Optimization arsenic immobilization in a sandy loam ...

Geoderma 232–234 (2014) 547–555

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Optimization arsenic immobilization in a sandy loam soil using iron-based amendments by response surface methodology Elham Naseri a, Adel Reyhanitabar a,⁎, Shahin Oustan a, Ali Akbar Heydari b, Leila Alidokht a a b

Department of Soil Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran Department of Statistics, Faculty of Mathematical Science, University of Tabriz, Iran

a r t i c l e

i n f o

Article history: Received 16 March 2014 Received in revised form 2 June 2014 Accepted 6 June 2014 Available online xxxx Keywords: Arsenic CCD Iron Soil remediation Zeolite

a b s t r a c t The survey of reports regarding high concentrations of arsenic in soils and groundwater around the world, which refers to increase of arsenic exposure to the living organisms, has been increased. In this research work arsenic immobilization process using three iron amendments (soluble Fe(II), zero-valent iron (ZVI), and Fe (II)-modified zeolite (Fe-Z)) was modeled and optimized in a spiked soil by response surface methodology (RSM). Three factors including initial concentration of As(III) (20 to 580 mg kg−1 of soil), amount of added Fe (0.5 to 2.5 wt.% of soil for both Fe(II) and ZVI, 0.05 to 0.2 wt.% of soil for loaded Fe on zeolite) and shaking time (15 to 960 min) were selected as the independent factors on arsenic immobilization efficiency. The five-level central composite design (CCD) was used for experiment design and optimization model parameters. Variance analysis showed that CCD models were statistically significant for all amendments (p b 0.01) with high accuracy (R2 = 0.98 for Fe(II), R2 = 0.89 for ZVI and R2 = 0.92 for Fe-Z) in predicting As(III) immobilization. Optimization results showed that at 200 mg As kg−1 soil and 600 minute shaking time, the immobilization of As(III) with Fe(II), ZVI and Fe-Z was 90.6%, 92% and 81.4%, respectively. However ZVI was most effective amendment, but with negligible difference in immobilization As(III), Fe(II) is more economical. In conclusion Fe(II) was more efficient and cost-effective than ZVI and Fe-Z in long-term immobilization. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Given the high toxicity of arsenic, it is necessary to remediate the contaminated soil as well as groundwater resources, thereby preventing adverse impacts on human and environmental health (Yong and Mulligan, 2004). As(V) and As(III) are the most common forms of arsenic in the environment depending on pH and redox conditions. However, arsenite is more toxic and mobile than arsenate (Wang and Mulligan, 2006). Soils containing arsenic concentrations higher than 20 mg kg−1 are assumed to be at the risk of widespread environmental contamination (Alloway, 1990). The distribution of arsenic in soils is largely governed by its adsorption on the surfaces of minerals (Sherman and Randall, 2003). In this regard, the effectiveness of Fe(II) (Ona-Nguema et al., 2010), ZVI (Gutiérrez et al., 2010) and Fe-Z (Elizalde-González et al., 2001a) in remediation of As-contaminated soils and ground waters has been previously proven. According to Yang et al. (2007), the content of soil iron oxides (extractable with dithionite-citrate-bicarbonate) is the most important factor controlling the As adsorption in soils. Bowell (1994) has reported

⁎ Corresponding author. E-mail addresses: [email protected], [email protected] (A. Reyhanitabar).

http://dx.doi.org/10.1016/j.geoderma.2014.06.009 0016-7061/© 2014 Elsevier B.V. All rights reserved.

that the affinity of arsenic for amorphous and poorly crystalline iron oxides was more than for well-crystallized one. The Fe(II) ions are converted to Fe(III) through hydrolysis and precipitate in soils as amorphous iron oxides (Yang et al., 2007). Lumsdon et al. (1984) suggested that iron oxides in the presence of arsenic can form insoluble ferric arsenate (FeAsO4). Moreover, ZVI has a high potential for arsenic immobilization in the environment (Farrell et al., 2001). Adsorption of arsenate and arsenite on ZVI or by trivalent iron hydroxides produced during the corrosion of ZVI may lead to the arsenic immobilization (Katsoyiannis et al., 2008; Melitas et al., 2002; Ona-Nguema et al., 2010). In addition, natural and synthetic alumina-silicates modified by Fe(II) and Fe(III) have been introduced as effective adsorbents of arsenic. This is due to high affinity of precipitated iron oxy-hydroxides to arsenic (Bruce et al., 1998). Another acceptable mechanism for arsenic immobilization is formation of surface complexes between hydroxyl groups of Fe(II)-modified zeolite and arsenic (Dzombak and Morel, 1990). Ferric arsenate/arsenite may be produced through reaction: −FeOH þ H3 AsO3 →Z−Fe−H2 AsO3 þ H2 O

ð1Þ

where Z and FeOH are zeolite and precipitated iron oxy-hydroxides on the zeolite surfaces, respectively.

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E. Naseri et al. / Geoderma 232–234 (2014) 547–555

In this study, the arsenic immobilization efficiency was optimized in a spiked soil using three amendments including Fe(II), ZVI and Z-Fe and as a function of three independent variables (initial concentration of As(III), amount of added Fe and shaking time) by the RSM. RSM is a set of mathematical and statistical techniques useful for appropriate designing of the experiments, modeling and determining the optimum operational conditions of the system (Montgomery, 2005). Central composite design (CCD) is one of the most well-known RSM designs and generally requires five levels for each factor (Box and Wilson, 1951). This method has been successfully used by many researchers for optimization and modeling of several remediation processes such as reductive removal of heavy metals (Alidokht et al., 2011 and Ramazanpour et al., 2014), Fenton oxidation (Ahmadi et al., 2005), photo catalysis (Khataee et al., 2011) and phyto-remediation (Feng et al., 2009). The aim of this research was modeling and optimization of arsenic immobilization process in a spiked soil by RSM. 2. Materials and methods

Fig. 1. XRD pattern of the natural Semnan zeolite.

2.1. Soil sampling and preparing As(III)-spiked soil In this study to omit the influence of soil organic matter and phosphorus on arsenic sorption–desorption characteristics, the studied soil with low organic carbon and available phosphorus was selected. Moreover, because of the role of carbonates in arsenic immobilization (Arai et al., 2003), a soil with low amounts of calcium carbonate equivalent (nil) was selected from North West of Iran, and sampled from surface layer (0–25 cm). Then this soil was air-dried, ground, passed through a 2-mm sieve and some physical and chemical analyses were carried out, which their results are presented in Table 1. For spiking the soil with arsenic, 66 mL (the volume required to raise the soil moisture field capacity) of Na2AsO3 solutions containing 303, 2023, 4546, 7068 and 8788 mg As(III) L−1 was added to the 1 kg soil to provide As(III) concentrations of 20, 133.5, 300, 440 and 580 mg kg−1, respectively. Also, the range of arsenic concentration in soil was chosen based on the guideline values for the minimum (20 mg kg−1) and maximum (N500 mg kg−1) concentrations of arsenic in soils (Alloway, 1990). The soil samples were mixed thoroughly and then underwent three cycles of wetting (up to FC moisture) and air drying (each cycle approximately for one week). 2.2. Fe-Z preparation The zeolite used in this research was supplied from zeolite mine located in Semnan province of Iran. The zeolite fragment were dried to 110 °C for 24 h and then was ground and sieved to obtain particle size of 0.250 to 0.106 mm in diameter. Characterization of the zeolite sample was performed by X-ray diffraction (XRD) analysis using Philips PW 1830/40 diffractometer with Cu-Kα2-radiation recorded from 4° to 72° (2θ). The diffractogram of the zeolite sample showed that clinoptilolite is the predominant mineral (Fig. 1). Also,

Fe-Z was prepared after isothermal study of Fe(II) sorption on the zeolite. At the first time, concentrations of Fe(II) ranging from 25 to 6000 mg L− 1 were prepared in the background solution of 0.01 M NaCl and FeSO4·7H2O was used as the iron source. Batch sorption experiments were performed in 50 mL centrifuge tubes containing 20 mL of Fe(II) solution and one gram of zeolite. The tubes were shaken at ambient temperature for 240 min on a reciprocating shaker at 130 rpm. Then, they were centrifuged for 15 min at 6000 rpm and filtered with 0.22 micron filter paper. Immediately, a drop of concentrated nitric acid was added to the solutions and the concentration of iron was measured by atomic absorption spectrometry (Shimadzo 6300) and adsorbed Fe was calculated by the following equation:

q¼

ðC i −C e ÞV m

ð2Þ

where q is the adsorbed Fe (mg kg−1), Ci and Ce are initial and equilibrium concentrations of Fe (mg L−1), respectively. V represents the volume of solution as liter and m is the mass of the added zeolite as kg (Buasri et al., 2008). According to the results of isothermal experiments on Fe(II) sorption by zeolite (Fig. 2), the maximum and minimum values of adsorbed Fe(II) were considered as 20 and 5 g kg−1 or 0.02 and 0.005 g per g of zeolite. As the amount of added zeolite was determined as 10% by weight of the soil (Huang and Petrovic, 1995), Fe

40 35

Method

Content

Property

Soil/DW (1:1)(US Salinity Laboratory Staff, 1954) Hydrometer method (Klute, 1986) Hydrometer method (Klute, 1986) Hydrometer method (Klute, 1986) Bower method (Bower, 1954) Wet digestion (Nelson and Sommers, 1996) Titration (Soil Conservation Service, 1992)

7.6 70 18 12 13.8 0.13 nil

Aqua regia (Chen and Ma, 2001) Olsen-P (Olsen and Sommer, 1982)

100 5.56

pH Sand (%) Silt (%) Clay (%) CEC (cmolc kg−1) Organic carbon (%) Equivalent calcium carbonate (%) Total arsenic (μg kg−1) Available phosphorus (mg kg−1)

q (g/kg)

30 Table 1 General physical and chemical properties of the soil sample.

25 20

21.482

15 10 5 0

0.41 0

100

200

300

400

C (mg/l) Fig. 2. Fe sorption isotherm on Semnan zeolite.

500

600


weight percentage of the soil in As(III) immobilization experiments by this amendment is 0.05 to 0.2. 2.3. Arsenic immobilization in As-spiked soil Immobilization of As in spiked soil was carried out in batch system. Two grams of air dried As-spiked soil (covering different concentrations of arsenic) and 20 mL of background solution (0.03 M NaCl) were placed into 50 mL centrifuge tubes containing different kinds and levels of Fe(II) and ZVI ranging from 0.5 to 2.5 percentages (by weight) separately, and 0.05 to 0.2 weight percentages of Fe loadings on zeolite, for Fe-Z experiments. The tubes were shaken for 16 h at 130 rpm using a reciprocal shaker. After centrifuging for 15 min at 6000 rpm and filtering through a 0.22 micron filter paper, arsenic concentration of the supernatants was determined by graphite furnace atomic absorption spectrometry (Varian model 220). Control tests, in the absence of amendments, were conducted to account the release of As(III) from soil during the experiments. The immobilization efficiency (as %) of arsenic (IE) was computed by the following equation: AsðIIIÞimmobilization efficiencyðIEÞ ¼

1−

c c0

100

ð3Þ

where, c is the supernatant As(III) residual concentration (mg L−1) and c0 represents the As(III) initial concentration (mg L−1) in control sample. 2.4. Design of experiments The effect of independent variables including As(III) initial concentration (mg kg− 1) in the soil (X1), percentage of amendment (by weight) (X2) and (X3) reaction time (minute) on As(III) immobilization efficiency as the response variable were investigated. Designing the experiments and process modeling were conducted by CCD approach as the most accepted class of second-order designs in RSM (Myers and Montgomery, 2002). The total number of experiments (N) was calculated by the following equation (Sarabia and Ortiz, 2009): k

N ¼ 2 þ 2k þ nc

ð4Þ

549

2.5. Regression model The methodology used in this research mainly involves quadratic model to explain the behavior of the system. This model is flexible and covers all linear, non-linear and interaction effects between the factors (Carley et al., 2004). The quadratic polynomial equation is as follows: y ¼ β0 þ

Xk i¼1

βi xi þ

Xk i¼1

2

βii xi þ

Xk Xk i¼1

i≠ j¼1

βij xi x j þ ε

where, y is the predicted response (as dependent variable), β0 is the offset term, βi is the ith linear coefficient, βii is the quadratic coefficient, βij is the ijth coefficient and ε is the error or residual value. Solving this equation and calculating the coefficients are done by using method of least squares. The ANOVA statistics (R2, adjusted R2, F-test and t-test) and residuals analysis were conducted to evaluate the fitness of the given models. Eventually, for providing an opportunity to find the most effective parameter governing the As(III) immobilization among the input parameters the Pareto analysis was also performed. The experimental design and statistical analysis of the data were done by Minitab statistical software package (Version 15). Finally, the two-dimensional (2D) and three-dimensional (3D) graphs of the response were plotted. 3. Results and discussion 3.1. The results of CCD model The experimental and predicted values for As(III) immobilization efficiency using three amendments corresponding to different combination of selected variables are presented in Table 3. The empirical relationships between the responses (As(III) immobilization efficiency using Fe(II) (y Fe(II)), ZVI (y ZVI) and Z-Fe (y Z-Fe)) and independent variables are expressed by the following equations: yðFeðIIÞÞ ¼ 72:012−9:3647 x 1 −1:7089x2 þ 4:4499 x3 þ 0:9525x1 x2 2 2 2 − 0:8575 x1 x3 −0:79x2 x3 þ 3:2941x1 þ 1:9577x2 − 0:9945x3 ð8Þ

yðZVIÞ ¼ 82:0841−10:1123x 1 þ 8:2627x2 þ 9:30006 x3 þ 4:595 x1 x2 2

where k is the number of factors, 2 represents the number of factorial points, 2k is the number of axial points, and nc is number of central points which is calculated by the following equation:

X i −X 0 ΔX

−

ð9Þ

2 5:1351x3

yðZ−FeÞ ¼ 6:0053−11:4585 x 1 þ 7:297x2 þ 1:5852 x3 − 0:1287 x1 x2 2

ð5Þ

To summarize the data and ease of statistical calculations the levels of all independent variables were coded as xi using Eq. (6) and are reported in Table 2.

xi ¼

2

− 0:9394 x1 x3 þ 0:6688 x2 x3 − 0:8326x1 − 3:2318x2

k

pffiffiffiffiffi 2 k 2k þ 2 −2 −2k: nc ¼ k

ð7Þ

ð6Þ

where, Xi is the actual value of variable, X0 is the actual value of Xi at the central point and ΔX is the step change. Therefore, three categories of experiments in this study (in the case of k = 3) are: 1. Factorial points (2k = 8) which includes all experiments with codified values +1 and −1; 2. Axial of star points (2k = 6) is codified values α = (2k)1/4 = ±1.682; 3. Central points consist of 6 experiments, codified as 0.

2

þ0:3313 x1 x3 − 0:2387 x2 x3 þ 4:298x1 þ 0:1049 x2 − 6:7611

ð10Þ

2 x3 :

Based on ANOVA results (Table 4) all obtained models are highly significant at the confidence level of 99% (calculated F-values are remarkably higher than tabulated one (4.65)). This indicates that the quadratic models were valid for the present optimization study. Additionally, the high R2 values imply high explanatory power of the model. Since R2 values increase by increasing the number of variables, whether significant or non-significant, the adjusted R2 overcomes the apparent rise in R2. Therefore, in a system with different numbers of independent variables it is preferred to use an adjusted R2, which takes into account the number of variables added to the model (Box and Behnken, 1960) and were close to corresponding R2 values (Table 4). The significance test of the coefficients was done by Student t test (Table 5) and the results concerning Fe(II) showed the statistical significance of all linear and quadratic coefficients (p b 0.05), while for ZVI, linear effect of all variables and quadratic effect of reaction time (b33) were significant. In the model regarding Z-Fe, the linear coefficients

550


Table 2 Range and levels of the independent variables for used amendments. Variables

Ranges and levels −1.682

[Amendment] (%w/w) (x1) [As(III)] (mg kg−1) (x2) Shaking time) minutes) (x3)

−1

0

1

1.682

Fe(II) and ZVI

Fe-Z

Fe(II) and ZVI

Fe-Z

Fe(II) and ZVI

Fe-Z

Fe(II) and ZVI

Fe-Z

Fe(II) and ZVI

Fe-Z

0.5 20 15

0.005 20 15

0.905 133.5 207

0.008 133.5 207

1.5 300 488

0.0125 300 488

2.095 466.5 769

0.017 466.5 769

2.5 580 960

0.02 580 960

related to variables of As concentration and the amount of added Fe-Z (b1, b2) as well as the non-linear coefficients related to variables of As concentration and shaking time (b33, b11) were significant. In all three models, the interaction effects between variables were not significant. The efficiency of the empirical models was assessed via the residual values obtained from difference between experimental data and predicted values (Fig. 3). A linear behavior was observed in the normal probability plot of the residuals, indicating their normal distribution. Furthermore, the plot of the residuals values vs. predicted values exhibited a random (non-symmetric) scatter above and below the zero line implying the constant variance of the residuals i.e. a uniform dispersion of the data points about the regression line. 3.2. The effects of variables (two and three-dimensional graphs) In this research, the effects of three factors on the response were studied. For each of amendments, one factor was fixed and the effects of the next two factors were studied with both two-dimensional and three-dimensional plots. 3.3. The Fe(II) amendment The combined effect of added Fe(II) and concentration of As(III) on the response (using a reaction time of 488 min) is drawn in Fig. 4. It can be seen that when the applied Fe(II) and concentration of As were in maximum amount (2.5%w/w and 580 mg kg− 1, respectively), the least immobilization of As occurred. Meanwhile, the lowest immobilization efficiency was obtained with the highest percent of Fe(II) and concentration of As. Naidu et al. (2003) reported a considerable release of proton during the oxidation of Fe(II) (Eq. (11)) in soils with low

buffering capacity. The produced protons, in turn, impede further oxidation of Fe(II): 2−

þ

4FeSO4 þ O2 þ 6H2 O↔4FeOOHðsÞ þ 4SO4 þ 8H :

ð11Þ

Probably production of more protons in the presence of high amounts of Fe(II) reduced As(III) immobilization. However, the model coefficients (Table 5) showed that the effect of increasing As concentration on the reduction of immobilization efficiency is more than that of added Fe(II). Fig. 5 shows the combined effect of reaction time and applied Fe(II) on As(III) immobilization (using 300 mg As kg−1 soil). In the presence of the lowest applied Fe(II) level, more than 65% of As(III) was immobilized at first minutes of the experiments. Also, the immobilization efficiency increased with increasing of shaking time. Yang et al. (2002) reported that during the initial stage of reaction (24 h), which is responsible for adsorbing high amounts of arsenic, almost 90% of arsenic was adsorbed. The rapid initial adsorption was followed by a slow reaction. Based on the findings of Arai et al. (2001) the latter adsorption stage could last up to one year. McLaren et al. (2006) reported that arsenic adsorption by Fe(II) showed non-linear kinetics behavior (two-phase reaction) in which the adsorption decreased as the reaction time increased. 3.4. The ZVI amendment The combined effect of added ZVI and initial concentration of As(III) (using a constant reaction time) on arsenic immobilization is shown in Fig. 6. According to the results, increasing the ZVI percent and decreasing As(III) concentration increased the immobilization of arsenic. It seems that increasing added ZVI from 0.5 to 2.5% increased the As(III)

Table 3 The central composite design and response values. Coded levels of variable Run no.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Response (y)

Time (min)

Amendments (%w/w)

As(III) (mg kg−1)

Fe(II) Experimental data

Predicted values

ZVI Experimental data

Predicted values

Experimental data

Fe-Z Predicted values

−1 −1 −1 −1 +1 +1 +1 +1 0 0 0 0 −1.682 +1.682 0 0 0 0 0 0

−1 −1 +1 +1 −1 −1 +1 +1 0 0 −1.682 +1.682 0 0 0 0 0 0 0 0

−1 +1 −1 +1 −1 +1 −1 +1 −1.682 +1.682 0 0 0 0 0 0 0 0 0 0

81.50 63.47 78.13 64.52 92.51 71.66 86.59 68.94 99.00 64.66 81.73 74.37 61.17 78.23 72.63 72.63 73.47 69.87 73.17 70.13

82.20 63.28 78.46 63.35 94.39 72.04 87.49 68.95 97.08 65.58 80.42 74.68 61.72 76.68 72.01 72.01 72.01 72.01 72.01 72.01

66.44 45.29 81.42 57.73 92.66 46.84 89.4 82.87 95.5 71.17 60.90 92.20 51.50 90.83 83.50 80.50 80.33 82.63 82.80 81.50

69.76 42.22 75.75 66.59 88.90 57.61 97.57 84.66 96.73 62.72 59.05 86.84 51.91 83.20 82.08 82.08 82.08 82.08 82.08 82.08

72.08 47.13 87.44 61.95 71.24 47.59 85.62 61.48 91.00 56.36 49.57 74.07 35.17 49.63 65.63 65.87 66.70 66.00 66.57 66.80

66.19 42.87 81.52 57.68 69.17 47.18 83.55 61.03 97.43 58.89 54.03 78.57 44.25 49.55 66.01 66.01 66.01 66.01 66.01 66.01


551

Table 4 Analysis of variance (ANOVA) for response of dependent variable. Amendment

Source of variation

Degree of freedom

Sum of squares

F-value

Fe(II)

Regression Residuals Total Regression Residuals Total Regression Residuals Total

9 10 19 9 10 19 9 10 19

194.76 2.79

69.81

464.75 50.34

9.22

397.32 28.658

13.86

ZVI

Fe-Z

R2

R2-adj

0.984

0.970

0.892

0.796

0.926

0.859

Table 5 Estimated regression coefficients and corresponding t-values from the data of CCD experiments for all three modifiers. Coefficients

b0 b1 b2 b3 b12 b13 b23 b11 b22 b33

Fe(II)

ZVI

Z-Fe

Parameter estimate

t-Value

Parameter estimate

t-Value

Parameter estimate

t-Value

72.012 −9.3647 −1.7089 4.4499 0.9525 −0.8578 −0.79 3.2941 1.9577 −0.9945

105.707⁎⁎ −20.719⁎⁎ −3.781⁎⁎ 9.845⁎⁎

82.0841 −10.1123 88.2627 9.3006 4.595 −0.9394 0.6688 −0.8326 −3.2318 −5.1351

28.366⁎⁎ −5.458⁎⁎ 4.374⁎⁎ 5.240⁎⁎

66.0053 −11.4585 7.4235 1.5852 −0.1287 0.3313 0.2387 4.298 0.1049 −6.2387

20.151⁎⁎ −5.700⁎⁎ 3.421⁎⁎

−1.613ns −1.452ns −1.338ns 7.487** 4.449** −2.260*

1.079ns −0.641ns 0.917ns 0.239ns −1.702ns −2.582⁎

ns, Not significant (confidence level of 95%). **, Significant ( Pb 0.01). *, Significant ( P b0.05).

Fig. 3. Residual plots for immobilization efficiency of As(III).

0.994ns 2.658ns 0.004ns −3.748ns −0.266⁎⁎ 0.073ns 0.136**

552


Fig. 6. The response surface and contour plots of As(III) immobilization efficiency (%) by ZVI as a function of both As(III) concentration (mg kg−1) and added ZVI (%w/w). Fig. 4. The response surface and contour plots of As(III) immobilization efficiency (%) by Fe(II) as a function of both As(III) concentration (mg kg−1) and added Fe(II) (%w/w).

oxidation as well as the production of newly formed amorphous iron oxides which finally tended to increase arsenic immobilization efficiency (Niu et al., 2005; Shu et al., 2007). The more presence of ZVI grains, the more active surfaces of iron oxides for scavenging arsenic. However, the results indicated that the sensitivity of response to changes in initial concentrations of As(III) was more than that of changes in ZVI application rate. The As(III) immobilization efficiency was influenced by added ZVI and reaction time (using 300 mg As kg−1 soil) as illustrated in Fig. 7. The highest immobilization was achieved at equilibrium times of more than 600 min and ZVI percent of more than 1.75% so that the efficiency of As(III) immobilization increased to more than 90%. Bang et al. (2005) reported that arsenic removal by ZVI increases gradually during the 120 h. They found that in a system containing As(III) and ZVI, the adsorption of As(III) was not a limiting factor in removal process and the production of iron oxides was a result of reaction between ZVI and water.

3.5. The Fe-Z amendment

Fig. 5. The response surface and contour plots of As(III) immobilization efficiency (%) by Fe(II) as a function of both added Fe(II) (%w/w) and shaking time (min).

Fig. 8 shows the simultaneous effect of added Z-Fe and initial concentration of As(III) on arsenic immobilization in the reaction time of 488 min. More than 90% of immobilization occurred in low concentration ranges of As(III) and higher percentages of Z-Fe. Contrary to the results reported by Elizalde-González et al. (2001a, 2001b) in which the degree of iron loading on zeolite did not affect arsenic immobilization, the results of this research showed an increasing effect of iron loading on the immobilization. The immobilization efficiency of arsenic as affected by added Z-Fe and reaction time using 300 mg As kg−1 soil is shown in Fig. 9 that the highest response was achieved after 450 min of reaction time and in the presence of more than 0.16% Fe. The two-dimensional plot showed that in higher degrees of iron loading on zeolite, the maximum


Fig. 7. The response surface and contour plots of As(III) immobilization efficiency (%) by ZVI as a function of both added ZVI (%w/w) and shaking time (min).

immobilization of As(III) was obtained in short periods of time. Initially the rate of immobilization was fast but then it slowed down and leveled off. This constant rate was followed by a decrease in immobilization

553

Fig. 9. The response surface and contour plots of As(III) immobilization efficiency (%) by Fe-Z as a function of both added Fe-Z (%w/w) and shaking time (min).

rate. Since more degrees of iron loadings on zeolite causes more As(III) immobilization, therefore, more amounts of iron is needed to reach more efficiency of arsenic immobilization. In this research, the Pareto graphic analysis was carried out for further interpretation of the obtained results. The percent of effectiveness for each parameter was calculated by the following equation: 0

1

2

b pi ¼ @Xi

2

bi

A 100

ð12Þ

where, b is the regression coefficient for each parameter. According to the results shown in Fig. 10, the following order was achieved for the effect of each parameter by percent: For Fe(II): As(III) concentration of (68.3%) N reaction time (15.5%) N quadratic effect of the As(III) initial concentration (8.4%), For ZVI: As(III) concentration (32.2%) N reaction time (27.3%) N added ZVI dosage (21.5%) and For Fe-Z: As(III) concentration (53.2%) N added Fe-Z dosage (22.3%) N quadratic effect of the reaction time (15.8%). 3.6. Optimizing As(III) immobilization process Optimizing the immobilization conditions for each amendment was one of the current research aims and was done in a given concentration of As(III) for all three amendments. Based on the results of efficiently immobilize As(III) in soil during 600 min (Table 6), more amounts of ZVI in comparison with Fe(II) is needed, while the immobilization efficiency increased only 2%. Fortunately, the Fe-Z amendment could immobilize 81.4% of As(III) using lower amounts of Fe (0.17%). 4. Conclusion

Fig. 8. The response surface and contour plots of As(III) immobilization efficiency (%) by Fe-Z as a function of both As(III) concentration (mg kg−1) and added Fe-Z (%w/w).

The results of this study indicated that the obtained polynomial regression models statistically were significant (p-value b 0.01) for all three amendments and RSM is a proper method for optimizing the

554


Effect of each factor(%)

100

Fe(II)

80 60 40 20 0

Effect of each factor (%)

40

ZVI

30 20 10

Acknowledgment

0

Hereby, this is to appreciate the research vice chancellorship at the University of Tabriz, Iran for supplying the necessary expenses. We are indebted to the Water and Waste Organization of East Azerbaijan, Iran, especially to its Department of Water Laboratory staffs.

60

Effect of each factor (%)

high efficiency and it is indicated that more amounts of iron would increase its efficiency within time. The results of optimizing showed that in same As(III) concentrations and reaction time, the As(III) immobilization efficiencies were 90.6, 92 and 81.4 in the presence of 0.5% Fe (as Fe(II)), 1.5% Fe (as ZVI) and 0.17% Fe (as Fe-Z), respectively. As the given results indicated, ZVI with more Fe concentration was the most effective amendment. Through the presence of very low amounts of carbonates in the investigated soil and releasing proton during Fe(II) oxidation process, Fe(II) with lower iron percentage works better than ZVI with negligible difference. Consumption of enough amounts of calcium carbonate within the lower amounts of Fe(II), could immobilize As(III) even at contamination levels higher than 200 mg As kg−1 soil. Fe-Z amendment even at the very lower amounts of Fe exhibited great immobilization efficiency too, but it needs more investigations to approach a distinctive result about it. This study showed that Fe(II) was more efficient and cost-effective than ZVI and Fe-Z in long-term immobilization, especially in calcareous soils.

Z-fe

50 40 30 20 10 0

Fig. 10. Pareto graphic analysis.

As(III) immobilization conditions. The Pareto analysis suggested that linear effect of initial As(III) concentration in soil has the most influence on all of amendment's immobilization efficiency. According to obtained results, all three amendments had acceptable performance in As(III) immobilization process and the most As(III) immobilization efficiency occurred in the presence of the lowest Fe(II) and the highest ZVI and Fe-Z concentrations. Since decreasing pH, induced by Fe(II) oxidation, leads to impeding As(III) immobilization, least Fe(II) concentration works better than higher ones during 16 h and more time reaction causes more immobilization efficiency. As time duration has direct effect on ZVI corrosive and production of active sites for As immobilization, high amounts of ZVI in high time reactions immobilize most As(III) from spiked soil. The results of As(III) immobilization induced by Fe-Z reactions showed that high amounts of iron loadings on zeolite has

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Table 6 Optimum values of As(III) immobilization efficiency. Amendments

Added amendment (%w/w)

As(III) concentration (mg kg−1)

Shaking time (minutes)

Predicted As(III) immobilization efficiency (%)

Fe(II) ZVI Fe-Z

0.5 1.5 0.17

200 200 200

600 600 600

90.559 91.949 81.361

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