fraction (Sulphur/Organic matter)

F2 = Reducible fraction (Fe-Mn Oxihidroxides)

F4 = Residual fraction

Fig. 4. Percentages of As, Cd, Co, Cr (A) and Cu, Ni, Pb and Zn (B) extracted in each step of the sequential extraction procedure BCR-modified on the soils of study area.

M.T. Guillén et al. / Journal of Geochemical Exploration 119–120 (2012) 32–43

B

39

7

7 43 69 76 77 90

Cu

91 97 107 117 121 124 134

91 97 107 117 121 124 134

135 136 144 145 146 149

135 136 144 145 146 149

160 163 182 184 190 219

160 163 182 184 190 219 0%

20%

40%

60%

80%

7 43 69 76 77 90

0%

100%

91 97 107 117 121 124 134

91 97 107 117 121 124 134

135 136 144 145 146 149

135 136 144 145 146 149

160 163 182 184 190 219

160 163 182 184 190 219 20%

40%

60%

80%

20%

40%

60%

80%

7 43 69 76 77 90

Pb

0%

Ni

43 69 76 77 90

100%

100%

Zn

0%

20%

40%

60%

80%

100%

SEQUENTIAL EXTRACTION PHASES F1 = Exchangeable water/acid condition Carbonates

F3 = Oxidisable fraction (Sulphur/Organic matter)

F2 = Reducible fraction (Fe-Mn Oxihidroxides)

F4 = Residual fraction

Fig. 4 (continued).

According to recent studies (Delgado et al., 2011; Pérez-López et al., 2008), the potentially mobile fraction is considered the sum of the first three steps of the SEP-BCR (F1 + F2 + F3) of the soils, i.e. the fraction soluble in water or weakly acidic conditions and carbonates (F1), the reducible fraction linked to the Fe-Mn oxyhydroxides (F2), and oxidisable fraction related to sulphur and organic matter. Considering the mobile fraction, high percentages of Cu (82%) >Co (81%) >Zn (71%) >Cd (68%) >Ni (50%) and medium values of Pb (36%)> Cr (29%) >As (19%) were recovered. These values usually result in high concentrations in the mobile fraction of soils: Cu (2170 mg kg − 1) >Zn

(907 mg kg− 1) >Pb (121 mg kg− 1) >As (34 mg kg− 1) Co (28 mg kg− 1)>Ni, Cr (8–10 mg kg− 1)>Cd (1.3 mg kg− 1). These concentrations, except Cr and As, exceed the background values (Table 1) established by Guillén et al. (2011) for soils in the Huelva municipality. Cobalt, Cr, and Ni have been frequently characterised as elements with an almost natural behaviour, and are associated with the finer fraction and Al content of soils/sediments in the study area (López-Gónzalez et al., 2006). The low concentrations of Cr and Ni commonly observed in the mobile fraction (8 and 9 mg·kg− 1 respectively) and the preferential association in the residual fraction, R of the SEP (71 and 50%

40

M.T. Guillén et al. / Journal of Geochemical Exploration 119–120 (2012) 32–43

respectively); confirm the low potential of ecological risk from these elements. However, Co typically is very enriched in the mobile fraction (80%), indicating that its source is mostly anthropogenic and hazardous to the environment in specific areas. Important concentrations in the mobile fraction of Co occur in samples 69, 76 and 107 (Fig. 4), with values of 230, 204 and 109 mg·kg− 1, respectively.

Arsenic and Pb were associated preferentially to residual fraction, R (81% and 64%, respectively), however they present relatively high concentrations in the mobile fraction (34 and 121 mg·kg− 1), representing 19% and 36% respectively over the pseudo-total content. For this reason they could pose potential environmental risk if changes occur in the soil parameters (pH, Eh), because present a percentage significant in

Fig. 5. Interpolated map showing the spatial distribution of the ecological risk indexes (Cd, Contamination Degree — PI, Pollution Index) based in eight elements (Hg, As, Cd, Cr, Cu, Ni, Pb and Zn).

M.T. Guillén et al. / Journal of Geochemical Exploration 119–120 (2012) 32–43

the reducible fraction (7% and 10%) and oxidisable fraction (8% to 23%), respectively. These two elements have been recovered in low proportions in F1 (3.42% and 1.86%, respectively). Cadmium, Cu and Zn usually show significant concentrations associated with the mobile fraction of the soils. These elements show similar behaviour, as described in the statistical analysis, reaching extraction percentages of 27.38%, 28.51% and 32.31%, respectively, and high mean concentrations (0.54 mg·kg− 1, 754 mg·kg− 1, and 412 mg·kg− 1) associated with the fraction soluble in slightly acid medium, F1 (Fig. 4). Cd in soils presents a high ecological risk even at concentrations considered as low as 17 mg·kg− 1. It has been shown concentrations between 8 and 17 mg·kg− 1 of Cd that represent a risk for cancer (Nawrot et al., 2006). The F1 fraction is considered the most important from an environmental perspective, since the metals contained are easily leached in neutral or slightly acid waters (Filgueiras et al., 2004; Marín et al., 1997), and they are thus available to be assimilated by organisms. Several authors have proposed the fraction F1 as having the most potential in bioavailable fraction (Álvarez-Valero et al., 2009; Delgado et al., 2011) in the environment, suggesting that it could therefore be used to assess the Potential Ecological Risk. Cadmium, Cu and Zn show also a significant percentage (Cd 41%–0.81 mg kg− 1, Cu 53%–1416 mg kg− 1, Zn 39%– 495 mg kg− 1) of recovery associated with the sum of F2 +F3, reflecting the high affinity of these three elements with Fe-Mn oxyhydroxides, sulphides and organo-metal complexes (Banerjee, 2003; Zemberyová et al., 2006). The percentages obtained in the residual fractions (R), are 32%, 18% and 28% of recovery for Cd, Cu and Zn, respectively. 3.3. Assessment of potential ecological risk Based on the interpolated map of the Cd values calculated for eight elements (As, Cd, Cu, Cr, Hg, Ni, Pb and Zn) (Fig. 5), Huelva township generally presents contamination varying from a moderate to a very high degree; the very high degree of contamination is mainly located in the floodplain of the Tinto-Odiel estuary. In the central sector of the study area, the other area pollution can be related to a farm, (sampling point 43, Fig. 1). As listed in Table S1 (supplementary data) and shown in Fig. 5, the low-intermediate Cd values (Cd ≤ 16) are distributed in the periurban areas (northern sector) coinciding with the material of tertiary age (gray-blue marlstone), which is not significantly affected by metal contamination and where agriculture is the principal field occupation. This unit is characterised by the absence of major sources of industrial pollution, nor is it affected by the AMD from the highlands of Rio Tinto and Odiel estuary. It is also interesting to note the low Cd values obtained along in the NE–SW transect of the city coinciding with the ancient seamounts and non urbanised areas, where presently a popular recreational area, Parque Moret is located. The highest values of Cd (high to very high ecological risk, 16b Cd ≤24) draw the gray-blue marlstone unit to the industrial complexes. Additionally, these areas usually surround the Quaternary infill of Huelva Estuary, which are strongly affected by AMD (e.g. Borrego et al., 2002; LópezGónzalez et al., 2006). This range clearly defines some industrial complexes in the study area (Fig. 1). The highlight industrial-railway area (El Rincón) situated in the E-NE sector of the town, the Huelva port zone, and other industrial complexes outside of the city (the Tejar-Colmenilla industrial complex in the NW sector and, the Fórtiz and Tartessos industrial complexes hosting the cellulose factory, both in the E-NE sector of the study area). Finally, the extremely high Contamination Degree values (845, 885 and 1098; see Table S1 for supplementary data) are seen in the samples 76, 134 and 136, respectively. The first sample coincides with the area where mining harbour activities were historically concentrated and the last two samples are located in the Punta del Sebo industrial complex (fertiliser and copper smelting).

41

Table 3 Risk assessment codes (RAC) obtained by the extractable F1-SEP for the selected samples of the Huelva Township.

Elements (% exchangeable, fraction 1 BCR steps) Sample

As

Cd

Co

Cr

Cu

Ni

Pb

Zn

7

1.4

43.9

23.9

3.7

70.6

10.4

12.7

29.8

43

5.0

48.6

12.2

1.3

11.4

10.3

4.9

26.4

69

5.5

36.1



1.1

10.0

4.4

5.0

0.5

76

2.7

24.3

3.7

0.7

1.1

5.4

1.2

2.7

77

3.0

39.0

5.8

0.9

51.5

4.6

4.3

3.3

90

2.5

45.8

12.4

1.4

19.1

9.5

2.6

23.9

91

3.0

30.8

15.9

0.7

2.9

3.6

2.0

13.7

97

6.7

31.8

9.6

1.2

9.6

10.4

1.5

40.8

107

6.5

46.0

9.6

2.3

26.2

19.0

1.0

41.9

117

5.0

52.5

23.3

2.1

18.7

11.8

3.9

42.8 14.8

121

10.0

7.8

6.1

0.0

32.3





124

3.0

12.3

1.2

2.1

14.1



4.3

5.3

134

13.8

17.9

0.0

1.6

33.1





29.0

135



7.1



47.9





38.9 40.2

100

136

2.5



16.4

0.7

20.1

6.2

1.3

144

0.6

58.5

17.9

0.8

16.7

15.1

2.5

51.5

145

0.5

56.2

0.9

1.1

16.6

20.1

1.0

46.8

146

6.1

30.4

11.1

0.6

17.3

13.2

0.7

44.9

149

3.5

62.7

48.4

0.9

24.1

30.7

2.0

52.1

160

0.6

30.5

9.3

0.5

2.6

5.4

1.3

10.2

163

6.4

17.4

18.0

0.7

41.2

6.7

1.7

24.6

182

0.4

60.4

1.0

0.8

15.2

17.6

1.1

51.3

184

0.4

64.2

0.8

0.8

14.7

16.4

1.0

59.8

190

6.7

42.6



0.8

1.9

3.8

4.7

2.4

219

5.5

40.8

17.6

1.3

2.3

5.2

4.5

36.0

Mean

4.22

41.7

11.8

1.17

20.8

10.94

2.97

29.3

Sta Dev

3.26

20.2

10.8

0.77

17.2

6.94

2.64

18.4

Moreover, the obtained RAC values (Table 3) have allowed for classification of elements as a function of their potential hazard as Cd> Zn > Cu > Co> Ni> As> Pb > Cr. Cadmium (F1 = 40%) represents a high ecological risk, and Zn (F1 = 29%) and Cu (F1 = 21%) pose a medium risk. The rest of the metals represent a low environmental risk (Table 3) in Huelva municipality area with values between 10.9% (Co) and 1.12% (Cr). It should be noted that the RAC does not takes into account the total metal concentration (Keller and Hammer, 2004), and only represents an approximation for potential ecological risk. Nonetheless, the RAC code can be useful to assess environmental risk using sequential extractions as a characterization method (Rodríguez et al., 2009). 4. Conclusions This study has shown that using a combination of multivariate statistical analyses, sequential extraction data, and the ecological risk indexes, it can be produced an effective assessment of environmental quality in areas affected by several sources of anthropogenic pollution. The PCA analysis suggest three main groups of elements that are distinguished based on their different sources: 1) As, Pb and Bi and related to traffic road, fossil fuel combustion, and agrochemical use, 2) Cd, Cu and Zn probably related to the industrial metallurgical plant located nearby Huelva city and 3) Elements such as Cr and Ni reflecting the characteristics of the bedrock. Similar results were obtained with the CA analysis.

42

M.T. Guillén et al. / Journal of Geochemical Exploration 119–120 (2012) 32–43

Due to the high percentages recovered in the mobile fraction (F1 + F2+ F3) elements such as Cd, Cu, Pb and Zn can pose a potential environmental risk when human activities interfere with environmental conditions. Cadmium presents significant percentages of recovery at the labile fraction (F1), as well as Cu and Zn. Therefore, the results show that Cd presents a serious risk to health because it is very toxic and it accumulates in the body. The mobility order of the heavy metals and As studied in the basis of the no residual content of the elements is: Cu>Co>Zn>Cd>Ni> Pb>Cr>As. Overall, environmental risk analysis for the studied trace elements presents a very high ecological risk in the floodplains of the Rio TintoOdiel estuary, where the high metal concentrations are caused by both AMD processes in the nearby Iberian Pyrite Belt, and to the industrial activities (Tejar-Colmenilla, Tartessos, and Fortis industrial complexes). Additionally, the study area in the central sector has metal concentrations that could be related to a single farm. The low ecologic risk values are concentrated in the periurban (not industrialised area) areas of the township, and coincide with the gray-blue marlstone unit. Based on the Cd values obtained in this study, the city of Huelva is presented with an important ecological risk (Cd > 24, very high degree of contamination) from the surrounding areas where the industrial and port activities are concentrated. The areas of Huelva that are not significantly affected by pollutants are mainly situated in the NE–SW city transect which coincides with the very important recreation area of Huelva (Parque Moret). Supplementary data to this article can be found online at http:// dx.doi.org/10.1016/j.gexplo.2012.06.009. Acknowledgements This work has been financed by the Spanish Ministry of Education and Science through project CGL2010-21956-C02-02. M.T.GUILLÉN was financially supported by the Secretaría Nacional de Ciencia Innovación y Tecnología (Senacyt) and the Instituto Nacional para la Formación de Recursos Humanos (Ifarhu). We also would like to thank Madeline Jones for the detailed English revision of the original manuscript. References Álvarez-Valero, A.M., Pérez-López, R., Matos, J., Capitán, M.A., Nieto, J.M., Sáez, R., Delgado, J., Caraballo, M., 2009. Potential environmental impact at São Domingos mining district (Iberian Pyrite Belt, SW Iberian Peninsula): evidence from a chemical and mineralogical characterization. Environmental Geology 55 (8), 1797–1809. Banerjee, A.D.K., 2003. Heavy metal levels and solid phase speciation in street dusts of Delhi, India. Environmental Pollution 123, 95–105. Barba-Brioso, C., Fernández-Caliani, J.C., Miras, A., Cornejo, J., Galán, E., 2010. Multisource water pollution in a highly anthropized wetland system associated with the estuary of Huelva (SW Spain). Marine Pollution Bulletin 60, 1259–1269. Borrego, J., Morales, J.M., de la Torre, M.L., Grande, J.A., 2002. Geochemical characteristic of heavy metal pollution in surface sediments of the Tinto and Odiel river estuary (southwestern Spain). Environmetal Geology 41, 785–796. Caeiro, S., Costa, M.H., Ramos, T.B., Fernandes, F., Silveira, N., Coimbra, A., Medeiros, G., Painho, M., 2005. Assessing heavy metal contamination in Sado Estuary sediment: an index analysis approach. Ecological Indicators 5 (2), 151–169. Césari, M., 2007. Estrategias de análisis y exploración de datos como soporte a la adquisición de conocimiento para modelización de sistemas expertos bayesianos causales. Trabajo Final de Especialidad en Ingeniería de Sistemas Expertos. ITBA. Chen, T.B., Zheng, y.m., Lei, M., Huang, Z.C., Wu, H.T., Chen, H., Fan, K., Yu, K., Wu, X., 2005. Assessment of heavy metal pollution in surface soils of urban parks in Beijing, China. Chemosphere 60 (4), 542–551. Chen, C., Lu, Y., Hong, J., Ye, M., Wang, Y., Lu, H., 2010. Metal and metalloid contaminant availability in Yundang Lagoon sediments, Xiamen Bay, China, after 20 years continuous rehabilitation. Journal of Hazardous Materials 175 (1–3), 1048–1055. Civis, J., Sierro, F.J., Flores, J.A., Andrés, I., Porta, J., Valle, M.F., 1987. El Neógeno marino de la provincia de Huelva: antecedentes y definición de las unidades litoestragráficas. In: de Salamanca, Universidad (Ed.), Paleontología del Neógeno de Huelva (W. Cuenca del Guadalquivir), pp. 9–23. Cuong, D., Obbard, J.P., 2006. Metal speciation in coastal marine sediments from Singapore using a modified BCR-sequential extraction procedure. Applied Geochemistry 21, 1335–1346.

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