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Heavy metal leaching of contaminatedsoils from a metallurgical plantPaolo Desogus a , Pier Paolo Manca a & Giampaolo Orrù aa Department of Geoengineering and Environmental Technologies(DIGITA) , University of Cagliari , Cagliari , ItalyPublished online: 30 Jul 2012.

To cite this article: Paolo Desogus , Pier Paolo Manca & Giampaolo Orrù (2013) Heavy metalleaching of contaminated soils from a metallurgical plant, International Journal of Mining,Reclamation and Environment, 27:3, 202-214, DOI: 10.1080/17480930.2012.708221

To link to this article: http://dx.doi.org/10.1080/17480930.2012.708221

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Heavy metal leaching of contaminated soils from a metallurgical plant

Paolo Desogus, Pier Paolo Manca* and Giampaolo Orr�u

Department of Geoengineering and Environmental Technologies (DIGITA), University ofCagliari, Cagliari, Italy

(Received 29 May 2012; final version received 25 June 2012)

Laboratory tests were carried out to determine the primary parametersaffecting the efficiency of the process of leaching heavy metals from naturalsoil collected inside a metallurgical plant in Italy. The soil samples testedconsisted primarily of medium density, fine silica sand that had beencontaminated with lead and zinc metallurgical wastes by percolating rainwater.Samples were obtained 1–2 m below ground level in an area where the surfaceconsists of artificial strata composed of soil mixed with metallurgical solidwaste. The soil layer tested hosts a low-permeability aquifer (at depthsgenerally greater than 10 m below ground level) that is also contaminated byheavy metals. Batch leaching experiments were conducted using acid solutions(acetic, nitric, hydrochloric and sulphuric) as extracting agents. Duringleaching tests, concentrations of Zn, Pb, Cd, Cu, Mn and Al were monitored.The results indicate that the best leaching solution varies for all analytes andsoil samples examined. Statistical analysis was used to identify correlationsbetween efficiency and leaching rate.

Keywords: metallurgical plant; contaminated soils; heavy metal leaching; soilwashing

1. Introduction

Heavy metals in soils may be present in several forms, with different levels ofsolubility: (a) dissolved (in soil solution), (b) exchangeable (in organic andinorganic components), (c) structural components of lattices in soils and (d)insolubly precipitated with other soil components [1]. The most toxic forms ofthese metals in their ionic species are also the most stable oxidation states, e.g.Cd2 þ, Pb2 þ, Hg2 þ, Agþ and As3 þ. In this form, they react with the body’s bio-molecules to form extremely stable biotoxic compounds which are difficult todissociate [2].

In general, the mobility and availability of heavy metals are controlled byadsorption and desorption characteristics of soils, which have been shown to beassociated with soil properties, including pH, speciation, organic matter content,cation exchange capacity, oxidation reduction status (Eh), the contents of clayminerals, calcium carbonate and Fe and Mn oxides [3–5].

*Corresponding author. Email: ppmanca@unica.it

� 201 Taylor & Francis

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In the past, smelting wastes, particularly slags, were considered relativelynonreactive because potential contaminants were surrounded by a layer ofcompounds with low solubility, such as silicates and oxides. Recent studies haveshown, however, that foundry slag may be more reactive than previously thoughtand may be a source of heavy metal contamination in surface water, soil andgroundwater [6–8].

Numerous techniques (containment, ex situ treatment, in situ treatment) havebeen tested for remediation of soils polluted by smelting wastes to minimiseenvironmental and health risks; these include thermal treatment, stabilisation andsolidification, soil washing, reactive barriers, soil flushing, electrokinetics, andphytoremediation [9,10–14]. Among these techniques, chemical extractionrepresents a very promising method, not only because it is one permanentsolution for soil pollution abatement, but it can also be used in largecontaminated areas due to its rapid kinetics, simple applicability and economicefficiency [15].

Soil washing involves separating pollutants from the soil matrix by solubilisingthem in a washing solution [16,17], usually implemented as an ex situ process. Thismethodology has been successfully used for the treatment of soils polluted by heavymetals, hydrocarbons and semi-volatile organic compounds, while it is less effectivein treating volatile organic compounds and pesticides [18].

For washing techniques, the selection of extractants (or agents) is the mostimportant step, as the effectiveness of each extractant varies, depending on its targetcontaminants and soil characteristics. Pollutants with low aqueous solubilities, suchas heavy metals, usually require the presence of strong acids or chelating agents, suchas ethylenediaminetetraacetic acid for effective removal [17,19,20]. There are fourways in which metals can be mobilised in soils: (a) changes in acidity, (b) changes insolution ionic strength, (c) changes in Redox potential and (d) formation ofcomplexes. The efficiency of metal removal depends mainly on soil and metalcharacteristics (e.g. crystalline, solubility), extractant chemistry and processingconditions [21].

This study focuses on soils at a metallurgical plant which was built in Italy in the1970s, and which produces lead and zinc. The plant is still active and has graduallyexpanded in size and in the types of treatment processes used. The soil stratigraphyof the site consists of a surface coating containing processed wastes. These wastes areunevenly distributed and can be transported by rainwater to a low-permeabilityshallow aquifer (generally 10 m below ground level). The soil and ground water inthe area of the plant are contaminated by heavy metals, both in solid and solubleforms. The main contaminants are cadmium, zinc and lead. The purpose of thisstudy was to establish the efficiency of various extracting agents in the removal ofheavy metals from contaminated soil at the site. To this end, batch testing was usedto evaluate extractants.

The experimental results obtained highlight the importance of preliminarylaboratory tests in evaluating the influence of some basic parameters on the efficiencyof soil washing, such as the types of contaminants, soil composition and leachingagents. The study of kinetics applied to the different soil samples permitshighlighting of any geochemical differences in the pollution process.

The experimental data obtained has also made it possible to establish the specialform of correlation existing between the efficiency of contaminant removal andreaction speed.

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2. Materials and methods

2.1. Soil

Soil stratigraphy at the site includes a thin surface layer largely consisting ofprocessing wastes. Processing wastes are also unevenly distributed in the underlyingnatural soil, which consists of the Quaternary deposits of southwest Sardinia:

. alluvial deposits (red-brown clasts varying from 2 to 3 cm in size);

. aeolian deposits (quartz sandstone and yellowish-white limestone, loose attimes, but more often cemented and partially stratified); and

. recent lacustrine deposits (silt-sand and clay).

Soil samples were collected from four locations in the upper soil layer about 1–2 m below the surface. The four samples (PZP4, PZP10, PZE9 and PZP25) showedsimilar particle size distribution and other physical characteristics (Tables 1 and 2)but had different pH values and concentrations of heavy metal contamination.

Each sample was subjected to X-ray diffraction (XRD) analysis to identify itsmineralogical components. To determine the phases, XRD results were compared tothe International Centre for Diffraction Data (ICDD) Index [22]. The results (Figure1) identify quartz, sanidine and plagioclase as major components and indicatesimilar mineralogical associations among the samples.

Additionally, each sample was chemically characterised to determine major andminor components and loss on ignition (LOI). Major components were identified asa percentage of the total sample by mixing lithium tetraborate with the sample inplatinum crucibles and then fusing the mixture in furnaces at 11508C. The fusedcrucible was placed in a mixture of 5% nitric acid; the resulting solution was

Table 1. Composition of soil samples.

Components Particle size (mm) Wt.%

Gravel 42 0Sand 0.0625–2 90–95Silt 0.002–0.0625 10–5Clay 50.002 0

Table 2. Soil sample characteristics.

Properties PZP25 PZP4 PZE9 PZP10

pH 7.9 5.3 9.0 8.3

D50 (mm) 0.300D10 (mm) 0.100D60 (mm) 0.400U 55k Hazen (m/s) 161074

Organic carbon (%) 0.10Total porosity (%) 40Bulk density (g/cm3) 1.5Specific weight (g/cm3) 2.4

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Figure

1.

XRD

analysisofsamples.

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analysed to determine the oxides of major elements. Minor components (measuredas mg/kg of dry mass of final samples) were isolated by acid digestion in a microwaveoven (Tables 3 and 4). In both cases, elements were determined by means ofinductively coupled plasma-optical emission spectroscopy (ICP/OES) (Perkin ElmerOptima 7000 DV ICP/OES spectrometer). The concentrations of Zn and Pb insample PZP25 and Cd and Zn in sample PZP4 exceeded Italian reference standards(Table 4) [23]; consequently, Zn, Pb and Cd were selected as the target metals forremediation. Although Cu, Mn and Al were not metals of primary concern, their co-existence in soils has been well noted, and their competition with target metalsduring soil washing was also investigated.

Loss on ignition was determined by comparing the mass of a portion of eachsample before and after being placed in a muffle furnace at 9508C for 1 h.

Table 3. Major components of samples used in leaching tests.

Component

Percentage by weight

PZP4 PZP25 PZP10 PZPE9

Al2O3 6.7 6.4 8.3 6.1Fe2O3 4.7 1.0 1.3 0.7MnO 0.0 0.0 0.0 0.0MgO 0.1 0.3 0.2 0.1CaO 3.3 1.4 1.3 0.6K2O 2.4 2.3 2.9 2.3Na2O 0.1 0.1 0.1 0.1TiO2 0.1 0.1 0.2 0.1P2O5 0.1 0.0 0.0 0.0SiO2 78.3 86.9 85.2 89.4LOI 3.9 1.8 1.2 0.7Total 99.7 100.5 100.8 100.1

Table 4. Minor components in the four samples (mg/kg).

Italian Standards Industrial Sites PZP25 PZP4 PZE9 PZP10

Ni 500 6.37 19.15 31.56 5.56Sb 30 4.90 0.00 2.34 1.74Cu 600 48.60 50.49 16.57 11.68Cr tot 800 13.89 41.70 12.49 16.01Se 15 0.77 0.00 1.14 1.14As 50 25.33 12.68 2.18 4.89Ag – 1.07 0.06 0.20 0.19Be 10 0.71 0.87 0.86 1.07Co 250 2.51 0.00 2.56 2.46Tl 10 3.26 7.01 2.01 3.65Cd 15 11.16 36.81 2.20 1.97Zn 1500 2169 2478 206.3 339.7Pb 1000 2881 20.33 75.71 155.6Cyanide 100 50.1 50.1 50.1 50.1Fluorides 2000 6.09 0.15 3.52 6.02Hg 5 2.38 50.05 1.27 1.93Stot – 834 18525 178 300SSO4 – 282 8588 102 178

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2.2. Batch Leaching

To verify the possibility of contaminant removal, batch leaching tests were carriedout on each of the four samples using water solutions of sulphuric acid (H2SO4),hydrochloric acid (HCl), nitric acid (HNO3) and acetic acid (CH3COOH). The batchleaching tests were conducted for 200–300 min in a 1 L, double-jacketed reactor(Pyrex glass thermostat) at a constant rotational speed of 500 rpm and a constanttemperature of 258C. After filtration, the leachate was analysed for Zn, Pb, Cd, Cu,Mn and Al using ICP/OES.

3. Results and discussion

3.1. Batch tests

The results of batch tests are summarised in Table 5. Data obtained from batch testswere used to evaluate the conditions providing the greatest leaching efficiency and tostudy correlation between the reaction rate and other key parameters.

Analysis of the data collected during the batch tests provided the extractionyield, a, a dimensionless value relating the concentration in the solution to theconcentration of the dry solid. The extraction yield varies depending on the analyte,soil sample and leaching agent used. Optimal conditions can be determined by

Table 5. Batch tests: extraction yield (a(i,j,k)max) obtained with different leaching solutions.

t (min) Zn Pb Cd Cu Mn Al

Leaching solution PZE9 S(a(i ¼ 1,3))j ¼ 1; k ¼ 1,4

CH3COOH, pH ¼ 4 240 0.246 0.041 0.394 0.035 0.067 0.000 0.681HNO3, pH ¼ 2 270 0.439 0.478 0.606 0.167 0.158 0.004 1.523HCl, pH ¼ 2 150 0.410 0.409 0.667 0.167 0.179 0.004 1.486H2SO4, pH ¼ 2 300 0.403 0.194 0.485 0.159 0.203 0.005 1.082S(a(k)) i ¼ 1,6; j ¼ 1 1.498 1.122 2.152 0.528 0.607 0.013Leaching solution PZP25 S(a(i ¼ 1,3))

j ¼ 2 k ¼ 1,4CH3COOH, pH ¼ 4 240 0.688 0.558 0.902 0.126 0.354 0.002 2.148HNO3, pH ¼ 2 225 0.738 0.155 0.842 0.315 0.434 0.008 1.735HCl, pH ¼ 2 240 0.852 0.190 0.890 0.398 0.519 0.010 1.932H2SO4, pH ¼ 2 210 0.923 0.007 0.950 0.405 0.565 0.011 1.880S(a(k)) i ¼ 1,6; j ¼ 2 3.201 0.91 3.584 1.244 1.872 0.031Leaching solution PZP4 S(a(i ¼ 1,3))

j ¼ 3; k ¼ 1,4CH3COOH, pH ¼ 4 240 0.473 0.026 0.768 0.013 0.412 0.000 1.267HNO3, pH ¼ 2 225 0.587 0.210 0.833 0.109 0.427 0.002 1.630HCl, pH ¼ 2 240 0.500 0.036 0.765 0.097 0.385 0.002 1.301H2SO4, pH ¼ 2 210 0.645 0.043 0.714 0.109 0.531 0.003 1.402S(a(k)) i ¼ 1,6; j ¼ 3 2.205 0.315 3.08 0.328 1.755 0.007Leaching solution PZP10 S(a(i ¼ 1,3))

j ¼ 4; k ¼ 1,4CH3COOH, pH ¼ 4 240 0.442 0.209 0.474 0.086 0.060 0.001 1.125HNO3, pH ¼ 2 210 0.547 0.490 0.440 0.177 0.269 0.004 1.477HCl, pH ¼ 2 240 0.419 0.427 0.508 0.205 0.310 0.004 1.354H2SO4, pH ¼ 2 300 0.434 0.090 0.575 0.194 0.429 0.006 1.099S(a(k)) i ¼ 1,6; j ¼ 4 1.842 1.216 1.997 0.662 1.068 0.015

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adding up the data in the rows or columns of Table 5. Thus, the extraction yield canbe assigned three indices (a(i,j,k)), where i is the index of the analyte (ranging from 1to 6), j is the index of the soil sample (ranging from 1 to 4) and k is the index of theleaching agent (ranging from 1 to 4).

Three parameters were used to express improved efficiency:

. (a(i,j,k))max represents the maximum concentration measured (among 96 values)in the 16 tests, including all six analytes considered. This parameter allows usto identify the analyte with the highest leaching efficiency and thus the mostefficient leaching agent and the corresponding sample.

. (S(a(k)) i ¼ 1,6; j ¼ 1,4)max represents the analyte that is most readily washable(among 24 values) as a result of leaching with the solutions considered and forthe four samples tested.

. (S(a(i ¼ 1,3)) j ¼ 1,4; k ¼ 1,4)max represents the most effective leaching agent(among four values) for each sample (j ¼ 1,4) for the three main analytes (Zn,Pb and Cd).

Results indicate the following (Table 5):

. Cd is leached with the greatest efficiency in the PZP25 sample when H2SO4 isused as a leaching agent ((ai,j,k))max ¼ 0.950);

. the analyte most readily leached by any solution and for any sample is Cd,where (S(a(k)) i ¼ 1,6; j ¼ 1,4)max is 3.584;

. the most efficient agent for leaching the three main analytes (Zn, Pb and Cd) isHNO3 for PZE9, PZP4 and PZP10, but CH3COOH for PZP25, where (S(a(i ¼ 1,3))j ¼ 1,4; k ¼ 1,4)max is 2.148 represents the highest absolute value.

Table 5 indicates that:

. efficiency values are between 40 and 95% for Cd and Zn, and are always lessthan 60% for all other analytes;

. the value for extraction efficiency does not remain constant for the sameanalyte in the various samples;

. the existence of linear correlation between pairs of analytes exists only betweenZn and Cd, with a correlation coefficient of 70%, and between Zn and Mn,with a correlation coefficient of 60%;

. sample PZP25 shows the best leaching performance;

. Pb compounds in sample PZP4 are not leachable with any of the four solutionsused;

. Pb compounds in sample PZP25 are leachable with CH3COOH.

3.2. Kinetic model of batch leaching reaction

Leaching time diagrams illustrate the time required to extract the main analytes fromthe two most contaminated samples, PZP25 and PZP4 (Figure 2).

For all 16 batch tests, the equation associated with the highest correlationcoefficient is:

a i;j;kð Þ ¼ ðaþ b� ðtÞnÞ0:5 ð1Þ

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Figure

2.

ExtractionofZn,PbandCdversustimeforsamplesPZP25andPZP4.

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where a(i,j,k) is the extraction yield variable as a function of time, dimensionless; t isthe reaction time, measured in minutes; a is the intercept of the equationcorresponding to the value of a at t ¼ 0 and should have a value of zero; b isthe slope, indicating reaction speed and having dimensions of [t0.5] (inverse of thesquare root of time); n is the exponent of the time variable, generally defined as theorder of the equation describing reaction kinetics. Calculations for batch reactionsalways obtained n ¼ 1/2.

The values of reaction rate b for the different solutions retain the same order ofmagnitude for the analytes Zn, Cd and Mn, but vary two orders of magnitude forPb, Cu and Al. This trend is illustrated by an example in the histogram of Figure 3for the solution with sulphuric acid. Similar behaviour is also observed for the othersolutions.

The maximum concentration reached by each analyte, a(i,j,k)max, varies with b: afaster reaction led to increased leaching.

This relationship is more clearly illustrated by the statistical correlation betweentwo variables, as shown in Equation (2) and Figure 4:

a i;j;kð Þmax¼ ðk0 þ k1 � ðbÞ0:5Þ ð2Þ

where a(i,j,k)max is the maximum value of the extraction yield obtained at the end ofthe test; b is the independent variable represented by the reaction rate calculated byEquation (1); k0 is the intercept for b ¼ 0; k1 is the slope.

The results of the statistical analysis performed are shown in Table 6.Because the P-value in the ANOVA table is less than 0.05, we can conclude

that there is a statistically significant relationship between a(i,j,k)max and b at the96.0% confidence level and that there is an indication of possible serial correlation atthis confidence level. The value of the correlation coefficient indicates a relativelystrong relationship between variables. The R-squared statistic indicates that themodel explains 96.15% of the variability in a(i,j,k)max.

Figure 3. Reaction speed b for analytes (Zn, Pb, Cd, Cu, Mn, Al) leached by H2SO4 in thesamples tested.

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Figure 4 shows the best fit for the 96 points listed in Table 5 and provides a cleargraphical comparison between the four samples. The points (analytes) located in theupper portion of Figure 4 correspond to a satisfactory leaching result, whereas thepoints located in the lower or middle portions of the figure correspond to a partial orincomplete leaching.

3.3. Changes in samples resulting from the leaching process

Petrographic analyses of sample PZP25 before and after its exposure to leachingsolutions illustrate the changes occurring as a result of leaching (Figure 5). Before

Table 6. Main statistical analysis parameters.

Coefficients

Parameter Least squares estimate SE t-Statistic P-value

Intercept 0.00539 0.01020 0.52829 0.5987Slope 4.20359 0.09176 45.8119 0.0000R2 96.1516R2 adjusted 96.1058

ANOVASource Sum of squares df Mean square F-ratio P-value

Model 5.94990 1 5.94990 2098.73 0.0000Residual 0.23814 84 0.00283Total (Corr.) 6.18804 85

Figure 4. Relationship of a(i,j,k)max as a function of the reaction rate.

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leaching (Figure 5a), a border of secondary hydroxides of heavy metals can be seenforming a stratiform structure around the nucleus of primary minerals. Suchinformation is confirmed by SEM analysis and EDS maps (Figure 5c,d). It is clearlyseen that the level of iron within the rim suggests the presence of a thin, uniform filmof hydroxides on the surface of the particle analysed.

After leaching (Figure 5b), the border and associated heavy metals are almostcompletely gone. This visual evidence corroborates the experimental resultsobtained.

4. Conclusions

The present study shed light on the efficiency of leaching on the soils tested andhelped to interpret the factors affecting the leaching process itself and its kinetics.

The use of sulphuric acid permits, for the most contaminated sample, the highestleaching efficiency for the removal of Cd and Zn. On the same sample, the bestreduction of Pb is achieved using acetic acid. The other three leaching agents havevery little effect on the removal of Pb.

Sample PZP25 was decontaminated (brought into total compliance with ECstandards) after being leached for about 4 h. The Cd analyte leaches more quicklythan Zn and Pb.

The differences highlighted in the discussion of the results, especially from thegrid that correlates efficiencies/leaching solutions/soil samples, show that the soil

Figure 5. Optical micrographs of solid sample PZP25: (a) before leaching; (b) after leachingwith HCl; (c) SEM image using secondary electrons (SE); (d) EDS mapping of Fe.

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samples tested were polluted by the main analytes Zn, Cd, Pb and Mn, which areaffected by different mobility conditions. A statistical analysis of experimental datapermitted identification of the form of correlation for both time and extractionyield, and efficiency and leaching rate. Despite differences in the mobility of theanalytes, only two exponential equations are valid for all samples and all leachingsolutions.

Acknowledgements

The authors would like to thank the Regional Government and the Italian Research NationalCouncil (IGAG Institute, CNR) for their financial support of this research.

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