Adsorptive Removal of Arsenic and Mercury from Aqueous Solutions

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Oct 28, 2017 - H. Rasoulzadeh. Department of Environmental Health Engineering, School of .... (2002) reported the use of the plant for the removal of lead in combination ..... free energy (E) of adsorption per mole of the adsor- bate (kJ/mol) ...... Sathishkumar, M., Binupriya, A. R., Vijayaraghavan, K., & Yun,. S. I. (2007).

Water Air Soil Pollut (2017) 228:429 https://doi.org/10.1007/s11270-017-3607-y

Adsorptive Removal of Arsenic and Mercury from Aqueous Solutions by Eucalyptus Leaves Mahmood Alimohammadi & Zhyar Saeedi & Bahman Akbarpour & Hassan Rasoulzadeh & Kaan Yetilmezsoy & Mohammad A. Al-Ghouti & Majeda Khraisheh & Gordon McKay

Received: 7 May 2017 / Accepted: 12 October 2017 # Springer International Publishing AG 2017

Abstract The study is a first-time investigation into the use of Eucalyptus leaves as a low-cost herbal adsorbent for the removal of arsenic (As) and mercury (Hg) from aqueous solutions. The adsorption capacity and efficiency M. Alimohammadi : Z. Saeedi : B. Akbarpour : H. Rasoulzadeh Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Z. Saeedi e-mail: [email protected] M. Alimohammadi (*) Center for Water Quality Research (CWQR), Institute for Environmental Research (IER), Tehran University of Medical Sciences, Tehran, Iran e-mail: [email protected] K. Yetilmezsoy Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, Davutpasa Campus, Esenler, 34220 Istanbul, Turkey e-mail: [email protected] M. A. Al-Ghouti Department of Biological and Environmental Sciences, College of Arts and Sciences, Qatar University, DohaP.O. Box 2713, Qatar M. Khraisheh Department of Chemical Engineering, College of Engineering, Qatar University, DohaP.O. Box 2713, Qatar G. McKay Division of Sustainability, College of Science and Engineering, Hamad Bin Khalifa University, Education City, Qatar Foundation, Doha, Qatar e-mail: [email protected]

were studied under various operating conditions within the framework of response surface methodology (RSM) by implementing a four-factor, five-level Box–Wilson central composite design (CCD). A pH range of 3–9, contact time (t) of 5–90 min, initial heavy metal (As or Hg) concentration (C0) of 0.5–3.875 mg/L, and adsorbent dose (m) of 0.5–2.5 g/L were studied for the optimization and modeling of the process. The adsorption mechanism and the relevant characteristic parameters were investigated by four two-parameter (Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich) isotherm models and four kinetic models (Lagergren’s pseudo-first order (PFO), Ho and McKay’s pseudo-second order (PSO), Weber– Morris intraparticle diffusion, and modified Freundlich). The new nonlinear regression-based empirical equations, which were derived within the scope of the study, showed that it might be possible to obtain a removal efficiency for As and Hg above 94% at the optimum conditions of the present process-related variables (pH = 6.0, t = 47.5 min, C 0 = 2.75 mg/L, and m = 1.5 mg/L). Based on the Langmuir isotherm model, the maximum adsorption or uptake capacity of As and Hg was determined as 84.03 and 129.87 mg/g, respectively. The results of the kinetic modeling indicated that the adsorption kinetics of As and Hg were very well described by Lagergren’s PFO kinetic model (R2 = 0.978) and the modified Freundlich kinetic model (R2 = 0.984), respectively. The findings of this study clearly concluded that the Persian Eucalyptus leaves demonstrated a higher performance compared to several other reported adsorbents used for the removal of heavy metals from the aqueous environment.

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Keywords Arsenic and mercury adsorption . Eucalyptus leaves . Heavy metal removal . Isotherm models . Kinetic models

1 Introduction Heavy metals, due to their high toxicity, carcinogenicity, and bioaccumulation potential in aquatic life, have unfavorable impacts on receiving waters used for various beneficial purposes (Ulmanu et al. 2003). Amongst the high-priority pollutants, mercury and arsenic are of particular interest because of their serious detrimental impacts on human health (Akhtar et al. 2010). Such pollutants are typically introduced to the aquatic environment and water resources via organic or nonorganic entry point sources such as mineral, industrial, and agricultural wastewater (organic); plastic paints; fluorescent bulbs; insecticides; pesticides; batteries; etc. (nonorganic). The effects of such heavy metals on humans are well documented. For example, Hg is commonly related to serious neurological disorders while As is associated to genetic disorders such as bladder, skin, and lung cancer (Smedley and Kinniburgh 2013; Mosaferi et al. 2008). Both chemicals are fatal at high doses. Correspondingly, the consent limits for mercury and arsenic are given as 0.001 and 0.1 mg/L, respectively, as reported in the World Health Organization’s (WHO) drinking water standards (Mosaferi et al. 2008; Yardim et al. 2003; Kadirvelu and Namasivayam 2003). Accordingly, a large amount of literature is devoted towards finding cost-effective technologies and processes for the removal of such heavy metals, including simultaneous sequestration and flotation (Yenial et al. 2014), ion exchange (Fu and Wang 2011), ultrafiltration (Weng et al. 2005), and reverse osmosis (Kang et al. 2000). Although great advantages have been reported for above technologies, issues related to costs, availability of raw materials, requirements for skilled personnel in addition to the need to handle considerable volumes of sludge meant that a large number of such technologies cannot be used effectively, especially in low-income countries where adsorption using indigenous, low-cost available adsorbents is required. Studies on the adsorption of heavy metals using a number of natural materials have been abundantly reported in the literature (Kurniawan et al. 2006; Babić et al. 2002); rice husk (Rocha et al. 2009), natural clinoptilolite zeolite, and

Water Air Soil Pollut (2017) 228:429

anthracite (Samadi et al. 2010) were found to have acceptable removal rates. For the removal of As, adsorbents such as activated carbon (Reed et al. 2000), activated alumina (Lin and Wu 2001), and neutralized red mud (Genç-Fuhrman et al. 2004) were reported. For example, Rocha et al. (2009) examined the elimination of heavy metals such as lead, zinc, cadmium, and mercury from industrial wastewaters using natural rice husk as an adsorbent, and showed that the highest adsorption was related to cadmium, copper, zinc, and mercury, respectively. A rapid adsorption process took place within 1.5 h at pH = 5 giving the highest adsorption capacity. In addition, Yaghmaeian et al. (2016) conducted experimental studies on the elimination of arsenate by using the modified chitosan adsorbent with zero-valent iron. The authors found that the highest removal efficiency was at pH = 7.16, optimum adsorbent dose of 3.04 g/L, contact time of 91.5 min, and initial arsenate concentration of 9.71 mg/L. A recent study carried out by Wang et al. (2015) demonstrated the removal of Pb using Eucalyptus leaf residue, and the regeneration of Pb-loaded magnetic biochar showed a desorption efficiency of 84.1% in 120 min with an iron leaching amount of 1.1 mg/g. Zhuang et al. (2015) reported plant extract was used as an eco-friendly alternative to chemical and physical methods for the synthesis of nanoparticles. The authors demonstrated that plant extracts could be used to synthesize Fe NPs using these plant extracts. The nanomaterial produced was used effectively for the removal of azo-dye from aqueous solution with a capacity upwards of 215.1 mg/g for Direct Black removal. In another application of Eucalyptus leaves, Al-Subu (2002) reported the use of the plant for the removal of lead in combination with other plants such as Cupressus semperirens and Pinushalepensis. The author reported that lead removal increased mainly with the increase in the concentration of lead in solution. The author also noted that both Freundlich and Langmuir isotherms best described the adsorption. Eucalyptus actually refers to a large genus of flowering trees that has over 700 different species, most of which are located in Australia and New Zealand, although some of the more widespread species can be found throughout Southeast Asia. Most of its species range from the size of a small bush to a medium-sized blooming tree, but all species have leaves that are covered in oil glands, from which the

Water Air Soil Pollut (2017) 228:429

majority of the health benefits are derived. For instance, it aids in curing respiratory issues, boosts the immune system, provides relief from anxiety and stress, helps to protect skin against infections, aids in managing and preventing diabetes, and prevents atherosclerosis and heart attacks. Additionally, this fast-growing genus of the plant makes it precious as a source of wood and paper. This tree is also used for preparation of Eucalyptus oil and Eucalyptus tea that have impressive benefits. Moreover, it is also considered an invasive species in certain areas, as the trees are hardy and can quickly overtake native populations of slowergrowing plants (Organic Information Services Pvt Ltd. 2017). Besides the above-mentioned benefits, the Persian Eucalyptus leaves are widely available and can potentially be used as a low-cost readily available adsorbent for the removal of heavy metals from aqueous solutions. On the other hand, to the best of the authors’ knowledge, no study has been conducted so far on the use of Eucalyptus leaves for the removal of As and Hg from aqueous solutions. The prime novelties of this work can be summarized as follows: (i) adsorption of As and Hg ions from aqueous solution is investigated for the first time using cellulosic Persian Eucalyptus leaves; (ii) this herbal adsorbent is a unique, versatile, and low-cost material having a higher surface area and higher adsorption or uptake capacity compared to several more expensive adsorbents; (iii) it is a novel and inexpensive natural adsorbent because it requires little pretreatment; and (iv) optimization established for As and Hg removal by Eucalyptus leaves is explored for the first time using the surface response methodology (RSM). Accordingly, the main objectives of this study were to (1) investigate the adsorption capacity and efficiency of Persian Eucalyptus leaves as a natural herbal adsorbent in removal of mercury and arsenic from the aqueous solutions; (2) determine the effect of adsorbent dose, contact time, concentrations of adsorbent and adsorbate (mercury and arsenic), and pH changes on the adsorption capacity of the used adsorbent; (3) explore the applicability of various isotherm models and kinetic equations for the determination of the adsorption mechanism and characteristic parameters; and (4) assess the performance and effectiveness of the studied Eucalyptus leaves by comparing their performance data with that of various adsorbents for removal of heavy metals from the aqueous solutions.

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2 Materials and Methods 2.1 Chemicals In this study, all the chemicals were provided with highpercentage purity from the Merck Group (Germany). Standard solutions of 1 g/L were prepared using mercury nitrate (Hg2(NO3)2) and sodium arsenate (Na3AsO4) using double-distilled water (ddH2O).Solutions were provided daily by making appropriate serial dilutions of the primary standard solution using the doubledistilled water. In order to measure the concentrations of arsenic and mercury, the ICP-AES (Spector Model ARCOS FHE12) method was used. The pH was measured by a Kent EIL7020 model digital pH meter. 2.2 Preparation of Biomass The Persian Eucalyptus leaves used in this study were obtained from the northern region of Iran. The raw samples were washed initially with distilled water to remove any dirt and particulate matter. The leaves were dried naturally for 2–3 days before being crushed using a rudimentary crusher (hammer type) and sieved to obtain homogeneous material (particle size 1–3 mm) ready for the adsorption tests. The textural and morphological features of the adsorbent surface were determined by means of scanning electron microscopy (SEM); transmission electron microscopy (TEM); Xray diffraction (XRD); and Brunauer, Emmett, and Teller (BET) analysis. The Fourier transform infrared spectroscopies (FTIR) of the samples were recorded on a PerkinElmer Spectrum 100 spectrophotometer to investigate the change in the functional groups of the material surface (using a KBr disk technique in the range of 500 to 4000 cm−1). 2.3 Adsorption Tests All tests were conducted in 250-mL Erlenmeyer flasks containing 100 mL of different concentrations of arsenic, mercury, and adsorbent at various pH values and contact times. The synthetically prepared wastewater samples were located in an orbital shaker (C-MAG HS 10 digital, Germany IKA) for appropriate mixing of the adsorbent and adsorbate at a fixed stirring speed of 120 rpm (≈ 12.57 rad/s). After a certain time (i.e., 30– 45 min), the solution was filtered using a 0.2-μm syringe filter and the remaining concentration of arsenic

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and mercury was measured using the ICP-AES method. All experiments were conducted at a room temperature of 25 ± 2 °C. The pH of the solution was adjusted by using HCl (0.1 N) and NaOH (0.1 N). The removal percentage (%) and adsorption capacity (q, mg/g) of arsenic and mercury were calculated from the following equations (Heibati et al. 2015): q¼

ðC 0 −C t ÞV m

ð1Þ 

 Ce percentage removal ð%Þ ¼ 1−  100 C0

ð2Þ

where C0 and Ct (mg/L) are the liquid phase adsorbate concentrations at the initial time and at a given time t, respectively, V is the volume of solution (L), m is the adsorbent mass (g), and Ce is the equilibrium concentration of arsenic or mercury after the adsorption process (mg/L). 2.4 Optimization by Box–Wilson Central Composite Design (CCD) In this study, optimization of the present adsorption process was conducted based on the Box–Wilson central composite design (CCD), usually referred to as CCD, by using the software R (version 3.1.2, 2014, Pumkin Helmet), running on a CPU N280 (Intel® Atom™ Processor 1.66 GHz, 0.99 GB of RAM) PC. In the present study, effects of four main factors (pH, contact time, initial As or Hg concentration, and adsorbent dose) on As or Hg removal efficiency, with the use of Eucalyptus leaves as a low-cost herbal adsorbent, were analyzed within the framework of response surface methodology (RSM) by implementing a four-factor, fivelevel CCD for a sample size of only n = 30, whereas a full-factorial three-level experimental design with four independent parameters requires to (3)4 = 81 runs. A contact time of 5–90 min, an adsorbent dose of 0.5– 2.5 g/L, pH of 3–9, and an initial As or Hg concentration of 0.5–3.875 mg/L were considered for the optimization and modeling of the process. The experiments were performed according to the CCD matrix designed by using the software R. The CCD matrix of four independent variables (expressed in natural or uncoded units) and the corresponding experimental values (RE, removal efficiency (%) for As and Hg) are presented for both arsenic and mercury in Table 1.

In the present study, the effect of the four main factors (X1 = pH, X2 = contact time (min), X3 = initial concentration of As or Hg (mg/L), and X4 = adsorbent dose (g/L)) on As or Hg removal efficiency (Y), with the use of Eucalyptus leaves as a low-cost herbal adsorbent, was optimized and modeled by the CCD approach. The following quadratic model structure was used for the analysis of the As and Hg data from the CCD matrix (Yetilmezsoy et al. 2009; Shabbiri et al. 2012a, b; Noori Sepehr et al. 2014; Dhawane et al. 2015): k

k

i¼1

i¼1

k−1

k

Y ¼ β 0 þ ∑ βi xi þ ∑ βii x2i þ ∑ ∑ β i xi x j þ ε ð3Þ i¼1 j¼iþ1

where Y is the process response or output (dependent variable); k is the number of the independent factors; i and j are the index numbers for the pattern; Xi and Xj define the natural (uncoded) independent factors; βi, βii, and βij represent coefficients of first-order (linear) and second-order terms (quadratic or squared) and interaction terms, respectively, and β0 indicates a free or offset term (also referred to as intercept term); and ε represents the random error or allows for discrepancies or uncertainties between predicted and measured values. For the implementation of the multiple regressionbased analysis of the quadratic models, DataFit® software package (V8.1.69, Oakdale Engineering, PA, USA) used the following values of the solution preferences: (a) regression tolerance = 1 × 10−10, maximum number of iterations = 250, and (b) diverging nonlinear iteration limit = 10. When performing the nonlinear regression, Richardson’s extrapolation method was conducted to calculate numerical derivatives for the solution of the models. The nonlinear regression analysis was conducted based on the Levenberg–Marquardt method with double precision.

2.5 Statistical Analysis Analysis of variance (ANOVA) was performed to obtain interactions between the dependent (Y) and independent (X1, X2, X3, and X4) variables, as well as to test the significance of the derived quadratic models for As and Hg. For this purpose, a solution script was written in the M-file Editor within the framework of MATLAB® R2009b software (V7.9.0.529, The

Water Air Soil Pollut (2017) 228:429

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Table 1 Central composite design (CCD) matrix with four independent variables expressed in natural (uncoded) units Run no.

Arsenic (As) pH

Mercury (Hg)

CT

IC

AD

RE

pH

CT

IC

AD

RE

1

6.00

90.00

2.75

1.50

94.15

3.00

47.50

2.75

1.50

78.46

2

6.00

47.50

2.75

1.50

95.70

9.00

47.50

2.75

1.50

91.50

3

6.00

5.00

2.75

1.50

90.15

6.00

47.50

2.75

1.50

94.66

4

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

5

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

6

3.00

47.50

2.75

1.50

77.64

6.00

90.00

2.75

1.50

93.18

7

9.00

47.50

2.75

1.50

92.20

6.00

47.50

2.75

1.50

94.66

8

6.00

47.50

2.75

1.50

95.70

6.00

5.00

2.75

1.50

88.18

9

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

10

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

11

6.00

47.50

0.50

1.50

93.88

6.00

47.50

2.75

1.50

94.66

12

6.00

90.00

2.75

1.50

94.15

6.00

47.50

2.75

1.50

94.66

13

6.00

47.50

2.75

1.50

94.50

6.00

47.50

2.75

1.50

94.66

14

6.00

47.50

3.875

1.50

95.14

6.00

47.50

0.50

1.50

92.66 93.18

15

6.00

47.50

2.75

1.50

95.70

6.00

90.00

2.75

1.50

16

6.00

5.00

2.75

1.50

90.15

6.00

47.50

3.875

1.50

93.66

17

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

18

6.00

47.50

2.75

1.50

95.70

6.00

5.00

2.75

1.50

88.18

19

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

20

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

21

6.00

47.50

2.75

2.50

94.50

3.00

47.50

2.75

1.50

78.46

22

6.00

47.50

2.75

1.50

95.70

9.00

47.50

2.75

1.50

91.50

23

6.00

47.50

2.75

0.50

92.50

6.00

47.50

2.75

1.50

94.66

24

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

25

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

26

3.00

47.50

2.75

1.50

77.64

6.00

47.50

2.75

2.50

92.46

27

9.00

47.50

2.75

1.50

92.20

6.00

47.50

2.75

1.50

94.66

28

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

0.50

91.22

29

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

30

6.00

47.50

2.75

1.50

95.70

6.00

47.50

2.75

1.50

94.66

Italicized data show the values of variables which are held at their optimum levels during runs 1–10, 11–20, and 21–30 for As or Hg CT contact time (min), IC initial concentration of arsenic or mercury (mg/L), AD adsorbent dose (g/L), RE removal efficiency for As or Hg (%)

MathWorks, Inc., Natick, MA, USA) to perform a multi-way (n-way) ANOVA on the vector of the dependent variable. Models were evaluated by an alpha (α) at 95% confidence level (p = 0.05) to appraise the statistical significance. It is noted that for any parameter to be a significant model component, its F value should be higher and its p value (probability value) should be less

than 0.05. Additionally, the significance of any processrelated parameter can also be analyzed using the sum of square (SS) value, and its higher value implies more importance of the corresponding variable (Dhawane et al. 2015). The quality of regression equation was determined by the coefficient of determination (R2), and its significance was judged by Fisher’s statistical test (F test). The

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F statistic calculated within the scope of ANOVA is expressed as follows: n  2 ∑ ni Y mean;i −Y pred;i =ðn−pÞ



i¼1 n

ni 

2

∑ ∑ Y ij −Y mean;i =ðN −nÞ

¼

ðSS LOF Þ=d 1 ðSS PE Þ=d 2

i¼1 j¼1

¼

n ðSS LOF Þ=d 1 →N ¼ ∑ ni ðSS E −SS LOF Þ=d 2 i¼1

ð4Þ

where p is the number of parameters in the model; i is an index of each of the n distinct x values; j is an index of the response variable observations for a given x value; ni is the number of Y values associated with the ith x value; Yij is the experimental response of run i, replicate j; Ypred,i is the response obtained from using the proposed polynomial at run i and Ymean,i is the observed mean of all the replicates at run i; SSLOF is the sum of squares due to lack of fit (LOF); SSPE is the sum of squares due to pure error (PE); SSE is the total sum of squares due to error (SSE = SSPE + SSLOF); d1 = (n − p) and d2 = (N − p) are the degrees of freedom; and N is the total number of observations. Furthermore, the coefficient of determination, R2, was used for judging the quality or goodness of the fitted quadratic models. Also, the R2 value should be in close agreement with the adjusted R2, which is denoted as Ra2. In the present analysis, R2 and R2adj values are determined by the following equations: R2 ¼ 1−

SS reg SS res SS tot −SS res ¼ ¼ SS tot SS tot SS res þ SS reg

ð5Þ

n  2 ∑ Y pred;i −Y obs;mean

R2 ¼

n



i¼1

∑ Y obs;i −Y pred;i

2

n  2 þ ∑ Y pred;i −Y obs;mean

i¼1

i¼1

 2 ∑ Y pred;i −Y obs;mean n

¼

i¼1 n

 2 ∑ Y obs;i −Y obs;mean

ð6Þ

i¼1

      n−1 SS res =df e 2 ¼ 1− ¼ 1− 1−R n−p−1 SS tot =df t      p ¼ R2 − 1−R2 ð7Þ n−p−1 

R2adj

where SStot is the total sum of squares (proportional to the variance of the data), SSres is the sum of squares of residuals (also referred to as the residual sum of squares), SSreg is the regression sum of squares (also referred to as the explained sum of squares), p is the total number of explanatory variables in the model (without including the constant term), n is the size of the sample, dft is the degrees of freedom (n − 1) of the estimate of the population variance of the dependent variable, and dfe is the degrees of freedom (n − p − 1) of the estimate of the population error variance. 2.6 Adsorption Isotherms The adsorption isotherm is one of the most important considerations in designing adsorption systems. In fact, the adsorption isotherm describes the interaction between an adsorbent and adsorbate. Thus, it is always considered as a major factor to determine the capacity of an adsorbent and optimization of the adsorbent consumption. For this reason, adsorption isotherms are very critical for the description of how solutes interrelate with the adsorbents, for optimizing the use of these materials (Thompson et al. 2015). In this study, equilibrium data collected were fitted to the well-known four twoparameter adsorption isotherm models, such as Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich equations, with the assumption of being a monocomponent system and thereby eliminating the possibility of multi-component competition. Several models are available, and the most common is the single-layer adsorption model that was proposed by Langmuir in 1918. Among the other models, the multi-layer adsorption model, described by Freundlich in 1906, is widely used and applicable in many adsorption studies. In the Langmuir isotherm, it is supposed that adsorption takes place in the homogeneous sites on the adsorbent. Accordingly, the model is best used to describe single-layer adsorption processes. In contrast, the Freundlich isotherm is based on multi-layer, nonuniform, and heterogeneous adsorption of the adsorbate onto the adsorbent (Vimonses et al. 2009). The Langmuir and Freundlich adsorption models can be expressed and linearized, respectively, as follows (Ho and McKay 1998; Yakout and Elsherif 2010; Mondal et al. 2013; Nethaji et al. 2013; Thompson et al. 2015): qe ¼

qmax k L C e 1 1 1 1 → ¼ þ ⋅ 1 þ k L C e qe qmax qmax ⋅k L C e

ð8Þ

Water Air Soil Pollut (2017) 228:429

1 qe ¼ k f ⋅C e 1=n →logðqe Þ ¼ logðk f Þ þ ⋅logðC e Þ n

Page 7 of 27 429

ð9Þ

where Ce is the equilibrium concentration of arsenic and mercury in solution (mg/L), qe is the adsorption or uptake capacity at equilibrium (mg/L), qmax is the maximum adsorption or uptake capacity (mg/g), kL is the Langmuir adsorption constant (L/mg), k f is the Freundlich constant [(mg/g)/(mg/L)1/n], and 1/n represents the exponent of nonlinearity (i.e., C-type, L-type, and S-type isotherms). In this study, the values of qmax and kL (Eq. (8)) are determined, respectively, from the slope (1/qmax) and intercept (1/(qmax∙kL)) of the linear graph of x = 1/Ce vs. y = 1/qe. The favorability of the adsorption process in the Langmuir model can be determined by means of the RL dimensionless factor (RL = 1/(1 + kL · C0)) as follows: RL = 0, 0 < RL < 1, RL = 1, and RL > 1 indicating irreversible, favorable, linear, and unfavorable adsorption isotherms, respectively. In Eq. (9), the values of n and kf are calculated, respectively, from the slope (1/n) and intercept (log(kf)) of the linear plot of x = log(Ce) vs. y = log(qe). The 1/n value derived from the Freundlich equation serves to describe the linearity of adsorption or alternatively the degree of curvature of the isotherms across the concentration range tested. It is noted that nonlinearity is observed, especially with chemicals which are not extremely hydrophobic and therefore not limited by solubility to extremely low concentrations. However, any sorption isotherm which covers a wide concentration range (i.e., more than two orders of magnitude), even if the whole range is at very low concentrations, will typically be nonlinear, presumably because a range of sorption processes are taking place. Typically, 1/n values range from 1 downwards. A value of 1 signifies that the relative adsorption (adsorption partition) of the chemical was the same across the whole range tested (C-type isotherm), which is unusual (especially across the concentration range of two orders of magnitude often used in regulatory studies). More normally, 1/n values will range from 0.7 to 1.0. These values show that when the concentration of the chemical under investigation increases, the relative adsorption decreases (L-type isotherm). This tends to be indicative of the saturation of the adsorption sites available to the chemical, resulting in relatively less adsorption. 1/n values of less than 0.7 describe highly curved isotherms, and 1/n values of greater than 1 are indicative of S-type isotherms. These are relatively uncommon but are often

observed at low concentration ranges for compounds containing a polar functional group. It has been hypothesized that, at low concentrations, such compounds are in competition with water for adsorption sites (ECETOC 2017). Temkin isotherm (proposed by Temkin and Pyzhev in 1939) is another two-parameter model that is usually used for heterogeneous adsorption of an adsorbate onto a surface of an adsorbent. The linearized form of the Temkin model is expressed by the following equation (Al-Meshragi et al. 2008; Varank et al. 2012; Mondal et al. 2013; Dehghani et al. 2016):   R⋅T R⋅T qe ¼ ¼ β T →qe lnðK T ⋅C e Þ→ bT bT ¼ βT lnðK T Þ þ β T lnðC e Þ

ð10Þ

where KT is the Temkin isotherm constant or equilibrium binding constant (L/mg) corresponding to the maximum binding energy and bT is the Temkin isotherm constant related to the heat of As or Hg adsorption onto Eucalyptus leaves due to adsorbent–adsorbate interaction (J/mol); R is the gas constant (8.314 J/mol/K); and T is the absolute temperature (herein 308 K). By plotting a linear graph of x = ln(Ce) vs. y = qe, the values of b and KT can be determined, respectively, from the slope (βT = (R · T)/b) and the intercept [β · ln(KT)] of the graph. The Dubinin–Radushkevich (proposed by Dubinin in 1960) isotherm is another popular model that is widely used for description of adsorption in microporous materials (especially those of a carbonaceous origin) based on the Polanyi potential theory (introduced by Polanyi in 1932) of adsorption, as well as for the analysis of isotherms of a high degree of regularity (Nguyen and Do 2001; Varank et al. 2012). This semiempirical isotherm can be generally defined and linearized in Eqs. (11)–(13), respectively, as follows (Nguyen and Do 2001; Al-Meshragi et al. 2008; Yakout and Elsherif 2010; Varank et al. 2012; Erhayem et al. 2015):     1 qe ¼ qD ⋅exp −BD ⋅εD 2 →εD ¼ R⋅T ⋅ln 1 þ ð11Þ Ce  2 ! 1 qe ¼ qD ⋅exp −BD ⋅ R⋅T ⋅ln 1 þ Ce 

lnðqe Þ ¼ lnðqD Þ−BD ⋅εD 2

ð12Þ ð13Þ

429

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Page 8 of 27

where qD is the maximum monolayer adsorption or uptake capacity (mg/g), BD is the activity coefficient related to the apparent free energy of As or Hg adsorption onto Eucalyptus leaves (mol2/kJ2), and εD is the Polanyi potential which is related to the equilibrium concentration, and others (qe, Ce, and R) are defined in previous equations. The values of BD and qD can be determined, respectively, from the slope (−BD) and the intercept [ln(qD)] of the linear graph of x = εD2 (as a function of R, T, and Ce) vs. y = ln(qe) (Yakout and Elsherif 2010; Erhayem et al. 2015). It has been reported that the characteristic of the adsorption is related to the porous structure of the adsorbent. In Eqs. (11)–(13), BD denotes the mean free energy (E) of adsorption per mole of the adsorbate (kJ/mol), when it is transferred to the surface of the solid from the infinite distance in the solution. This energy can be obtained from the following relationship (Al-Meshragi et al. 2008; Yakout and Elsherif 2010; Varank et al. 2012; Mondal et al. 2013; Erhayem et al. 2015): E ¼ 1=

pffiffiffiffiffiffiffiffi 2BD

ð14Þ

In the Dubinin–Radushkevich isotherm, the magnitude of E is very useful for prediction and interpretation of the underlying mechanism of the adsorption process. If E < 8 kJ/mol, the adsorption process may be affected by physical forces. In the case of E being between 8 and 16 kJ/mol, adsorption is governed by the ion exchange mechanism, and for the value of E > 16 kJ/mol may be dominated by particle diffusion phenomenon (Patel and Vashi 2014).

present kinetic mechanism behind As and Hg adsorption by Eucalyptus leaves. The equations of PFO (proposed by Lagergren in 1898) and PSO (proposed by Ho and McKay in 1999) models can be described and linearized, respectively, as follows (Ho and McKay 1999, 2002; Varank et al. 2012; Mondal et al. 2013; Qi et al. 2015): dqt ¼ k 1 ðqe −qt Þ→lnðqe −qt Þ ¼ lnðqe Þ−k 1 ⋅t dt

ð15Þ

dqt q 2k2t t 1 1 þ t ¼ k 2 ðqe −qt Þ2 →qt ¼ e → ¼ dt 1 þ qe k 2 t qt k 2 qe 2 qe

ð16Þ where qe and qt are the adsorption capacities at equilibrium and time t (mg/g), and k1 is the rate constant (min−1), respectively. In Eq. (15), ln(qe) and k1 are the intercept and slope of the linear graph of x = t vs. y = ln(qe − qt), respectively. In Eq. (16), k2 is the pseudo-second-order constant (mg/(g.min)). The values of qe and k2 can be determined, respectively, from the slope (1/qe) and intercept [1/(k2 · qe2)] of the linear graph of x = t vs. y = t/qt. Since the pseudo-first-order and pseudo-secondorder are not sufficient to fully identify the diffusion mechanism behind the adsorption process, the kinetic results must therefore be analyzed by using the Weber– Morris intraparticle diffusion model (proposed by Weber and Morris in 1963). The linearized form of the Weber–Morris intraparticle diffusion model is expressed by the following equation (Yakout and Elsherif 2010; Varank et al. 2012; Mondal et al. 2013; Gupta et al. 2015; Thompson et al. 2015; Dehghani et al. 2016):

2.7 Adsorption Kinetics

qt ¼ k int ⋅t 1=2 þ C

ð17Þ

The adsorption kinetics of the present system were examined for a better understanding of arsenic and mercury adsorption dynamics on Eucalyptus leaves, and providing a predictive model that allows the estimation of the amount of ions adsorbed during the process. The information can also be used to design the large systems (Türk et al. 2009; Al Rmalli et al., 2008). In this study, four kinetic models, such as Lagergren’s pseudo-first-order (PFO) kinetics, Ho and McKay’s pseudo-second-order (PSO) kinetics, the Weber–Morris intraparticle diffusion model, and the modified Freundlich kinetic model, were selected for the evaluation of the

where qt is the adsorption or uptake capacity at time t (mg/g), kint is the intraparticle diffusion rate constant (mg∙g−1∙min−1/2), and C is a constant related to the thickness of the boundary layer (mg/g). The values of kint and C can be directly calculated, respectively, from the slope (kint) and the intercept (C) of the linear graph of x = t1/2 vs. y = qt. It is noted that larger kint values illustrate better adsorption, which is related to improved bonding between adsorbate and adsorbent particles (Demirbas et al. 2004), and may simply be due to higher adsorbate concentrations, which adds to a greater driving force for diffusion of adsorbate molecules into the pores

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(Bajpai and Jain 2010; Thompson et al. 2015). Likewise, the value of the intercept (C) may give a useful idea about the thickness of the boundary layer, i.e., the larger the intercept, the greater the boundary layer effect (Kavitha and Namasivayam 2007; Varank et al. 2012). Finally, the modified Freundlich equation (developed by Kuo and Lotse in 1973) is expressed and linearized, respectively, by the following equations (Varank et al. 2012; Sampranpiboon and Feng 2016): qt ¼ k mf ⋅C 0 ⋅t 1=mmf lnðqt Þ ¼ lnðk mf ⋅C 0 Þ þ

ð18Þ 1 ⋅lnðt Þ mmf

ð19Þ

where qt is the adsorption or uptake capacity at time t (mg/g), kmf is the apparent adsorption rate constant (L/g/ min), C0 is the initial As or Hg concentration (mg/L), and mmf is the Kuo–Lotse constant for the modified Freundlich model (L/g/min). The values of kmf and mmf are used empirically to evaluate the effect of surface loading and ionic strength on the adsorption process. By plotting a linearized graph of x = ln(t) vs. y = ln(qt), the values of mmf and kmf can be obtained, respectively, from the slope (1/mmf) and the intercept [ln(kmf ∙ C0)] of the straight-line plot.

3 Results and Discussion 3.1 Adsorbent Characteristics

leaves can be of a great advantage as the surface area obtained is nearly tenfold higher than that for the bark, if the adsorption process is surface area dependent. 3.2 Derivation of Quadratic Models and Statistical Results With the application of multiple regression-based analysis on the CCD matrix (Table 1), the following secondorder polynomial models were derived for the natural (uncoded) form of independent factors (X1 = pH, X2 = contact time (CT: min), X3 = initial As or Hg concentration (IC: mg/L), and X4 = adsorbent dose (AD: g/L)) and the dependent variable (Y: As or Hg removal efficiency, RE: %)): Y ¼ β 0 þ ðβ 1 X 1 þ β 2 X 2 þ β 3 X 3 þ β 4 X 4 Þ   þ β 5 X 21 þ β 6 X 22 þ β 7 X 23 þ β 8 X 24 þðβ 9 X 1 X 2 þ β 10 X 1 X 3 þ β 11 X 1 X 4 þ β 12 X 2 X 3 þ β 13 X 2 X 4 þ β 14 X 3 X 4 Þ

ð20Þ

REAs ð%Þ ¼ 71:16 þ 1:19ðpHÞ  1:16ðCTÞ þ 10:65ðICÞ þ 34:39ðADÞ      1:19 pH2  0:0030 CT2      0:36 IC2  2:13 AD2 þ 0:44ðpHÞðCTÞ  1:49ðpHÞðICÞ  0:82ðpHÞðADÞ  0:086ðCTÞðICÞ

The SEM analysis of the adsorbent revealed that the adsorbent had a porous structure. Based on the TEM analysis, it was determined that the adsorbent particles are spherical in shape. These spherical particles had a tendency to adhere together and form chains. The X-ray diffraction spectra of the adsorbent showed that the peak at 43° is related to the graphene structure in the adsorbent matrix. The results of proximate and elemental analyses of the adsorbent are given in Table 2. The specific surface area (obtained by the BET method) of the adsorbent was found to be 770 m2/g. The average pore diameter (nm) and total pore volume (m3/g) were 0.66 and 0.71, respectively. In addition, the moisture and ash content were 12 and 5.7% w/w, respectively. Rajamohan et al. (2014) reported a surface area of 70.9 m2/g for Eucalyptus camaldulensis barks for the removal of aluminum. Accordingly, it can be seen that the use of the plant

 0:63ðCTÞðADÞ þ 2:87ðICÞðADÞ ð21Þ

REHg ð%Þ ¼ 71:52 þ 0:68ðpHÞ  1:14ðCTÞ þ 10:99ðICÞ þ 35:17ðADÞ      1:08 pH2  0:0036 CT2      0:53 IC2  2:82 AD2 þ 0:43ðpHÞðCTÞ  1:64ðpHÞðICÞ  1:07ðpHÞðADÞ  0:074ðCTÞðICÞ  0:61ðCTÞðADÞ þ 3:29ðICÞðADÞ ð22Þ The results of ANOVA of the multiple regressionbased models (Eqs. (21) and (22)) showed that the quadratic models were highly significant and valid, as

429

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Table 2 Physicochemical analysis of Persian Eucalyptus leaves Type of analysis Elemental analysis Proximate analysis Porous structure parameters

Components and values N (%)

C (%)

P (%)

14

12

3

Moisture (%)

Ash content (%)

12

5.70

Average pore diameter (nm)

Total pore volume (m3/g)

BET surface area (m2/g)

0.66

0.71

770

was evident from the Fisher F test conducted for As or Hg adsorption data (Fmodels > 500) with a very low probability value (p < Fmodels = 0.00001). According to Eq. (4), the calculated F values (> 500) were found to be greater than the tabulated F value (Fα,df(n − df + 1) = F0.05,14,17 = Sr2/Se2 = Ftabulated = 2.329) at α = 0.05. This indicated that the computed Fisher’s variance ratio at this level was large enough to corroborate a very high degree of fit of the quadratic models, and the selected combinations were highly significant, as similarly reported in previous works (Yetilmezsoy et al. 2009; Noori Sepehr et al. 2014). Since Fmodels > Ftabulated, the Fisher F test concluded at a confidence level of 95% that the second-order polynomial models explained a significant amount of the variation in the dependent factors (REAs or REHg). Moreover, the statistical analysis for the response surface quadratic models showed the standard error of estimate was below 0.35 for the present case. The models’ p value indicated that the derived second-order models for removal of As or Hg was significant, whereas the p values of for the lack of fit (LOF) showed insignificancy of the models’ failure (pLOF > 0.05 and was almost equal to 0.99, indicating a nonsignificant LOF), so that the independent variables or parameters had considerable effects on the removal of As or Hg. As seen from the multiple regression-based equations (Eqs. (21) and (22)), all factors, such as X1 (pH), X2 (contact time, CT), X3 (initial concentration, IC, of As or Hg), and X4 (adsorbent dose, AD) influence As or Hg removal efficiency (REAs or REHg). According to the multiple regression-based analysis for both REAs and REHg models, the model terms X1, X2, X3, X4, X12, X22, X32, X42, X1 ∙ X2, X1 ∙ X3, X1 ∙ X4, X2 ∙ X3, X2 ∙ X4, and X3 ∙ X4 were significant (p < 0.05). The second-order (quadratic or squared) effects of X12, X22, X32, and X42 were the most significant, all having a p value < 0.0015, thus corroborating the results of Shabbiri et al. (2012a,

b) and Wang et al. (2008). From the ANOVA, these results indicated that X1 (pH) and X2 (contact time, CT) had a direct relationship to both As and Hg removal efficiency, since the sum of square (SS) values of these variables (SSpH = 587.62 and 476.70 and SSCT = 98.40 and 137.42 for As and Hg models, respectively) were much higher than those of other first-order terms, X3 (SSIC = 3.07 and 4.55 and SSAD = 10.19 and 15.08 for As and Hg models, respectively). According to Eqs. (5)–(7), the goodness of fit of the models was tested by means of the determination coefficient (R2) and the adjusted determination coefficient (Ra2). The values of R2 = 0.998 and R2 ≈ 1.0 revealed with 95% certainty that Eqs. (21) and (22) satisfactorily predicted the expected responses with very small deviations (maximum residuals < 0.07) for As and Hg data sets, respectively. It is noted that the Ra2 corrects the R2 value for the sample size and the number of terms in the model. If there are many terms in the model and the sample size is not very large, the Ra2 may be noticeably smaller than the R2 (Liu et al. 2004; Yetilmezsoy et al. 2009). For the present case, the values of adjusted determination coefficient (Ra2 = 0.996 and Ra2 ≈ 1.0 for As and Hg, respectively) were also very high, implying a high significance of the derived quadratic models. Finally, the Durbin–Watson (DW) statistics (DW = 1.938 and DW = 1.438 for As and Hg, respectively) were determined to be close to 2, showing the goodness of fit of the second-order polynomial models and indicating positive autocorrelations (DW ≤ 2) for each model between errors (Hewings et al. 2002; Yetilmezsoy et al. 2009). 3.3 Effect of Interactions Between pH and Contact Time on the Adsorption of As and Hg Figure 1 shows the effect of various pH values on the adsorption of arsenic and mercury by the Eucalyptus leaves at different contact times. As seen from Fig. 1, the

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values, the hydroxyl ion concentration in the solution is increased, as well as competing with the arsenic and mercury ions for adsorption on the adsorbent active sites, so the adsorption of these ions is reduced (Henke 2009). Since the pHzpc (pH of zero point charge) value of the used adsorbent is 5.0–6.0, the decreased adsorption efficiency is reasonable in alkaline conditions. Other studies have also shown that the optimum pH for adsorption of arsenic and mercury was between 6.0 and 8.0 (Ngah and Hanafiah 2008; Mohan and Pittman 2007; Bulut and Aydın 2006). In Fig. 1, at pH = 3.0 and 47.5 min contact time, the removal efficiency of arsenic is equal to 77.6%, and the removal efficiency of mercury is equal to 78.5% (Fig. 1a,

96.0

Run no for As: 1–10 (Table 1)

94.0

X1 = CT 90.0 47.5 5.0 47.5 47.5 47.5 47.5 47.5 47.5 47.5

92.0 90.0 88.0 86.0 84.0

(a)

82.0 80.0 78.0

76.0 80.0 Co 70.0 nta 60.0 ct 50.0 .0 tim 40 .0 e ( 30 .0 mi 20 .0 n) 10 0.0

RE As (%) (R2

Removal efficiency for Hg (%)

Fig. 1 3D response surface diagrams showing the effects of the mutual interactions between pH and contact time on removal efficiency of arsenic (a) and mercury (b). Other variables were held at their optimum levels: adsorbent dose = 1.5 g/L and initial concentration of arsenic and mercury = 2.75 mg/L

Removal efficiency for As (%)

pH change affects the adsorption of arsenic and mercury, since it determines the type of ion species of arsenate and mercury and the adsorbent surface charge. This scenario will have an impact on the reaction between adsorbent and adsorbate. In other words, in the case of a positive adsorbent surface charge, the adsorbent affinity to absorb the anions is increased, and there will be electrostatic adsorption. Thus, the pH of the solution has an impact on both the adsorbent surface charge and the adsorbate species charge, and these are the controlling conditions for the adsorption of arsenic and mercury (Mosaferi et al. 2014). On the other hand, the adsorbent surface charge is negative at lower pH values, so the tendency to adsorb the desired ions through the electrostatic process is decreased. Also, at higher pH

9.0

8.0

7.0

6.0 pH

5.0

0.00001

RE = Yexp 94.15 95.70 90.15 95.70 95.70 77.64 92.20 95.70 95.70 95.70

RE = Ypred 94.15 95.70 90.15 95.70 95.70 77.64 92.20 95.70 95.70 95.70

4.0

56.31 11.72 ln(t ) 1.69 [ln(t )]2

1.0, p

X2 = pH 6.0 6.0 6.0 6.0 6.0 3.0 9.0 6.0 6.0 6.0

179.60 pH

234.32 pH 2

878.41 pH 3

0.05)

96.0

Run no for Hg: 1–10 (Table 1)

94.0

X1 = CT 47.5 47.5 47.5 47.5 47.5 90.0 47.5 5.0 47.5 47.5

92.0 90.0 88.0 86.0 84.0

(b)

82.0 80.0 78.0 80.0 Co 70.0 nta 60.0 ct 50.0 .0 ti m 40 .0 e ( 30 .0 mi 20 .0 n ) 10 0.0

9.0

8.0

7.0

6.0 pH

5.0

1.0, p

0.00001

0.05)

RE = Yexp 78.46 91.50 94.66 94.66 94.66 93.18 94.66 88.18 94.66 94.66

RE = Ypred 78.46 91.50 94.66 94.66 94.66 93.18 94.66 88.18 94.66 94.66

4.0

REHg (%) 56.83 12.71 ln(t ) 1.80 [ln(t )]2 (R2

X2 = pH 3.0 9.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0

127.65 pH

23.31 1096.45 pH 2 pH 3

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Page 12 of 27

b). Under the same conditions, the empirical equations (shown on Fig. 1) derived within the framework of DataFit® software package (V8.1.69, Oakdale Engineering, PA, USA) yield a very good fit (R2 ≈ 1.0 and p = 0.00001 < α = 0.05 at 95% confidence level) with a removal efficiency of 77.64 and 78.46% for arsenic and mercury, respectively. In the derivation of these empirical formulations, the same nonlinear convergence criteria and computational techniques (Richardson’s extrapolation and Levenberg–Marquardt methods) were conducted, where they were also considered in derivation of the second-order polynomial models based on the CCD matrix. According to the empirical equations shown in Fig. 1a, b, at the optimum conditions of pH and contact time (pH = 6.0 and t = 47.5 min), it may be possible to obtain a removal efficiency of 95.7 and 94.66% for arsenic and mercury, respectively. It is noted that the removal efficiency of arsenic and mercury showed a decreasing growth trend by increasing the pH from 3.0 to 7.0. The possible reason for the decreased removal efficiency at higher pH (from 6.0 to 9.0) may be attributed to the decreased chelation process of the amine groups available in the Eucalyptus leaves (Fig. 1a, b). Accordingly, the process may cause lower competitive adsorption of hydrogen ions at high pH. Furthermore, maximum removal or uptake capacity was obtained at pH = 6.0 (at an initial concentration (m) of 1.5 mg/L), where adsorption efficiencies of 95.7% for arsenic and 94.7% for mercury were achieved. A similar trend and pH dependency were reported by Mousavi and Lotfi (2011) for the adsorption of cadmium, nickel, and cobalt by the ashes of Eucalyptus leaves. In addition, Mosaferi et al. (2014) reported that the highest adsorption capacity of arsenic occurred at a pH between 6.0 and 8.0. According to the trends and data reported in Fig. 1a, b, it might be concluded that the adsorption process using the Eucalyptus leaves might be governed by electrostatic repulsion between the negative adsorbent surface and the cationic nature of arsenic or mercury ions. 3.4 Effect of Interactions Between Contact Time and Initial Adsorbate Concentration on the Adsorption of As and Hg The effect of contact time on the adsorption of arsenic and mercury onto the adsorbent at various initial concentrations of arsenic and mercury is shown in Fig. 2. As can be seen, the adsorption of arsenic and mercury is

Water Air Soil Pollut (2017) 228:429

enhanced by increasing the contact time up to an equilibrium contact time. The results indicate that the removal efficiency of arsenic and mercury increased over this contact time, and the maximum removal was observed to occur within the first 50 min of the experimental time. As seen in Fig. 2a, b, the adsorption of arsenic and mercury within the first 50 min is faster, and by increasing the contact time from 50 to 90 min, the slope of this curve then starts to gradually decrease and finally becomes constant after 70 min. The adsorption process was rapid at the initial stages of the contact period, but thereafter, it became slower towards equilibrium. The adsorption sites become less available as the contact time increased, resulting in a slow adsorption phase, as similarly stated by Thompson et al. (2015). The initial rapid phase may be due to the availability of more adsorption/vacant sites at the initial stage, resulting in an increased concentration gradient between the adsorbate in solution and the adsorbate on the adsorbent. This can be attributed to strong attractive forces between As and Hg ions and the Persian Eucalyptus leaves, and also by the fast diffusion into the interparticle matrix to attain rapid equilibrium, as similarly emphasized by others (Sathishkumar et al. 2007; Arivoli et al. 2008; Binupriya et al. 2008; Varank et al. 2012). For the present case, no significant change in As and Hg removal was observed after about 47.5 min. In other words, longer times (herein > 47.5 min) unnecessarily prolonged the process to obtain similar results. Therefore, further studies were conducted using a period of 47.5 min as the optimal contact time for As and Hg adsorption. It can be observed from Fig. 2a, b that, after a certain period of time, removal versus time curves are single, smooth, and continuous leading to saturation, thus suggesting the possibility of monolayer coverage of As and Hg on the outer surface of the adsorbent. The results showed that by increasing the initial concentrations of arsenic and mercury, the adsorption capacity and removal efficiency could be increased to a certain level (95.7 and 94.7% for As and Hg, respectively), as the initial concentration of As and Hg was increased (herein from 0.5 to 2.75 mg/L). This can be explained that, at the initial stage of the adsorption process, there are plenty of readily accessible sites, but eventually a plateau is reached, thus indicating that the adsorbent is saturated at high initial concentrations of arsenic and mercury (i.e., 3.875 mg/L), as similarly reported by Bhowmick et al. (2014). For the present case, Fig. 2a, b shows that the increased initial

Water Air Soil Pollut (2017) 228:429

96.0 Removal efficiency for As (%)

Fig. 2 3D response surface diagrams showing the effects of the mutual interactions between contact time and initial adsorbate concentration on removal efficiency of arsenic (a) and mercury (b). Other variables were held at their optimum levels: pH = 6.0 and adsorbent dose = 1.5 g/L

Page 13 of 27 429

Run no for As: 11–20 (Table 1)

X1 = m 0.50 2.75 2.75 3.875 2.75 2.75 2.75 2.75 2.75 2.75

95.0 94.0 93.0 92.0

(a)

91.0

Ini tia 90.0 la ds 3.5 or ba 3.0 .5 te 2 co 2.0 nc en 1.5 tra 1.0 t io n ( 0.5 mg / L)

90

.0

80

0.869, p

0.0018

Removal efficiency for Hg (%)

28.39 ln(t )

Run no for Hg: 11–20 (Table 1)

X1 = m 2.75 2.75 2.75 0.50 2.75 3.875 2.75 2.75 2.75 2.75

94.0 93.0 92.0

(b)

90.0 89.0

Ini tia 88.0 la ds 3.5 or ba 3.0 .5 te 2 co 2.0 nc en 1.5 tra 1.0 t io n ( 0.5 mg / L)

90

.0

80 70.0 .0

(R2

0.923, p

X2 = CT 47.5 47.5 47.5 47.5 90.0 47.5 47.5 5.0 47.5 47.5

RE = Yexp 94.66 94.66 94.66 92.66 93.18 93.66 94.66 88.18 94.66 94.66

RE = Ypred 94.39 94.39 94.39 92.66 94.73 93.66 94.39 88.27 94.39 94.39

10 30 20.0 .0 50 40.0 .0 . ) 0 .0 e (min ct tim Conta

60

REHg (%) 95.15 1.50 ln( m) 1.53 [ln( m)]2

concentration of As and Hg has a direct impact on the removal efficiency of the studied heavy metal ions, and the possible reason for the adsorbent saturation at high initial concentrations of As and Hg may be ascribed to the filling of the adsorbent pores or the difficult access of As and Hg ions to the active sites on the adsorbent surface, as well as to a decrease in the mass transfer driving force, hence the decreased rate at which As and Hg ions pass from the solution to the adsorbent surface. In general, it can be said that at the low arsenic and mercury concentrations, the ratio of adsorbate ions to the active sites is lower and, as a result, the adsorption is independent of their initial concentration. On the contrary, at the high concentrations of arsenic and mercury,

RE = Ypred 93.88 95.59 95.31 95.14 95.31 90.23 95.31 95.31 95.31 95.31

0.05)

95.0

91.0

RE = Yexp 93.88 94.15 94.50 95.14 95.70 90.15 95.70 95.70 95.70 95.70

10 30 20.0 .0 50 40.0 .0 70 60.0 .0 in) (m e ct ti m .0 .0 Conta

RE As (%) 95.52 1.05 ln(m) 0.65 [ln(m)]2 (R2

X2 = CT 47.5 90.0 47.5 47.5 47.5 5.0 47.5 47.5 47.5 47.5

0.00096

34.21 ln(t )

0.05)

the access to the adsorption sites is lower, and so the arsenic and mercury removal depends on their initial concentration. The results of the present study showed that an increase in the initial concentration of arsenic and mercury has a positive impact on the adsorption capacity, so that increasing the initial concentration from 0.5 to 2.75 mg/L increased the adsorption capacity. The reason could be the increased concentration driving force due to the increase in the number of arsenic and mercury ions (Gupta and Bhattacharyya 2011). In this study, the effect of different concentrations of arsenic and mercury on the adsorption efficiency at various times indicated the fact that the equilibrium adsorption capacity of the Eucalyptus leaves for

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adsorption of arsenic and mercury was enhanced by increasing the initial concentration of these elements, and also, the adsorption kinetics of arsenic and mercury had two phases. The adsorption phase was implemented rapidly in the initial adsorption phase, and in the second phase, the adsorption was slower and finally reached the equilibrium conditions. The high rate of adsorption for arsenic and mercury in the first phase might be due to the adsorbent dosage available level at the start of the process or, in other words, the available active adsorption sites that absorbed the arsenic and mercury ions. However, the number of active adsorption sites gradually decreases with increasing contact time and the increasing concentration of arsenic and mercury ions adsorbed onto the adsorbent, so the adsorption rate decreased significantly and the second phase started and involved diffusion. These second phase active adsorption sites might be located in the deeper parts of the adsorbent. Thus, at the beginning of the adsorption reaction, all the sites were prepared for adsorption, but the external surface sites were easily exposed to the arsenic and mercury ions with higher chance to face the above-mentioned ions. So it increased the speed of adsorption, but gradually, with the saturation of the external surface sites, the adsorption continued through the deep and internal parts of the leaf and would reduce the adsorption rate. In fact, all sites are involved in the adsorption, but the adsorption speed in the initial phase was controlled through the adsorbent surface sites. The increase in adsorption capacity by increasing the concentration of arsenic and mercury could be due to the high probability of collision between the arsenic and mercury ions (Kanel et al. 2005). Based on the empirical equations given in Fig. 2a, b, at the optimum conditions of initial concentration of arsenic and mercury and contact time (C0 = 2.75 mg/L and t = 47.5 min), a removal efficiency of 95.31 and 94.39% can be achieved for arsenic and mercury, respectively. This is in line with the results obtained for the mutual interactions of BpH and contact time.^ In a study on the removal of arsenic by zero-valent iron nanoparticles as the permeable active barriers in the groundwater resources, Kanel et al. (2005) reported the equilibrium time of 90 min for the initial concentration of 6 mg/L, which is consistent with the results of the present study. Likewise, Al Rmalli et al. (2008) reported the time of 1 h for removal of mercury on the surface of willow leaves with the maximum adsorption capacity of 37.2 mg/g, which is also in agreement with results of the

Water Air Soil Pollut (2017) 228:429

present study. Furthermore, Sinha and Khare (2012) reported the use of moss as a natural adsorbent in the removal of mercury. They obtained the maximum adsorption rate at pH = 5.5, a contact time of 60 min, an adsorbent dose of 4 g/L, and temperature of 20 °C. The equilibrium time differences might be due to the difference in the initial concentrations of arsenic and mercury, since the removal efficiency is reduced by increasing the initial concentration of the pollutant, and reaching the equilibrium occurred within the lesser time.

3.5 Effect of Interaction Between Adsorbent Dose and pH on the Adsorption of As and Hg The effect of various concentrations of adsorbent on the adsorption of arsenic and mercury at different various pH values is depicted in Fig. 3. As shown in Fig. 3a, b, the increased dose of adsorbent is associated with an increase in the removal efficiency. The reason for an increase in the removal efficiency of arsenic and mercury with an increasing dose of Eucalyptus leaves could be explained by the increase in adsorbent surface area, resulting in more active sites for arsenic and mercury ions to adsorb in the active pores on the adsorbent. On the other hand, according to Eq. (1), increasing the adsorbent dose reduces the specific adsorbent loading, so that, for the present case, increasing the dose of adsorbent over 2 g/L did not yield an increase (no significant impact for arsenic and a slight decrease for mercury) of the adsorption capacity. The reason might be the saturation of active sites of the adsorbent during the adsorption process (Heibati et al. 2015). From the empirical equations presented in Fig. 3a, b, at the optimum conditions of adsorbent dose and pH (m = 1.5 mg/ L and pH = 6.0), a removal efficiency of 95.70 and 94.66% can be achieved for arsenic and mercury, respectively. This is in agreement with the previous results obtained for the mutual interactions of both BpH and contact time^ and Bcontact time and initial adsorbate concentration.^ To summarize, the experimental findings indicated that removal efficiencies reached their maximum values for both As and Hg at pH 6.0, and then showed a decrease from 95.70 to 92.20% for As and from 94.66 to 91.50% for Hg within the pH range of 6.0–9.0. The declining trends in removal efficiencies at high pH can be better seen in 3D response surface diagrams (Figs. 1 and 3). Based on the experimental results, it is

Fig. 3 3D response surface diagrams showing the effects of the mutual interactions between adsorbent dose and pH on removal efficiency of arsenic (a) and mercury (b). Other variables were held at their optimum levels: contact time = 47.5 min and initial concentration of arsenic and mercury = 2.75 mg/L

Page 15 of 27 429

Removal efficiency for As (%)

Water Air Soil Pollut (2017) 228:429

96.0

Run no for As: 21–30 (Table 1)

94.0

X1 = m 2.50 1.50 0.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50

92.0 90.0 88.0 86.0 84.0

(a)

82.0 80.0 78.0

76.0 2.4 Ad 2.2 .0 so 2 1.8 rb en 1.61.4 td os 1.21.0 e( g/ L 0.80.6 ) 0.4

9.0

8.0

7.0

6.0 pH

5.0

Removal efficiency for Hg (%)

1.0, p

0.000001

RE = Yexp 94.50 95.70 92.50 95.70 95.70 77.64 92.20 95.70 95.70 95.70

RE = Ypred 94.50 95.70 92.50 95.70 95.70 77.64 92.20 95.70 95.70 95.70

4.0

RE As (%) 64.47 7.60 m 2.20 m 2 (R2

X2 = pH 6.0 6.0 6.0 6.0 6.0 3.0 9.0 6.0 6.0 6.0

277.20 pH

771.12 pH 2

0.05)

96.0

Run no for Hg: 21–30 (Table 1)

94.0

X1 = m 1.50 1.50 1.50 1.50 1.50 2.50 1.50 0.50 1.50 1.50

92.0 90.0 88.0 86.0

(b)

84.0 82.0 80.0 78.0 2.4 Ad 2.2 .0 so 2 1.8 rb en 1.61.4 td os 1.21.0 e( g/L 0.80.6 ) 0.4

9.0

8.0

7.0

6.0 pH

5.0

confirmed that there was no visual evidence of precipitation in the solutions for the present case. 3.6 Adsorption Isotherms Equilibrium isotherm parameters for arsenic and mercury adsorption onto the adsorbent are presented in Table 3. Additionally, the linear diagrams of Langmuir and Freundlich adsorption isotherms are shown for arsenic and mercury in Fig. 4. The results of the adsorption equilibrium isotherms given in Table 3 show that the 1/n values (1.745 and 1.730 for As and Hg, respectively) obtained from the Freundlich equation are indicative of S-type isotherms, since the values of 1/n are higher than 1. This can be ascribed that some compounds containing

1.0, p

0.000001

RE = Yexp 78.46 91.50 94.66 94.66 94.66 92.46 94.66 91.22 94.66 94.66

RE = Ypred 78.46 91.50 94.66 94.66 94.66 92.46 94.66 91.22 94.66 94.66

4.0

REHg (%) 65.10 9.08 m 2.82 m 2 (R2

X2 = pH 3.0 9.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0 6.0

249.48 pH

693.36 pH 2

0.05)

a polar functional group may be in competition with water for adsorption sites at low concentration ranges. As seen from Table 3, the RL values (0.40 and 0.53 for As and Hg, respectively) obtained from the Langmuir model are between 0 and 1, indicating a favorable adsorption isotherm for both arsenic and mercury. On the basis of determination coefficients, the Freundlich model (R2 = 0.9897 for arsenic and R2 = 0.9849 for mercury) fitted the adsorption isotherm data better than the Langmuir model (R2 = 0.9815 for arsenic and R2 = 0.9802 for mercury). In recent research on arsenic adsorption by the zerovalent iron nanoparticles stabilized by the montmorillonite minerals, Bhowmick et al. (2014) reported an appropriate Langmuir model with a maximum

429

Water Air Soil Pollut (2017) 228:429

Page 16 of 27

Table 3 Coefficients, constants, and statistical values of two-parameter isotherm models for As and Hg adsorption onto the Persian Eucalyptus leaves Two-parameter isotherm models (linearized forms)

Isotherm parameters and statistics

Arsenic (As)a

Mercury (Hg)a

Langmuir model 1 1 þ q 1⋅k L ⋅ C1e q ¼ q

qmax

84.03

129.87

kL

0.150

0.090

RL

0 < 0.40 < 1

0 < 0.53 < 1

R2

0.9815

0.9802

F

159.40

148.39

e

max

max

Freundlich model logðqe Þ ¼ logðk f Þ þ 1n ⋅logðC e Þ

Temkin model qe ¼ β T lnðK T Þ þ βT lnðC e Þ βT ¼ ðR⋅T Þ =bT (R = 8.314 J/mol/K, T = 308 K)

Dubinin–Radushkevich model pffiffiffiffiffiffiffiffi lnðqe Þ ¼ lnðqD Þ−BD ⋅εD 2 E ¼ 1= 2BD

p

0.0011 < 0.05

0.0012 < 0.05

kf

13.97

13.16

n

0.573

0.578

R2

0.9897

0.9849

F

478.60

326.86

p

< 0.0010

< 0.0010

KT

1.691

1.637

βT

35.89

35.74

bT

71.36

71.65

R2

0.8637

0.8769

F

31.69

35.63

p

0.0025 < 0.05

0.0019 < 0.05

qD

58.21

59.29

BD

0.3835

0.4090

E

1.142

1.106

R

0.9292

0.9293

F

65.66

79.91

p

0.0005 < 0.05

0.0005 < 0.05

2

a

Units of isotherm parameters are previously defined in the text

adsorption of 45.5 mg/g. Similarly, Krishnan et al. (2011) reported a maximum capacity of 1.33 mg/g for the adsorption of mercury onto the surface of the rice husk. In a study on the arsenic adsorption with composite adsorbent of zero-valent iron and porous carbon, Baikousi et al. (2015) reported the maximum adsorption capacity of 26.8 mg/g based on the Langmuir isotherm model. Furthermore, in another study on the arsenic removal with starch-stabilized zero-valent iron nanoparticles and carboxy methyl cellulose, the maximum adsorption capacity for arsenic based on the Langmuir isotherm model was reported equal to 14 mg/g (Mosaferi et al. 2014). In the present study, the maximum adsorption capacity based on the Langmuir isotherm model was determined as 84.03 mg/g for As and 129.87 mg/g for Hg. The differences with the present study show that the Eucalyptus leaf particles have higher adsorption or uptake capacity compared to the

starch-stabilized zero-valent iron nanoparticles for arsenic adsorption. As seen from both Table 3 and Fig. 4, the Temkin isotherm model had a poorer fit (R2 = 0.8637 for As and R2 = 0.8769 for Hg) of experimental data than the other two-parameter isotherm equations (Langmuir, Freundlich, and Dubinin–Radushkevich models). For the present experimental data, the Dubinin– Radushkevich isotherm showed that the magnitudes of E were found as 1.142 kJ/mol for As and 1.106 kJ/mol for Hg. Since these values are determined to be less than 8 kJ/mol, the present adsorption process may be ascribed to be affected by physical forces rather than by the ion exchange mechanism or particle diffusion phenomenon. Considering the statistical parameters (R2, F statistic, and p value) obtained for the studied twoparameter isotherm models, the order of prediction performance is presented as follows: Freundlich >

Water Air Soil Pollut (2017) 228:429

Page 17 of 27 429

0.7

0.7 Experimental data (As) Langmuir isotherm model (As)

0.6 0.5

0.5

0.4

1/qe

1/qe

Experimental data (Hg) Langmuir isotherm model (Hg)

0.6

(a )

0.3 0.2

y

0.1

0.9815, F

159.40, p

2

4

y

0.1

0.0011)

0.0 0

( b)

0.3 0.2

0.0793 x 0.0119

(R2

0.4

6

0.0860 x 0.0077

(R2

0.9802, F

0

2

4

1/Ce 2.0

2.0

y 1.7441 x 1.1453 (R2

0.9897 , F

478 .60, p

1.6

1.4

1.4

( c)

1.2 1.0

(R2

0.9849 , F

326 .86, p

0.001)

(d )

1.2 1.0

0.8

0.8

Experimental data (As) Freundlich isotherm model (As)

0.6 -0.2

0.0

0.2

Experimental data (Hg) Freundlich isotherm model (Hg)

0.6

0.4

-0.2

0.0

log(Ce)

0.2

0.4

log(Ce)

70

70

Experimental data (As) Temkin isotherm model (As)

60 50

35.8858x 18.8612 2 (R 0.8637, F 31.6880, p

y

40

0.0025)

(e )

30

50

y

40

(R2

20

10

10

-0.5

0.0

0.5

35.7394 x 17.6114 0.8769, F

35.6262, p

0.0019)

(f )

30

20

0 -1.0

Experimental data (Hg) Temkin isotherm model (Hg)

60

qe

qe

6

y 1.7303 x 1.1194

1.8

0.001)

1.6

0 -1.0

1.0

-0.5

ln(Ce)

0.0

0.5

1.0

ln(Ce)

5

5 Experimental data (As) D & R isotherm model (As)

4 3

(g ) 2

y

1

(R2

0.3835 x 4.0641 0.9292 , F 2

3

(h ) 2

y

1

65.66, p

(R2

0.0005 )

0 0

Experimental data (Hg) D & R isotherm model (Hg)

4

ln(qe)

ln(qe)

0.0012)

1/Ce

log(qe)

log(qe)

1.8

148.39, p

0.0

8

4 D

6

8

0.4090 x 4.0824 0.9293, F

79.91, p

0.0003 )

0 0

2

4

6

8

D

Fig. 4 Linearized plots of Langmuir, Freundlich, Temkin, and Dubinin–Radushkevich isotherm models for the adsorption of arsenic (a, b, c, e, and g, respectively) and mercury (b, d, f, and h, respectively) onto Eucalyptus leaves

429

Water Air Soil Pollut (2017) 228:429

Page 18 of 27

Langmuir > Dubinin–Radushkevich > Temkin. To summarize, the isotherm parameters and statistical indicators (Table 3) corroborate that arsenic and mercury adsorption onto the Eucalyptus leaves can be very well described by the Freundlich isotherm model at a confidence level of 95% (FFreundlich = 478.60 and 326.86 for As and Hg, respectively).

model. The linearized plots provided in Fig. 5 also demonstrate that the empirical data obtained from the adsorption tests are consistent with the PFO equation, and the present experimental data can be described better by using this kinetic model. It can be seen from Table 4 that the calculated values, qe,cal, of arsenic and mercury onto the Eucalyptus leaves best agreed with the experimental values, qe,exp, in the case of the PFO model. When the experimental results are applied to Lagergren’s PFO model, therefore, the dominant mechanism in the adsorption process of arsenic and mercury is physical adsorption. The domination of physical forces was also corroborated by the results of the Dubinin–Radushkevich isotherm, where the value of the mean free energy (E) of adsorption per mole of the adsorbate was determined to be less than 8 kJ/mol for both As and Hg. According to the kinetic parameters (kmf and mmf) obtained from the modified Freundlich kinetic model,

3.7 Adsorption Kinetics The values of kinetic parameters obtained for the adsorption of arsenic and mercury onto the Persian Eucalyptus leaves are given in Table 4. Although Ho and McKay’s PSO kinetic model (R2 = 0.930 and 0.976 for As ang Hg, respectively) provides a good fitting to the experimental data points for the adsorption of As and Hg, Lagergren’s PFO kinetic model (R2 = 0.978 and 0.983 for As and Hg, respectively) described the present adsorption process better than the Ho and McKay’s PSO

Table 4 Coefficients, constants, and statistical values of different kinetic models for As and Hg adsorption onto the Persian Eucalyptus leaves Kinetic models (linearized forms)

Lagergren’s pseudo-first-order (PFO) kinetic model ln(qe − qt) = ln(qe) − k1 ⋅ t

Ho and McKay’s pseudo-second-order (PSO) kinetic model t 1 1 q ¼ k 2 q2 þ q t t

e

e

Modified Freundlich kinetic model lnðqt Þ ¼ lnðk mf ⋅C 0 Þ þ m1mf ⋅lnðt Þ (C0 = 10 mg/L)

Weber–Morris intraparticle diffusion model qt = kint ⋅ t1/2 + C

a

Units of kinetic parameters are previously defined in the text

Kinetic parameters and statistics

Arsenic (As)a

Mercury (Hg)a

qe, exp

1.555

1.635

qe, cal

1.697

1.530

k1

0.0302

0.0302

R2

0.9779

0.9825

F

88.64

112.16

p

0.0111 < 0.05

0.0088 < 0.05

qe, exp

1.555

1.635

qe, cal

2.457

2.174

k2

0.0069

0.0146

R2

0.9300

0.9757

F

53.12

160.66

p

0.0019 < 0.05

0.0002 < 0.05

kmf

0.0088

0.0199

mmf

1.593

2.171

R2

0.9743

0.9840

F

151.87

245.33

p

0.0002

< 0.0001

kint

0.1529

0.1470

C

0.0246

0.1926

R2

0.9332

0.9633

F

27.93

52.42

p

0.0340 < 0.05

0.0185 < 0.05

Water Air Soil Pollut (2017) 228:429

Page 19 of 27 429

0.5

0.5

Experimental data (As) PFO kinetic model (As)

0.0

-0.5

ln(qe-qt)

ln(qe-qt)

0.0

Experimental data (Hg) PFO kinetic model (Hg)

(a )

-1.0

y

-1.5

0.9779 , F

0

(b)

-1.0

0.0302 x 0.5289

(R2

-0.5

88.64, p

20

0.0111)

y

-1.5

40

60

0.0302 x 0.4251

(R2

0.9825, F

0

112.16, p

20

40

t (min) 80 Experimental data (As) PSO kinetic model (As)

Experimental data (Hg) PSO kinetic model (Hg)

60

60

40

(c)

20

y

t/qt

t/qt

60

t (min)

80

0.9300 , F

20

40

60

53.12, p

80

(d ) y

0.0019 )

100

120

0.4599 x 14.5138

(R2

0 0

40

20

0.4070 x 23.9881

(R2

0.9757, F

140

0

20

40

60

0.0002)

80

100

120

140

t (min)

3

Experimental data (As) Modified Freundlich kinetic model (As)

2

160.66, p

0

t (min)

Experimental data (Hg) Modified Freundlich kinetic model (Hg)

2 1

ln(qt)

ln(qt)

0

( e) -2

0

(f )

-1

y -4

0.6277 x 2.4306

(R2

0.9743, F

0

1

151.87, p 2

0.0002) 3

4

-2

y

-3

(R2

5

0.4606 x 1.6148 0.9840, F

0

1

245.33, p

0.0001)

2

3

ln(t) 2.0

y (R2

1.5

2.0

0.1529 x 0.0246 0.9332 , F

27.93, p

4

5

ln(t)

y (R2

0.0340 ) 1.5

1.0

(g )

qt

qt

0.0088)

0.5

0.1470 x 0.1926 0.9633, F

52.42, p

0.0185 )

1.0

(h )

0.5 Experimental data (As) W & M intra particle diffusion model (As)

Experimental data (Hg) W & M intra particle diffusion model (Hg)

0.0 0

2

4

6

t

8

10

12

0.0 0

2

4

6

8

10

12

t

Fig. 5 Linearized plots of Lagergren’s PFO, Ho, and McKay’s PSO, modified Freundlich, and Weber–Morris intraparticle diffusion kinetic models for the adsorption of arsenic (a, b, c, e, and g, respectively) and mercury (b, d, f, and h, respectively) onto Eucalyptus leaves

429

Page 20 of 27

the effects of surface loading and ionic strength were more pronounced for the adsorption of Hg ions onto the Eucalyptus leaves. The results of the kinetic studies also showed that the best-fitted kinetic models were Lagergren’s PFO kinetic model and the modified Freundlich kinetic model for As and Hg, respectively (Table 4). For the Weber–Morris intraparticle diffusion model, Kavitha and Namasivayam (2007) have reported that the thickness of the boundary layer may be interpreted in terms of the magnitude values of the intercept (i.e., the larger the intercept, the greater is the boundary layer effect). Considering this fact, it may be concluded that the boundary layer effect for Hg (C = 0.1946) seems to have a higher impact that for As (C = 0.0246). The linearized plots of the Weber– Morris intraparticle diffusion model (Fig. 5g, h) do not pass through the origin for both As and Hg, indicating that the intraparticle diffusion is not the only ratelimiting step, but other kinetic models may control the rate of adsorption, as similarly stated by Sampranpiboon and Feng (2016). 3.8 Comparisons with Literature Data Table 5 presents performance data regarding the comparison of adsorption tests conducted with different materials and experimental conditions on the elimination of As or Hg ions from aqueous media. The collected data reveal that a broad spectrum of experimental conditions has been studied for the removal of As or Hg using cost-effective adsorbents, and the conditions for optimum initial pH have been investigated between 1 and 12. The influence of different doses of used materials, ranging from 0.1 mg/mL to 40 g/L, has been studied. Most of the investigations related to the present study have been conducted at a temperature above 20 °C. A broad spectrum of contact time, from 2 min to 72 h (3 days), has been examined at different mixing speeds up to 300 rpm. The collected literature data clearly demonstrated that the elimination of heavy metal ions from aqueous media could be successfully enhanced up to about 100% by using different cost-effective adsorbents and biosorbents (Chen et al. 2008; Dutta et al. 2009; Gupta et al. 2015; Inbaraj and Sulochana 2006; Silva et al. 2010; Iakovleva et al. 2016; Mudasir et al. 2016). The possible differences in results can be ascribed to the varying properties and doses of used materials,

Water Air Soil Pollut (2017) 228:429

initial pH, heavy metal ion concentrations, operating temperatures, and also applied reaction times. According to the maximum As or Hg removals, the present results seem to be in line with those reported by other researchers. The results show that the Eucalyptus leaves show an advantage over many adsorbents used in other studies, indicating that the present adsorbent is an encouraging material for As or Hg elimination in real-scale implementation. The obtained maximum adsorption capacities (q0) for the adsorption of As or Hg onto other adsorbents as cost-effective materials studied in the literature are tabulated in Table 6. As observed from this data, the Eucalyptus leaves make a preferable adsorbent in comparison with other cheap materials used by other researchers (Thanawatpoontawee et al. 2016; Yaghmaeian et al. 2015; Liu et al. 2013; Zeng 2004). This demonstrates that the Eucalyptus leaves possess a satisfactory As or Hg adsorption or uptake performance than several low-cost adsorbents and biosorbents, such as sugarcane bagasse, bamboo leaf powder, coconut shell, and bone char; therefore, it can be beneficial using this material for the removal of As and Hg from aqueous media. Although higher values of q0 have also been reported by some researchers (Asasian et al. 2012; Fakhri 2015; Hadavifar et al. 2016; Iakovleva et al. 2016), it should be stated that possible distinctions may be due to the characteristics of each material such as structure, functional groups, and surface area. 3.9 Discussion on the Economic Benefits Eucalyptus, a diverse genus of flowering trees, is typically native to Australia. A number of Eucalyptus species are cultivated outside Australia including countries in the Middle East and North America and some countries in the Mediterranean. The Eucalyptus leaf is widely available and can potentially be used as a low-cost and readily available adsorbent for the remediation of heavy metals from aqueous solutions. From the economic point of view, this can make it a preferable and more cost-effective material compared to other adsorbents for the removal of As or Hg from aqueous solutions. At this point, it can be noted that additional investigations may be required for both desorption and real-scale application. However, in other parts of the world (i.e., India, China, Morocco, Iran, etc.), where Eucalyptus leaves may be available at no cost, regeneration is not needed, so that As- or Hg-loaded leaves can be eliminated by

Water Air Soil Pollut (2017) 228:429

Page 21 of 27 429

Table 5 Comparison of adsorption experiments conducted with various materials and operating conditions for removal or uptake of arsenic or mercury from aqueous solutions Adsorbent/biosorbent

WoWWT

pH

CT

tbcolw75ptMRoUE

tbcolw90ptReference and region

Persian Eucalyptus leaves

SWW

3–9

5–90

Present study, Iran

Granular iron oxide with PVAc binder

SWW

2–10 72 h

95.7 for As 94.7 for Hg 70 for As(III) and As(V)

Iron(III)-loaded zein beads

RWW (TK-80, TK-81, DW, and PW)

3–9

15–960

Fe–Mn binary oxide-impregnated chitosan bead Qi et al. (2015), China

SWW

7.0

± 0.1

RH (industrial sand) and CaFe-Cake (sulfate tailings)

SS and AWW from a 2–10 0.5–72 h mining site

> 90 for As(V)

Thanawatpoontawee et al. (2016), Thailand

36 h

NS

RH: 93 for As(III), 72 for As(V) CaFe-Cake: 100 for As(III) and As(V) Up to 99.36 for Hg(II)

Iakovleva et al. (2016), Finland

Dithizone-immobilized natural zeolite (DIZ) SS (RW)

3–9

Copper oxide nanoparticles

SWW

2–12 10–95

> 90 for Hg(II)

Fakhri (2015), Iran

Mix-ZC: activated carbon prepared from agricultural wastes

SWW

2–12 2–540

NS

Asasian et al. (2012), Iran

Carbon sorbent derived from fruit shell of Indian almond (Terminalia catappa)

SWW

1–10 12 h

98.6 for Hg(II)

Inbaraj and Sulochana (2006), India

Modified activated carbons (AC, AC-H2SO4, AC-CS2)

SWW

3–10 NS

> 97 for Hg(II)

Soé Silva et al. (2010), Argentina

MWCN

SWW

3–9

10–180

> 85 for Hg2+

Yaghmaeian et al. (2015), Iran

Thiol-functionalized Saccharum officinarum bagasse

ACGW

7.0

20–120

100 As(III) and As(V)

Gupta et al. (2015), India

Sodium dodecyl sulfate (SDS)-modified bamboo leaf powder

SWW

2–10 10–70

80 for Hg(II)

Mondal et al. (2013), India

Bone char

SWW

2–13 10–60

99.18 for As(V)

Chen et al. (2008), China

Charcoal-immobilized papain (CIP)

IWW

4–9

99.4 for Hg

Dutta et al. (2009), India

Coconut shell-based activated carbon

SWW

2–10 20 h

Up to 100 for Hg(II) Goel et al. (2004), India

Camel bone char

RWW

1–9

5–90

95.8–98.5% for Hg(II)

Bone char

SWW

4.0

10–2880

NS

Hydrated ferric oxide-treated sugarcane ba- SWW gasse (Saccharum officinarum L.)

5–180

Mangwandi et al. (2016), UK

2–10

2–10 15 min–24 h 98 for As(V)

Mudasir et al. (2016), Indonesia

Hassan et al. (2008), Egypt Liu et al. (2014), China Pehlivan et al. (2013), Germany

Different units are indicated in the table WoWT water or wastewater type, CT contact time (min), MRoUE maximum removal or uptake efficiency (%), SWW synthetic wastewater, RWW real wastewater, DW drinking water, PW pond water, RW river water, AWW acidic wastewater, SS synthetic solution, ACGW arseniccontaminated ground water, IWW industrial wastewater, NS not specified

hazardous waste incineration or encapsulation and landfilling (Yetilmezsoy and Demirel 2008; Akinbiyi 2000). Consequently, it is believed that this cost-

effective and green product can add substantial economic value to Eucalyptus cultivating countries for the removal of heavy metals from aqueous solutions.

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Table 6 Maximum adsorption capacities obtained for adsorption of arsenic or mercury onto various adsorbents (Langmuir isotherm model) Adsorbent/biosorbent

WoWWT

tbcolw130ptMAoUC

tbcolw100ptReference and region

Persian Eucalyptus leaves

SWW

84.03 for As, 129.87 for Hg

Present study, Iran

Iron(III)-loaded zein beads

RWW (TK-80, 1.95 for As(V) TK-81, DW, and PW)

Thanawatpoontawee et al. (2016), Thailand

Fe–Mn binary oxide-impregnated chitosan bead

SWW

Qi et al. (2015), China

39.1 for As(V), 54.2 for As(III)

RH (industrial sand) and CaFe-Cake (sulfate tailings) SS and AWW from RH: 215 mmol/g for As(III), Iakovleva et al. (2016), a mining site 248 mmol/g for As(V) Finland CaFe-Cake: 26.66 mmol/g for As(III), 36.66 mmol/g for As(V) Thiolated MWCN SWW Single metal ion: 204.64 for Hg(II), Hadavifar et al. (2016), binary metal ions: 35.89 for Hg(II) Iran Fe(III)-Si binary oxide adsorbent

SWW

Copper oxide nanoparticles

SWW

21.1 to 21.5 (mg As/g) for As(III) and Zeng (2004), Canada 11.3 to 14.9 (mg As/g) for As(V) 825.21 for Hg(II) Fakhri (2015), Iran

Mix-ZC: activated carbon prepared from agricultural SWW wastes (pistachio-nut shells and licorice residues)

147.1 for Hg(II)

Asasian et al. (2012), Iran

Carbon sorbent derived from fruit shell of Indian almond (Terminalia catappa)

SWW

94.43 for Hg(II)

Inbaraj and Sulochana (2006), India

MWCN

SWW

25.641 for Hg2+

Yaghmaeian et al. (2015), Iran

Hybrid mesoporous aluminosilicate sieve prepared with fly ash Activated carbon of Eichhornia crassipes biomass

SWW

20 for Hg(II)

Liu et al. (2013), China

SWW

32.81 for Hg(II)

Giri and Patel (2011), India

Thiol-functionalized Saccharum officinarum bagasse ACGW

28.57 for As(III), 34.48 for As(V)

Gupta et al. (2015), India

Sodium dodecyl sulfate (SDS)-modified bamboo leaf SWW powder

27.1 for Hg(II)

Mondal et al. (2013), India

Charcoal-immobilized papain (CIP)

IWW

0.002 mg Hg/mg CIP

Dutta et al. (2009), India

Coconut shell-based activated carbon

SWW

47.74 for Hg(II)

Goel et al. (2004), India

Camel bone char

RWW

28.24 for Hg(II)

Hassan et al. (2008), Egypt

Bone char

SWW

0.335 for As(V)

Liu et al. (2014), China

Hydrated ferric oxide-treated sugarcane bagasse (Saccharum officinarum L.)

SWW

22.1 for As(V)

Pehlivan et al. (2013), Germany

Different units are indicated in the table WoWT water or wastewater type, MAoUC maximum adsorption or uptake capacity (q, mg/g), SWW synthetic wastewater, RWW real wastewater, DW drinking water, PW pond water, SS synthetic solution, MWCN multi-walled carbon nanotubes, ACGW arsenic-contaminated ground water

3.10 Mechanism of Adsorption By using the FTIR technique, we can determine the interaction between the active sites on the surface of the adsorbent and the adsorbate. Furthermore, FTIR

analysis (not shown) was used for the determination of the functional groups which are responsible for the adsorption process. The FTIR spectrum of the Eucalyptus leaves indicates the presence of linear chain aliphatic compounds which are at 2928 and 2854 cm −1

Water Air Soil Pollut (2017) 228:429

Page 23 of 27 429

Fig. 6 Binding mechanisms between the cellulose main component in the Eucalyptus leaves and the mercury and arsenate

[asymmetric and symmetric vibrations of the methylene groups, CH2; νa(CH2) and νs(CH2), respectively], as well as a smaller band at 1454 cm−1 [CH2 scissoring and rocking vibrations; δscis(CH2) and δrock(CH2), respectively]. These vibrations together with the vibrations at

1035 and 1062 cm−1 are attributed to asymmetric and symmetric C–O–C stretching vibrations of the ether bonds: νa(C–O–C) ether and νs(C–O–C) ether, respectively. The low-intensity bands at 1538 and 1455 cm−1 were related to the stretching of aromatic rings [ν(C–C)

429

Water Air Soil Pollut (2017) 228:429

Page 24 of 27

conjugated with (C=C) or ν(C–C)/(C=C)]. The broad bands appearing at 3428 and 3357 cm−1 [H-bonded, O– H stretching vibration; ν(O–H…O)] were assigned to hydroxyl functional groups. Figure 6 proposes the binding mechanisms between the cellulose main component in the Eucalyptus leaves and the mercury and arsenate.

for recovery and re-use for both heavy metals and Eucalyptus leaves.

Acknowledgements The authors would like to thank the personnel of the Environmental Health Laboratory, Tehran University of Medical Sciences. Funding Information The authors would like to thank Tehran University of Medical Sciences for financial support.

4 Conclusions The present study was conducted for the first time to investigate the adsorption capacity and efficiency of the Persian Eucalyptus leaves as a low-cost herbal adsorbent for the removal of mercury and arsenic from aqueous solutions. The adsorption mechanisms and characteristic parameters for the present application were also explored as an important objective using isotherm and kinetic models. Accordingly, the following can be concluded: &

&

&

&

&

Arsenic and mercury adsorption was highly dependent on pH, and the ability of amine groups available in Eucalyptus leaves for protonation was decreased by increasing pH above 7.0, resulting in a decrease in the adsorption efficiency of arsenic or mercury at high pH levels. The absorbed arsenic and mercury could be enhanced by increasing the initial concentration of ions. The optimum adsorption conditions based on the multiple regression-based methodology were obtained as pH = 6.0, adsorbent dose of 1.5 g/L, contact time of 47.5 min, and initial concentration of arsenic and mercury of 2.75 mg/L. The equilibrium isotherms showed that both the arsenic and mercury adsorption onto the Eucalyptus leaves followed the Freundlich isotherm model (R2 = 0.9897 and R2 = 0.9849, respectively). The results of the kinetic modeling demonstrated that Lagergren’s PFO kinetics model (R2 = 0.9779) and the modified Freundlich kinetics (R2 = 0.9840) were found as the best-fitted models to describe the experimental data of As and Hg, respectively. The disposal/regeneration of the As/Hg-laden Eucalyptus leaves must be considered due to its hazardous nature. It could be disposed of as hazardous landfill waste or by degradation of the adsorbed As and Hg into nonhazardous compounds or using a more sustainable solution by developing a method

Compliance with Ethical Standard Conflict of Interest conflict of interest.

The authors declare that they have no

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