adsorptive removal of arsenic and mercury from aqueous solutions by eucalyptus leaves ·...

Adsorptive Removal of Arsenic and Mercury from AqueousSolutions 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 theuse of Eucalyptus leaves as a low-cost herbal adsorbentfor the removal of arsenic (As) and mercury (Hg) fromaqueous solutions. The adsorption capacity and efficiency

were studied under various operating conditions withinthe framework of response surface methodology (RSM)by implementing a four-factor, five-level Box–Wilsoncentral composite design (CCD). A pH range of 3–9,contact time (t) of 5–90 min, initial heavy metal (As orHg) concentration (C0) of 0.5–3.875 mg/L, and adsorbentdose (m) of 0.5–2.5 g/L were studied for the optimizationand modeling of the process. The adsorption mechanismand the relevant characteristic parameters were investigat-ed by four two-parameter (Langmuir, Freundlich, Temkin,and Dubinin–Radushkevich) isotherm models and fourkinetic 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 equa-tions, which were derived within the scope of the study,showed that it might be possible to obtain a removalefficiency for As and Hg above 94% at the optimumconditions of the present process-related variables(pH = 6.0, t = 47.5 min, C0 = 2.75 mg/L, andm = 1.5 mg/L). Based on the Langmuir isotherm model,the maximum adsorption or uptake capacity of As andHg was determined as 84.03 and 129.87 mg/g, respec-tively. The results of the kinetic modeling indicated thatthe adsorption kinetics of As and Hg were very welldescribed by Lagergren’s PFO kinetic model(R2 = 0.978) and the modified Freundlich kinetic model(R2 = 0.984), respectively. The findings of this studyclearly concluded that the Persian Eucalyptus leavesdemonstrated a higher performance compared to severalother reported adsorbents used for the removal of heavymetals from the aqueous environment.

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

M. Alimohammadi : Z. Saeedi : B. Akbarpour :H. RasoulzadehDepartment of Environmental Health Engineering, School ofPublic Health, Tehran University of Medical Sciences, Tehran,Iran

Z. Saeedie-mail: [email protected]

M. Alimohammadi (*)Center for Water Quality Research (CWQR), Institute forEnvironmental Research (IER), Tehran University of MedicalSciences, Tehran, Irane-mail: [email protected]

K. YetilmezsoyDepartment of Environmental Engineering, Faculty of CivilEngineering, Yildiz Technical University, Davutpasa Campus,Esenler, 34220 Istanbul, Turkeye-mail: [email protected]

M. A. Al-GhoutiDepartment of Biological and Environmental Sciences, College ofArts and Sciences, Qatar University, DohaP.O. Box 2713, Qatar

M. KhraishehDepartment of Chemical Engineering, College of Engineering,Qatar University, DohaP.O. Box 2713, Qatar

G. McKayDivision of Sustainability, College of Science and Engineering,Hamad Bin Khalifa University, Education City, Qatar Foundation,Doha, Qatare-mail: [email protected]

http://crossmark.crossref.org/dialog/?doi=10.1007/s11270-017-3607-y&domain=pdf

Keywords Arsenicandmercuryadsorption .Eucalyptusleaves .Heavymetal removal . Isothermmodels .Kineticmodels

1 Introduction

Heavymetals, due to their high toxicity, carcinogenicity,and bioaccumulation potential in aquatic life, have un-favorable impacts on receiving waters used for variousbeneficial purposes (Ulmanu et al. 2003). Amongst thehigh-priority pollutants, mercury and arsenic are of par-ticular interest because of their serious detrimental im-pacts on human health (Akhtar et al. 2010). Such pol-lutants are typically introduced to the aquatic environ-ment and water resources via organic or nonorganicentry point sources such as mineral, industrial, andagricultural wastewater (organic); plastic paints; fluores-cent bulbs; insecticides; pesticides; batteries; etc. (non-organic). The effects of such heavy metals on humansare well documented. For example, Hg is commonlyrelated to serious neurological disorders while As isassociated to genetic disorders such as bladder, skin,and lung cancer (Smedley and Kinniburgh 2013;Mosaferi et al. 2008). Both chemicals are fatal at highdoses. Correspondingly, the consent limits for mercuryand arsenic are given as 0.001 and 0.1 mg/L, respective-ly, 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 devotedtowards finding cost-effective technologies and process-es for the removal of such heavy metals, includingsimultaneous sequestration and flotation (Yenial et al.2014), ion exchange (Fu andWang 2011), ultrafiltration(Weng et al. 2005), and reverse osmosis (Kang et al.2000).

Although great advantages have been reported forabove technologies, issues related to costs, availabilityof raw materials, requirements for skilled personnel inaddition to the need to handle considerable volumes ofsludge meant that a large number of such technologiescannot be used effectively, especially in low-incomecountries where adsorption using indigenous, low-costavailable adsorbents is required. Studies on the adsorp-tion of heavy metals using a number of natural materialshave been abundantly reported in the literature(Kurniawan et al. 2006; Babić et al. 2002); rice husk(Rocha et al. 2009), natural clinoptilolite zeolite, and

anthracite (Samadi et al. 2010) were found to haveacceptable removal rates. For the removal of As,adsorbents such as activated carbon (Reed et al.2000), activated alumina (Lin and Wu 2001), andneutralized red mud (Genç-Fuhrman et al. 2004) werereported. For example, Rocha et al. (2009) examinedthe elimination of heavy metals such as lead, zinc,cadmium, and mercury from industrial wastewatersusing natural rice husk as an adsorbent, and showedthat the highest adsorption was related to cadmium,copper, zinc, and mercury, respectively. A rapid ad-sorption process took place within 1.5 h at pH = 5giving the highest adsorption capacity. In addition,Yaghmaeian et al. (2016) conducted experimentalstudies on the elimination of arsenate by using themodified chitosan adsorbent with zero-valent iron.The authors found that the highest removal efficiencywas at pH = 7.16, optimum adsorbent dose of 3.04 g/L,contact time of 91.5 min, and initial arsenate concentra-tion of 9.71 mg/L.

A recent study carried out by Wang et al. (2015)demonstrated the removal of Pb using Eucalyptus leafresidue, and the regeneration of Pb-loaded magneticbiochar showed a desorption efficiency of 84.1% in120 min with an iron leaching amount of 1.1 mg/g.Zhuang et al. (2015) reported plant extract was used asan eco-friendly alternative to chemical and physicalmethods for the synthesis of nanoparticles. The authorsdemonstrated that plant extracts could be used to syn-thesize Fe NPs using these plant extracts. Thenanomaterial produced was used effectively for the re-moval of azo-dye from aqueous solution with a capacityupwards of 215.1 mg/g for Direct Black removal. Inanother application of Eucalyptus leaves, Al-Subu(2002) reported the use of the plant for the removal oflead in combination with other plants such as Cupressussemperirens and Pinushalepensis. The author reportedthat lead removal increased mainly with the increase inthe concentration of lead in solution. The author alsonoted that both Freundlich and Langmuir isotherms bestdescribed the adsorption.

Eucalyptus actually refers to a large genus offlowering trees that has over 700 different species,most of which are located in Australia and NewZealand, although some of the more widespread spe-cies can be found throughout Southeast Asia. Most ofits species range from the size of a small bush to amedium-sized blooming tree, but all species haveleaves that are covered in oil glands, from which the

429 of 27 Water Air Soil Pollut (2017) 228:429

majority of the health benefits are derived. For in-stance, it aids in curing respiratory issues, boosts theimmune system, provides relief from anxiety andstress, helps to protect skin against infections, aidsin managing and preventing diabetes, and preventsatherosclerosis and heart attacks. Additionally, thisfast-growing genus of the plant makes it precious asa source of wood and paper. This tree is also used forpreparation of Eucalyptus oil and Eucalyptus tea thathave impressive benefits.Moreover, it is also consideredan invasive species in certain areas, as the trees are hardyand can quickly overtake native populations of slower-growing plants (Organic Information Services Pvt Ltd.2017).

Besides the above-mentioned benefits, the PersianEucalyptus leaves are widely available and can po-tentially be used as a low-cost readily available ad-sorbent for the removal of heavy metals from aqueoussolutions. On the other hand, to the best of the au-thors’ knowledge, no study has been conducted so faron the use of Eucalyptus leaves for the removal of Asand Hg from aqueous solutions. The prime novelties ofthis work can be summarized as follows: (i) adsorptionof As and Hg ions from aqueous solution is investigatedfor the first time using cellulosic Persian Eucalyptusleaves; (ii) this herbal adsorbent is a unique, versatile,and low-cost material having a higher surface area andhigher adsorption or uptake capacity compared to sev-eral more expensive adsorbents; (iii) it is a novel andinexpensive natural adsorbent because it requires littlepretreatment; and (iv) optimization established for Asand Hg removal byEucalyptus leaves is explored for thefirst time using the surface response methodology(RSM).

Accordingly, the main objectives of this study wereto (1) investigate the adsorption capacity and efficiencyof Persian Eucalyptus leaves as a natural herbal adsor-bent in removal of mercury and arsenic from the aque-ous solutions; (2) determine the effect of adsorbent dose,contact time, concentrations of adsorbent and adsorbate(mercury and arsenic), and pH changes on the adsorp-tion capacity of the used adsorbent; (3) explore theapplicability of various isotherm models and kineticequations for the determination of the adsorption mech-anism and characteristic parameters; and (4) assess theperformance and effectiveness of the studied Eucalyptusleaves by comparing their performance data with that ofvarious adsorbents for removal of heavy metals from theaqueous solutions.

2 Materials and Methods

2.1 Chemicals

In this study, all the chemicals were provided with high-percentage purity from the Merck Group (Germany).Standard solutions of 1 g/L were prepared using mercu-ry nitrate (Hg2(NO3)2) and sodium arsenate (Na3AsO4)using double-distilled water (ddH2O).Solutions wereprovided daily by making appropriate serial dilutionsof the primary standard solution using the double-distilled water. In order to measure the concentrationsof arsenic and mercury, the ICP-AES (Spector ModelARCOS FHE12) method was used. The pH was mea-sured by a Kent EIL7020 model digital pH meter.

2.2 Preparation of Biomass

The Persian Eucalyptus leaves used in this study wereobtained from the northern region of Iran. The rawsamples were washed initially with distilled water toremove any dirt and particulate matter. The leaves weredried naturally for 2–3 days before being crushed usinga rudimentary crusher (hammer type) and sieved toobtain homogeneous material (particle size 1–3 mm)ready for the adsorption tests. The textural and morpho-logical features of the adsorbent surface were deter-mined by means of scanning electron microscopy(SEM); transmission electron microscopy (TEM); X-ray diffraction (XRD); and Brunauer, Emmett, and Tell-er (BET) analysis. The Fourier transform infrared spec-troscopies (FTIR) of the samples were recorded on aPerkinElmer Spectrum 100 spectrophotometer to inves-tigate the change in the functional groups of the materialsurface (using a KBr disk technique in the range of 500to 4000 cm−1).

2.3 Adsorption Tests

All tests were conducted in 250-mL Erlenmeyer flaskscontaining 100 mL of different concentrations of arse-nic, mercury, and adsorbent at various pH values andcontact times. The synthetically prepared wastewatersamples were located in an orbital shaker (C-MAG HS10 digital, Germany IKA) for appropriate mixing of theadsorbent and adsorbate at a fixed stirring speed of120 rpm (≈ 12.57 rad/s). After a certain time (i.e., 30–45 min), the solution was filtered using a 0.2-μm sy-ringe filter and the remaining concentration of arsenic

Water Air Soil Pollut (2017) 228:429 of 27 429

and mercury was measured using the ICP-AES method.All experiments were conducted at a room temperatureof 25 ± 2 °C. The pH of the solution was adjusted byusing HCl (0.1 N) and NaOH (0.1 N). The removalpercentage (%) and adsorption capacity (q, mg/g) ofarsenic and mercury were calculated from the followingequations (Heibati et al. 2015):

q ¼ C0−Ctð ÞVm

ð1Þ

percentage removal %ð Þ ¼ 1−Ce

C0

� �� 100 ð2Þ

where C0 and Ct (mg/L) are the liquid phase adsorbateconcentrations at the initial time and at a given time t,respectively, V is the volume of solution (L), m is theadsorbent mass (g), and Ce is the equilibrium concen-tration of arsenic or mercury after the adsorption process(mg/L).

2.4 Optimization by Box–Wilson Central CompositeDesign (CCD)

In this study, optimization of the present adsorptionprocess was conducted based on the Box–Wilson cen-tral composite design (CCD), usually referred to asCCD, by using the software R (version 3.1.2, 2014,Pumkin Helmet), running on a CPU N280 (Intel® At-om™ Processor 1.66 GHz, 0.99 GB of RAM) PC. In thepresent study, effects of four main factors (pH, contacttime, initial As or Hg concentration, and adsorbent dose)on As or Hg removal efficiency, with the use of Euca-lyptus leaves as a low-cost herbal adsorbent, were ana-lyzed within the framework of response surface meth-odology (RSM) by implementing a four-factor, five-level CCD for a sample size of only n = 30, whereas afull-factorial three-level experimental design with fourindependent parameters requires to (3)4 = 81 runs. Acontact 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 concentrationof 0.5–3.875 mg/L were considered for the optimizationand modeling of the process. The experiments wereperformed according to the CCD matrix designed byusing the software R. The CCD matrix of four indepen-dent variables (expressed in natural or uncoded units)and the corresponding experimental values (RE, remov-al efficiency (%) for As and Hg) are presented for botharsenic and mercury in Table 1.

In the present study, the effect of the four mainfactors (X1 = pH, X2 = contact time (min), X3 = initialconcentration of As or Hg (mg/L), and X4 = adsorbentdose (g/L)) on As or Hg removal efficiency (Y), withthe use of Eucalyptus leaves as a low-cost herbaladsorbent, was optimized and modeled by the CCDapproach. The following quadratic model structurewas used for the analysis of the As and Hg data fromthe CCD matrix (Yetilmezsoy et al. 2009; Shabbiriet al. 2012a, b; Noori Sepehr et al. 2014; Dhawaneet al. 2015):

Y ¼ β0 þ ∑k

i¼1βixi þ ∑

k

i¼1βiix

2i þ ∑

k−1

i¼1∑k

j¼iþ1βixix j þ ε ð3Þ

where Y is the process response or output (dependentvariable); k is the number of the independent factors; iand 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) andinteraction terms, respectively, and β0 indicates a freeor offset term (also referred to as intercept term); and εrepresents the random error or allows for discrepan-cies or uncertainties between predicted and measuredvalues.

For the implementation of the multiple regression-based analysis of the quadratic models, DataFit® soft-ware package (V8.1.69, Oakdale Engineering, PA,USA) used the following values of the solution prefer-ences: (a) regression tolerance = 1 × 10−10, maximumnumber of iterations = 250, and (b) diverging nonlineariteration limit = 10. When performing the nonlinearregression, Richardson’s extrapolation method was con-ducted to calculate numerical derivatives for the solutionof the models. The nonlinear regression analysis wasconducted based on the Levenberg–Marquardt methodwith double precision.

2.5 Statistical Analysis

Analysis of variance (ANOVA)was performed to obtaininteractions between the dependent (Y) and independent(X1, X2, X3, and X4) variables, as well as to test thesignificance of the derived quadratic models for Asand Hg. For this purpose, a solution script was writtenin the M-file Editor within the framework ofMATLAB® R2009b software (V7.9.0.529, The


MathWorks, Inc., Natick, MA, USA) to perform amulti-way (n-way) ANOVA on the vector of the depen-dent variable.

Models were evaluated by an alpha (α) at 95%confidence level (p = 0.05) to appraise the statisticalsignificance. It is noted that for any parameter to be asignificant model component, its F value should behigher and its p value (probability value) should be less

than 0.05. Additionally, the significance of any process-related parameter can also be analyzed using the sum ofsquare (SS) value, and its higher value implies moreimportance of the corresponding variable (Dhawaneet al. 2015).

The quality of regression equation was determinedby the coefficient of determination (R2), and its signifi-cance was judged by Fisher’s statistical test (F test). The

Table 1 Central composite design (CCD) matrix with four independent variables expressed in natural (uncoded) units

Run no. Arsenic (As) Mercury (Hg)

pH 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

15 6.00 47.50 2.75 1.50 95.70 6.00 90.00 2.75 1.50 93.18

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(%)


F statistic calculated within the scope of ANOVA isexpressed as follows:

F ¼∑n

i¼1ni Ymean;i−Ypred;i� �2

= n−pð Þ

∑n

i¼1∑j¼1

niY ij−Ymean;i� �2

= N−nð Þ¼ SSLOFð Þ=d1

SSPEð Þ=d2

¼ SSLOFð Þ=d1SSE−SSLOFð Þ=d2 →N ¼ ∑

n

i¼1ni ð4Þ

where p is the number of parameters in the model; i is anindex of each of the n distinct x values; j is an index ofthe response variable observations for a given x value; niis the number of Yvalues associated with the ith x value;Yij is the experimental response of run i, replicate j; Ypred,iis the response obtained from using the proposed poly-nomial at run i and Ymean,i is the observedmean of all thereplicates at run i; SSLOF is the sum of squares due tolack of fit (LOF); SSPE is the sum of squares due to pureerror (PE); SSE is the total sum of squares due to error(SSE = SSPE + SSLOF); d1 = (n − p) and d2 = (N − p) arethe degrees of freedom; and N is the total number ofobservations.

Furthermore, the coefficient of determination, R2,was used for judging the quality or goodness of thefitted quadratic models. Also, the R2 value should bein close agreement with the adjusted R2, which is denot-ed as Ra

2. In the present analysis, R2 and R2adj values are

determined by the following equations:

R2 ¼ 1−SSresSStot

¼ SStot−SSresSStot

¼ SSregSSres þ SSreg

ð5Þ

R2 ¼∑n

i¼1Ypred;i−Y obs;mean� �2

∑n

i¼1Y obs;i−Ypred;i� �2 þ ∑

n


¼∑n


∑n

i¼1Y obs;i−Y obs;mean� �2 ð6Þ

R2adj ¼ 1−

SSres=df eSStot=df t

� �¼ 1− 1−R2

� � n−1n−p−1

� ��

¼ R2− 1−R2� � p

n−p−1

� �� ð7Þ

where SStot is the total sum of squares (proportional tothe variance of the data), SSres is the sum of squares ofresiduals (also referred to as the residual sum ofsquares), SSreg is the regression sum of squares (alsoreferred to as the explained sum of squares), p is the totalnumber of explanatory variables in the model (withoutincluding the constant term), n is the size of the sample,dft is the degrees of freedom (n − 1) of the estimate of thepopulation variance of the dependent variable, and dfe isthe degrees of freedom (n − p − 1) of the estimate of thepopulation error variance.

2.6 Adsorption Isotherms

The adsorption isotherm is one of the most importantconsiderations in designing adsorption systems. In fact,the adsorption isotherm describes the interaction be-tween an adsorbent and adsorbate. Thus, it is alwaysconsidered as a major factor to determine the capacity ofan adsorbent and optimization of the adsorbent con-sumption. For this reason, adsorption isotherms are verycritical for the description of how solutes interrelate withthe adsorbents, for optimizing the use of these materials(Thompson et al. 2015). In this study, equilibrium datacollected were fitted to the well-known four two-parameter adsorption isotherm models, such as Lang-muir, Freundlich, Temkin, and Dubinin–Radushkevichequations, with the assumption of being a mono-component system and thereby eliminating the possibil-ity of multi-component competition.

Several models are available, and the most commonis the single-layer adsorption model that was proposedby Langmuir in 1918. Among the other models, themulti-layer adsorption model, described by Freundlichin 1906, is widely used and applicable in many adsorp-tion studies. In the Langmuir isotherm, it is supposedthat adsorption takes place in the homogeneous sites onthe adsorbent. Accordingly, the model is best used todescribe single-layer adsorption processes. In contrast,the Freundlich isotherm is based on multi-layer, nonuni-form, and heterogeneous adsorption of the adsorbateonto the adsorbent (Vimonses et al. 2009). The Lang-muir and Freundlich adsorption models can beexpressed and linearized, respectively, as follows (Hoand McKay 1998; Yakout and Elsherif 2010; Mondalet al. 2013; Nethaji et al. 2013; Thompson et al. 2015):

qe ¼qmaxkLCe

1þ kLCe→

1

qe¼ 1

qmaxþ 1

qmax⋅kL⋅1

Ceð8Þ


qe ¼ k f ⋅Ce1=n→log qeð Þ ¼ log k fð Þ þ 1

n⋅log Ceð Þ ð9Þ

where Ce is the equilibrium concentration of arsenic andmercury in solution (mg/L), qe is the adsorption oruptake capacity at equilibrium (mg/L), qmax is the max-imum adsorption or uptake capacity (mg/g), kL is theLangmuir adsorption constant (L/mg), kf is theFreundlich constant [(mg/g)/(mg/L)1/n], and 1/n repre-sents 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)) aredetermined, respectively, from the slope (1/qmax) andintercept (1/(qmax∙kL)) of the linear graph of x = 1/Ce

vs. y = 1/qe. The favorability of the adsorption process inthe Langmuir model can be determined by means of theRL dimensionless factor (RL = 1/(1 + kL ·C0)) as follows:RL = 0, 0 < RL < 1, RL = 1, and RL > 1 indicatingirreversible, favorable, linear, and unfavorable adsorp-tion isotherms, respectively. In Eq. (9), the values of nand 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 Freundlichequation serves to describe the linearity of adsorption oralternatively the degree of curvature of the isothermsacross the concentration range tested.

It is noted that nonlinearity is observed, especiallywith chemicals which are not extremely hydrophobicand therefore not limited by solubility to extremely lowconcentrations. However, any sorption isotherm whichcovers a wide concentration range (i.e., more than twoorders of magnitude), even if the whole range is at verylow concentrations, will typically be nonlinear, presum-ably because a range of sorption processes are takingplace. Typically, 1/n values range from 1 downwards. Avalue of 1 signifies that the relative adsorption (adsorp-tion partition) of the chemical was the same across thewhole range tested (C-type isotherm), which is unusual(especially across the concentration range of two ordersof magnitude often used in regulatory studies). Morenormally, 1/n values will range from 0.7 to 1.0. Thesevalues show that when the concentration of the chemicalunder investigation increases, the relative adsorptiondecreases (L-type isotherm). This tends to be indicativeof the saturation of the adsorption sites available to thechemical, resulting in relatively less adsorption. 1/nvalues of less than 0.7 describe highly curved isotherms,and 1/n values of greater than 1 are indicative of S-typeisotherms. These are relatively uncommon but are often

observed at low concentration ranges for compoundscontaining a polar functional group. It has been hypoth-esized that, at low concentrations, such compounds arein competition with water for adsorption sites (ECETOC2017).

Temkin isotherm (proposed by Temkin and Pyzhevin 1939) is another two-parameter model that is usuallyused for heterogeneous adsorption of an adsorbate ontoa surface of an adsorbent. The linearized form of theTemkin model is expressed by the following equation(Al-Meshragi et al. 2008; Varank et al. 2012; Mondalet al. 2013; Dehghani et al. 2016):

qe ¼R⋅TbT

� �ln KT⋅Ceð Þ→ R⋅T

bT¼ βT→qe

¼ βTln KTð Þ þ βTln Ceð Þ ð10Þwhere KT is the Temkin isotherm constant or equilibri-um binding constant (L/mg) corresponding to the max-imum binding energy and bT is the Temkin isothermconstant related to the heat of As or Hg adsorption ontoEucalyptus leaves due to adsorbent–adsorbate interac-tion (J/mol); R is the gas constant (8.314 J/mol/K); and Tis the absolute temperature (herein 308 K). By plotting alinear graph of x = ln(Ce) vs. y = qe, the values of b andKT can be determined, respectively, from the slope(βT = (R · T)/b) and the intercept [β · ln(KT)] of thegraph.

The Dubinin–Radushkevich (proposed by Dubininin 1960) isotherm is another popular model that iswidely used for description of adsorption in micropo-rous materials (especially those of a carbonaceous ori-gin) based on the Polanyi potential theory (introducedby Polanyi in 1932) of adsorption, as well as for theanalysis of isotherms of a high degree of regularity(Nguyen and Do 2001; Varank et al. 2012). This semi-empirical isotherm can be generally defined and linear-ized in Eqs. (11)–(13), respectively, as follows (Nguyenand Do 2001; Al-Meshragi et al. 2008; Yakout andElsherif 2010; Varank et al. 2012; Erhayem et al. 2015):

qe ¼ qD⋅exp −BD⋅εD2� �

→εD ¼ R⋅T ⋅ln 1þ 1

Ce

� �ð11Þ

qe ¼ qD⋅exp −BD⋅ R⋅T ⋅ln 1þ 1

Ce

� �� 2 !ð12Þ

ln qeð Þ ¼ ln qDð Þ−BD⋅εD2 ð13Þ


where qD is the maximum monolayer adsorption oruptake capacity (mg/g), BD is the activity coefficientrelated to the apparent free energy of As or Hg adsorp-tion onto Eucalyptus leaves (mol2/kJ2), and εD is thePolanyi potential which is related to the equilibriumconcentration, and others (qe, Ce, and R) are defined inprevious equations. The values of BD and qD can bedetermined, respectively, from the slope (−BD) and theintercept [ln(qD)] of the linear graph of x = εD

2 (as afunction of R, T, and Ce) vs. y = ln(qe) (Yakout andElsherif 2010; Erhayem et al. 2015).

It has been reported that the characteristic of theadsorption is related to the porous structure of theadsorbent. In Eqs. (11)–(13), BD denotes the meanfree energy (E) of adsorption per mole of the adsor-bate (kJ/mol), when it is transferred to the surface ofthe solid from the infinite distance in the solution.This energy can be obtained from the followingrelationship (Al-Meshragi et al. 2008; Yakout andElsherif 2010; Varank et al. 2012; Mondal et al.2013; Erhayem et al. 2015):

E ¼ 1=ffiffiffiffiffiffiffiffi2BD

pð14Þ

In the Dubinin–Radushkevich isotherm, the magni-tude of E is very useful for prediction and interpretationof the underlying mechanism of the adsorption process.If E < 8 kJ/mol, the adsorption process may be affectedby physical forces. In the case of E being between 8 and16 kJ/mol, adsorption is governed by the ion exchangemechanism, and for the value of E > 16 kJ/mol may bedominated by particle diffusion phenomenon (Patel andVashi 2014).

2.7 Adsorption Kinetics

The adsorption kinetics of the present system wereexamined for a better understanding of arsenic andmercury adsorption dynamics on Eucalyptus leaves,and providing a predictive model that allows the esti-mation of the amount of ions adsorbed during the pro-cess. The information can also be used to design thelarge systems (Türk et al. 2009; Al Rmalli et al., 2008).In this study, four kinetic models, such as Lagergren’spseudo-first-order (PFO) kinetics, Ho and McKay’spseudo-second-order (PSO) kinetics, the Weber–Morrisintraparticle diffusion model, and the modified Freundlichkinetic model, were selected for the evaluation of the

present kinetic mechanism behind As and Hg adsorptionby Eucalyptus leaves.

The equations of PFO (proposed by Lagergren in1898) and PSO (proposed by Ho and McKay in 1999)models can be described and linearized, respectively, asfollows (Ho andMcKay 1999, 2002; Varank et al. 2012;Mondal et al. 2013; Qi et al. 2015):

dqtdt

¼ k1 qe−qtð Þ→ln qe−qtð Þ ¼ ln qeð Þ−k1⋅t ð15Þ

dqtdt

¼ k2 qe−qtð Þ2→qt ¼qe

2k2t1þ qek2t

→tqt

¼ 1

k2qe2þ 1

qet

ð16Þwhere qe and qt are the adsorption capacities at equilib-rium and time t (mg/g), and k1 is the rate constant(min−1), respectively. In Eq. (15), ln(qe) and k1 are theintercept and slope of the linear graph of x = t vs.y = ln(qe − qt), respectively. In Eq. (16), k2 is thepseudo-second-order constant (mg/(g.min)). The valuesof qe and k2 can be determined, respectively, from theslope (1/qe) and intercept [1/(k2 · qe

2)] of the linear graphof x = t vs. y = t/qt.

Since the pseudo-first-order and pseudo-second-order are not sufficient to fully identify the diffusionmechanism behind the adsorption process, the kineticresults must therefore be analyzed by using the Weber–Morris intraparticle diffusion model (proposed by We-ber and Morris in 1963). The linearized form of theWeber–Morris intraparticle diffusionmodel is expressedby 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):

qt ¼ k int⋅t1=2 þ C ð17Þwhere 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 thethickness of the boundary layer (mg/g). The values ofkint and C can be directly calculated, respectively, fromthe slope (kint) and the intercept (C) of the linear graph ofx = t1/2 vs. y = qt.

It is noted that larger kint values illustrate better ad-sorption, which is related to improved bonding betweenadsorbate and adsorbent particles (Demirbas et al.2004), and may simply be due to higher adsorbateconcentrations, which adds to a greater driving forcefor diffusion of adsorbate molecules into the pores


(Bajpai and Jain 2010; Thompson et al. 2015). Like-wise, the value of the intercept (C) may give a usefulidea about the thickness of the boundary layer, i.e., thelarger the intercept, the greater the boundary layer effect(Kavitha and Namasivayam 2007; Varank et al. 2012).

Finally, the modified Freundlich equation (developedby Kuo and Lotse in 1973) is expressed and linearized,respectively, by the following equations (Varank et al.2012; Sampranpiboon and Feng 2016):

qt ¼ kmf ⋅C0⋅t1=mmf ð18Þ

ln qtð Þ ¼ ln kmf ⋅C0ð Þ þ 1

mmf⋅ln tð Þ 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 modifiedFreundlich model (L/g/min).

The values of kmf and mmf are used empirically toevaluate the effect of surface loading and ionic strengthon the adsorption process. By plotting a linearized graphof x = ln(t) vs. y = ln(qt), the values of mmf and kmf canbe obtained, respectively, from the slope (1/mmf) and theintercept [ln(kmf ∙ C0)] of the straight-line plot.

3 Results and Discussion

3.1 Adsorbent Characteristics

The SEM analysis of the adsorbent revealed that theadsorbent had a porous structure. Based on the TEManalysis, it was determined that the adsorbent particlesare spherical in shape. These spherical particles had atendency to adhere together and form chains. The X-raydiffraction spectra of the adsorbent showed that the peakat 43° is related to the graphene structure in the adsor-bent matrix.

The results of proximate and elemental analyses ofthe adsorbent are given in Table 2. The specific surfacearea (obtained by the BETmethod) of the adsorbent wasfound to be 770 m2/g. The average pore diameter (nm)and total pore volume (m3/g) were 0.66 and 0.71, re-spectively. In addition, the moisture and ash contentwere 12 and 5.7% w/w, respectively. Rajamohan et al.(2014) reported a surface area of 70.9 m2/g for Euca-lyptus camaldulensis barks for the removal of alumi-num. Accordingly, it can be seen that the use of the plant

leaves can be of a great advantage as the surface areaobtained is nearly tenfold higher than that for the bark, ifthe adsorption process is surface area dependent.

3.2 Derivation of Quadratic Models and StatisticalResults

With the application of multiple regression-based anal-ysis on the CCDmatrix (Table 1), the following second-order polynomial models were derived for the natural(uncoded) form of independent factors (X1 = pH,X2 = contact time (CT: min), X3 = initial As or Hgconcentration (IC: mg/L), and X4 = adsorbent dose(AD: g/L)) and the dependent variable (Y: As or Hgremoval efficiency, RE: %)):

Y ¼ β0 þ β1X 1 þ β2X 2 þ β3X 3 þ β4X 4ð Þþ β5X

21 þ β6X

22 þ β7X

23 þ β8X

24

� �þ β9X 1X 2 þ β10X 1X 3 þ β11X 1X 4 þ β12X 2X 3 þ β13X 2X 4 þ β14X 3X 4ð Þ

ð20Þ

REAs %ð Þ ¼ 71:16þ 1:19 pHð Þ � 1:16 CTð Þþ 10:65 ICð Þ þ 34:39 ADð Þ� 1:19 pH2

� �� 0:0030 CT2� �

� 0:36 IC2� �� 2:13 AD2

� �þ 0:44 pHð Þ CTð Þ � 1:49 pHð Þ ICð Þ� 0:82 pHð Þ ADð Þ � 0:086 CTð Þ ICð Þ� 0:63 CTð Þ ADð Þ þ 2:87 ICð Þ ADð Þ ð21Þ

REHg %ð Þ ¼ 71:52þ 0:68 pHð Þ � 1:14 CTð Þþ 10:99 ICð Þ þ 35:17 ADð Þ� 1:08 pH2

� �� 0:0036 CT2� �

� 0:53 IC2� �� 2:82 AD2

� �þ 0:43 pHð Þ CTð Þ � 1:64 pHð Þ ICð Þ� 1:07 pHð Þ ADð Þ � 0:074 CTð Þ ICð Þ� 0:61 CTð Þ ADð Þ þ 3:29 ICð Þ ADð Þ ð22Þ

The results of ANOVA of the multiple regression-based models (Eqs. (21) and (22)) showed that thequadratic models were highly significant and valid, as


was evident from the Fisher F test conducted for As orHg adsorption data (Fmodels > 500) with a very lowprobability value (p < Fmodels = 0.00001). Accordingto Eq. (4), the calculated F values (> 500) were found tobe greater than the tabulated F value (Fα,df(n − df + 1) =F0.05,14,17 = Sr

2/Se2 = Ftabulated = 2.329) at α = 0.05. This

indicated that the computed Fisher’s variance ratio atthis level was large enough to corroborate a very highdegree of fit of the quadratic models, and the selectedcombinations were highly significant, as similarly re-ported in previous works (Yetilmezsoy et al. 2009;Noori Sepehr et al. 2014). Since Fmodels > Ftabulated, theFisher F test concluded at a confidence level of 95% thatthe second-order polynomial models explained a signif-icant amount of the variation in the dependent factors(REAs or REHg). Moreover, the statistical analysis forthe response surface quadratic models showed the stan-dard error of estimate was below 0.35 for the presentcase. The models’ p value indicated that the derivedsecond-order models for removal of As or Hg wassignificant, 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, indicatinga nonsignificant LOF), so that the independent variablesor parameters had considerable effects on the removal ofAs 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 orHg), and X4 (adsorbent dose, AD) influence As or Hgremoval efficiency (REAs or REHg). According to themultiple regression-based analysis for both REAs andREHg models, the model terms X1, X2, X3, X4, X1

2, X22,

X32, X4

2, X1 ∙ X2, X1 ∙ X3, X1 ∙ X4, X2 ∙ X3, X2 ∙ X4, andX3 ∙ X4 were significant (p < 0.05). The second-order(quadratic or squared) effects of X1

2, X22, X3

2, 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, theseresults indicated that X1 (pH) and X2 (contact time, CT)had a direct relationship to both As and Hg removalefficiency, since the sum of square (SS) values of thesevariables (SSpH = 587.62 and 476.70 and SSCT = 98.40and 137.42 for As and Hg models, respectively) weremuch higher than those of other first-order terms, X3

(SSIC = 3.07 and 4.55 and SSAD = 10.19 and 15.08 forAs and Hg models, respectively).

According to Eqs. (5)–(7), the goodness of fit of themodels was tested by means of the determination coeffi-cient (R2) and the adjusted determination coefficient (Ra

2).The values of R2 = 0.998 and R2 ≈ 1.0 revealed with 95%certainty that Eqs. (21) and (22) satisfactorily predicted theexpected responses with very small deviations (maximumresiduals < 0.07) for As and Hg data sets, respectively. It isnoted that the Ra

2 corrects the R2 value for the sample sizeand the number of terms in the model. If there are manyterms in themodel and the sample size is not very large, theRa

2 may be noticeably smaller than the R2 (Liu et al. 2004;Yetilmezsoy et al. 2009). For the present case, the values ofadjusted determination coefficient (Ra

2 = 0.996 andRa

2 ≈ 1.0 for As and Hg, respectively) were also very high,implying a high significance of the derived quadraticmodels. Finally, the Durbin–Watson (DW) statistics(DW = 1.938 and DW = 1.438 for As and Hg, respective-ly) were determined to be close to 2, showing the goodnessof fit of the second-order polynomial models and indicat-ing positive autocorrelations (DW ≤ 2) for each modelbetween errors (Hewings et al. 2002; Yetilmezsoy et al.2009).

3.3 Effect of Interactions Between pH and Contact Timeon the Adsorption of As and Hg

Figure 1 shows the effect of various pH values on theadsorption of arsenic and mercury by the Eucalyptusleaves at different contact times. As seen from Fig. 1, the

Table 2 Physicochemical analysis of Persian Eucalyptus leaves

Type of analysis Components and values

Elemental analysis N (%) C (%) P (%)

14 12 3

Proximate analysis Moisture (%) Ash content (%)

12 5.70

Porous structure parameters Average pore diameter (nm) Total pore volume (m3/g) BET surface area (m2/g)

0.66 0.71 770


pH change affects the adsorption of arsenic and mercury,since it determines the type of ion species of arsenate andmercury and the adsorbent surface charge. This scenariowill have an impact on the reaction between adsorbent andadsorbate. In other words, in the case of a positive adsor-bent surface charge, the adsorbent affinity to absorb theanions is increased, and there will be electrostatic adsorp-tion. Thus, the pH of the solution has an impact on both theadsorbent surface charge and the adsorbate species charge,and these are the controlling conditions for the adsorptionof arsenic andmercury (Mosaferi et al. 2014). On the otherhand, the adsorbent surface charge is negative at lower pHvalues, so the tendency to adsorb the desired ions throughthe electrostatic process is decreased. Also, at higher pH

values, the hydroxyl ion concentration in the solution isincreased, as well as competing with the arsenic andmercury 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 theused adsorbent is 5.0–6.0, the decreased adsorption effi-ciency is reasonable in alkaline conditions. Other studieshave also shown that the optimum pH for adsorption ofarsenic and mercury was between 6.0 and 8.0 (Ngah andHanafiah 2008; Mohan and Pittman 2007; Bulut andAydın 2006).

In Fig. 1, at pH = 3.0 and 47.5 min contact time, theremoval efficiency of arsenic is equal to 77.6%, and theremoval efficiency ofmercury is equal to 78.5% (Fig. 1a,

Rem

oval

effi

cien

cy fo

r As

(%)

pH

Contact time (min) 9.08.0

7.06.0

5.04.0

0.010.020.030.040.050.060.070.080.076.0

78.0

80.0

82.0

84.0

86.0

88.0

90.0

92.0

94.0

96.0

(a)

Run no for As: 1–10 (Table 1)X1 = CT

X2 = pH

RE =Yexp

RE =Ypred

90.0 6.0 94.15 94.1547.5 6.0 95.70 95.705.0 6.0 90.15 90.1547.5 6.0 95.70 95.7047.5 6.0 95.70 95.7047.5 3.0 77.64 77.6447.5 9.0 92.20 92.2047.5 6.0 95.70 95.7047.5 6.0 95.70 95.7047.5 6.0 95.70 95.70

Rem

oval

effi

cien

cy fo

r Hg

(%)

pH

Contact time (min) 9.08.0

7.06.0

5.04.0

0.010.020.030.040.050.060.070.080.078.0

80.0

82.0

84.0

86.0

88.0

90.0

92.0

94.0

96.0

(b)

Run no for Hg: 1–10 (Table 1)X1 = CT

X2 = pH

RE =Yexp

RE =Ypred

47.5 3.0 78.46 78.4647.5 9.0 91.50 91.5047.5 6.0 94.66 94.6647.5 6.0 94.66 94.6647.5 6.0 94.66 94.6690.0 6.0 93.18 93.1847.5 6.0 94.66 94.665.0 6.0 88.18 88.1847.5 6.0 94.66 94.6647.5 6.0 94.66 94.66

2

2 3

2

179.60 234.32 878.41(%) 56.31 11.72 ln( ) 1.69 [ln( )]

( 1.0, 0.00001 0.05)

As ttERpH pH pH

R p

2

2 3

2

127.65 23.31 1096.45(%) 56.83 12.71 ln( ) 1.80 [ln( )]

( 1.0, 0.00001 0.05)

Hg ttERpH pH pH

R p

Fig. 1 3D response surfacediagrams showing the effects ofthe mutual interactions betweenpH and contact time on removalefficiency of arsenic (a) andmercury (b). Other variables wereheld at their optimum levels:adsorbent dose = 1.5 g/L andinitial concentration of arsenicand mercury = 2.75 mg/L


b). Under the same conditions, the empirical equations(shown on Fig. 1) derived within the framework ofDataFit® software package (V8.1.69, Oakdale Engi-neering, PA, USA) yield a very good fit (R2 ≈ 1.0 andp = 0.00001 < α = 0.05 at 95% confidence level) with aremoval efficiency of 77.64 and 78.46% for arsenic andmercury, respectively. In the derivation of these empir-ical formulations, the same nonlinear convergencecriteria and computational techniques (Richardson’s ex-trapolation and Levenberg–Marquardt methods) wereconducted, where they were also considered in deriva-tion of the second-order polynomial models based onthe CCD matrix. According to the empirical equationsshown in Fig. 1a, b, at the optimum conditions of pHand contact time (pH = 6.0 and t = 47.5 min), it may bepossible to obtain a removal efficiency of 95.7 and94.66% for arsenic and mercury, respectively.

It is noted that the removal efficiency of arsenic andmercury showed a decreasing growth trend by increas-ing the pH from 3.0 to 7.0. The possible reason for thedecreased removal efficiency at higher pH (from 6.0 to9.0) may be attributed to the decreased chelation processof the amine groups available in the Eucalyptus leaves(Fig. 1a, b). Accordingly, the process may cause lowercompetitive adsorption of hydrogen ions at high pH.Furthermore, maximum removal or uptake capacitywas obtained at pH = 6.0 (at an initial concentration(m) of 1.5 mg/L), where adsorption efficiencies of95.7% for arsenic and 94.7% for mercury wereachieved. A similar trend and pH dependency werereported byMousavi and Lotfi (2011) for the adsorptionof cadmium, nickel, and cobalt by the ashes ofEucalyptus leaves. In addition, Mosaferi et al. (2014)reported that the highest adsorption capacity of arsenicoccurred at a pH between 6.0 and 8.0. According to thetrends and data reported in Fig. 1a, b, it might beconcluded that the adsorption process using the Euca-lyptus leaves might be governed by electrostatic repul-sion between the negative adsorbent surface and thecationic nature of arsenic or mercury ions.

3.4 Effect of Interactions Between Contact Timeand Initial Adsorbate Concentration on the Adsorptionof As and Hg

The effect of contact time on the adsorption of arsenicand mercury onto the adsorbent at various initial con-centrations of arsenic andmercury is shown in Fig. 2. Ascan be seen, the adsorption of arsenic and mercury is

enhanced by increasing the contact time up to an equi-librium contact time. The results indicate that the re-moval efficiency of arsenic and mercury increased overthis contact time, and the maximum removal was ob-served to occur within the first 50 min of the experi-mental time. As seen in Fig. 2a, b, the adsorption ofarsenic and mercury within the first 50 min is faster, andby increasing the contact time from 50 to 90 min, theslope of this curve then starts to gradually decrease andfinally becomes constant after 70 min. The adsorptionprocess was rapid at the initial stages of the contactperiod, but thereafter, it became slower towards equilib-rium. The adsorption sites become less available as thecontact time increased, resulting in a slow adsorptionphase, as similarly stated by Thompson et al. (2015).The initial rapid phase may be due to the availability ofmore adsorption/vacant sites at the initial stage, resultingin an increased concentration gradient between the ad-sorbate in solution and the adsorbate on the adsorbent.This can be attributed to strong attractive forces betweenAs and Hg ions and the Persian Eucalyptus leaves, andalso by the fast diffusion into the interparticle matrix toattain rapid equilibrium, as similarly emphasized byothers (Sathishkumar et al. 2007; Arivoli et al. 2008;Binupriya et al. 2008; Varank et al. 2012). For thepresent case, no significant change in As and Hg remov-al was observed after about 47.5 min. In other words,longer times (herein > 47.5 min) unnecessarilyprolonged the process to obtain similar results. There-fore, further studies were conducted using a period of47.5 min as the optimal contact time for As and Hgadsorption. It can be observed from Fig. 2a, b that, aftera certain period of time, removal versus time curves aresingle, smooth, and continuous leading to saturation,thus suggesting the possibility of monolayer coverageof As and Hg on the outer surface of the adsorbent.

The results showed that by increasing the initialconcentrations of arsenic and mercury, the adsorptioncapacity and removal efficiency could be increased to acertain level (95.7 and 94.7% for As and Hg, respec-tively), as the initial concentration of As and Hg wasincreased (herein from 0.5 to 2.75 mg/L). This can beexplained that, at the initial stage of the adsorptionprocess, there are plenty of readily accessible sites, buteventually a plateau is reached, thus indicating that theadsorbent is saturated at high initial concentrations ofarsenic and mercury (i.e., 3.875 mg/L), as similarlyreported by Bhowmick et al. (2014). For the presentcase, Fig. 2a, b shows that the increased initial


concentration of As and Hg has a direct impact on theremoval efficiency of the studied heavy metal ions, andthe possible reason for the adsorbent saturation at highinitial concentrations of As and Hg may be ascribed tothe filling of the adsorbent pores or the difficult access ofAs and Hg ions to the active sites on the adsorbentsurface, as well as to a decrease in the mass transferdriving force, hence the decreased rate at which As andHg ions pass from the solution to the adsorbent surface.In general, it can be said that at the low arsenic andmercury concentrations, the ratio of adsorbate ions tothe active sites is lower and, as a result, the adsorption isindependent of their initial concentration. On the con-trary, at the high concentrations of arsenic and mercury,

the access to the adsorption sites is lower, and so thearsenic and mercury removal depends on their initialconcentration. The results of the present study showedthat an increase in the initial concentration of arsenic andmercury has a positive impact on the adsorption capac-ity, so that increasing the initial concentration from 0.5to 2.75 mg/L increased the adsorption capacity. Thereason could be the increased concentration drivingforce due to the increase in the number of arsenic andmercury ions (Gupta and Bhattacharyya 2011).

In this study, the effect of different concentrations ofarsenic and mercury on the adsorption efficiency atvarious times indicated the fact that the equilibriumadsorption capacity of the Eucalyptus leaves for

Rem

oval

effi

cien

cy fo

r As

(%)

Contact time (min)

Initial adsorbate concentration (mg/L)

90.080.0

70.060.0

50.040.0

30.020.0

10.0

0.51.0

1.52.0

2.53.0

3.590.0

91.0

92.0

93.0

94.0

95.0

96.0

(a)

2

2

28.39(%) 95.52 1.05 ln( ) 0.65 [ln( )]

ln( )

( 0.869, 0.0018 0.05)

AsRE m mt

R p

Run no for As: 11–20 (Table 1)X1 = m

X2 = CT

RE = Yexp

RE =Ypred

0.50 47.5 93.88 93.882.75 90.0 94.15 95.592.75 47.5 94.50 95.313.875 47.5 95.14 95.142.75 47.5 95.70 95.312.75 5.0 90.15 90.232.75 47.5 95.70 95.312.75 47.5 95.70 95.312.75 47.5 95.70 95.312.75 47.5 95.70 95.31

Rem

oval

effi

cien

cy fo

r Hg

(%)

Contact time (min)

Initial adsorbate concentration (mg/L)

90.080.0

70.060.0

50.040.0

30.020.0

10.0

0.51.0

1.52.0

2.53.0

3.588.0

89.0

90.0

91.0

92.0

93.0

94.0

95.0

(b)

2

2

34.21(%) 95.15 1.50 ln( ) 1.53 [ln( )]

ln( )

( 0.923, 0.00096 0.05)

Hg mmERt

R p

Run no for Hg: 11–20 (Table 1)X1 = m

X2 = CT

RE = Yexp

RE = Ypred

2.75 47.5 94.66 94.392.75 47.5 94.66 94.392.75 47.5 94.66 94.390.50 47.5 92.66 92.662.75 90.0 93.18 94.733.875 47.5 93.66 93.662.75 47.5 94.66 94.392.75 5.0 88.18 88.272.75 47.5 94.66 94.392.75 47.5 94.66 94.39

Fig. 2 3D response surfacediagrams showing the effects ofthe mutual interactions betweencontact time and initial adsorbateconcentration on removalefficiency of arsenic (a) andmercury (b). Other variables wereheld at their optimum levels:pH = 6.0 and adsorbentdose = 1.5 g/L


adsorption of arsenic and mercury was enhanced byincreasing the initial concentration of these elements,and also, the adsorption kinetics of arsenic and mercuryhad two phases. The adsorption phase was implementedrapidly in the initial adsorption phase, and in the secondphase, the adsorption was slower and finally reached theequilibrium conditions. The high rate of adsorption forarsenic and mercury in the first phase might be due tothe adsorbent dosage available level at the start of theprocess or, in other words, the available active adsorp-tion sites that absorbed the arsenic and mercury ions.However, the number of active adsorption sites gradu-ally decreases with increasing contact time and theincreasing concentration of arsenic and mercury ionsadsorbed onto the adsorbent, so the adsorption ratedecreased significantly and the second phase startedand involved diffusion. These second phase active ad-sorption sites might be located in the deeper parts of theadsorbent. Thus, at the beginning of the adsorptionreaction, all the sites were prepared for adsorption, butthe external surface sites were easily exposed to thearsenic and mercury ions with higher chance to facethe above-mentioned ions. So it increased the speed ofadsorption, but gradually, with the saturation of theexternal surface sites, the adsorption continued throughthe deep and internal parts of the leaf and would reducethe adsorption rate. In fact, all sites are involved in theadsorption, but the adsorption speed in the initial phasewas controlled through the adsorbent surface sites. Theincrease in adsorption capacity by increasing the con-centration of arsenic and mercury could be due to thehigh probability of collision between the arsenic andmercury ions (Kanel et al. 2005). Based on the empiricalequations given in Fig. 2a, b, at the optimum conditionsof initial concentration of arsenic and mercury and con-tact time (C0 = 2.75 mg/L and t = 47.5 min), a removalefficiency of 95.31 and 94.39% can be achieved forarsenic and mercury, respectively. This is in line withthe results obtained for the mutual interactions of BpHand contact time.^

In a study on the removal of arsenic by zero-valentiron nanoparticles as the permeable active barriers in thegroundwater resources, Kanel et al. (2005) reported theequilibrium time of 90 min for the initial concentrationof 6 mg/L, which is consistent with the results of thepresent study. Likewise, Al Rmalli et al. (2008) reportedthe time of 1 h for removal of mercury on the surface ofwillow leaves with the maximum adsorption capacity of37.2 mg/g, which is also in agreement with results of the

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

3.5 Effect of Interaction Between Adsorbent Doseand pH on the Adsorption of As and Hg

The effect of various concentrations of adsorbent on theadsorption of arsenic and mercury at different variouspH values is depicted in Fig. 3. As shown in Fig. 3a, b,the increased dose of adsorbent is associated with anincrease in the removal efficiency. The reason for anincrease in the removal efficiency of arsenic and mercu-ry with an increasing dose ofEucalyptus leaves could beexplained by the increase in adsorbent surface area,resulting in more active sites for arsenic and mercuryions to adsorb in the active pores on the adsorbent. Onthe other hand, according to Eq. (1), increasing theadsorbent dose reduces the specific adsorbent loading,so that, for the present case, increasing the dose ofadsorbent over 2 g/L did not yield an increase (nosignificant impact for arsenic and a slight decrease formercury) of the adsorption capacity. The reason mightbe the saturation of active sites of the adsorbent duringthe adsorption process (Heibati et al. 2015). From theempirical equations presented in Fig. 3a, b, at the opti-mum conditions of adsorbent dose and pH (m = 1.5 mg/L and pH = 6.0), a removal efficiency of 95.70 and94.66% can be achieved for arsenic and mercury, re-spectively. This is in agreement with the previous resultsobtained for the mutual interactions of both BpH andcontact time^ and Bcontact time and initial adsorbateconcentration.^

To summarize, the experimental findings indicatedthat removal efficiencies reached their maximum valuesfor both As and Hg at pH 6.0, and then showed adecrease from 95.70 to 92.20% for As and from 94.66to 91.50% for Hg within the pH range of 6.0–9.0. Thedeclining trends in removal efficiencies at high pH canbe better seen in 3D response surface diagrams (Figs. 1and 3). Based on the experimental results, it is


confirmed that there was no visual evidence of precip-itation in the solutions for the present case.

3.6 Adsorption Isotherms

Equilibrium isotherm parameters for arsenic and mercu-ry adsorption onto the adsorbent are presented inTable 3.Additionally, the linear diagrams of Langmuir andFreundlich adsorption isotherms are shown for arsenicand mercury in Fig. 4. The results of the adsorptionequilibrium isotherms given in Table 3 show that the1/n values (1.745 and 1.730 for As and Hg, respectively)obtained from the Freundlich equation are indicative ofS-type isotherms, since the values of 1/n are higher than1. This can be ascribed that some compounds containing

a polar functional group may be in competition withwater for adsorption sites at low concentration ranges.As seen from Table 3, the RL values (0.40 and 0.53 forAs and Hg, respectively) obtained from the Langmuirmodel are between 0 and 1, indicating a favorableadsorption isotherm for both arsenic and mercury. Onthe basis of determination coefficients, the Freundlichmodel (R2 = 0.9897 for arsenic and R2 = 0.9849 formercury) fitted the adsorption isotherm data better thanthe Langmuir model (R2 = 0.9815 for arsenic andR2 = 0.9802 for mercury).

In recent research on arsenic adsorption by the zero-valent iron nanoparticles stabilized by the montmoril-lonite minerals, Bhowmick et al. (2014) reported anappropriate Langmuir model with a maximum

Rem

oval

effi

cien

cy fo

r As

(%)

pH

Adsorbent dose (g/L) 9.08.0

7.06.0

5.04.0

0.40.60.81.01.21.41.61.82.02.22.476.0

78.0

80.0

82.0

84.0

86.0

88.0

90.0

92.0

94.0

96.0

(a)

Run no for As: 21–30 (Table 1)X1 = m

X2 = pH

RE = Yexp

RE = Ypred

2.50 6.0 94.50 94.501.50 6.0 95.70 95.700.50 6.0 92.50 92.501.50 6.0 95.70 95.701.50 6.0 95.70 95.701.50 3.0 77.64 77.641.50 9.0 92.20 92.201.50 6.0 95.70 95.701.50 6.0 95.70 95.701.50 6.0 95.70 95.70

Rem

oval

effi

cien

cy fo

r Hg

(%)

pH

Adsorbent dose (g/L) 9.08.0

7.06.0

5.04.0

0.40.60.81.01.21.41.61.82.02.22.478.0

80.0

82.0

84.0

86.0

88.0

90.0

92.0

94.0

96.0

(b)

Run no for Hg: 21–30 (Table 1)X1 = m

X2 = pH

RE = Yexp

RE = Ypred

1.50 3.0 78.46 78.461.50 9.0 91.50 91.501.50 6.0 94.66 94.661.50 6.0 94.66 94.661.50 6.0 94.66 94.662.50 6.0 92.46 92.461.50 6.0 94.66 94.660.50 6.0 91.22 91.221.50 6.0 94.66 94.661.50 6.0 94.66 94.66

2

2

2

277.20 771.12(%) 64.47 7.60 2.20

( 1.0, 0.000001 0.05)

AsRE m mpH pH

R p

2

2

2

249.48 693.36(%) 65.10 9.08 2.82

( 1.0, 0.000001 0.05)

HgRE m mpH pH

R p

Fig. 3 3D response surfacediagrams showing the effects ofthe mutual interactions betweenadsorbent dose and pH onremoval efficiency of arsenic (a)and mercury (b). Other variableswere held at their optimum levels:contact time = 47.5min and initialconcentration of arsenic andmercury = 2.75 mg/L


adsorption of 45.5 mg/g. Similarly, Krishnan et al.(2011) reported a maximum capacity of 1.33 mg/g forthe adsorption of mercury onto the surface of the ricehusk. In a study on the arsenic adsorption with compos-ite adsorbent of zero-valent iron and porous carbon,Baikousi et al. (2015) reported the maximum adsorptioncapacity of 26.8 mg/g based on the Langmuir isothermmodel. Furthermore, in another study on the arsenicremoval with starch-stabilized zero-valent iron nanopar-ticles and carboxy methyl cellulose, the maximum ad-sorption capacity for arsenic based on the Langmuirisotherm model was reported equal to 14 mg/g(Mosaferi et al. 2014). In the present study, the maxi-mum adsorption capacity based on the Langmuir iso-therm model was determined as 84.03 mg/g for As and129.87 mg/g for Hg. The differences with the presentstudy show that the Eucalyptus leaf particles havehigher adsorption or uptake capacity compared to the

starch-stabilized zero-valent iron nanoparticles for arse-nic adsorption.

As seen from both Table 3 and Fig. 4, the Temkinisotherm model had a poorer fit (R2 = 0.8637 for As andR2 = 0.8769 for Hg) of experimental data than the othertwo-parameter isotherm equations (Langmuir,Freundlich, and Dubinin–Radushkevich models). Forthe present experimental data, the Dubinin–Radushkevich isotherm showed that the magnitudes ofE were found as 1.142 kJ/mol for As and 1.106 kJ/molfor Hg. Since these values are determined to be less than8 kJ/mol, the present adsorption process may be as-cribed to be affected by physical forces rather than bythe ion exchange mechanism or particle diffusion phe-nomenon. Considering the statistical parameters (R2, Fstatistic, and p value) obtained for the studied two-parameter isotherm models, the order of prediction per-formance is presented as follows: Freundlich >

Table 3 Coefficients, constants, and statistical values of two-parameter isotherm models for As and Hg adsorption onto the PersianEucalyptus leaves

Two-parameter isotherm models (linearized forms) Isotherm parametersand statistics

Arsenic (As)a Mercury (Hg)a

Langmuir model1qe¼ 1

qmaxþ 1

qmax ⋅kL⋅ 1Ce

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

p 0.0011 < 0.05 0.0012 < 0.05

Freundlich modellog qeð Þ ¼ log k fð Þ þ 1

n ⋅log Ceð Þkf 13.97 13.16

n 0.573 0.578

R2 0.9897 0.9849

F 478.60 326.86

p < 0.0010 < 0.0010

Temkin modelqe ¼ βTln KTð Þ þ βTln Ceð Þ βT ¼ R⋅Tð Þ =bT(R = 8.314 J/mol/K, T = 308 K)

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

Dubinin–Radushkevich model

ln qeð Þ ¼ ln qDð Þ−BD⋅εD2 E ¼ 1=ffiffiffiffiffiffiffiffi2BD

p qD 58.21 59.29

BD 0.3835 0.4090

E 1.142 1.106

R2 0.9292 0.9293

F 65.66 79.91

p 0.0005 < 0.05 0.0005 < 0.05

a Units of isotherm parameters are previously defined in the text


log(Ce)

-0.2 0.0 0.2 0.4

log(

q e)

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Experimental data (As)Freundlich isotherm model (As)

)001.0,60.478,9897.02(

1453.17441.1

pFR

xy

)c(

log(Ce)

-0.2 0.0 0.2 0.4

log(

q e)

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Experimental data (Hg)Freundlich isotherm model (Hg)

)001.0,86.326,9849.02(

1194.17303.1

pFR

xy

)d(

ln(Ce)

-1.0 -0.5 0.0 0.5 1.0

q e

0

10

20

30

40

50

60

70Experimental data (As)Temkin isotherm model (As)

)0025.0,6880.13,8637.02(

8612.188858.35

pFR

xy

)e(

ln(Ce)

-1.0 -0.5 0.0 0.5 1.0

q e

0

10

20

30

40

50

60

70Experimental data (Hg)Temkin isotherm model (Hg)

)0019.0.6262,53,8769.02(

6114.177394.35

pFR

xy

)f(

0 2 4 6 8

ln(q

e)

0

1

2

3

4

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

)0005.0,66.56,9292.02(

0641.43835.0

pFR

xy

)g(

D

0 2 4 6 8

ln(q

e)

0

1

2

3

4

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

)0003.0,91.79,9293.02(

0824.44090.0

pFR

xy

)h(

D

1/Ce

0 2 4 6 8

1/q e

0.0

0.1

0.2

0.3

0.4

0.5

0.6

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

)0011.0,40.159,9815.02(

0119.00793.0

pFR

xy

)a(

1/Ce

0 2 4 6

1/q e

0.0

0.1

0.2

0.3

0.4

0.5

0.6

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

)0012.0,39.148,9802.02(

0077.00860.0

pFR

xy

)b(

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


Langmuir > Dubinin–Radushkevich > Temkin. To sum-marize, the isotherm parameters and statistical indicators(Table 3) corroborate that arsenic and mercury adsorp-tion onto the Eucalyptus leaves can be very well de-scribed by the Freundlich isotherm model at a confi-dence level of 95% (FFreundlich = 478.60 and 326.86 forAs and Hg, respectively).

3.7 Adsorption Kinetics

The values of kinetic parameters obtained for the ad-sorption of arsenic and mercury onto the Persian Euca-lyptus leaves are given in Table 4. Although Ho andMcKay’s PSO kinetic model (R2 = 0.930 and 0.976 forAs ang Hg, respectively) provides a good fitting to theexperimental data points for the adsorption of As andHg, Lagergren’s PFO kinetic model (R2 = 0.978 and0.983 for As and Hg, respectively) described the presentadsorption process better than the Ho andMcKay’s PSO

model. The linearized plots provided in Fig. 5 alsodemonstrate that the empirical data obtained from theadsorption tests are consistent with the PFO equation,and the present experimental data can be describedbetter by using this kinetic model. It can be seen fromTable 4 that the calculated values, qe,cal, of arsenic andmercury onto the Eucalyptus leaves best agreed with theexperimental values, qe,exp, in the case of the PFOmodel. When the experimental results are applied toLagergren’s PFO model, therefore, the dominant mech-anism in the adsorption process of arsenic and mercuryis physical adsorption. The domination of physicalforces was also corroborated by the results of theDubinin–Radushkevich isotherm, where the value ofthe mean free energy (E) of adsorption per mole of theadsorbate was determined to be less than 8 kJ/mol forboth As and Hg.

According to the kinetic parameters (kmf and mmf)obtained from the modified Freundlich kinetic model,

Table 4 Coefficients, constants, and statistical values of different kinetic models for As and Hg adsorption onto the Persian Eucalyptusleaves

Kinetic models (linearized forms) Kinetic parametersand statistics

Arsenic (As)a Mercury (Hg)a

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

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

Ho and McKay’s pseudo-second-order (PSO) kinetic modeltqt¼ 1

k2q2eþ 1

qet

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

Modified Freundlich kinetic modelln qtð Þ ¼ ln kmf ⋅C0ð Þ þ 1

mmf⋅ln tð Þ

(C0 = 10 mg/L)

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

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

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

a Units of kinetic parameters are previously defined in the text


t (min)0 20 40 60

ln(q

e-q t)

-1.5

-1.0

-0.5

0.0

0.5Experimental data (As)PFO kinetic model (As)

)0111.0,64.88,9779.02(

5289.00302.0

pFR

xy

)a(

t (min)0 20 40 60

ln(q

e-q t)

-1.5

-1.0

-0.5

0.0

0.5Experimental data (Hg)PFO kinetic model (Hg)

)0088.0,16.112,9825.02(

4251.00302.0

pFR

xy

)b(

t (min)0 20 40 60 80 100 120 140

t/qt

0

20

40

60

80Experimental data (Hg)PSO kinetic model (Hg)

)0002.0,66.160,9757.02(

5138.144599.0

pFR

xy

)d(

t (min)0 20 40 60 80 100 120 140

t/qt

0

20

40

60

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

)0019.0,12.53,9300.02(

9881.234070.0

pFR

xy

)c(

0 2 4 6 8 10 12

q t

0.0

0.5

1.0

1.5

2.0

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

)0340.0,93.27,9332.02(

0246.01529.0

pFR

xy

)g(

t0 2 4 6 8 10 12

q t

0.0

0.5

1.0

1.5

2.0

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

)0185.0,42.52,9633.02(

1926.01470.0

pFR

xy

)h(

t

ln(t)0 1 2 3 4 5

ln(q

t)

-3

-2

-1

0

1

2

3Experimental data (Hg)Modified Freundlich kinetic model (Hg)

)0001.0,33.245,9840.02(

6148.14606.0

pFR

xy

)f(

ln(t)0 1 2 3 4 5

ln(q

t)

-4

-2

0

2Experimental data (As)Modified Freundlich kinetic model (As)

)0002.0,87.151,9743.02(

4306.26277.0

pFR

xy

)e(

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


the effects of surface loading and ionic strength weremore pronounced for the adsorption of Hg ions onto theEucalyptus leaves. The results of the kinetic studies alsoshowed that the best-fitted kinetic models wereLagergren’s PFO kinetic model and the modifiedFreundlich kinetic model for As and Hg, respectively(Table 4). For the Weber–Morris intraparticle diffusionmodel, Kavitha and Namasivayam (2007) have reportedthat the thickness of the boundary layer may beinterpreted in terms of the magnitude values of theintercept (i.e., the larger the intercept, the greater is theboundary layer effect). Considering this fact, it may beconcluded that the boundary layer effect for Hg(C = 0.1946) seems to have a higher impact that forAs (C = 0.0246). The linearized plots of the Weber–Morris intraparticle diffusion model (Fig. 5g, h) do notpass through the origin for both As and Hg, indicatingthat the intraparticle diffusion is not the only rate-limiting step, but other kinetic models may control therate of adsorption, as similarly stated by Sampranpiboonand Feng (2016).

3.8 Comparisons with Literature Data

Table 5 presents performance data regarding thecomparison of adsorption tests conducted with dif-ferent materials and experimental conditions on theelimination of As or Hg ions from aqueous media.The collected data reveal that a broad spectrum ofexperimental conditions has been studied for theremoval of As or Hg using cost-effective adsorbents,and the conditions for optimum initial pH have beeninvestigated between 1 and 12. The influence ofdifferent doses of used materials, ranging from0.1 mg/mL to 40 g/L, has been studied. Most ofthe investigations related to the present study havebeen conducted at a temperature above 20 °C. Abroad spectrum of contact time, from 2 min to 72 h(3 days), has been examined at different mixingspeeds up to 300 rpm. The collected literature dataclearly demonstrated that the elimination of heavymetal ions from aqueous media could be successful-ly enhanced up to about 100% by using differentcost-effective adsorbents and biosorbents (Chenet 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). Thepossible differences in results can be ascribed tothe varying properties and doses of used materials,

initial pH, heavy metal ion concentrations, operatingtemperatures, and also applied reaction times. Ac-cording to the maximum As or Hg removals, thepresent results seem to be in line with those reportedby other researchers. The results show that the Eu-calyptus leaves show an advantage over many ad-sorbents used in other studies, indicating that thepresent adsorbent is an encouraging material for Asor Hg elimination in real-scale implementation.

The obtainedmaximum adsorption capacities (q0) forthe adsorption of As or Hg onto other adsorbents ascost-effective materials studied in the literature are tab-ulated in Table 6. As observed from this data, the Euca-lyptus leaves make a preferable adsorbent in comparisonwith other cheap materials used by other researchers(Thanawatpoontawee et al. 2016; Yaghmaeian et al.2015; Liu et al. 2013; Zeng 2004). This demonstratesthat the Eucalyptus leaves possess a satisfactory As orHg adsorption or uptake performance than severallow-cost adsorbents and biosorbents, such as sugar-cane bagasse, bamboo leaf powder, coconut shell, andbone char; therefore, it can be beneficial using thismaterial for the removal of As and Hg from aqueousmedia. Although higher values of q0 have also beenreported by some researchers (Asasian et al. 2012;Fakhri 2015; Hadavifar et al. 2016; Iakovleva et al.2016), it should be stated that possible distinctionsmay be due to the characteristics of each material suchas structure, functional groups, and surface area.

3.9 Discussion on the Economic Benefits

Eucalyptus, a diverse genus of flowering trees, is typi-cally native to Australia. A number of Eucalyptus spe-cies are cultivated outside Australia including countriesin the Middle East and North America and some coun-tries in theMediterranean. The Eucalyptus leaf is widelyavailable and can potentially be used as a low-cost andreadily available adsorbent for the remediation of heavymetals from aqueous solutions. From the economicpoint of view, this can make it a preferable and morecost-effective material compared to other adsorbents forthe removal of As or Hg from aqueous solutions. At thispoint, it can be noted that additional investigations maybe required for both desorption and real-scale applica-tion. However, in other parts of the world (i.e., India,China, Morocco, Iran, etc.), where Eucalyptus leavesmay be available at no cost, regeneration is not needed,so that As- or Hg-loaded leaves can be eliminated by


hazardous waste incineration or encapsulation andlandfilling (Yetilmezsoy and Demirel 2008; Akinbiyi2000). Consequently, it is believed that this cost-

effective and green product can add substantial econom-ic value to Eucalyptus cultivating countries for the re-moval of heavy metals from aqueous solutions.

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

Adsorbent/biosorbent WoWWT pH CT tbcolw75ptMRoUE tbcolw90ptReferenceand region

Persian Eucalyptus leaves SWW 3–9 5–90 95.7 for As94.7 for Hg

Present study, Iran

Granular iron oxide with PVAc binder SWW 2–10 72 h 70 for As(III) andAs(V)

Mangwandi et al.(2016), UK

Iron(III)-loaded zein beads RWW (TK-80,TK-81, DW, andPW)

3–9 15–960 > 90 for As(V) Thanawatpoontaweeet al. (2016), Thai-land

Fe–Mn binary oxide-impregnated chitosanbead

SWW 7.0 ± 0.1 36 h NS

Qi et al. (2015), China

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

SS and AWW from amining site

2–10 0.5–72 h RH: 93 for As(III),72 for As(V)

CaFe-Cake: 100 forAs(III) and As(V)

Iakovleva et al. (2016),Finland

Dithizone-immobilized natural zeolite (DIZ) SS (RW) 3–9 5–180 Up to 99.36 forHg(II)

Mudasir et al. (2016),Indonesia

Copper oxide nanoparticles SWW 2–12 10–95 > 90 for Hg(II) Fakhri (2015), Iran

Mix-ZC: activated carbon prepared fromagricultural wastes

SWW 2–12 2–540 NS Asasian et al. (2012),Iran

Carbon sorbent derived from fruit shell ofIndian 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 Saccharumofficinarum bagasse

ACGW 7.0 20–120 100 As(III) andAs(V)

Gupta et al. (2015), In-dia

Sodium dodecyl sulfate (SDS)-modifiedbamboo 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), Chi-na

Charcoal-immobilized papain (CIP) IWW 4–9 2–10 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% forHg(II)

Hassan et al. (2008),Egypt

Bone char SWW 4.0 10–2880 NS Liu et al. (2014), China

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

SWW 2–10 15 min–24 h 98 for As(V) Pehlivan et al. (2013),Germany

Different units are indicated in the table

WoWTwater 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 arsenic-contaminated ground water, IWW industrial wastewater, NS not specified


3.10 Mechanism of Adsorption

By using the FTIR technique, we can determine theinteraction between the active sites on the surface ofthe adsorbent and the adsorbate. Furthermore, FTIR

analysis (not shown) was used for the determination ofthe functional groups which are responsible for theadsorption process. The FTIR spectrum of the Eucalyp-tus leaves indicates the presence of linear chain aliphaticcompounds which are at 2928 and 2854 cm−1

Table 6 Maximum adsorption capacities obtained for adsorption of arsenic or mercury onto various adsorbents (Langmuir isothermmodel)

Adsorbent/biosorbent WoWWT tbcolw130ptMAoUC tbcolw100ptReferenceand region

Persian Eucalyptus leaves SWW 84.03 for As, 129.87 for Hg Present study, Iran

Iron(III)-loaded zein beads RWW (TK-80,TK-81, DW, andPW)

1.95 for As(V) Thanawatpoontaweeet al. (2016), Thai-land

Fe–Mn binary oxide-impregnated chitosan bead SWW 39.1 for As(V), 54.2 for As(III) Qi et al. (2015), China

RH (industrial sand) and CaFe-Cake (sulfate tailings) SS and AWW froma mining site

RH: 215 mmol/g for As(III),248 mmol/g for As(V)

CaFe-Cake: 26.66 mmol/g forAs(III), 36.66 mmol/g for As(V)

Iakovleva et al. (2016),Finland

Thiolated MWCN SWW Single metal ion: 204.64 for Hg(II),binary metal ions: 35.89 for Hg(II)

Hadavifar et al. (2016),Iran

Fe(III)-Si binary oxide adsorbent SWW 21.1 to 21.5 (mg As/g) for As(III) and11.3 to 14.9 (mg As/g) for As(V)

Zeng (2004), Canada

Copper oxide nanoparticles SWW 825.21 for Hg(II) Fakhri (2015), Iran

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

SWW 147.1 for Hg(II) Asasian et al. (2012),Iran

Carbon sorbent derived from fruit shell of Indianalmond (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 preparedwith fly ash

SWW 20 for Hg(II) Liu et al. (2013), China

Activated carbon of Eichhornia crassipes biomass 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), In-dia

Sodium dodecyl sulfate (SDS)-modified bamboo leafpowder

SWW 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), In-dia

Coconut shell-based activated carbon SWW 47.74 for Hg(II) Goel et al. (2004), In-dia

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

WoWTwater or wastewater type,MAoUCmaximum adsorption or uptake capacity (q, mg/g), SWW synthetic wastewater,RWW real wastewater,DW drinking water, PW pond water, SS synthetic solution,MWCNmulti-walled carbon nanotubes, ACGW arsenic-contaminated ground water


[asymmetric and symmetric vibrations of the methylenegroups, CH2; νa(CH2) and νs(CH2), respectively], aswell as a smaller band at 1454 cm−1 [CH2 scissoring androcking vibrations; δscis(CH2) and δrock(CH2), respec-tively]. These vibrations together with the vibrations at

1035 and 1062 cm−1 are attributed to asymmetric andsymmetric C–O–C stretching vibrations of the etherbonds: νa(C–O–C) ether and νs(C–O–C) ether, respec-tively. The low-intensity bands at 1538 and 1455 cm−1

were related to the stretching of aromatic rings [ν(C–C)

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


conjugated with (C=C) or ν(C–C)/(C=C)]. The broadbands appearing at 3428 and 3357 cm−1 [H-bonded, O–H stretching vibration; ν(O–H…O)] were assigned tohydroxyl functional groups. Figure 6 proposes the bind-ing mechanisms between the cellulose main componentin the Eucalyptus leaves and the mercury and arsenate.

4 Conclusions

The present study was conducted for the first time toinvestigate the adsorption capacity and efficiency of thePersian Eucalyptus leaves as a low-cost herbal adsor-bent for the removal of mercury and arsenic from aque-ous solutions. The adsorption mechanisms and charac-teristic parameters for the present application were alsoexplored as an important objective using isotherm andkinetic models. Accordingly, the following can beconcluded:

& Arsenic and mercury adsorption was highly depen-dent on pH, and the ability of amine groups avail-able in Eucalyptus leaves for protonation was de-creased by increasing pH above 7.0, resulting in adecrease in the adsorption efficiency of arsenic ormercury at high pH levels.

& The absorbed arsenic and mercury could be en-hanced by increasing the initial concentration ofions. The optimum adsorption conditions based onthe multiple regression-based methodology wereobtained as pH = 6.0, adsorbent dose of 1.5 g/L,contact time of 47.5 min, and initial concentration ofarsenic and mercury of 2.75 mg/L.

& The equilibrium isotherms showed that both thearsenic and mercury adsorption onto the Eucalyptusleaves followed the Freundlich isotherm model(R2 = 0.9897 and R2 = 0.9849, respectively).

& The results of the kinetic modeling demonstratedthat 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 theexperimental data of As and Hg, respectively.

& The disposal/regeneration of the As/Hg-laden Euca-lyptus leaves must be considered due to its hazard-ous nature. It could be disposed of as hazardouslandfill waste or by degradation of the adsorbed Asand Hg into nonhazardous compounds or using amore sustainable solution by developing a method

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

Acknowledgements The authors would like to thank the per-sonnel of the Environmental Health Laboratory, Tehran Universityof Medical Sciences.

Funding Information The authors would like to thank TehranUniversity of Medical Sciences for financial support.

Compliance with Ethical Standard

Conflict of Interest The authors declare that they have noconflict of interest.

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