Heavy metal removal by GLDA washing: Optimization

Science of the Total Environment 569–570 (2016) 557–568

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Heavy metal removal by GLDA washing: Optimization, redistribution, recycling, and changes in soil fertility Guiyin Wang a, Shirong Zhang a,⁎, Xiaoxun Xu a, Qinmei Zhong a, Chuer Zhang a, Yongxia Jia b, Ting Li b, Ouping Deng b, Yun Li b a b

College of Environmental Science, Sichuan Agricultural University, Wenjiang, 611130, China College of Resources, Sichuan Agricultural University, Wenjiang, 611130, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• N,N-bis (carboxymethyl)-L-glutamic acid (GLDA) was employed for soil remediation. • RSM approach was used successfully for optimizing of metals removal process. • Environmental risk posed by metals reduced significantly after GLDA-washing. • Compared to EDTA-washing, GLDAwashing retained most of nutrients in soils. • No N 5% loss in metals extraction efficiencies by recovered GLDA.

a r t i c l e

i n f o

Article history: Received 9 May 2016 Received in revised form 19 June 2016 Accepted 20 June 2016 Available online xxxx Editor: Jay Gan Keywords: Soil washing N,N-bis (carboxymethyl)-L-glutamic acid Optimization Chelate recovery Soil fertility

⁎ Corresponding author. E-mail address: [email protected] (S. Zhang).

http://dx.doi.org/10.1016/j.scitotenv.2016.06.155 0048-9697/© 2016 Elsevier B.V. All rights reserved.

a b s t r a c t Soil washing, an emerging method for treating soils contaminated by heavy metals, requires an evaluation of its efficiency in simultaneously removing different metals, the quality of the soil following remediation, and the reusability of the recycled washing agent. In this study, we employed N,N-bis (carboxymethyl)-L-glutamic acid (GLDA), a novel and readily biodegradable chelator to remove Cd, Pb, and Zn from polluted soils. We investigated the influence of washing conditions, including GLDA concentration, pH, and contact time on their removal efficiencies. The single factor experiments showed that Cd, Pb, and Zn removal efficiencies reached 70.62, 74.45, and 34.43% in mine soil at a GLDA concentration of 75 mM, a pH of 4.0, and a contact time of 60 min, and in polluted farmland soil, removal efficiencies were 69.12, 78.30, and 39.50%, respectively. We then employed response surface methodology to optimize the washing parameters. The optimization process showed that the removal efficiencies were 69.50, 88.09, and 40.45% in mine soil and 71.34, 81.02, and 50.95% in polluted farmland soil for Cd, Pb, and Zn, respectively. Moreover, the overall highly effective removal of Cd and Pb was connected mainly to their highly effective removal from the water-soluble, exchangeable, and carbonate fractions. GLDA-washing eliminated the same amount of metals as EDTA-washing, while simultaneously retaining most of the soil nutrients. Removal efficiencies of recycled GLDA were no N 5% lower than those of the fresh GLDA. Therefore, GLDA could potentially be used for the rehabilitation of soil contaminated by heavy metals. © 2016 Elsevier B.V. All rights reserved.

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G. Wang et al. / Science of the Total Environment 569–570 (2016) 557–568

1. Introduction Concern over heavy metal contamination of soil has dramatically increased globally with the intensification in the past decades of anthropogenic activities that lead to such contamination, such as mining, smelting, and electroplating (Mukwaturi and Lin, 2015; Rosestolato et al., 2015; Domínguez et al., 2016; Huber et al., 2016). Of these heavy metals, cadmium (Cd), lead (Pb), and zinc (Zn) are of particular concern because they are non-biodegradable and are persistent in the soil environment (Adrees et al., 2015). Moreover, these metals are highly toxic to plants through inhibiting plant growth and yield by restricting the chlorophyll synthesis and photosynthetic rate (Adrees et al., 2015; Rizwan et al., 2016a; Rizwan et al., 2016b). Additionally, they pose serious threats to human health and environmental sustainability because they accumulated in living organisms (Wang et al., 2014), and bring about huge economic implications with respect to the reclamation and restoration (Akcil et al., 2015). Consequently it is imperative to clean the contaminated soil. Soil washing with a chelator is an emerging method for the treatment of contaminated soil that has recently attracted a great deal of interest among researchers (Dermont et al., 2008; Wang et al., 2014; Im et al., 2015; Kulikowska et al., 2015a; Mukhopadhyay et al., 2016). Among these chelators, ethylenediaminetetracetic acid (EDTA) is the most frequently cited chelating agent for the extraction of toxic metals from contaminated soils (Dermont et al., 2008; Pinto et al., 2014; Jelusic et al., 2014; Jez and Lestan, 2016; Kim et al., 2016) because it has a strong capacity to dissolve and mobilize various heavy metals (Pinto et al., 2014; Satyro et al., 2016). Unfortunately, EDTA is poorly biodegradable and quite persistent in the soil environment, which might have an adverse effect on the microorganisms and plant, and lead to secondary pollution via leaching to groundwater (Jez and Lestan, 2016; Suanon et al., 2016). Hence, the [S,S]-stereoisomer of ethylenediaminedisuccinic acid (EDDS) has recently emerged as a safe and environmentally-friendly substitute of EDTA in soil washing processes (Yip et al., 2010; Cao et al., 2013; Ferraro et al., 2015; Race et al., 2016) in view of its readily biodegradable in soils and relatively strong complexing ability with heavy metals (Fabbricino et al., 2013). However, relatively high price and weak chelating capability with heavy metals of EDDS makes it unsuitable for in situ application. Therefore, an efficiently and readily biodegradable extracting agent for soil remediation purposes is highly desirable. Recently, a new kind of chelating agent, N,N-bis(carboxymethyl)-Lglutamic acid (GLDA), has drawn the attention of researchers (Itrich et al., 2015; Wu et al., 2015; Suanon et al., 2016; van Ginkel and Geerts, 2016; Wu et al., 2016). Compared with EDTA and EDDS, GLDA exhibits excellent biodegradability, with N 80% being degraded within 28 days (Kołodyńska, 2011). Moreover, the amount of CO2 released during biodegradation is far lower than that released during the biodegradation of traditional chelating agents (Wu et al., 2015). Furthermore, a toxicity test has demonstrated that it does not pose health risks for organisms (Kołodyńska, 2011). As reported in previous studies, GLDA could effectively remove heavy metal ions from wastewater (Kołodyńska, 2011) and industrial sludge (Wu et al., 2015; Suanon et al., 2016). However, there are limited studies on the utilization of GLDA as a washing agent to remove heavy metals from contaminated soil. Additionally, the traditional method used for the optimization of soil washing, one-factor-ata-time, fails to consider the interaction effects between the washing factors. Thus, in this study, we employed response surface methodology (RSM), a useful experimental design and analysis tool that evaluate the relationship between an independent variable and a response variable to identify the optimum conditions (Wang et al., 2015; Ates and Erginel, 2016) for GLDA-washing. The aim of soil washing is to reduce the concentrations of total and bioavailable heavy metals, and to subsequently use the washed soil for planting crops. However, previous studies have shown that soil properties were distinctly different after soil washing (Jelusic and Lestan,

2014; Im et al., 2015; Ren et al., 2015; Chiang et al., 2016; Suanon et al., 2016). Im et al. (2015) suggested that the washed soils might need time to allow soil microorganisms to recover because soil enzyme activity and plant growth were restrained after soil washing. Because metal distribution in soils is highly dependent on such soil properties, such changes may be accompanied by changes in metal distribution as well as changes in soil shear strength and permeability, which will influence soil functions in turn (Im et al., 2015). Additionally, it is important for soil remediation costs to be kept as low as possible, especially for large contaminated sites. Recovery and reuse of washing agents is one such way to reduce the remediation costs (Ferraro et al., 2015). The objectives of this study were: (1) to evaluate and optimize the washing factors including GLDA concentration, pH and contact time for metals removal; (2) to assess changes in the metal distribution, major soil mineral element concentrations, and fertility parameters with GLDA-washing; and (3) to explore the feasibility of using recovered GLDA for subsequent metal removal. 2. Materials and methods 2.1. Soil sampling and characterization We examined two typical contaminated soil samples: a mine soil sample obtained from the Tangjia Pb-Zn mine in Hanyuan, Sichuan, China (29°24′ N, 102°39′ E), and a polluted farmland soil sample obtained near a former non-ferrous metal refinery plant in Pengzhou, Sichuan, China (30°59′ N, 103°57′ E). The samples were passed through a 2-mm sieve and homogenized and stored in plastic cans following air-dried. Total metal contents in the soils were determined by an atomic absorption spectrophotometer (AAS, Thermo Solaar M6, Thermo Fisher Scientific Ltd., USA) after digesting the samples with a HNO3-HCl-HClO4 mixture at a ratio of 1:2:2 (v:v:v) (Wang et al., 2014). Soil texture was performed using a pipette method (Gee and Bauder, 1986). In this study, several soil fertility properties before and after specific washing treatments were examined, including pH, soil organic matter (SOM), total nitrogen (TN), cation exchange capacity (CEC), electrical conductivity (EC), plant-available ammonium (NH+ 4 N + NO− 3 -N) and phosphorus (AP), exchangeable K, Na, Ca, and Mg, exchangeable sodium percentage (ESP) and Ca2+/Mg2+ ratio. Soil pH and EC were measured in a 1:2.5 (w/v) aqueous suspension by a digital pH meter (pHS-3C, Shanghai INESA Scientific Instrument Co., Ltd., China) and an electrical conductivity meter (DDS-307A, Shanghai INESA Scientific Instrument Co., Ltd., China), respectively. SOM was determined by the wet dichromate oxidation method (Nelson and Sommers, 1996). TN content was analyzed by the semi-micro Kjeldahl method (Bremmer and Mulvaney, 1982). CEC and exchangeable cation (K+, Na+, Ca2+, and Mg2+) were measured after 1.0 M ammonium acetate extraction (pH = 7.0) (Gogo et al., 2010). Available ammonium − (NH+ 4 -N + NO3 -N) was extracted using 2 M KCl for 1 h (Wheatley et al., 1989) and measured by an auto analyzer (FIAstar 5000 Analyzer, Germany). Available phosphorus was determined by the molybdenum antimony colorimetry method (Olsen and Sommers, 1982). The ESP was calculated as the ratio of exchangeable Na+ to the CEC and the Ca2+/Mg2+ ratio was obtained by dividing the exchangeable Ca2+ by exchangeable Mg2 +. The physicochemical characteristics of the soils are presented in Table 1. 2.2. Batch washing experimental design Batch experiments were implemented in acid-rinsed polyethylene tubes to investigate the effects of GLDA concentration (Akzo Nobel Chemicals Co., Ltd., Shanghai, China), pH, and contact time on the removal of soil Cd, Pb, and Zn. For each run, soils (5.00 g) were mixed with a measured volume of chelating agent solution (50.00 mL) in 100-mL acid-rinsed polyethylene tubes and shaken at 180 rpm in a mechanical shaker at a preset time. The effect of GLDA concentration


559

Table 1 Variations in Cd, Pb and Zn contents and physicochemical characteristics of the tested soil samples before and after varieties washing treatments (70 mM, 25 °C, pH 3.0, solid/liquid =1:10 and washing time 120 min). Characteristic

Mine soil

Farmland soil GLDA

EDDS

EDTA

Citric acid

ASa

Original

GLDA

EDDS

EDTA

Citric acid

ASa

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

−

16.70 ±

6.59 ±

12.95 ±

6.42 ±

12.87 ±

16.09 ±

65.2, 16.7, 18.1 Sandy loam 32.93 ±

10.86 ±

18.81 ±

10.24 ±

17.68 ±

31.86 ±

Pb (mg kg−1)

0.25a 1640.23 ±

0.06c 187.31 ±

0.27b 267.36 ±

0.02c 23.51 ±

0.62b 1071.28 ±

0.56a 1586.39 ±

0.48a 680.63 ±

0.21c 99.38 ±

0.16b 193.77 ±

0.54c 97.62 ±

0.17b 461.77 ±

0.51a 658.71 ±

Zn (mg kg−1)

73.89a 2588.74 ±

5.58d 1601.22 ±

7.80c 1487.69 ±

4.68e 1351.36 ±

16.44b 1550.83 ±

65.84a 2551.91 ±

26.73a 367.59 ±

0.68d 219.26 ±

5.38c 244.12 ±

5.57d 210.08 ±

10.78b 209.40 ±

11.52a 352.48 ±

60.03a 6.25–6.31 19.20 ±

35.51b 4.63–4.89 16.40 ±

10.36d 4.23–4.48 17.82 ±

15.19e 3.68–3.81 15.96 ±

6.71c 3.17–3.39 22.63 ±

56.79a 4.86–5.16 18.69 ±

2.44a 6.97–7.16 23.94 ±

2.03c 8.03b 5.13–5.39 4.64–4.83 20.42 ± 21.82 ±

3.71c 1.16c 4.48–4.53 3.58–3.71 18.68 ± 26.68 ±

2.48a 5.34–5.56 23.08 ±

Total nitrogen (g

0.20b 1.02 ±

0.12d 0.91 ±

0.21c 0.85 ±

0.73d 0.74 ±

1.12a 0.81 ±

0.36b 0.99 ±

0.17b 1.51 ±

0.50d 1.29 ±

0.33c 1.18 ±

0.46e 1.15 ±

0.44a 1.58 ±

0.52a 1.46 ±

kg−1) CEC (cmol kg −

0.07a 12.40 ±

0.02b 9.46 ±

0.04bc 8.91 ±

0.03d 8.13 ±

0.02 cd 10.13 ±

0.08a 11.85 ±

0.04a 17.98 ±

0.03b 16.50 ±

0.02b 15.47 ±

0.05b 13.35 ±

0.58a 16.13 ±

0.15ab 16.52 ±

1) EC (dS cm−1)

0.19a 1.62 ±

0.22c 1.34 ±

0.11d 1.39 ±

0.12e 0.92 ±

0.20b 1.40 ±

0.27a 1.57 ±

0.13a 2.91 ±

0.09b 2.09 ±

0.12c 2.32 ±

0.43d 1.11 ±

0.22b 2.44 ±

0.29b 2.76 ±

0.04a 29.69 ±

0.02c 26.19 ±

0.01b 28.17 ±

0.03d 26.00 ±

0.02b 24.94 ±

0.09a 28.56 ±

0.04a 64.22 ±

0.07c 57.58 ±

0.10b 60.58 ±

0.06d 58.96 ±

0.06b 54.34 ±

0.08a 61.25 ±

1.15a 11.34 ±

0.21c 12.33 ±

0.16b 12.37 ±

0.33c 14.50 ±

0.73c 13.17 ±

0.74b 9.14 ±

0.98a 29.09 ±

0.88d 30.49 ±

0.59b 31.41 ±

0.42c 33.69 ±

0.77e 31.16 ±

0.76b 25.49 ±

0.09d 126.32 ±

0.30c 104.54 ±

0.14c 83.60 ±

0.14a 55.39 ±

0.13b 111.44 ±

0.21e 120.29 ±

0.19d 347.59 ±

0.36c 293.36 ±

1.49b 305.83 ±

0.51a 200.67 ±

0.10bc 318.58 ±

0.64e 325.49 ±

1.25a 126.67 ±

1.27c 70.83 ±

3.98d 83.33 ±

0.89e 28.75 ±

0.72b 77.92 ±

1.59a 118.75 ±

4.72a 259.58 ±

5.48a 160.42 ±

10.45a 179.58 ±

9.62b 42.92 ±

2.52a 171.67 ±

6.14a 236.25 ±

5.91a 1801.87 ±

2.60c 445.20 ±

1.91b 520.94 ±

2.50d 118.63 ±

4.73b 786.12 ±

3.47a 1645.69 ±

6.41a 1343.71 ±

3.82c 412.41 ±

5.05b 493.48 ±

2.60d 251.61 ±

5.91b 523.08 ±

2.14a 1036.54 ±

17.27a 410.75 ±

5.63d 128.71 ±

7.51c 153.27 ±

4.14e 64.40 ±

4.92b 252.64 ±

14.75a 386.74 ±

44.19a 264.00 ±

8.86d 132.38 ±

5.95c 148.59 ±

9.70e 135.88 ±

9.70c 204.81 ±

10.14b 249.52 ±

15.59a 4.39 4.44 ±

3.10d 3.46 3.26 ±

8.22c 3.40 4.07 ±

6.09e 1.84 1.54 ±

2.92b 3.11 3.35 ±

7.19a 4.25 4.36 ±

9.12a 5.09 6.28 ±

7.99d 3.12 4.23 ±

2.00c 3.32 5.05 ±

4.14d 1.85 1.40 ±

7.45b 2.55 4.63 ±

4.75a 4.15 6.22 ±

0.14a

0.17c

0.13b

0.16d

0.22c

0.29a

0.13a

0.11d

0.18b

0.08e

0.11c

0.20a

Original Clay, Silt, Sand (%) 52.2, 7.5, 40.3 Texture Sandy clay −1

Cd (mg kg

)

pH Soil organic matter (g kg−1)

− NH+ 4 -N + NO3 -N (mg kg−1)

Available P (mg kg−1) Exchangeable K (mg kg−1) Exchangeable Na (mg kg−1) Exchangeable Ca (mg kg−1) Exchangeable Mg (mg kg−1) Ca2+/Mg2+ ESP (%)b

Different lowercase letters in the same line represent the results with statistical difference according to the Fisher's protected LSD test at P b 0.05 in the same soil. a AS, Aqueous solution. b ESP (exchangeable sodium percentage) = exchange sodium/cation exchange capacity × 100.

(1.00–100.00 mM) on metal removal was studied under the constant conditions (pH 4.0 and contact time 60 min). In order to investigate the effect of pH on metals removal, experiments were performed under different pH regimes (3.0–10.0). The pH value of the suspension system was adjusted by a diluted HNO3 or/and NaOH solution and this experiment was conducted by maintaining other factors at constant conditions (GLDA concentration 50 mM and contact time 60 min). The kinetic study was examined by incubation for different periods of time (5.00–480.00 min), constant conditions of this experiment (GLDA concentration 50 mM and pH 4.0). Controls with Milli-Q water were also set up. The suspensions were then centrifuged (10 min, 4000 rpm), filtered and analyzed for metal concentrations using AAS. Additionally, we introduced the second-order kinetic model (Kulikowska et al., 2015b) to assess the kinetic process of the metal removal.

Ates and Erginel, 2016). Design Expert software (Version 8.0.7, Stat. Ease. Inc., USA) was applied for regression and graphical analysis of the data obtained. The quadratic equation model for predicting the optimum value and elucidating the interaction between the factors within the specified range are expressed as:

2.3. Optimization of the heavy metal washing procedure

2.4. Comparison experiment

Response surface methodology (RSM) based on Box–Behnken design (BBD) was employed to optimize the washing factors. The ranges and levels of independent variables investigated are given in Table S1. The experimental design included 17 combinations of variables (Table 2), including 12 factorial experiments and 5 replicates at a central point to provide a more precise estimation of the experimental error and to assess the lack of fit of the proposed models. All the experiments were performed randomly and in triplicate to minimize the influence of unexplained variability on the observed responses (Wang et al., 2015;

To compare the metal removal efficiency by GLDA with the conventional chelating agents such as EDDS, EDTA, and citric acid, experiments were performed with a 1:10 (w/v) ratio of soil to agent, a chelating agent concentration of 70.00 mM, pH of 3.00, and washing time of 120 min. These parameters selected based on the results of the optimization experiments. The other operating steps were done as mentioned above. Subsequently, the washed soils were collected and air-dried and analyzed the concentrations of metals (Cd, Pb, and Zn) and soil major mineral elements (Ca, Fe, Al, Mg and Mn), as well as soil fertility.

Y¼

n X i¼1

βi X i þ

n X i¼1

βii X 2i þ

n −1 X

n X

βij X i X j þ B0

ð1Þ

i¼1 j¼iþ1

where Y is the predicted response; β0 is constant of the equation; βi, βj, and βij are the linearity, quadratic and interactive coefficients of the model, respectively; n is the number of variables; Xi and Xj are the coded independent variables.

560


Table 2 The Box–Behnken design with experimental data for the Cd, Pb and Zn removal efficiencies (%) from the mine and polluted farmland soils. Assays

1 2 3 4 5 6 7 8 9 10 11 12 13(C) 14(C) 15(C) 16(C) 17(C)

Independent variable

Mine soil

Farmland soil

X1

X2

X3

Cd

Pb

Zn

Cd

Pb

Zn

−1(25) 1(75) −1(25) 1(50) −1(25) 1(75) −1(25) 1(75) 0(50) 0(50) 0(50) 0(50) 0(50) 0(50) 0(50) 0(50) 0(50)

−1(3.0) −1(3.0) 1(5.0) 1(5.0) 0(4.0) 0(4.0) 0(4.0) 0(4.0) −1(3.0) 1(5.0) −1(3.0) 1(5.0) 0(4.0) 0(4.0) 0(4.0) 0(4.0) 0(4.0)

0(90) 0(90) 0(90) 0(90) −1(30) −1(30) 1(150) 1(150) −1(30) −1(30) 1(150) 1(150) 0(90) 0(90) 0(90) 0(90) 0(90)

56.83 ± 3.54de 69.46 ± 1.05a 53.77 ± 0.58f 65.07 ± 0.71b 50.41 ± 0.06 g 61.64 ± 0.21c 56.62 ± 1.92de 68.61 ± 1.22a 60.81 ± 0.74c 55.57 ± 1.76ef 64.21 ± 1.03b 59.24 ± 1.92 cd 66.87 ± 0.58ab 66.21 ± 0.13ab 67.32 ± 1.07ab 67.42 ± 0.61ab 67.09 ± 0.50ab

68.99 ± 3.25b 87.37 ± 0.38a 66.51 ± 2.62bc 81.01 ± 7.04a 57.38 ± 0.99d 68.44 ± 3.93b 66.70 ± 3.09bc 85.15 ± 2.43a 72.02 ± 1.75b 60.38 ± 0.98 cd 82.45 ± 3.17a 65.77 ± 0.08bc 73.29 ± 0.75b 73.48 ± 3.03b 73.61 ± 4.68b 73.10 ± 0.14b 72.71 ± 0.68b

28.33 ± 1.47bcd 37.72 ± 0.58a 22.05 ± 0.76f 26.04 ± 0.56de 24.82 ± 2.67e 27.83 ± 0.96bcd 28.05 ± 1.19bcd 36.47 ± 2.19a 29.10 ± 0.26bcd 21.11 ± 1.83f 36.84 ± 0.25a 27.07 ± 2.95cde 31.31 ± 1.93b 30.29 ± 0.91bc 30.34 ± 0.81bc 30.24 ± 1.59bc 30.54 ± 1.02bc

65.44 ± 1.76bc 71.62 ± 1.38a 55.82 ± 3.09e 62.91 ± 1.55 cd 54.76 ± 0.95e 61.59 ± 1.80d 66.22 ± 2.65bc 70.71 ± 1.91a 60.94 ± 2.24d 55.11 ± 0.52e 67.90 ± 0.67ab 62.99 ± 2.57 cd 68.26 ± 0.51ab 67.27 ± 1.19ab 68.34 ± 0.52ab 68.14 ± 0.89ab 68.29 ± 1.43ab

78.13 ± 1.79ab 81.47 ± 3.20a 68.67 ± 0.46e 71.75 ± 1.53cde 63.97 ± 1.71f 70.56 ± 0.67de 74.74 ± 1.84bcd 77.30 ± 2.46ab 74.98 ± 0.95bcd 65.10 ± 1.13f 76.15 ± 1.46bc 72.03 ± 0.75cde 77.46 ± 2.50ab 77.94 ± 1.34ab 77.96 ± 4.04ab 77.89 ± 1.22ab 77.27 ± 0.94ab

42.86 ± 1.14bc 48.67 ± 1.39a 27.66 ± 0.96e 29.71 ± 1.41de 13.54 ± 1.30 g 15.15 ± 0.10g 41.88 ± 0.77bc 44.91 ± 0.59bc 18.70 ± 0.58f 11.08 ± 0.96h 49.77 ± 1.06a 31.80 ± 1.11d 40.68 ± 1.31c 41.37 ± 1.88c 42.40 ± 2.95bc 41.25 ± 0.94c 42.06 ± 1.02bc

(C) = central points. Different lowercase letters in the same column represent results with statistical difference according to the Fisher's protected LSD test at P b 0.05.

2.5. Sequential extraction procedure Chemical fractionations of soil Cd, Pb, and Zn before and after GLDA washing were performed according to Tessier's sequential extraction procedure with a slight modification (Suanon et al., 2016) as shown in Table S2. This was done by separating the metals into fractions, namely, water-soluble (F1), exchangeable (F2), bound to carbonates (F3), bound to Fe-Mn oxides (F4), bound to organic matters (F5), and residual (F6) fractions. Additionally, the ecological risk (Eir) of each individual metal was calculated according to the following formula (Kulikowska et al., 2015b): Eir ¼ T ir

C iD Ω

their pH value would eventually rise to 11.00–12.00. Then mixtures were held for an additional 30 min prior to filtration to remove insoluble phosphate salts (M = Ca, Fe) from the solutions. Finally, 10.00 mL of these filtrates were stored for metals analyses, and the rest (about 216 mL) was adjusted pH to 5.00 for the further experiment. The reactions involved in the regeneration of GLDA to be reused (abbreviated as L in the equations) are listed in Eqs. (3)–(5): FeSðsÞþL‐M2 →L‐Fe2þ þ MSðsÞ L‐Fe2þ þ 1

4

O2 ðgÞþHþ →L‐Fe3þ þ 1

ð3Þ 2

H2 O

ð4Þ

ð2Þ

3L‐Fe2þ þCa3 ðPO4 Þ2 ðsÞþ3OH− →3L‐Ca2þ þ2FePO4 ðsÞ þ FeðOHÞ3 ðsÞ: ð5Þ

where Tiris the toxic-response factor (Cd = 30, Pb = 5, Zn = 1); CiR is the threshold limits in soil for the metal (Cd, 0.45 mg kg−1; Pb, 80 mg kg−1; Zn, 250 mg kg−1); CiD (mg kg−1) is the metal concentration; Ω is the modified index of heavy metal concentration calculated asAδ + B (A, the sum percentage of F1, F2 and F3 fraction according to the modified Tessier's procedure; B = 1-A; and δ is the toxic index for a given metal related to the sum percentage of F1, F2, and F3 fraction). The δ values depending on the sum percentage of metal in the F1, F2, and F3 fraction: 1.0 (F1 + F2 + F3 ≤ 10%); 1.2 (10% b F1 + F2 + F3 ≤ 30%); 1.4 (30% b F1 + F2 + F3 ≤ 50%); 1.6 (N50%). Eirof b40 means that a single metal poses a low risk, 40–80 is moderate risk, 80–160 is considerable risk, 160–320, high risk; and N 320, very high risk (Zhu et al., 2012).

Batch experiments were carried out to assess the metal removal efficiencies by the recovered GLDA. Combined the above filtrates with a fresh GLDA solution (34 mL, 50.00 mM, pH 5.00) extracted another 25.00 g of contaminated soil in a mechanical shaker for 120 min. An additional 34 mL of fresh GLDA solution (50.00 mM and pH 5.00) acted as control. All experiments were performed in triplicated.

C iR

2.6. Soil washing with the recovered GLDA GLDA recycling was performed using the method of Efligenir et al. (2013) with a few modifications. Briefly, soil (25.00 g) was extracted with 250.00 mL of GLDA solution (50.00 mM) at pH 5.00 in a mechanical shaker for 120 min. After extraction, the soil suspension was separated from the spent soil washing solution through centrifugation and filtration. The filtrates (about 236 mL) were collected and divided into two parts: 10.00 mL were kept at 4 °C before analyzing the metal concentrations, and subsequently the rest was treated for regeneration. In order to obtain the recovered GLDA, the above filtrates were acidified to pH 4.0 and placed for overnight, followed by adding a small amount of FeS powder into the filtrates and stirring gently the mixture for 12 h. Then the formed insoluble metal sulfide MS (M = Cd, Pb, Zn, etc.) were removed by filtration. The pH of the filtrates was adjusted to 3.50 with diluted HNO3 or/and NaOH solution before adding Ca3(PO4)2. Subsequently, the mixtures were stirred for 20 min, and

2.7. Statistical analysis All mean values reported were the average of three independent samples of which the error limits of the parameters were below 5.0%. Standard reference material (GBW07405) was used for QA/QC during the digestion procedure. Analysis of variance and regression were conducted on experimental data using SPSS version 19.0 (SPSS Inc., Chicago, Illinois). For mean separations, Fisher's least significant difference test was used at significance level of P b 0.05. 3. Results and discussion 3.1. Washing of the contaminated soils with GLDA 3.1.1. Effect of GLDA concentration In the absence of GLDA, only 0.08–2.58% of Cd, Pb, and Zn were washed away from the contaminated soil (Fig. 1a and b). Removal efficiencies for all three metals increased significantly with an increase GLDA concentrations from 1.00 to 50.00 mM (P b 0.05); however, their changes were insignificant at GLDA concentrations above 50.00 mM (P N 0.05). The Cd, Pb, and Zn removal efficiencies were 66.07, 73.64, and 29.47%, respectively, for mine soil and 67.52, 77.65, and 39.11%, respectively, for polluted farmland soil at a GLDA


concentration of 50.0 mM. While they increased by b 5.0% at the highest chelator concentration (100.00 mM, P N 0.05). The results suggest that a higher GLDA concentration (N50.00 mM) lead to more effective removal of heavy metals, where the removal efficiencies obtained with GLDA for Cd and Pb were among the best observed at various conditions. A higher concentration of chelating agent could bind to more heavy metal ions and directly facilitate metal ion–ligand complexing reaction to move toward the direction leading to the formation of chelate (Begum et al., 2012a; Wu et al., 2015), which increase the removal efficiency. However, co-solubilization effects between the target heavy metals and co-existing metals such as Ca, Mg, Fe, and Al might result in reducing the removal efficiency (Nowack et al., 2006; Subirés-Muñoz et al., 2011; Wang et al., 2015; Wu et al., 2015), especially under a low concentration of chelating agent. 3.1.2. Effect of pH The pH of the washing solution may affect the capability of the chelator to remove metals by regulating the stability constants of the metalchelant complexes, the aqueous metal species concentration, ion-exchange and the sorption/desorption reactions (Wang et al., 2014; Rahman et al., 2015). The removal efficiencies of soil Cd, Pb, and Zn decreased as solution pH increased from 3.0 to 10.0 (Fig. 1c and d). The extraction efficiencies at pH 3.00 in mine soil were 69.53, 77.76, and 34.79%, and in polluted farmland soil were 66.83, 76.51, and 44.44% for Cd, Pb, and Zn, respectively. To understand the metal ion removal in the presence of GLDA, we introduced the stability constant (log K) of the metal-GLDA (abbreviated as L) complex, and the log K values occurred in the following order: log KCuL (13.03) N log KNiL (12.74) N log KPbL (11.60) N log KZnL (11.52) N log KCdL (10.31) (Begum et al., 2012a, 2012b). However, this order presents a slight difference compared to the one displayed in our work (Pb N Cd N Zn), which might be justified by the different concentration level of heavy metals within the soil and the prevailing environmental conditions (chemical fraction of heavy metals and non-target constituent existing) (Suanon et al., 2016). Results from this investigation also performed similarities to those reported by Wu et al. (2015). In their study, approximate 89% Cd was removed at pH 4.0 in the presence of GLDA, while the Zn removal efficiency remained relatively low throughout the experiments. Heavy metal ions bound to soil colloids may be dissolved by lowering the washing solution of pH. H+ ions present on the soil colloid

561

surface increase the concurrent release of the other metal ions via the cation exchange mechanism, and the soil particle surface becomes increasingly protonated as the quantity of H+ ions increases, which also promotes the desorption of metal ions (Wang et al., 2014; Chauhan et al., 2015). Therefore, the Cd, Pb, and Zn removal efficiencies were higher at lower values of solution pH. However, at a pH of 10.0, removal efficiencies for Cd, Pb, and Zn were only 36.79, 39.39, and 15.25%, respectively in the mine soil, while they slightly increased in the polluted farmland soil (except for Zn) compared to the mine soil. An alkaline conditions may lead to the formation of metal hydroxyl complexes and a low solubility of metal oxides (Begum et al., 2012a; Begum et al., 2013), which are possible reasons for the low metal removal efficiencies at a high pH level. 3.1.3. Kinetics of heavy metal removal with GLDA The soil Cd, Pb, and Zn removal efficiencies significantly increased with washing time up to 90 min (P b 0.05, Fig. 1e and f). At 90 min, removal efficiencies for Cd, Pb, and Zn were 67.00, 79.13, and 29.26%, respectively, in the mine soil and 67.11, 76.87, and 39.54%, respectively, in the polluted farmland soil. After that time, the removal efficiencies approached equilibrium and remained almost constant as washing time increased to 480 min (P N 0.05). These results demonstrate that the removal of Cd, Pb, and Zn from soils by GLDA occurs in a two-step kinetic process: a relatively fast initial step that occurs within 90 min, followed by a more gradual extraction in the subsequent hours. This finding is consistent with other studies that have examined washing time (Tandy et al., 2004). This phenomenon could be explained by the mobilization of weakly-bound metals (labile forms) that occurs at the beginning of the process, following which extraction efficiency is influenced by the replenishment of the labile pool with more recalcitrant species (Kirpichtchikova et al., 2006; Wang et al., 2014). The kinetics of the removal processes for soil Cd, Pb, and Zn followed the second-order model well (Fig. 1e and f). The equilibrium concentrations (qe values) of Cd that were removed from the polluted soils were lower than the individual concentrations of Pb and Zn that were removed (Table S3), the values of which were closely related to individual concentrations. The concentration of Cd was lower than those of the other metals (Table 1). Interestingly, the k2 value was the highest for Cd and lowest for Zn, regardless of soil type (Table S3), indicating that Cd was the most mobile in soil, and Zn, the least mobile. These findings

Fig. 1. Effect of the GLDA concentration, pH and washing time on the removal efficiencies of Cd, Pb and Zn from the mine soil (a, c and e) and polluted farmland soil (b, d and f), respectively. Values represent mean ± standard deviation of three replicates.

0.0207 0.0012

DF: Degree of freedom. a

0.0001 0.0351

0.3374

Zn Cd Zn Pb

0.0015 0.0002 0.0032 0.0016 0.5365 0.2557 0.4265 0.5151 0.2719 0.0129

Cd

b0.0001 b0.0001 0.0004 0.0002 0.5157 0.7073 0.8934 0.0003 0.0011 b0.000

9 1 1 1 1 1 1 1 1 1 7 3 4 16 Model X1 X2 X3 X1 X2 X1 X3 X2 X3 X21 X22 X23 Residual Lack of fit Pure error Core total

Pb

Zn

Pb

Zn

P-value

0.0005 0.0080 0.0001 0.0005 0.9334 0.2212 0.0964 0.0702 0.1635 0.0005

Pb Cd Zn Pb Cd Cd Cd

Pb

Sum of squares Sum of squares

Zn

F-value Farmland soil

F-value Mine soil DFa Source

3.2.2. Contour analysis To further investigate the interactive influences of the independent variables on the responses, we produced 2D contour plots (Figs. 3 and 4) based on the fitted model equations. A circular contour plot implies that the interactions between the corresponding variables are negligible, while an elliptical contour plot indicates otherwise (Mohammadi et al., 2016). As shown in Figs. 3 and 4, the oval contour plot showed that there was a strong interaction between pH and GLDA concentration. The Cd, Pb, and Zn removal efficiencies for both mine and polluted farmland soils increased steadily as the GLDA concentration rose from 25.00 to 75.00 mM at pH 5.00. However, this effect was more noticeable at lower pH level. These results were in agreement with those of our previous study (Wang et al., 2015) and of Wu et al. (2015). The smallest ellipse in the contour plot confined the maximum predicted value, indicating that there was a perfect interaction between the variables (Mohammadi et al., 2016). All soil metal removal efficiencies increased

Table 3 Analysis of variance for the fitted quadratic polynomial model of Cd, Pb and Zn removal efficiencies from the mine and polluted farmland soils.

3.2.1. Model fitting and analysis of variance (ANOVA) The removal efficiencies for Cd, Pb, and Zn using GLDA amendment and with different combinations of selected variables are presented in Table 2. The data in Table 2 were used to fit the polynomial model representing the metal removal efficiencies. The ANOVA analysis of the fitted model was shown in Table 3. The models were statistically significant with F-values of 63.53, 12.61, and 159.79 in the mine soil and 27.20, 18.28, and 116.32 in the polluted farmland soil for Cd, Pb, and Zn, respectively (Table 3, P b 0.01). This indicates that the quadratic models were valid for the present optimization study. Moreover, the R2 and R2adj values for both mine and polluted farmland soils were close to 1.0, indicating the high explanatory power of the models. Furthermore, the coefficient of variation (C.V.) for all metals was b5.00%, showing a high degree of precision and good reliability of the conducted experiments (Kostić et al., 2016). We also assessed the model through “adequate precision”, which measures the signal to-noise ratio; a ratio above 4 indicates adequate model discrimination (Kostić et al., 2016; Mohammadi et al., 2016). The experimental ratios were all N 4.00, indicating an adequate signal. These statistical parameters demonstrate the reliability of the models. Normally, it is necessary to assess whether the selected model provides an adequate approximation of the real system. If the normal probability plot obeys a linear behavior, this indicates that the residuals follow a normal distribution (Wang et al., 2015). As shown in Fig. S1, the experimental data were normally distributed, and there was no requirement for response transformation and no apparent problem with normality in all such cases. Meanwhile, the plot of studentized residual versus predicted values exhibited a non-symmetric scatter (Fig. 2), implying that the models were significant (P b 0.05). Additionally, the predicted values of soil Cd, Pb, and Zn removal efficiencies obtained from the models and the actual experimental data were split evenly at the 45° line (Fig. 2), indicating that the quadratic models were appropriate for fitting the heavy metals removal. The above results demonstrate the accuracy of the Box-Behnken model when applied to Cd, Pb, and Zn removal optimization.

440.56 370.42 2588.54 27.20 18.28 116.32 0.0001 75.58 30.30 19.53 42.00 13.46 7.90 0.0003 105.63 137.61 446.26 58.70 61.13 180.48 0.0001 156.82 81.98 1509.48 87.15 36.42 610.47 b0.0001 0.21 0.02 3.53 0.12 0.01 1.43 0.7444 1.37 4.06 0.50 0.76 1.80 0.20 0.4120 0.21 8.29 216.78 0.12 3.68 10.83 0.7417 6.72 10.26 11.45 3.74 4.56 4.63 0.0945 34.17 5.46 30.28 18.99 2.42 12.25 0.0033 50.88 85.29 512.88 28.28 37.89 207.42 0.0011 12.60 15.76 17.31 11.79 15.35 15.45 19.61 50.71 11.11 0.0074 0.80 0.40 1.85 453.16 386.18 2605.85 Cd: R2 =0.9722, R2adj =0.9365, CV(%)=2.08, Adequate precision =16.51 2 2 Pb: R =0.9592, R adj =0.9067, CV(%)=2.02, Adequate precision =15.54 Zn: R2 =0.9934, R2adj =0.9848, CV(%)=4.58, Adequate precision =35.16

P-value

3.2. Optimization of Cd, Pb, and Zn removal

b0.0001 b0.0001 b0.0001 b0.0001 0.0009 0.0009 0.1125 0.0351 0.0007 0.0340

are in agreement with those of a study demonstrating Cd removal by humic substances (Kulikowska et al., 2015b). Additionally, although the rate constant k2 for removal of the metals differed between the two soil types by as much as an order of magnitude in this study especially for Cd, the equilibrium concentration for all metals was reached after 90 min of extraction. This may be because k2 values are affected mainly in the first several minutes of washing (Kulikowska et al., 2015b). Therefore, the washing time needed to obtain equilibrium conditions could be more useful than the k2 value from a practical point of view.

b0.0001 0.0261 b0.0001 b0.0001 0.2708 0.6653 0.0133 0.0684 0.0100 b0.0001


539.73 1011.75 346.01 63.53 12.61 159.79 277.89 486.56 76.94 294.38 54.60 319.78 38.98 172.61 159.49 41.30 19.37 662.87 51.26 218.93 81.73 54.30 24.57 339.68 0.44 3.76 7.29 0.47 0.42 30.30 0.14 13.65 7.32 0.15 1.53 30.41 0.02 6.35 0.79 0.02 0.71 3.29 42.27 4.19 1.64 44.78 0.47 6.80 26.97 12.67 8.09 28.57 1.42 33.60 85.02 97.73 1.66 90.06 10.92 6.91 6.61 62.38 1.68 5.68 6.88 0.90 8.18 165.86 1.53 0.93 0.50 0.79 546.34 1074.13 347.69 Cd: R2 =0.9879, R2adj =0.9724, CV(%)=1.56, Adequate precision =25.00 2 2 Pb: R =0.9419, R adj =0.8673, CV(%)=4.13, Adequate precision =13.61 Zn: R2 =0.9952, R2adj =0.9889, CV(%)=1.67, Adequate precision =42.89

562


563

Fig. 2. Correlation of actual and predicted values of response and plot of residuals versus the predicted values for Cd, Pb and Zn removal from the mine and polluted farmland soils, respectively.

steadily with increasing washing time up to 120 min, followed by a slight increase in higher GLDA concentrations. The maximum metal removal efficiencies were achieved at a GLDA concentration of 65 mM and a washing time of approximately 120 min, except for Pb and Zn in mine soil. An increase in the concentration of the chelating agent and contact time would contribute to breaking the chemical bonds of the metals in the contaminated soil (Naoum et al., 2001), and therefore result in an increase in the metals removal. Furthermore, the effect of the interaction between washing time and pH on soil Cd, Pb, and Zn removal was significant for Pb and Zn. While for Cd, we observed a circular contour plot. At lower pH conditions, a greater quantity of H+ ions resulted in the de-sorption of metal ions from the soil colloid surface, a process that would be enhanced by an increase washing time (Naoum et al., 2001; Wang et al., 2015). Fig. S2 demonstrated the interactive effects of the three independent variables on the removal of soil Cd, Pb, and Zn. The perturbation plot illustrated the change in the response variable as each factor moves from the preferred reference, with all other factors held constant at the middle of the design space (Sedighi et al., 2012). The flat curves indicated that GLDA concentration and washing time substantially influenced Cd, Pb, and Zn removal in both the mine and polluted farmland soils as they changed from the reference point (Fig. S2). High pH values had an antagonistic effect on all metal removal. The removal efficiencies of metals between pH 3.00 and 5.00 occurred in the following order: Pb N Cd N Zn. A similar trend was also observed by Wu et al. (2015) and Rahman et al. (2015).

3.2.3. Validation of the models The main purpose of RSM is to obtain the maximized response value with optimal independent variables. Numerical optimization was performed to find a maximum point for the desirability function (Ahmadi et al., 2014), by setting the GLDA concentration, pH, and contact time within their ranges and maximizing the metal removal efficiencies. The optimal conditions for metal removal from mine soil were GLDA concentration of 74.83 mM, pH of 3.03, and washing time of 131.29 min. Under these conditions, the predicted removal efficiencies of soil Cd, Pb, and Zn were 69.50, 88.09, and 40.45%, respectively. For the polluted farmland soil, the optimal conditions were GLDA concentration of 69.45 mM, pH of 3.11, and washing time of 108.63 min; under these conditions, the metal removal efficiencies reached 71.34, 81.02, and 50.95%, respectively. To evaluate the suitability and validity of the model, the triplicate confirmatory experiments were done under the optimal conditions (Table S4). The results were closely related to the data obtained from the above numerical optimization and the measured values for all factors fitted within the 95% confidence intervals and prediction intervals (Table S4). These results indicate that the BBD is a reliable tool for the optimization of heavy metal removal.

3.3. Heavy metal distribution and environmental risk before and after with GLDA amendment The metal distribution in soils amended with GLDA was considerably altered compared with the original soils (Table 4). In the original mine and polluted farmland soils, the highest concentrations of Cd

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Fig. 3. The contour plots showing effects of two independent variables on the Cd, Pb and Zn removal from the mine soil (holding another variable at central values).

and Pb occurred in F2, followed by F3. The most stable fractions (F4 + F5 + F6) contained 42.81 and 19.05%, respectively, of the total concentrations in mine soil, and 42.91 and 27.42% in polluted farmland soil. Substantial quantities of Cd and Pb (N 50% in total) were weakly bound, occurring in F1, F2, and F3 (Table 4), corroborating evidence from other studies demonstrating that they are easily extracted by washing agents (Begum et al., 2012a; Wang et al., 2014; Suanon et al., 2016). In contrast to Cd and Pb, Zn occurred predominantly in F6 (51.22 and 47.98%, in mine and polluted farmland soil, respectively) followed by F2 (24.54% and 23.19%, respectively). The relatively high proportion of Zn in the residual fraction indicates that the metal is strongly incorporated within the crystalline lattice of the soil, which may be less easily extractable, even by chemical-enhanced washing (Hartley et al., 2014; Tsang and Hartley, 2014). Compared with the initial distribution, metals in the F1, F2, and F3 fractions were the most easily extractable, as shown by a corresponding significant reduction of 26.80–61.66% for all metals (Table 4). The FeMn oxides fraction of Cd and Pb in the polluted farmland soil was also

extracted by GLDA to some extent (about 3%), but extraction of that fraction in mine soil was negligible. By contrast, the proportion of all metals in F5 and F6 fractions obviously increased after GLDA extraction in the present study. The share of Cd, Pb, and Zn in F5 in mine soil and polluted farmland soil was increased by 2.60–3.40% and 3.33–12.07%, respectively; whereas in F6, it reached 24.06–55.53% and 27.59– 46.63%, respectively. The increase in the proportion of F5 and F6 among the left heavy metals after washing could be attributed to the loss of more easily extractable heavy metals. The distribution of metals not only demonstrates the efficiency of soil washing, but also the environmental risk of the soil, because the individual metal fractions have different chemical reactivities (Kulikowska et al., 2015a). Thus, a risk assessment was needed. Prior to remediation, the environmental risk from Cd in both soils was very high, according to the Eir index, while the risk from Pb was moderate and from Zn was low. The occurrence of these metals in the mobile fractions increases their associated risks. The highest risk from Cd (ECd r N ErPb NEZn r ) is a result of the higher toxicity of Cd compared with Pb and


565

Fig. 4. The contour plots showing effects of two independent variables on the Cd, Pb and Zn removal from the polluted farmland soil (holding another variable at central values).

Zn, and its lower permissible soil concentration (Cd, 0.45 mg kg−1; Pb, 80 mg kg−1; Zn, 250 mg kg−1). Soil washing with GLDA substantially decreased Cd, Pb, and Zn concentrations in the most easily-extractable fractions, leading to a reduction in the environmental risk posed by these metals.

ecosystems, for example promoting eutrophication (Leštan et al., 2008; Chauhan et al., 2015). In contrast, GLDA as a chelating agent is biodegradable and non-toxic to ecosystems (Kołodyńska, 2011). Accordingly, GLDA is potentially a practical and eco-friendly washing agent for the decontamination of soils that are heavily contaminated by heavy metals.

3.4. Comparison of the GLDA and other chelating agents The comparison study on the removal efficiency of Cd, Pb, and Zn under the optimized conditions in the presence of GLDA, EDDS, EDTA, and citric acid was conducted. EDTA showed superior extraction performance for Cd (61.55–68.89%), Pb (85.66–92.47%) and Zn (40.09– 42.85%) compared with GLDA, EDDS, and citric acid (Table 1). The superiority of EDTA can be attributed to its higher carboxylic acid density and its capacity to form more stable complexes with metals (Begum et al., 2013). However, GLDA removes the same amount of Cd and Zn as EDTA and a substantial amount of Pb. Although EDTA could efficiently extract different metals, it poses a risk to the environment because of it is non-biodegradable and it may have deleterious effects on

3.5. Solubilization of soil mineral elements The solubilization of soil mineral elements (i.e., Ca, Fe, Al, Mg, and Mn) by GLDA, EDDS, EDTA, and citric acid was also measured (Fig. 5a and b). A large amount of Ca released into the washing solutions, especially in the EDTA treatment, in which N 50% of Ca was removed; this was in agreement with findings of other studies (Begum et al., 2013; Hartley et al., 2014). The proportions of Mg and Mn solubilized were lower than Ca. Additionally, a small amount of Fe (6.42–18.26%) and Al (0.40–3.29%) were solubilized. These data demonstrate that GLDA solubilized a smaller proportion of these metals than did EDTA and

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Table 4 Chemical form of Cd, Pb and Zn in the original and remediated soils with GLDA (Solid-to-liquid ratio, 1:10; GLDA concentration, 70 mM; pH, 3.0; and contact time, 120 min). Mine soil

Farmland soil

Parameter

Original (mg kg−1)

After washing (mg kg−1)

Removal (%)

Original(mg kg−1)

After washing(mg kg−1)

Removal (%)

Total Cd F1 F2 F3 F4 F5 F6 SEE(%)a ECdb r Total Pb F1 F2 F3 F4 F5 F6 SEE(%)a EPbb r Total Zn F1 F2 F3 F4 F5 F6 SEE(%)a EZnb r

16.70 ± 0.25 0.40 ± 0.01 5.56 ± 0.26 3.86 ± 0.09 3.29 ± 0.11 0.63 ± 0.04 3.23 ± 0.12 101.62 1506.12 1640.23 ± 73.89 42.32 ± 5.21 912.21 ± 56.32 336.98 ± 26.35 42.67 ± 5.47 56.39 ± 7.96 213.36 ± 33.69 97.79 150.95 2588.74 ± 60.03 9.06 ± 0.65 635.24 ± 45.94 353.24 ± 36.57 63.21 ± 5.48 245.28 ± 24.94 1325.86 ± 60.27 101.67 11.95

6.59 ± 0.06 0.00 ± 0.00 0.89 ± 0.02 0.63 ± 0.01 2.06 ± 0.16 0.42 ± 0.04 2.86 ± 0.19 104.10 459.60 187.31 ± 5.58 0.00 ± 0.00 22.69 ± 3.26 9.28 ± 0.94 13.36 ± 1.86 12.36 ± 1.61 128.25 ± 24.42 99.27 12.11 1601.22 ± 35.51 0.46 ± 0.02 81.2 ± 12.36 106.3 ± 19.68 43.21 ± 6.41 206.21 ± 29.31 1225.14 ± 58.91 103.83 6.56

60.60 100.00 83.99 83.68 37.39 33.33 11.46 – – 88.58 100.00 97.51 97.25 68.69 78.08 39.89 – – 38.15 94.92 87.22 69.91 31.64 15.93 7.60 – –

32.93 ± 0.48 0.38 ± 0.02 14.36 ± 1.24 3.69 ± 0.95 1.29 ± 0.32 3.15 ± 0.54 9.69 ± 0.96 98.88 2932.57 680.63 ± 26.73 1.71 ± 0.42 356.21 ± 26.28 145.41 ± 12.98 25.14 ± 2.75 36.25 ± 3.91 125.21 ± 14.75 101.37 61.41 367.59 ± 2.44 0.30 ± 0.03 85.25 ± 14.96 66.35 ± 11.61 16.54 ± 4.87 38.96 ± 6.91 176.36 ± 20.17 104.40 1.71

10.86 ± 0.21 0.00 ± 0.00 0.25 ± 0.08 0.14 ± 0.02 0.16 ± 0.01 2.35 ± 0.48 8.26 ± 1.29 102.76 724.00 99.38 ± 0.68 0.01 ± 0.00 15.47 ± 1.06 11.25 ± 1.53 0.36 ± 0.07 12.08 ± 1.09 64.25 ± 11.38 104.06 6.55 219.26 ± 2.03 0.00 ± 0.00 4.19 ± 0.57 3.96 ± 0.41 15.29 ± 1.29 35.26 ± 7.48 165.69 ± 23.64 102.34 0.88

67.02 100.00 98.26 96.21 87.60 25.40 14.76 – – 85.40 99.41 95.66 92.26 98.57 66.68 48.69 – – 40.35 100.00 95.08 94.03 7.567 9.50 6.05 – –

a b

SEE is sequential extraction efficiency in expressed as [(F1 + F2 + F3 + F4 + F5 + F6)/Total metal content] × 100. Eri represent the potential ecological risk of an individual metal.

Fig. 5. The concentrations changes of Ca, Fe, Al, Mg and Mn from the mine soil (a) and polluted farmland soil (b) before and after washing with different agents, respectively, and the Cd, Pb and Zn removal efficiencies from the mine soil (c) and polluted farmland soil (d) using fresh and recycled GLDA. Error bars represent standard deviation (n = 3). Means labeled with different letters are significantly different (P b 0.05).


EDDS. Lower quantities of mineral elements released in the GLDAwashed soil are beneficial for revitalization of the remediated soil. Previous studies have shown that the co-dissolution of soil Ca, Fe, Mg, Al, and Mn is an important factor that could result in the release of heavy metal ions from soil minerals by chelating agent (Subirés-Muñoz et al., 2011; Wu et al., 2015). The mineral element Ca is the main competition cation, and therefore its concentration in the washing solution was very high compared with those of the target heavy metals (Zhang et al., 2013). In the current study, the extraction efficiencies of Cd, Pb, and Zn by GLDA were close to EDTA at their concentrations of 70 mM, pH of 3.0, and contact time of 120 min (Table 1). However, compared with EDTA, GLDA solubilized less Al, Ca, Fe, Mn, and Mg except for Fe from the polluted farmland soil (Fig. 5a and b). This was probably the result of the selective complexing ability of GLDA for Cd, Pb, and Zn. Thus, the minerals present in soil may not interfere with the GLDA extraction of Cd, Pb, and Zn, confirming the potential applicability of GLDA for the remediation of heavy metal-contaminated soil. 3.6. Variation in the soil fertility The soil fertility parameters changed markedly following various washing treatments (Table 1), and could be used as indicators to evaluate the effectiveness of washing treatments on soil (Wang et al., 2014; Chiang et al., 2016). The pH, organic matter, and total nitrogen content declined significantly in both mine and polluted farmland soil following GLDA, EDDS, and EDTA washing (P b 0.05). However, following citric acid washing, organic matter content increased by 11.45–17.86%. This increase in soil organic matter content could be attributed to the persistence of citric acid in soil particles (Wang et al., 2014). In all washing treatments, there was also a noticeable decrease in soil CEC and EC (P b 0.05), indicating the organic chelators solutions did not add excess base ions (Chiang et al., 2016). Available nitrogen, the major nutrient for plant growth, decreased slightly. However, available P contents in both the mine and polluted farmland soils increased by factor of 1.09–1.28 and 1.05–1.16, respectively, following washing. Acid washing conditions might enhance the amount of available P by dissolving and transforming unavailable P into an available form (Ren et al., 2015). The contents of exchangeable K, Na, Ca, and Mg in soil significantly decreased as the soils underwent washing by the chelators (Table 1). They declined by 11.78–56.15%, 34.21–77.30%, 56.37–93.42%, and 38.49–84.32% for mine soil and 8.35–42.27%, 30.82–83.47%, 61.07– 81.28%, and 22.42–49.86% for polluted farmland soil, respectively. Their losses could be attributed to replacement by protons (H+). However, compared with EDTA-washing, decreases in the initial exchangeable K, Na, Ca, and Mg were lower following GLDA-washing. In addition, the decline in the Ca2+/Mg2+ ratio became noticeable, implying that more Ca2+ was lost than Mg2+ during the washing process. As a result of the significant loss of Na+, all observed ESP levels decreased to below 5.10%. Our results suggest that GLDA eliminates the same amount of toxic metals as EDTA does, while simultaneously retaining most of the nutrients in the soil (Table 1). For practical applications, bioaugmentation (Chen et al., 2016) or cultivation (Jelusic et al., 2014; Ye et al., 2016) could be applied to further improve the properties of the washed soil. 3.7. Reusability of recycled GLDA The Cd, Pb, and Zn removal efficiencies from soils using fresh and recycled GLDA were assessed (Fig. 5c and d). Their removal efficiencies reached 53.56, 69.59, and 24.56% in mine soil and 59.26, 74.58, and 28.69% in polluted farmland soil, respectively, following washing with the recycled GLDA, and the extraction efficiency of the recycled GLDA was not N 5% less than that of the fresh GLDA. We observed no statistically significant differences in the extraction metal efficiencies in any of the metals between the recycled GLDA solution and freshly prepared

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GLDA solution (P N 0.05). Therefore, the regenerated GLDA could remove heavy metals from contaminated soil nearly as the fresh GLDA. 4. Conclusion N,N-bis(carboxymethyl)-L-glutamic acid (GLDA), an environmentally friendly chelating reagent was employed to remediate soils contaminated with heavy metals. Results showed that GLDA concentration, solution pH and washing time markedly influenced removal efficiencies of Cd, Pb, and Zn. We employed response surface methodology (RSM) based on the Box–Behnken design to optimize these parameters. The optimization process indicated that in mine soil, 69.50% of Cd, 88.09% of Pb, and 40.45% of Zn could be removed at a GLDA concentration of 74.83 mM, a pH of 3.03, and a washing time of 131.29 min. For farmland soil, 71.34, 81.02, and 50.95% of Cd, Pb, and Zn, respectively was removed under at the optimal conditions: GLDA concentration of 69.45 mM, pH of 3.11, and washing time of 108.63 min. The GLDA-extracted metals came mainly from the easily-extractable fractions, such as the water-soluble, exchangeable, and carbonate fractions, and the metals' environmental risk (Eir) substantially decreased as the proportion of the metals occurring in these easily-extractable fractions removed following GLDA-washing. However, in comparison to EDTA, GLDA tended to moderate remove soil organic matter, total and plantavailable ammonium, and exchangeable K, Na, Ca, and Mg, all of which would support the revitalization of the washed soils. Additionally, the regenerated GLDA removed Cd, Pb, and Zn nearly as effective as the fresh GLDA. Therefore, the biodegradable chelant GLDA is a potential substitute for the conventionally-used EDTA in the removal of metals from heavily polluted soils. Conflict of interest The authors declare no competing financial interests. Acknowledgments The authors acknowledge the funding provided by the Projects of Sci-tech Support, Sichuan, China (No. 2014NZ0044) and the Projects of National Sci-tech Support, China (2012BAD14B18-2) for carrying out this research. We wish to thank Ping Yao, Yue Chen, Rui Ma, LinXian Li, Yi-Jun Wang, Ya-Ru Cao and Guang-Ron Xu of Sichuan Agricultural University, for supporting the investigation and research work. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2016.06.155. Reference Adrees, M., Ali, S., Rizwan, M., Zia-ur-Rehman, M., Ibrahim, M., Abbas, F., Farid, M., Qayyum, M.F., Irshad, M.K., 2015. Mechanisms of silicon-mediated alleviation of heavy metal toxicity in plants: a review. Ecotoxicol. Environ. Saf. 119, 186–197. Ahmadi, A., Heidarzadeh, S., Mokhtari, A.R., Darezereshki, E., Harouni, H.A., 2014. Optimization of heavy metal removal from aqueous solutions by maghemite (γ-Fe2O3) nanoparticles using response surface methodology. J. Geochem. Explor. 147, 151–158. Akcil, A., Erust, C., Ozdemiroglu, S., Fonti, V., Beolchini, F., 2015. A review of approaches and techniques used in aquatic contaminated sediments: metal removal and stabilization by chemical and biotechnological processes. J. Clean. Prod. 86, 24–36. Ates, F., Erginel, N., 2016. Optimization of bio-oil production using response surface methodology and formation of polycyclic aromatic hydrocarbons (PAHs) at elevated pressures. Fuel Process. Technol. 142, 279–286. Begum, Z.A., Rahman, I.M.M., Tate, Y., Sawai, H., Maki, T., Hasegawa, H., 2012a. Remediation of toxic metal contaminated soil by washing with biodegradable aminopolycarboxylate chelants. Chemosphere 87, 1161–1170. Begum, Z.A., Rahman, I.M.M., Tate, Y., Egawa, Y., Maki, T., Hasegawa, H., 2012b. Formation and stability of binary complexes of divalent ecotoxic ions (Ni, Cu, Zn, Cd, Pb) with biodegradable aminopolycarboxylate chelants (dl-2-(2carboxymethyl)nitrilotriacetic acid, GLDA, and 3-hydroxy-2,2′-iminodisuccinic acid, HIDS) in aqueous solutions. J. Solut. Chem. 41, 1713–1728.

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