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Evaluation of arsenic in soils and plant uptake using various chemical extraction methods in soils affected by old mining activities
Geoderma 160 (2011) 535–541
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Geoderma j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g e o d e r m a
Evaluation of arsenic in soils and plant uptake using various chemical extraction methods in soils affected by old mining activities M.J. Martínez-Sánchez, S. Martínez-López, M.L. García-Lorenzo, L.B. Martínez-Martínez, C. Pérez-Sirvent ⁎ Department of Agricultural Chemistry, Geology and Pedology, University of Murcia, Spain
a r t i c l e
i n f o
Article history: Received 29 November 2009 Received in revised form 24 October 2010 Accepted 6 November 2010 Available online 3 December 2010 Keywords: Chemical extraction methods Bioavailable arsenic Plant uptake Mine soils Sediments
a b s t r a c t The aims of this study were to assess arsenic concentrations and solubility in soils, and its uptake by plant species growing in an area strongly affected by mining activities in Murcia (SE, Spain) paying attention to the mineralogical composition of the soils and the relation of the same with the bioavailability of this element. For this, extractions were made with several chemical reagents, to determine the bioavailability of the element in different environmental conditions (natural or potential) and, especially, the arsenic bioavailable for the plant uptake. Maximum extraction was achieved using the Mehra and Jackson procedure, while the least effective extractant was an ammonium sulphate solution. The strongest relationship between the As determined by soil chemical extractions and the As measured in plant biomass was found using the oxidisable-organic matter extraction procedure. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Arsenic, one of the most pollutant of toxins, is broadly distributed in nature. Background concentrations vary between 0.5 mg kg− 1 and 80 mg kg− 1 (average 10 mg kg− 1) in soils (Kabata-Pendias and Mukherjee, 2007), although higher concentrations are frequently found in soils and sediments that have been affected by anthropogenic activities such as mining or agriculture, or where the soils and sediments are derived from As-rich mineralized rocks. In ores, this element is usually present in the sulphide form associated to pyrite, arsenopyrite or pyrrhotite, while, in soils, two main oxidation states, arsenate [As(V)] and arsenite [As (III)], are usually found (Nriagu et al., 2007). Arsenic is naturally found in plants, where its concentration seldom exceeds 1 mg kg− 1 (Adriano, 2001); indeed, it has been reported that at very low concentrations it can even be beneficial for plant growth (Gulz et al., 2005). It can be absorbed from soils or from the matter deposited on leaves. Arsenic uptake by plants depends on its concentration and speciation in soil. Uptake is usually in the form of arsenate, which produces stress in the plants, including growth inhibition (Gunes et al., 2008). Some plant species are able to grow in soils with a very high arsenic content, demonstrating that they have developed tolerance mechanisms (Jedynak et al., 2009). Plant tolerance is normally 2 mg kg− 1 (Kabata-Pendias and Mukherjee, 2007). Arsenic contamination is a subject of great importance due to
⁎ Corresponding author. Tel.: + 34 868887449; fax: +34868884148. E-mail address: [email protected] (C. Pérez-Sirvent). 0016-7061/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2010.11.001
its carcinogenic nature and its adverse effect on the ecosystem. The most serious risk posed by this carcinogenic metalloid is associated with the forms that are biologically available for absorption, or “bioavailable,” to plants. Bioavailability of a substance is a function of the abundance, chemical form, and the way in which it is bound to soil particles (Traina and Laperche, 1999). The extractable fraction of As may provide a better indication of its bioavailability and mobility in soils (Adriano, 2001). A direct relationship between different chemical forms of As in soils (extracted by single chemical extraction methods) and the level of the element measured in plants has seldom been reported. It is therefore necessary to determine the best chemical extractant and the form of As in soils that will decide the availability, solubility and bioavailability of the element for plant uptake. Therefore, it was thought interesting to develop analytical techniques to evaluate the As species in the most soluble and mobilizable fractions, which are the most bioavailable (Anawar et al., 2007). The fate and transfer of metals are a complex subject, and depend on the physical transport process involved and the soil mineralogy (Navarro et al., 2006). The mineralogical composition of the soil is one important factor for the mobility of metals and, possibly, for their bioavailability to plants (Martínez-Sánchez and Pérez-Sirvent, 2007). Various chemical methods are available to extract arsenic from the different fractions of soil, but their capacity to estimate the As available for plants in mining soils is largely unknown. Single chemical extractions can be used to estimate and predict bioavailable As and plant uptake from soils affected by mining activities, since different environmental situations can be simulated (Ure et al., 1992).
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2. Materials and methods 2.1. Sample characteristics For this study, fourteen samples were collected in the surrounding area of Sierra Minera and Portman Bay (Murcia, SE Spain) (Fig. 1). The studied zone is close to the mining region of La Unión (SE Spain) (Martínez-Sánchez et al., 2008). The entire area is located between Cabo de Palos and Cartagena and has been subjected to mining for many centuries. It constituted one of the most important mining districts of Spain and produced substantial amounts of lead, silver, zinc and pyrites until 1991 (Navarro et al., 2008). The area presents a central zone with sulfides, carbonates, silica, oxides and hydroxides, and an intermediate zone with sulfide mineralizations, carbonates, greenalite and magnetite. The mineralization occurs (i) within a strongly altered zone above the Miocene footwall; (ii) in pebbly mudstone beds where the hydrothermal activity led to dissolution, void formation and mineral deposition; and (iii) in fault breccias along the normal faults that bound the Miocene sediments (Navarro et al., 2008). The average annual temperature is 17 °C and precipitation does not exceed 300 mm, with occasional torrential rainfall, which frequently occurs in the period between the end of summer and autumn. Samples of five plant species, which grow naturally in the studied area, were collected. Plants were collected from soil sampling points, taking three plants of each species (the results for which were averaged). The following species were collected: Limonium carthaginens, an endemic specie of the study zone which usually grows in semiarid areas with low nutritional needs; Arthrocnemun macrostachyum, with a similar
behaviour to the species mentioned but which usually grows in wet saline areas; Dittrichia viscosa, Zygophyllum fabago and Glaucium flavum, all of which are abundant along roadsides and in the studied area. The first four species are resistant and perennial, while G. flavum is a short cycle plant. The soils studied have been exposed to serious interventions in the form of mining (Antrosol), although natural soils developed on the volcanic rocks still remain. The soils around Sierra Minera are calcareous and may occasionally be flooded after rain, when they receive substantial quantities of materials from the mining area (Navarro et al., 2008). The samples were taken from the rizosphere. 2.2. Sample preparation Soil samples were air dried and sieved to b2 mm for general analytical determinations. The pH was determined in a 1:5 suspension of soil in pure water and in a 1 M KCl solution (Peech, 1965) using a Crison GLP21 pH meter. Electrical conductivity (EC) (mS cm− 1) was measured using a Crison GlP31 conductivity meter in the extracts obtained by filtering the 1:5 suspensions through a 0.45 μm cellulose acetate disk filter. Equivalent calcium carbonate (%) was determined by the volumetric method using a Bernard calcimeter previously calibrated against Na2CO3 (Hulseman, 1996). Organic carbon was determined by sulfochromic oxidation (Nelson and Sommers, 1982) according to the NF X31-109 standard (Norme Française) (AFNOR, 1993). A semi quantitative estimation of the mineralogical composition of the samples was made by X Ray Diffraction (XRD) analysis using Cu-Kα radiation with a PW3040 Philips Diffractometer. X-powder software
Fig. 1. Studied zone.
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(Martín, 2004) was used to analyse the X-ray diffraction diagrams obtained by the crystalline powder method. The powder diffraction file (PDF2) database was used for peak identification, taking into account that the determination of minerals from soils by XRD analysis is not accurate below a limit of 5% of the total weight in a sample (depending on the crystallinity of individual minerals). The software incorporates precise quantitative studies done by methods of nonlinear least squares on a full profile of the full diffractogram and takes full advantage of the information contained in the database records. Weighting was achieved with the standard Reference Intensity Ratios (RIR) method described by Chung (1974). The automatic use of this method assumes that the database contains the chemical composition of each phase. To determine the total metal content (Pb, Zn, Cd, Fe and As), soil samples were first ground to a fine powder using an agate ball mill. Samples were placed in Teflon vessels and 5 ml of concentrated hydrofluoric acid solution, 2 ml of concentrated nitric acid solution and 5 ml of water were added. When digestion (15 min at 1000 W in a Milestone ETHOS PLUS microwave) was complete, the samples were transferred to a volumetric flask and brought to 50 ml. In the case of plant material, the aerial fraction was separated from the root, washed and then lyophilized. Then, 200 mg of lyophilized vegetal tissue were placed in Teflon vessels, and 3 ml water, 2 ml concentrated H2O2 and 5 ml concentrated HNO3 acid solution were added. After the digestion stage, 50 ml solutions were obtained, as indicated for soil samples. The Zn and Fe content was determined by flame atomic absorption spectrometry (FAAS) using a Perkin-Elmer 1100B Atomic Absorption Spectrophotometer, and the Pb and Cd content was determined by electrothermal atomization atomic absorption spectrometry (ETAAS) using a Unicam 929 AA Spectrometer. Arsenic levels were obtained by using atomic fluorescence spectrometry with an automated continuous flow hydride generation (HG-AFS) spectrometer (PSA Millenium Excalibur 10055). The experimental conditions and instrumental parameters of arsenic analysis were as follows: wavelength (nm)= 197.3, primary current (mA) = 27.5, boost current (mA)= 35, delay time (s) = 15, analysis time (s) = 30, memory time (s) = 30, air flow rate (ml/min)= 300. The reliability of the results was verified through the analysis of two standard reference materials (SRM 2711 Montana Soil and SRM 1515 Apple leaves). Spikes, duplicates and reagent blanks were also used as a part of our quality control. 2.3. Chemical extraction methods Nine chemical extractions methods were used to assess arsenic availability in soil: water medium (1:5 extract), nitric acid medium (1 g of solid in 50 ml 0.1 M HNO3), hydrochloric acid medium (1 g of solid in 25–50 ml 0.5 M HCl), (an adaptation of the chemical extraction method of Adriano, 2001), complexing–reducing medium (Mehra and Jackson, 1960), oxidising medium (step 3 BCR, (Community Bureau of Reference)) (Sutherland and Tack, 2002), 0.5 M HNaCO3 (Olsen et al., 1954), ammonium acetate (5 g of solid in 112.5 ml 1 M CH3COONH4) (Adriano, 2001), 0.05 M (NH4)2SO4 (Wenzel et al., 2002) and 0.005 M DTPA (Lindsay and Norvell, 1978). 2.4. Data analysis A statistical analysis of the data was made with MINITAB 14 and SYSTAT V11 software. The Pearson product-moment correlation test was used to establish possible relationships between the soluble arsenic content and the mineralogical composition of the soil samples and possible relationships between the former and the arsenic content of plant samples. In order to contrast the normality of the samples, the normal probability test was used together with Ryan–Joiner's contrast statistics. To study the way in which arsenic is mobilized in the different extraction
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Table 1 Characteristics of the soil samples (N = 14). Sample
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 Maximum Mean Minimum Standard deviation
E.C
CaCO3
O.M
E.h
Clay
H20
KCl
(mS/cm)
%
(%)
(m.V.)
(%)
4.98 8.10 7.51 7.37 4.25 8.12 7.91 7.71 7.54 9.06 7.77 6.86 6.64 5.27 9.06 7.08 4.25 1.36
4.76 7.89 7.29 7.22 4.15 7.88 7.75 7.45 7.33 8.52 7.54 6.36 6.37 4.54 8.52 6.79 4.15 1.37
7.4 2.8 0.2 3.5 2.5 0.5 0.4 0.7 0.5 0.2 2.9 1.4 1.7 0.5 7.40 1.80 0.20 1.97
0.0 10.1 0.3 0.0 0.0 13.3 28.7 0.0 4.9 60.1 0.0 0.0 0.0 0.0 60.10 8.39 0.00 16.99
0.3 0.8 0.8 1.9 1.0 1.2 0.5 1.3 0.7 0.2 0.5 1.0 0.5 1.0 1.90 0.84 0.20 0.45
299 136 170 126 257 215 228 206 214 206 178 282 255 215 299.00 213.36 126.00 50.2
1.2 1.0 0.2 0.3 1.9 0.6 1.0 0.1 0.1 0.3 0.1 0.1 0.1 0.1 1.90 0.51 0.10 0.56
pH
methods, the Kruskal–Wallis test was used. This is a non-parametric test which is used when the distribution of the samples is not normal. Differences at p b 0.05 level were considered significant. In addition, an analysis of the main components was carried out using the Varimax rotated factor loadings.
3. Results 3.1. Soils properties and metal concentrations The general characteristics of the soil samples are shown in Table 1 and the total metal content is shown in Table 2. The samples showed average pH values close to neutrality, although two of them had acidic pH values. In accordance to the consulted bibliography and the results obtained with the Pearson product-moment correlation test, the pH was negatively correlated with total As. Most samples showed a very low organic matter content (mean value of 0.8%). The soils with the highest organic matter content were those containing dolomite, while those with high phyllosilicates and pyrite levels showed the lowest values. The results showed that the organic matter is attacked by oxidising agents and that the greater the
Table 2 Total metal content of soil samples (N = 14). Sample
Pb (mg/Kg)
Zn (mg/Kg)
Fe (%)
Cd (mg/Kg)
As (mg/Kg)
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 Maximum Minimum Mean Standard deviation Quantification limit (cl)
3990 1241 1700 2330 554 1940 538 2040 3000 63 3600 4700 6750 13,500 13,500.0 63.0 3282.0 3457.0 0.174
8980 3153 8400 9900 2390 1200 1490 3670 3050 135 7900 7060 7200 4000 9900.0 135.0 4894.8 3224.0 0.193
25.11 26.12 38.63 50.59 4.33 7.39 4.63 11.81 18.99 16.12 19.21 24.53 24.27 19.26 50.59 4.33 20.83 12.77 0.002
88 123 165 74 2 11 12 14 14 5 33 19 20 15 165.0 2.4 43.3 50.0 0.188
752 669 1553 1868 3115 303 327 563 664 40 454 561 530 644 3115.0 40.0 860.2 805.0 0.01
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Fig. 2. Mineralogical composition. a. Semi-quantitative mineralogical composition obtained from the low-size fraction (b2 mm). b. X-ray diffraction patterns of two samples: a carbonate-rich soil (sample S10) and a mining soil (sample S4).
organic matter content, the lower the content of mobilizable arsenic. Electrical conductivity was low in most samples, as was the calcium carbonate content. The samples with the highest conductivity values in the topmost layer were those with a high phyllosilicate content.
As regards the intercorrelations of the total metals, the good correlation between iron, cadmium and zinc reflected the mineralogical source of these metals, all of which are associated to sulfides. Lead and arsenic were negatively correlated with pH, since these very
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Table 3 Soluble arsenic content of the soil samples (mg/kg). Sample
H2O
HNO3
HCl
Oxidisable
Mehra and Jackson
DTPA
(NH4)2SO4
Olsen
NH4Ac
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 Maximum Mean Minimum Standard deviation (cl)
0.1 0.1 bcl bcl bcl bcl bcl bcl 0.1 bcl bcl bcl bcl bcl 0.1 0.02 0.0 0.04 0.01
15.7 6.8 7.1 10.6 0.3 0.2 b cl b cl 0.3 b cl b cl 0.1 0.1 b cl 15.7 2.9 0.0 5.07 0.1
65.2 44.4 37.9 68.6 90.3 16.5 39.6 16.1 28.6 0.5 27.2 26.2 24.6 6.5 90.3 35.2 0.5 25.12 0.1
4.6 1.1 5.9 23.5 1.3 2.0 4.3 2.1 1.7 1.6 2.0 0.7 0.9 2.0 23.5 3.84 0.7 5.86 0.1
110.7 38.1 220.6 167.1 151.9 6.2 6.9 6.8 13.4 4.9 9.3 11.2 7.6 13.3 220.6 54.9 4.9 74.4 0.1
0.1 0.1 0.2 0.2 0.1 0.1 0.1 1.2 b cl b cl 0.1 0.1 0.1 0.3 1.2 0.2 0.0 0.29 0.004
0.01 b cl b cl 0.01 0.01 b cl b cl 0.02 b cl b cl b cl b cl b cl b cl 0.02 0.0 0.0 0.01 0.005
0.4 0.6 1.3 1.0 0.1 1.4 1.9 0.3 1.0 0.1 0.6 0.4 0.5 0.2 1.9 0.7 0.1 0.54 0.02
0.1 bcl 0.1 0.1 bcl 0.1 0.3 bcl 1.1 bcl 0.1 bcl bcl bcl 1.1 0.1 0.0 0.29 0.04
(cl): Quantification limit of arsenic.
mineralized materials were associated to acid pH values. The samples showing the highest contents of these metals corresponded to very mineralized soils and to the lowest pH values. A negative correlation existed between the CaCO3 content and the iron and zinc levels. The mineralogical analysis showed that the main minerals were quartz, muscovite, kaolinite and illite, while the minority minerals were products of mining activities (iron oxides and hydroxides, siderite, jarosite and gypsum), calcite and feldspars (Fig. 2). As regards the correlations within the mineralogical composition, there was a very close correlation between gypsum and jarosite (Ro = 0.685) and goethite (Ro = 0.846) all formed by alteration of the original mineralogy. Calcite and quartz showed good correlation (Ro = 0.513), although not very significant, because they are original minerals, while there was also a good (but negative) correlation between calcite and jarosite (Ro = −0.718) and gypsum and quartz (Ro = −0.663). The concentration of soluble arsenic was determined after nine different extractions, which gave the following mean values: 0.69 mg kg− 1 for the Olsen extraction, 0.14 mg kg− 1 for extraction by ammonium acetate, 35 mg kg− 1 for HCl acid, 3.8 mg kg− 1 in the oxidising medium, 0.2 mg kg− 1 for DTPA extraction, 3.2 mg kg− 1 for HNO3, 55 mg kg− 1 for the Mehra and Jackson extraction and 0.03 mg kg− 1 with water. Finally, the ammonium sulphate extraction provided values below the quantification limit. The grade of effectiveness of the extractants used was: oxidisableN Mehra and JacksonN HNO3 N HClN NH4AcN DTPAN OlsenN ammonium sulphateN water (the results are showed in Table 3). The Pearson product-moment correlation test was used to establish possible relationships between the arsenic extracted and the mineral-
ogical composition of the soil samples (Table 4). The results showed that samples which contained phyllosilicates in their mineralogical composition were positively correlated with the DTPA, ammonium sulphate, water and HNO3 extractions. Secondly, the samples which contained gypsum were positively correlated with the HCl and Mehra–Jackson extractions. Thirdly, the samples which contained goethite were positively correlated with the HCl extractions and, finally, the samples which contained hematite, siderite and pyrite were positively correlated with the oxidisable and Mehra and Jackson extractions, while the samples which contained quartz and carbonates were negatively correlated with the Mehra and Jackson extraction. In the carbonated soil studied, arsenic is not mobilized and so there was no arsenic uptake by plants. However, arsenic was linked with the iron oxides in the studied soils (affected by mining activities), so that an increase in acidity in such conditions may cause arsenic to be mobilized. The samples with the highest arsenic content were those containing minerals derived from the mining activity; the same soils also showed high sulfate (gypsum) and iron oxihydroxide (goethite) values and contained little or no quartz. This high and negative correlation of quartz with arsenic (Ro = −0.727) was difficult to evaluate because quartz was a very abundant mineral in all the studied samples and its variations were not very significant. The mineralogical composition, as well as the size of the particles of the samples, influenced arsenic mobility. In order to study the methodology of arsenic mobilization by the different chemical extraction methods applied, it was first necessary to contrast the normality of the samples, for which the normal probability test was used together with Ryan–Joiner's statistics of contrast (see Table 5).
Table 4 Relationships (Ro) between mobilized arsenic, arsenic content in soil and plant samples and mineralogical composition (mg/kg). Olsen As Total soil As Total Leave As Total Root Phyllosicate14 Phyllosicate10 Quartz Gypsum Calcite Goethite Siderite Pyrite ⁎ p b 0.05. ⁎⁎ p b 0.002.
− 0.153 0.310 − 0.085 − 0.089 0.187 0.435 − 0.399 0.568⁎ 0.318 0.337 0.316
NH4Ac − 0.103 −0.131 − 0.224 0.147 − 0.170 0.082 − 0.320 0.385 − 0.155 − 0.052 −0.051
HCl 0.813⁎⁎ 0.294 0.708⁎ − 0.030 0.373 −0.480 0.694⁎ − 0.272 0.632⁎ 0.329 0.353
Oxidisable
(NH4)2SO4
DTPA
0.368 0.629⁎ 0.641⁎ 0.132 0.242 − 0.284 0.030 − 0.262 − 0.124 0.844⁎⁎ 0.901⁎⁎
0.386 0.075 0.145 0.605 0.113 −0.207 −0.189 0.553⁎ 0.293 0.128 0.165
− 0.051 − 0.019 − 0.140 0.725⁎ − 0.061 0.067 − 0.185 − 0.031 −0.089 0.010 0.010
HNO3 0.246 0.217 0.622 0.212 0.827⁎⁎ − 0.173 0.073 − 0.190 − 0.150 0.505 0.511
H2O
Mehra and Jackson
− 0.111 − 0.240 − 0.012 0.191 0.599⁎ 0.049 − 0.180 0.204 − 0.148 − 0.212 − 0.207
0.791⁎⁎ 0.334 0.591 0.112 0.379 − 0.626⁎ 0.539⁎ − 0.546⁎ 0.375 0.771⁎⁎ 0.003
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Table 5 Normality test. Variable
Ryan–Joiner's coefficient (RJ)
p-value
Normality of data accepted?
MEQ1 MEQ2 MEQ3 MEQ4 MEQ5 MEQ6 MEQ7 MEQ8 MEQ9
0.643 0.675 0.944 0.709 0.732 0.605 0.760 0.794 0.752
p-value b 0.01 p-value b 0.01 p-value b 0.012 p-value b 0.01 p-value b 0.01 p-value b 0.01 p-value b 0.01 p-value b 0.01 p-value b 0.01
No No Yes No No No No No No
Significance level α = 0.05 or 0.01. MEQ1: Olsen, MEQ2: CH3COONH4, MEQ3: HCl, MEQ4: oxidisable, MEQ5: (NH4)2SO4, MEQ6: DTPA MEQ7: HNO3, MEQ8: H2O, MEQ9: Mehra– Jackson.
The behaviour of the different extractants was studied and the statistics proved the data were not normally distributed, so that the initial hypotheses of the analysis of the variance were not verified and ANOVA tests were not applicable. Therefore, a Kruskal–Wallis contrast was carried out to detect statistic differences. The results obtained after applying the Kruskal–Wallis contrast (Table 5) showed that there were two different methods of arsenic mobilization. These two chemical extraction methods (HCl and Mehra and Jackson) differ from the rest because they mobilized a higher arsenic percentage: mean values of 27.94 and 12.22, respectively. The rest of the methods showed a similar way of mobilizing arsenic (Fig. 2 Box plot). Attention should be drawn to method 4 (oxidisable), with a mean of 2, because, although this was not the extraction method that mobilized most arsenic, it best correlated with the total content of the element in the roots and leaves of the plant. A principal component analysis was made to group the different variables and check the distribution of the samples as a function of the same. This permitted us to group the samples as a function of their chemical and mineralogical composition in order to ascertain whether As mobility in the respective soils was dependent on these variables. To select the variables that make up each factor, values above 0.500 were chosen. The samples were also selected by reference to different types of variables. The factors obtained after carrying out the principal components analysis using Varimax rotated factor loadings were: • For Factor 1, with a Total Variance of 22.1%, the variables selected were: on the positive side, O.M. content, leaf As and Fe content, mobilizable arsenic with Olsen, oxidisable and Mehra and Jackson extractions, siderite and pyrite content. On the negative side: the hematite content. • For Factor 2, with a Total Variance of 18.3%, the variables selected were: on the positive side, total arsenic content of soil, total As content of roots, mobilizable arsenic with HCl extraction and goethite content. On the negative side, pH soils, carbonates content and mobilizable arsenic with Olsen extraction. • For Factor 3, with a Total Variance of 13.8%, the variables selected were: on the positive side, mobilizable arsenic with ammonium acetate, ammonium sulphate and water extractions. On the negative side, jarosite and magnetite content. • For Factor 4, with a Total Variance 16.7%, the variables selected on the positive side were: electrical conductivity in soils and mobilizable arsenic with HNO3 extraction. On the negative side, mobilizable arsenic with DTPA extraction. Factors 1 and 2 showed the variances of greatest weight in the component analysis. Fig. 3 shows that the samples with the highest pH values and lowest As values in soil were grouped in the negative part. A series of samples were grouped in the intermediate part since they did not vary greatly as regards these variables. Samples S3 and S4, which had a greater metal content (pyrite and siderite), were grouped differently.
Fig. 3. Graphic representation of Factor 1 vs. Factor 2.
Their grouping may have been influenced by their different localisation since they came from Portman Bay, while the other samples came from the Sierra Minera. Sample S5 was grouped in the positive part of F2, illustrating its low pH, high As content and low content of other metals. This sample shows good correlations with the extractions carried out with HCl and Mehra and Jackson reagents.
3.2. Arsenic level in plants Total arsenic concentrations in terrestrial plants varied greatly within plant species and sampling location. In general, plants growing in the contaminated area were found to have high arsenic concentrations (1.14 to 1750 mg kg− 1) compared with specimens collected from the uncontaminated area (ranging from 0.06 to 0.58 mg kg− 1) (Ruiz-Chancho et al., 2008). Plant samples were collected in all the soils studied. The samples were from five species: L. carthaginens, A. macrostachyum, D. viscosa, G. flavum and Z. fabago. The results showed that the plants did not absorb arsenic to the same extent and suggest that there is a good relationship between the total As content of the soil and that found in the plants. The average content in roots and leaves was 18.1 mg kg− 1 and 23.5 mg kg− 1, respectively. In short, the soils that had a greater arsenic content corresponded to the plants that contained more arsenic in their leaves (Table 6). The Pearson product-moment correlation test was used to establish possible relationships between the soluble arsenic content and the concentration of metalloid, measured in plant samples (Table 4). The Table 6 Total metal content of plant samples (N = 14). Sample
Plant
[As] Leave (mg/kg)
[As] Root (mg/kg)
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 Quantification limit (cl)
Dittrichia viscosa Dittrichia viscosa Limonium carthaginens Arthrocnemum macrostachyum Dittrichia viscosa Dittrichia viscosa Zygophyllum fabago Dittrichia viscosa Zygophyllum fabago Dittrichia viscosa Limonium carthaginens Glaucium flavum Limonium carthaginens Zygophyllum fabago
3 1 6 44 15 38 0.1 3 1 1 2 2 1 0.1 0.01
60 7 22 119 86 44 0.2 7 2 20 1 85 10 0.3 0.01
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results showed that the arsenic content in roots was positively correlated with the element extracted by HCl, in oxidising medium (third step of the sequential extraction procedure) and with the HNO3 and Mehra and Jackson extractions. The arsenic concentration in leaves was positively correlated with the arsenic extracted in the sulfides fraction (third step of the sequential extraction procedure), with Mehra and Jackson and with the arsenic extracted by Olsen method. The extraction carried out with DTPA and NH4Ac cannot be considered appropriate for studying As mobility since they were not comparable and no correlation was obtained. 4. Discussion The Pearson product-moment correlation test was used to establish possible relationships between of mineralogical composition of the soil and the arsenic content plant samples. A high and negative correlation (Ro = −0.472) was observed in the studied samples between the As content of leaves and the presence of hematite. The negative correlation of hematite with siderite and pyrite was due to them all being iron phases that compete amongst themselves. In the roots, good correlations were obtained between the As content and the presence of sulfates [gypsum (Ro = 0.493) and jarosite (Ro = 0.342)] and goethite (Ro = 0.289) in the soils. Gypsum and jarosite are secondary sulfates formed as a consequence of the alteration of sulfides at acid pH values. The high values observed for these minerals corresponded to mineralized samples with lower pHs. The content of As in the plant showed good correlations with the oxidised phases. The phases that are mobilized from the root to the leaves are the oxidised phases, which are particularly mobilized in acid soils. According to our results, As is accumulated in the leaves of the plants and is linked with the iron oxihydroxides contained in these mining-affected soils. 5. Conclusion Arsenic in the studied soils (which were affected by mining activity) shows different degrees of mobility, depending on the soil conditions. The arsenic fraction extracted was independent of the total content and depended, rather, on the properties of the extractants used, the highest values being obtained with acid (HCl) followed by a complexing-reductant medium. The behaviour of As in the mineral compounds of the soils studied is related with the behaviour of iron, both elements showing a low degree of mobility and remaining in the residual materials of the alteration processes. Both elements are contained in the silicates, carbonates and sulfides of the non-altered rock, and are concentrated during weathering in the new hydrated phases in the form of iron oxihydroxides and jarosites. After applying the different methods according to the recommendations described in the literature to study the bioavailability the element, little correlation was found between the As concentrations calculated by the different methods and that in the plants studied. It was therefore impossible to decide what may be considered the “best” method. Having said this, it was observed that the arsenic extracted by HCl extraction and in oxidising medium showed good correlations with the arsenic in the roots of plants. In the leaves, the best correlation between method and As concentration was obtained with the extractions carried out with the sulfide fraction (third step of sequential extraction procedure), Mehra and Jackson and the Olsen method.
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Acknowledgement The authors are grateful to the Spanish Ministerio de Ciencia e Innovación (CTM2008-04567) for the financial support.
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Geoderma j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g e o d e r m a
Evaluation of arsenic in soils and plant uptake using various chemical extraction methods in soils affected by old mining activities M.J. Martínez-Sánchez, S. Martínez-López, M.L. García-Lorenzo, L.B. Martínez-Martínez, C. Pérez-Sirvent ⁎ Department of Agricultural Chemistry, Geology and Pedology, University of Murcia, Spain
a r t i c l e
i n f o
Article history: Received 29 November 2009 Received in revised form 24 October 2010 Accepted 6 November 2010 Available online 3 December 2010 Keywords: Chemical extraction methods Bioavailable arsenic Plant uptake Mine soils Sediments
a b s t r a c t The aims of this study were to assess arsenic concentrations and solubility in soils, and its uptake by plant species growing in an area strongly affected by mining activities in Murcia (SE, Spain) paying attention to the mineralogical composition of the soils and the relation of the same with the bioavailability of this element. For this, extractions were made with several chemical reagents, to determine the bioavailability of the element in different environmental conditions (natural or potential) and, especially, the arsenic bioavailable for the plant uptake. Maximum extraction was achieved using the Mehra and Jackson procedure, while the least effective extractant was an ammonium sulphate solution. The strongest relationship between the As determined by soil chemical extractions and the As measured in plant biomass was found using the oxidisable-organic matter extraction procedure. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Arsenic, one of the most pollutant of toxins, is broadly distributed in nature. Background concentrations vary between 0.5 mg kg− 1 and 80 mg kg− 1 (average 10 mg kg− 1) in soils (Kabata-Pendias and Mukherjee, 2007), although higher concentrations are frequently found in soils and sediments that have been affected by anthropogenic activities such as mining or agriculture, or where the soils and sediments are derived from As-rich mineralized rocks. In ores, this element is usually present in the sulphide form associated to pyrite, arsenopyrite or pyrrhotite, while, in soils, two main oxidation states, arsenate [As(V)] and arsenite [As (III)], are usually found (Nriagu et al., 2007). Arsenic is naturally found in plants, where its concentration seldom exceeds 1 mg kg− 1 (Adriano, 2001); indeed, it has been reported that at very low concentrations it can even be beneficial for plant growth (Gulz et al., 2005). It can be absorbed from soils or from the matter deposited on leaves. Arsenic uptake by plants depends on its concentration and speciation in soil. Uptake is usually in the form of arsenate, which produces stress in the plants, including growth inhibition (Gunes et al., 2008). Some plant species are able to grow in soils with a very high arsenic content, demonstrating that they have developed tolerance mechanisms (Jedynak et al., 2009). Plant tolerance is normally 2 mg kg− 1 (Kabata-Pendias and Mukherjee, 2007). Arsenic contamination is a subject of great importance due to
⁎ Corresponding author. Tel.: + 34 868887449; fax: +34868884148. E-mail address: [email protected] (C. Pérez-Sirvent). 0016-7061/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2010.11.001
its carcinogenic nature and its adverse effect on the ecosystem. The most serious risk posed by this carcinogenic metalloid is associated with the forms that are biologically available for absorption, or “bioavailable,” to plants. Bioavailability of a substance is a function of the abundance, chemical form, and the way in which it is bound to soil particles (Traina and Laperche, 1999). The extractable fraction of As may provide a better indication of its bioavailability and mobility in soils (Adriano, 2001). A direct relationship between different chemical forms of As in soils (extracted by single chemical extraction methods) and the level of the element measured in plants has seldom been reported. It is therefore necessary to determine the best chemical extractant and the form of As in soils that will decide the availability, solubility and bioavailability of the element for plant uptake. Therefore, it was thought interesting to develop analytical techniques to evaluate the As species in the most soluble and mobilizable fractions, which are the most bioavailable (Anawar et al., 2007). The fate and transfer of metals are a complex subject, and depend on the physical transport process involved and the soil mineralogy (Navarro et al., 2006). The mineralogical composition of the soil is one important factor for the mobility of metals and, possibly, for their bioavailability to plants (Martínez-Sánchez and Pérez-Sirvent, 2007). Various chemical methods are available to extract arsenic from the different fractions of soil, but their capacity to estimate the As available for plants in mining soils is largely unknown. Single chemical extractions can be used to estimate and predict bioavailable As and plant uptake from soils affected by mining activities, since different environmental situations can be simulated (Ure et al., 1992).
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2. Materials and methods 2.1. Sample characteristics For this study, fourteen samples were collected in the surrounding area of Sierra Minera and Portman Bay (Murcia, SE Spain) (Fig. 1). The studied zone is close to the mining region of La Unión (SE Spain) (Martínez-Sánchez et al., 2008). The entire area is located between Cabo de Palos and Cartagena and has been subjected to mining for many centuries. It constituted one of the most important mining districts of Spain and produced substantial amounts of lead, silver, zinc and pyrites until 1991 (Navarro et al., 2008). The area presents a central zone with sulfides, carbonates, silica, oxides and hydroxides, and an intermediate zone with sulfide mineralizations, carbonates, greenalite and magnetite. The mineralization occurs (i) within a strongly altered zone above the Miocene footwall; (ii) in pebbly mudstone beds where the hydrothermal activity led to dissolution, void formation and mineral deposition; and (iii) in fault breccias along the normal faults that bound the Miocene sediments (Navarro et al., 2008). The average annual temperature is 17 °C and precipitation does not exceed 300 mm, with occasional torrential rainfall, which frequently occurs in the period between the end of summer and autumn. Samples of five plant species, which grow naturally in the studied area, were collected. Plants were collected from soil sampling points, taking three plants of each species (the results for which were averaged). The following species were collected: Limonium carthaginens, an endemic specie of the study zone which usually grows in semiarid areas with low nutritional needs; Arthrocnemun macrostachyum, with a similar
behaviour to the species mentioned but which usually grows in wet saline areas; Dittrichia viscosa, Zygophyllum fabago and Glaucium flavum, all of which are abundant along roadsides and in the studied area. The first four species are resistant and perennial, while G. flavum is a short cycle plant. The soils studied have been exposed to serious interventions in the form of mining (Antrosol), although natural soils developed on the volcanic rocks still remain. The soils around Sierra Minera are calcareous and may occasionally be flooded after rain, when they receive substantial quantities of materials from the mining area (Navarro et al., 2008). The samples were taken from the rizosphere. 2.2. Sample preparation Soil samples were air dried and sieved to b2 mm for general analytical determinations. The pH was determined in a 1:5 suspension of soil in pure water and in a 1 M KCl solution (Peech, 1965) using a Crison GLP21 pH meter. Electrical conductivity (EC) (mS cm− 1) was measured using a Crison GlP31 conductivity meter in the extracts obtained by filtering the 1:5 suspensions through a 0.45 μm cellulose acetate disk filter. Equivalent calcium carbonate (%) was determined by the volumetric method using a Bernard calcimeter previously calibrated against Na2CO3 (Hulseman, 1996). Organic carbon was determined by sulfochromic oxidation (Nelson and Sommers, 1982) according to the NF X31-109 standard (Norme Française) (AFNOR, 1993). A semi quantitative estimation of the mineralogical composition of the samples was made by X Ray Diffraction (XRD) analysis using Cu-Kα radiation with a PW3040 Philips Diffractometer. X-powder software
Fig. 1. Studied zone.
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(Martín, 2004) was used to analyse the X-ray diffraction diagrams obtained by the crystalline powder method. The powder diffraction file (PDF2) database was used for peak identification, taking into account that the determination of minerals from soils by XRD analysis is not accurate below a limit of 5% of the total weight in a sample (depending on the crystallinity of individual minerals). The software incorporates precise quantitative studies done by methods of nonlinear least squares on a full profile of the full diffractogram and takes full advantage of the information contained in the database records. Weighting was achieved with the standard Reference Intensity Ratios (RIR) method described by Chung (1974). The automatic use of this method assumes that the database contains the chemical composition of each phase. To determine the total metal content (Pb, Zn, Cd, Fe and As), soil samples were first ground to a fine powder using an agate ball mill. Samples were placed in Teflon vessels and 5 ml of concentrated hydrofluoric acid solution, 2 ml of concentrated nitric acid solution and 5 ml of water were added. When digestion (15 min at 1000 W in a Milestone ETHOS PLUS microwave) was complete, the samples were transferred to a volumetric flask and brought to 50 ml. In the case of plant material, the aerial fraction was separated from the root, washed and then lyophilized. Then, 200 mg of lyophilized vegetal tissue were placed in Teflon vessels, and 3 ml water, 2 ml concentrated H2O2 and 5 ml concentrated HNO3 acid solution were added. After the digestion stage, 50 ml solutions were obtained, as indicated for soil samples. The Zn and Fe content was determined by flame atomic absorption spectrometry (FAAS) using a Perkin-Elmer 1100B Atomic Absorption Spectrophotometer, and the Pb and Cd content was determined by electrothermal atomization atomic absorption spectrometry (ETAAS) using a Unicam 929 AA Spectrometer. Arsenic levels were obtained by using atomic fluorescence spectrometry with an automated continuous flow hydride generation (HG-AFS) spectrometer (PSA Millenium Excalibur 10055). The experimental conditions and instrumental parameters of arsenic analysis were as follows: wavelength (nm)= 197.3, primary current (mA) = 27.5, boost current (mA)= 35, delay time (s) = 15, analysis time (s) = 30, memory time (s) = 30, air flow rate (ml/min)= 300. The reliability of the results was verified through the analysis of two standard reference materials (SRM 2711 Montana Soil and SRM 1515 Apple leaves). Spikes, duplicates and reagent blanks were also used as a part of our quality control. 2.3. Chemical extraction methods Nine chemical extractions methods were used to assess arsenic availability in soil: water medium (1:5 extract), nitric acid medium (1 g of solid in 50 ml 0.1 M HNO3), hydrochloric acid medium (1 g of solid in 25–50 ml 0.5 M HCl), (an adaptation of the chemical extraction method of Adriano, 2001), complexing–reducing medium (Mehra and Jackson, 1960), oxidising medium (step 3 BCR, (Community Bureau of Reference)) (Sutherland and Tack, 2002), 0.5 M HNaCO3 (Olsen et al., 1954), ammonium acetate (5 g of solid in 112.5 ml 1 M CH3COONH4) (Adriano, 2001), 0.05 M (NH4)2SO4 (Wenzel et al., 2002) and 0.005 M DTPA (Lindsay and Norvell, 1978). 2.4. Data analysis A statistical analysis of the data was made with MINITAB 14 and SYSTAT V11 software. The Pearson product-moment correlation test was used to establish possible relationships between the soluble arsenic content and the mineralogical composition of the soil samples and possible relationships between the former and the arsenic content of plant samples. In order to contrast the normality of the samples, the normal probability test was used together with Ryan–Joiner's contrast statistics. To study the way in which arsenic is mobilized in the different extraction
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Table 1 Characteristics of the soil samples (N = 14). Sample
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 Maximum Mean Minimum Standard deviation
E.C
CaCO3
O.M
E.h
Clay
H20
KCl
(mS/cm)
%
(%)
(m.V.)
(%)
4.98 8.10 7.51 7.37 4.25 8.12 7.91 7.71 7.54 9.06 7.77 6.86 6.64 5.27 9.06 7.08 4.25 1.36
4.76 7.89 7.29 7.22 4.15 7.88 7.75 7.45 7.33 8.52 7.54 6.36 6.37 4.54 8.52 6.79 4.15 1.37
7.4 2.8 0.2 3.5 2.5 0.5 0.4 0.7 0.5 0.2 2.9 1.4 1.7 0.5 7.40 1.80 0.20 1.97
0.0 10.1 0.3 0.0 0.0 13.3 28.7 0.0 4.9 60.1 0.0 0.0 0.0 0.0 60.10 8.39 0.00 16.99
0.3 0.8 0.8 1.9 1.0 1.2 0.5 1.3 0.7 0.2 0.5 1.0 0.5 1.0 1.90 0.84 0.20 0.45
299 136 170 126 257 215 228 206 214 206 178 282 255 215 299.00 213.36 126.00 50.2
1.2 1.0 0.2 0.3 1.9 0.6 1.0 0.1 0.1 0.3 0.1 0.1 0.1 0.1 1.90 0.51 0.10 0.56
pH
methods, the Kruskal–Wallis test was used. This is a non-parametric test which is used when the distribution of the samples is not normal. Differences at p b 0.05 level were considered significant. In addition, an analysis of the main components was carried out using the Varimax rotated factor loadings.
3. Results 3.1. Soils properties and metal concentrations The general characteristics of the soil samples are shown in Table 1 and the total metal content is shown in Table 2. The samples showed average pH values close to neutrality, although two of them had acidic pH values. In accordance to the consulted bibliography and the results obtained with the Pearson product-moment correlation test, the pH was negatively correlated with total As. Most samples showed a very low organic matter content (mean value of 0.8%). The soils with the highest organic matter content were those containing dolomite, while those with high phyllosilicates and pyrite levels showed the lowest values. The results showed that the organic matter is attacked by oxidising agents and that the greater the
Table 2 Total metal content of soil samples (N = 14). Sample
Pb (mg/Kg)
Zn (mg/Kg)
Fe (%)
Cd (mg/Kg)
As (mg/Kg)
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 Maximum Minimum Mean Standard deviation Quantification limit (cl)
3990 1241 1700 2330 554 1940 538 2040 3000 63 3600 4700 6750 13,500 13,500.0 63.0 3282.0 3457.0 0.174
8980 3153 8400 9900 2390 1200 1490 3670 3050 135 7900 7060 7200 4000 9900.0 135.0 4894.8 3224.0 0.193
25.11 26.12 38.63 50.59 4.33 7.39 4.63 11.81 18.99 16.12 19.21 24.53 24.27 19.26 50.59 4.33 20.83 12.77 0.002
88 123 165 74 2 11 12 14 14 5 33 19 20 15 165.0 2.4 43.3 50.0 0.188
752 669 1553 1868 3115 303 327 563 664 40 454 561 530 644 3115.0 40.0 860.2 805.0 0.01
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Fig. 2. Mineralogical composition. a. Semi-quantitative mineralogical composition obtained from the low-size fraction (b2 mm). b. X-ray diffraction patterns of two samples: a carbonate-rich soil (sample S10) and a mining soil (sample S4).
organic matter content, the lower the content of mobilizable arsenic. Electrical conductivity was low in most samples, as was the calcium carbonate content. The samples with the highest conductivity values in the topmost layer were those with a high phyllosilicate content.
As regards the intercorrelations of the total metals, the good correlation between iron, cadmium and zinc reflected the mineralogical source of these metals, all of which are associated to sulfides. Lead and arsenic were negatively correlated with pH, since these very
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539
Table 3 Soluble arsenic content of the soil samples (mg/kg). Sample
H2O
HNO3
HCl
Oxidisable
Mehra and Jackson
DTPA
(NH4)2SO4
Olsen
NH4Ac
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 Maximum Mean Minimum Standard deviation (cl)
0.1 0.1 bcl bcl bcl bcl bcl bcl 0.1 bcl bcl bcl bcl bcl 0.1 0.02 0.0 0.04 0.01
15.7 6.8 7.1 10.6 0.3 0.2 b cl b cl 0.3 b cl b cl 0.1 0.1 b cl 15.7 2.9 0.0 5.07 0.1
65.2 44.4 37.9 68.6 90.3 16.5 39.6 16.1 28.6 0.5 27.2 26.2 24.6 6.5 90.3 35.2 0.5 25.12 0.1
4.6 1.1 5.9 23.5 1.3 2.0 4.3 2.1 1.7 1.6 2.0 0.7 0.9 2.0 23.5 3.84 0.7 5.86 0.1
110.7 38.1 220.6 167.1 151.9 6.2 6.9 6.8 13.4 4.9 9.3 11.2 7.6 13.3 220.6 54.9 4.9 74.4 0.1
0.1 0.1 0.2 0.2 0.1 0.1 0.1 1.2 b cl b cl 0.1 0.1 0.1 0.3 1.2 0.2 0.0 0.29 0.004
0.01 b cl b cl 0.01 0.01 b cl b cl 0.02 b cl b cl b cl b cl b cl b cl 0.02 0.0 0.0 0.01 0.005
0.4 0.6 1.3 1.0 0.1 1.4 1.9 0.3 1.0 0.1 0.6 0.4 0.5 0.2 1.9 0.7 0.1 0.54 0.02
0.1 bcl 0.1 0.1 bcl 0.1 0.3 bcl 1.1 bcl 0.1 bcl bcl bcl 1.1 0.1 0.0 0.29 0.04
(cl): Quantification limit of arsenic.
mineralized materials were associated to acid pH values. The samples showing the highest contents of these metals corresponded to very mineralized soils and to the lowest pH values. A negative correlation existed between the CaCO3 content and the iron and zinc levels. The mineralogical analysis showed that the main minerals were quartz, muscovite, kaolinite and illite, while the minority minerals were products of mining activities (iron oxides and hydroxides, siderite, jarosite and gypsum), calcite and feldspars (Fig. 2). As regards the correlations within the mineralogical composition, there was a very close correlation between gypsum and jarosite (Ro = 0.685) and goethite (Ro = 0.846) all formed by alteration of the original mineralogy. Calcite and quartz showed good correlation (Ro = 0.513), although not very significant, because they are original minerals, while there was also a good (but negative) correlation between calcite and jarosite (Ro = −0.718) and gypsum and quartz (Ro = −0.663). The concentration of soluble arsenic was determined after nine different extractions, which gave the following mean values: 0.69 mg kg− 1 for the Olsen extraction, 0.14 mg kg− 1 for extraction by ammonium acetate, 35 mg kg− 1 for HCl acid, 3.8 mg kg− 1 in the oxidising medium, 0.2 mg kg− 1 for DTPA extraction, 3.2 mg kg− 1 for HNO3, 55 mg kg− 1 for the Mehra and Jackson extraction and 0.03 mg kg− 1 with water. Finally, the ammonium sulphate extraction provided values below the quantification limit. The grade of effectiveness of the extractants used was: oxidisableN Mehra and JacksonN HNO3 N HClN NH4AcN DTPAN OlsenN ammonium sulphateN water (the results are showed in Table 3). The Pearson product-moment correlation test was used to establish possible relationships between the arsenic extracted and the mineral-
ogical composition of the soil samples (Table 4). The results showed that samples which contained phyllosilicates in their mineralogical composition were positively correlated with the DTPA, ammonium sulphate, water and HNO3 extractions. Secondly, the samples which contained gypsum were positively correlated with the HCl and Mehra–Jackson extractions. Thirdly, the samples which contained goethite were positively correlated with the HCl extractions and, finally, the samples which contained hematite, siderite and pyrite were positively correlated with the oxidisable and Mehra and Jackson extractions, while the samples which contained quartz and carbonates were negatively correlated with the Mehra and Jackson extraction. In the carbonated soil studied, arsenic is not mobilized and so there was no arsenic uptake by plants. However, arsenic was linked with the iron oxides in the studied soils (affected by mining activities), so that an increase in acidity in such conditions may cause arsenic to be mobilized. The samples with the highest arsenic content were those containing minerals derived from the mining activity; the same soils also showed high sulfate (gypsum) and iron oxihydroxide (goethite) values and contained little or no quartz. This high and negative correlation of quartz with arsenic (Ro = −0.727) was difficult to evaluate because quartz was a very abundant mineral in all the studied samples and its variations were not very significant. The mineralogical composition, as well as the size of the particles of the samples, influenced arsenic mobility. In order to study the methodology of arsenic mobilization by the different chemical extraction methods applied, it was first necessary to contrast the normality of the samples, for which the normal probability test was used together with Ryan–Joiner's statistics of contrast (see Table 5).
Table 4 Relationships (Ro) between mobilized arsenic, arsenic content in soil and plant samples and mineralogical composition (mg/kg). Olsen As Total soil As Total Leave As Total Root Phyllosicate14 Phyllosicate10 Quartz Gypsum Calcite Goethite Siderite Pyrite ⁎ p b 0.05. ⁎⁎ p b 0.002.
− 0.153 0.310 − 0.085 − 0.089 0.187 0.435 − 0.399 0.568⁎ 0.318 0.337 0.316
NH4Ac − 0.103 −0.131 − 0.224 0.147 − 0.170 0.082 − 0.320 0.385 − 0.155 − 0.052 −0.051
HCl 0.813⁎⁎ 0.294 0.708⁎ − 0.030 0.373 −0.480 0.694⁎ − 0.272 0.632⁎ 0.329 0.353
Oxidisable
(NH4)2SO4
DTPA
0.368 0.629⁎ 0.641⁎ 0.132 0.242 − 0.284 0.030 − 0.262 − 0.124 0.844⁎⁎ 0.901⁎⁎
0.386 0.075 0.145 0.605 0.113 −0.207 −0.189 0.553⁎ 0.293 0.128 0.165
− 0.051 − 0.019 − 0.140 0.725⁎ − 0.061 0.067 − 0.185 − 0.031 −0.089 0.010 0.010
HNO3 0.246 0.217 0.622 0.212 0.827⁎⁎ − 0.173 0.073 − 0.190 − 0.150 0.505 0.511
H2O
Mehra and Jackson
− 0.111 − 0.240 − 0.012 0.191 0.599⁎ 0.049 − 0.180 0.204 − 0.148 − 0.212 − 0.207
0.791⁎⁎ 0.334 0.591 0.112 0.379 − 0.626⁎ 0.539⁎ − 0.546⁎ 0.375 0.771⁎⁎ 0.003
540
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Table 5 Normality test. Variable
Ryan–Joiner's coefficient (RJ)
p-value
Normality of data accepted?
MEQ1 MEQ2 MEQ3 MEQ4 MEQ5 MEQ6 MEQ7 MEQ8 MEQ9
0.643 0.675 0.944 0.709 0.732 0.605 0.760 0.794 0.752
p-value b 0.01 p-value b 0.01 p-value b 0.012 p-value b 0.01 p-value b 0.01 p-value b 0.01 p-value b 0.01 p-value b 0.01 p-value b 0.01
No No Yes No No No No No No
Significance level α = 0.05 or 0.01. MEQ1: Olsen, MEQ2: CH3COONH4, MEQ3: HCl, MEQ4: oxidisable, MEQ5: (NH4)2SO4, MEQ6: DTPA MEQ7: HNO3, MEQ8: H2O, MEQ9: Mehra– Jackson.
The behaviour of the different extractants was studied and the statistics proved the data were not normally distributed, so that the initial hypotheses of the analysis of the variance were not verified and ANOVA tests were not applicable. Therefore, a Kruskal–Wallis contrast was carried out to detect statistic differences. The results obtained after applying the Kruskal–Wallis contrast (Table 5) showed that there were two different methods of arsenic mobilization. These two chemical extraction methods (HCl and Mehra and Jackson) differ from the rest because they mobilized a higher arsenic percentage: mean values of 27.94 and 12.22, respectively. The rest of the methods showed a similar way of mobilizing arsenic (Fig. 2 Box plot). Attention should be drawn to method 4 (oxidisable), with a mean of 2, because, although this was not the extraction method that mobilized most arsenic, it best correlated with the total content of the element in the roots and leaves of the plant. A principal component analysis was made to group the different variables and check the distribution of the samples as a function of the same. This permitted us to group the samples as a function of their chemical and mineralogical composition in order to ascertain whether As mobility in the respective soils was dependent on these variables. To select the variables that make up each factor, values above 0.500 were chosen. The samples were also selected by reference to different types of variables. The factors obtained after carrying out the principal components analysis using Varimax rotated factor loadings were: • For Factor 1, with a Total Variance of 22.1%, the variables selected were: on the positive side, O.M. content, leaf As and Fe content, mobilizable arsenic with Olsen, oxidisable and Mehra and Jackson extractions, siderite and pyrite content. On the negative side: the hematite content. • For Factor 2, with a Total Variance of 18.3%, the variables selected were: on the positive side, total arsenic content of soil, total As content of roots, mobilizable arsenic with HCl extraction and goethite content. On the negative side, pH soils, carbonates content and mobilizable arsenic with Olsen extraction. • For Factor 3, with a Total Variance of 13.8%, the variables selected were: on the positive side, mobilizable arsenic with ammonium acetate, ammonium sulphate and water extractions. On the negative side, jarosite and magnetite content. • For Factor 4, with a Total Variance 16.7%, the variables selected on the positive side were: electrical conductivity in soils and mobilizable arsenic with HNO3 extraction. On the negative side, mobilizable arsenic with DTPA extraction. Factors 1 and 2 showed the variances of greatest weight in the component analysis. Fig. 3 shows that the samples with the highest pH values and lowest As values in soil were grouped in the negative part. A series of samples were grouped in the intermediate part since they did not vary greatly as regards these variables. Samples S3 and S4, which had a greater metal content (pyrite and siderite), were grouped differently.
Fig. 3. Graphic representation of Factor 1 vs. Factor 2.
Their grouping may have been influenced by their different localisation since they came from Portman Bay, while the other samples came from the Sierra Minera. Sample S5 was grouped in the positive part of F2, illustrating its low pH, high As content and low content of other metals. This sample shows good correlations with the extractions carried out with HCl and Mehra and Jackson reagents.
3.2. Arsenic level in plants Total arsenic concentrations in terrestrial plants varied greatly within plant species and sampling location. In general, plants growing in the contaminated area were found to have high arsenic concentrations (1.14 to 1750 mg kg− 1) compared with specimens collected from the uncontaminated area (ranging from 0.06 to 0.58 mg kg− 1) (Ruiz-Chancho et al., 2008). Plant samples were collected in all the soils studied. The samples were from five species: L. carthaginens, A. macrostachyum, D. viscosa, G. flavum and Z. fabago. The results showed that the plants did not absorb arsenic to the same extent and suggest that there is a good relationship between the total As content of the soil and that found in the plants. The average content in roots and leaves was 18.1 mg kg− 1 and 23.5 mg kg− 1, respectively. In short, the soils that had a greater arsenic content corresponded to the plants that contained more arsenic in their leaves (Table 6). The Pearson product-moment correlation test was used to establish possible relationships between the soluble arsenic content and the concentration of metalloid, measured in plant samples (Table 4). The Table 6 Total metal content of plant samples (N = 14). Sample
Plant
[As] Leave (mg/kg)
[As] Root (mg/kg)
S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 Quantification limit (cl)
Dittrichia viscosa Dittrichia viscosa Limonium carthaginens Arthrocnemum macrostachyum Dittrichia viscosa Dittrichia viscosa Zygophyllum fabago Dittrichia viscosa Zygophyllum fabago Dittrichia viscosa Limonium carthaginens Glaucium flavum Limonium carthaginens Zygophyllum fabago
3 1 6 44 15 38 0.1 3 1 1 2 2 1 0.1 0.01
60 7 22 119 86 44 0.2 7 2 20 1 85 10 0.3 0.01
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results showed that the arsenic content in roots was positively correlated with the element extracted by HCl, in oxidising medium (third step of the sequential extraction procedure) and with the HNO3 and Mehra and Jackson extractions. The arsenic concentration in leaves was positively correlated with the arsenic extracted in the sulfides fraction (third step of the sequential extraction procedure), with Mehra and Jackson and with the arsenic extracted by Olsen method. The extraction carried out with DTPA and NH4Ac cannot be considered appropriate for studying As mobility since they were not comparable and no correlation was obtained. 4. Discussion The Pearson product-moment correlation test was used to establish possible relationships between of mineralogical composition of the soil and the arsenic content plant samples. A high and negative correlation (Ro = −0.472) was observed in the studied samples between the As content of leaves and the presence of hematite. The negative correlation of hematite with siderite and pyrite was due to them all being iron phases that compete amongst themselves. In the roots, good correlations were obtained between the As content and the presence of sulfates [gypsum (Ro = 0.493) and jarosite (Ro = 0.342)] and goethite (Ro = 0.289) in the soils. Gypsum and jarosite are secondary sulfates formed as a consequence of the alteration of sulfides at acid pH values. The high values observed for these minerals corresponded to mineralized samples with lower pHs. The content of As in the plant showed good correlations with the oxidised phases. The phases that are mobilized from the root to the leaves are the oxidised phases, which are particularly mobilized in acid soils. According to our results, As is accumulated in the leaves of the plants and is linked with the iron oxihydroxides contained in these mining-affected soils. 5. Conclusion Arsenic in the studied soils (which were affected by mining activity) shows different degrees of mobility, depending on the soil conditions. The arsenic fraction extracted was independent of the total content and depended, rather, on the properties of the extractants used, the highest values being obtained with acid (HCl) followed by a complexing-reductant medium. The behaviour of As in the mineral compounds of the soils studied is related with the behaviour of iron, both elements showing a low degree of mobility and remaining in the residual materials of the alteration processes. Both elements are contained in the silicates, carbonates and sulfides of the non-altered rock, and are concentrated during weathering in the new hydrated phases in the form of iron oxihydroxides and jarosites. After applying the different methods according to the recommendations described in the literature to study the bioavailability the element, little correlation was found between the As concentrations calculated by the different methods and that in the plants studied. It was therefore impossible to decide what may be considered the “best” method. Having said this, it was observed that the arsenic extracted by HCl extraction and in oxidising medium showed good correlations with the arsenic in the roots of plants. In the leaves, the best correlation between method and As concentration was obtained with the extractions carried out with the sulfide fraction (third step of sequential extraction procedure), Mehra and Jackson and the Olsen method.
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Acknowledgement The authors are grateful to the Spanish Ministerio de Ciencia e Innovación (CTM2008-04567) for the financial support.
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