Journal of Environmental Chemical Engineering 1 (2013) 813–821

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Journal of Environmental Chemical Engineering

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2,4-Dichlorophenoxyacetic acid removal from aqueous solutionsvia adsorption in the presence of biological contamination

Duygu Ova, Bikem Ovez *

Ege University, Engineering Faculty, Chemical Engineering Department, 35100, Bornova-Izmir, Turkey

A R T I C L E I N F O

Article history:

Received 5 April 2013

Accepted 19 July 2013

Keywords:

Herbicide

Granular activated carbon

Sorption

Microorganisms

Biological pollutants

A B S T R A C T

In this study, the adsorption of the herbicide 2,4-dichlorophenoxyacetic acid (2,4-D), a chemical

agricultural pollutant, onto granular activated carbon (GAC) was accomplished in the presence of

biological contaminants in a batch and continuous system. In the batch studies, the maximum sorption

capacities (mg/g) exhibited by GAC were found to be 5.9, 76.8, 124.0, 173.1, and 177.6 in the presence of

Acidovorax avenae subsp. avenae LMG 17238, Gracilaria verrucosa, a group of aquarium-isolated

microorganisms, Spirulina platensis, and in the absence of microorganisms, respectively. Two and three-

parameter non-linear equilibrium models—Langmuir, Freundlich, Redlich–Peterson, Sips, and Toth—

were applied to describe the batch sorption process. In the continuous-flow column studies,

breakthrough curves were plotted as a function of influent 2,4-D concentration (50–200 mg/L), flow

rate (0.2–0.4 mL/min), GAC mass (0.75–1.5 g), and microorganism load. The highest bed capacity was

obtained by using 200 mg/L inlet 2,4-D concentration, 0.2 mL/min flow rate and 1.5 g GAC mass. In the

presence of biological contaminants, the order of adsorption of 2,4-D in terms of the maximum

adsorption capacity (mg/g) from the least to the greatest was as follows: no microorganism < S.

platensis < the aquarium-isolated group of microorganisms < G. verrucosa < LMG 17238. Among the

kinetic models applied to the fixed-bed column data, the Thomas and Yoon–Nelson models showed a

better fit than the Bohart–Adams.

� 2013 Elsevier Ltd. All rights reserved.

Introduction

In recent years, chemical (fertilizers, herbicides, pesticides,heavy metals, and dioxins) and biological (microorganisms andorganic matter) contaminants have become the most significantenvironmental pollutants because of rapid industrialization and adramatic increase in the human population. Despite strict standardregulations regarding the prevention of chemical and microbialpathogen spread in individual and public water distributionsystems, they continue to contaminate drinking water suppliesand cause waterborne diseases. Phenoxyalkanoic acid herbicidesare widely used in agriculture to control the growth of broad-leafweeds, to remove aquatic plants from lake drainage ditches, asgrowth regulators, and as substances for prolonging fruitdurability [1]. The metabolism and environmental fate ofphenoxyalkanoic acid herbicides have been extensively reviewed[2,3]. In addition, the acute toxicity of these pollutants has beendetermined on a laboratory scale, mainly using individual bacteria,green algae, and mammalian cells [4]. Regarded as the most

* Corresponding author. Tel.: +90 232 311 14 83; fax: +90 232 388 77 76.

E-mail addresses: duygu.ova@ege.edu.tr (D. Ova), bikem.ovez@ege.edu.tr,

zolansilverspear@gmail.com (B. Ovez).

2213-3437/$ – see front matter � 2013 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.jece.2013.07.024

commonly used phenoxy herbicide in the world, 2,4-D has provento be toxic to humans and animals [5]. The European Commission(1980) defined a maximum admissible concentration of 0.1 mg/Lfor individual pesticides and 0.5 mg/L for the total of all pesticidespresent, while the World Health Organization has recommended70 mg/L as the maximum permissible concentration of 2,4-D indrinking water [6]. Several conventional treatment methods forthe removal of this herbicide from aqueous solutions, includingchemical oxidation with ozone [7], photodegradation [8], coagu-lation [9], biological degradation [10], and adsorption [11,12] havebeen investigated with varying success. Among these processes,adsorption has been found to be superior to other techniques forwater reuse in terms of initial cost, flexibility and simplicity ofdesign, ease of operation and insensitivity to toxic pollutants. Inaddition to these, not resulting in the formation of harmfulsubstances makes adsorption a well known equilibrium separationprocess and an effective method for water decontaminationapplications. By the help of adsorption, pollutants can be removedfrom both aqueous and gaseous streams by using soils [13,14],bentonites [15], clays [16,17], activated carbons [18–20], datestones [21], oil shale ash [22], calcined hydrotalcite [23],organohydrotalcite [24], [Zn-Al-Cl] layered double hydroxides[25], goethite and humic acid-coated goethite [26], and diatomite[27].

Fig. 1. ESEM micrograph of GAC at 2000� magnification.

D. Ova, B. Ovez / Journal of Environmental Chemical Engineering 1 (2013) 813–821814

Drinking water is a particular ecosystem with special char-acteristics, such as the absence of light, the presence ofdisinfectants, and the existence of low nutrient levels. In additionto chemicals, microorganisms originating from sewage, fertilizers,and other organic wastes, can increase the potential pathogen loadin resource waters and cause severe problems in the quality ofdrinking water [28]. For the treatment of industrial wastewatersand raw drinking water supplies contaminated with these types ofchemical and biological pollutants, activated carbon, in eithergranular (GAC) or its powdered (PAC) form is the most widely usedadsorbent owing to its highly developed surface properties such assurface area, porosity, and surface chemistry. Not being a generalprinciple, a typical activated carbon particle has a porous structureconsisting of a network of interconnected macropores, mesopores,and micropores that provide a good capacity for the adsorption oforganic molecules due to its high surface area. There are threefactors that determine the nature of bonding mechanisms as wellas the extent and strength of adsorption which are the surfacechemistry of activated carbon (the kind, concentration of adsor-bent surface groups, and point of zero charge), the chemicalcharacteristics of the adsorbate (polarity, ionic nature, functionalgroups, and solubility), and the properties of the adsorptionsolution (pH, concentration of adsorbate, and the presence of otherspecies). Also a variety of physicochemical forces, such as van derWaals, H-bonding, dipole–dipole interactions, ion exchange andcovalent bonding, can lead to the adsorption of organic compoundson activated carbon [11]. Coal activated GAC was preferred as anadsorbent in this study with a relatively larger particle sizecompared to PAC, and consequently, presenting a smaller externalsurface besides the economical considerations. Another reason isthat having a non-polar carbon surface enables it to exhibit ahigher affinity for non-polar adsorbates and other carbon-basedimpurities and contrasting polar adsorbents such as silica gel andactivated alumina [29].

Sufficient information on the adsorption of individual pollu-tants is required to be able to design optimal active carbonadsorption units and develop mathematical models that can beused to compare the capacities of different adsorbents underdifferent operating conditions and accurately describe theseprocesses. To examine the relationship between sorption andaqueous concentration at equilibrium, linear regression wasfrequently used to determine the best fitted isotherm [30]. Thelinear least-squares method is widely applied to confirm theexperimental data and isotherms using the coefficient ofdetermination. Depending on the way an isotherm equation islinearized, the error distribution changes either for the worse or forthe better. Different outcomes may result for different linearizedforms of the equation because the error structure varies uponlinearizing the non-linear equation due to the different axialsettings which influences the whole determination process. On theother hand, in the non-linear method, the error distribution doesnot get altered, as all the isotherm parameters are fixed on thesame axis [31,32]. Thus, this situation addresses the need for theuse of non-linear models to estimate the parameters.

Although various kinds of activated carbon are known to havethe potential to decrease herbicides to very low concentrations, thestudies focusing specifically on 2,4-D adsorption by GAC inthe presence of biological species which are highly importantin the design of adsorption treatment systems, are very limited.The present study focuses on the modeling of 2,4-D adsorptionfrom drinking water in the presence of biological contaminants,including a group of aquarium-isolated microorganisms, Gracilaria

verrucosa, Acidovorax avenae subsp. avenae LMG 17238, andSpirulina platensis, both in batch and column systems. TheLangmuir, Freundlich, Redlich–Peterson, Sips, and Toth isothermmodels were employed for fitting the batch data, while the

Thomas, Yoon–Nelson, and Bohart–Adams kinetic models wereapplied to the column data in order to determine the models thatbest represent the characteristics of GAC adsorption.

Experimental

Adsorbent

Granular activated carbon (GAC) (coal activated and Aqualine)was oven-dried at 110 8C for 24 h and stored in a desiccator untilneeded. The BET surface area and porous properties of the GACwere determined from N2 adsorption experiments. The GAC wascharacterized by N2 adsorption at 77 K in the relative pressurerange (P/P0) of 0.001–0.98 using a surface analyzer (QS-7Quantasorb Surface Analyzer). The carbon sample was outgassedfor 24 h at 573 K to remove any moisture or adsorbed con-taminants that may have been present on its surface. The BETsurface area of GAC with a particle size of 1–2 mm was measuredto be 671 m2 where its particle density was determined to be2.35 g/mL using an autopycnometer (AccuPyc He-pycnometer).Fig. 1 shows an environmental scanning electron microscope(ESEM) (Philips XL 30 FEG) micrograph of the rough areas of thesurface from a GAC sample at 2000� magnification. A distinctroughness with oval patterns was characterized and within eachoval section, the presence of the macropores was clearlynoticeable.

Microorganism preparation and inoculation methods

Since the aqueous environment of an ornamental aquariumreplete held at a favorable temperature and pH with dissolvednutrients provides a unique medium for bacterial growth, afreshwater 10 L fiber aquarium located in laboratory conditions at20 � 2 8C was used as a bacteria reservoir. The agar plates containingthe isolated bacteria were then incubated at 20 8C for 24 h forappropriate colony formation. After the incubation, a single colonyfrom each plate was selected for re-isolation to a pure culture.Enterobacter, Staphlyococcus aureus, Total Coliform (E. coli), Bacillus

cereus, and Salmonella sp. were detected in the aquarium sample. Foroptimum accuracy of a count, the preferred range for total CFU/platewas between 30 and 300 colonies/plate.

The red macroalga, G. verrucosa used in this study whichbelongs to the botanical classification of Rhodophyte, was collectedfrom Izmir Bay, Izmir, Turkey. After collection, the alga was washedwith deionized water, shade dried for 24 h, oven-dried at 30 8Cuntil a constant weight was obtained and then stored inpolyethylene bottles. When needed, it was immersed in a beakerfull of deionized water which was then added to the system as a[(Fig._1)TD$FIG]

Table 1Bacteria groups associated with Gracilaria verrucosa.

Bacteria that perform

nitrate reduction

Other bacteria groups

Paracoccus spp. Planococcus spp.

Pseudomonas sp. and

Pseudomonas putida

Planococcus citreus

Arthrobacter sp. Aeromonas sp. and Aeromonas punctata

Stenotrophomonas maltophilia

Vibrio harveyi

Exiguobacterium acetylicum

and Exiguobacterium sp.

D. Ova, B. Ovez / Journal of Environmental Chemical Engineering 1 (2013) 813–821 815

contaminant. In addition to being a red alga, G. verrucosa is a sourceof mixed bacterial cultures. Limited studies of the differentbacterial cultures in G. verrucosa have been carried out incollaboration with the Institute of Functional Interfaces at theKarlsruhe Institute of Technology using denaturing gradient gelelectrophoresis (DGGE) and a DNA fingerprint technique (Table 1).

The freeze–dried pure bacterial strain A. avenae subsp. avenae

LMG 17238 was obtained from the BCCMTM/LMG BacteriaCollection, Ghent University, Faculty of Sciences, Belgium. LMG17238 is a special bacterial culture, classified as a Pseudomonas

Gram-negative group of bacteria that has been successfully usedfor the assessment of the biodegradability of synthetic plasticssuch as poly e-caprolactone (PCL), as discussed by Nalcaci et al.[33] and for the first time in this study, it has been used withactivated carbon. An optical microscope (Olympus CX31) image ofLMG 17238 with cell size approximately 1–2 mm, following aGram-staining procedure is presented in Fig. 2a.

The cyanobacteria S. platensis was grown in Zarrouk’s Mediumunder constant light exposure (3000 lx) at 25 � 1 8C. The culturewas then filtered using a Whatman No. 1 filter paper and washed withdeionized water to remove the growth medium, as described by Sekeret al. [34]. An optical microscope (Olympus CX31) image of S. platensis

with terminal cells broadly rounded; having regular three-dimen-sional spirals which are 40–50 mm distant can be seen in Fig. 2b.

Batch mode sorption studies

A stock solution was prepared by dissolving analytical grade2,4-D (98%, Sigma Aldrich) in ethanol (96%, Merck) to overcome thesolubility issues of 2,4-D in water and CaCl2 (10�2 M, Merck) topromote flocculation and maintain a constant backgroundelectrolyte concentration and then diluted to 1 L with distilledwater. Test solutions of various concentrations were equilibratedto a defined pH and a temperature in 100 mL flasks with Teflon

[(Fig._2)TD$FIG]

Fig. 2. Optical microscopic image of microorganisms at 100� magnification: (a

caps. Blank adsorption experiments were carried out by mixingGAC (0.01 � 10�4 g) and solutions of 2,4-D (50 mL) at 25 � 0.1 8C in athermostatic shaker at a 90 rpm constant shaking rate to ensurethat equilibrium was reached. For the biological contaminationstudies, the sample solutions containing bacteria and algae cellswere filtered through a cellulose filter with a pore size 0.45 mmusing a Sartorius glass vacuum pump. The equilibrium concentra-tions of the herbicide were calculated from the calibration curvesafter measuring the absorbance values of the solutions where theaverage values of the duplicate experiments were used in thecalculations.

The data obtained in the batch sorption studies was used tocalculate the equilibrium herbicide sorption quantity according tothe following equation [35]:

q ¼ ðC0 � Cf ÞVm

(1)

where q (mg/g) is the equilibrium amount adsorbed on theadsorbent, V (L) is the sample volume, C0 (mg/L) is the initialherbicide concentration, Cf (mg/L) is the equilibrium herbicideconcentration, and m (g) is the weight of the granular activatedcarbon.

In order to simplify a sorption process where it is impossible topredict the rate-determining step, sorption isotherm equations areapplied to the condition resulting after the adsorbate-containingphase has been in contact with the adsorbent for a sufficient timeto reach equilibrium at constant temperature. The differentequilibrium isotherm parameters and the underlying thermody-namic assumptions of these models provide some insight into thesorption mechanism, the surface properties, and affinity of thesorbent [30]. The most commonly used isotherms that can explaindifferent solid–liquid adsorption systems are Langmuir [36],Freundlich [37], Redlich–Peterson [38], Toth [39], and Sips [40].The criteria for choosing these isotherm models for the adsorptiondata is mainly based on the goodness of curve fitting which isevaluated by statistical analysis. The model equations andparameters determined using the MATLAB Version 7.7.0 CurveFitting Tool are listed in Table 2. The degree of fitting of theexperimental data to the theoretical models was measured byapplying four functions, namely, the correlation coefficient R2,the adjusted R2, the root mean square error (RMSE, Eq. (2)), and thesum of squared errors (SSE, Eq. (3)), which are defined in thefollowing equations [41]:

RMSE ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

N

XN

i¼1

ðqcal � qexpÞ2

vuut (2)

) Acidovorax avenae subsp. avenae LMG 17238, and (b) Spirulina platensis.

Table 2Batch isotherm model equations and parameters.

Isotherm

models

Model equation Parameters Units Reference

Langmuir qe ¼qmKaCf

1þ KaCfqm mg/g [36]

Ka L/mg

Freundlich qe = KFCf1/n KF (mg/g)(L/mg)1/n [37]

n –

Redlich–

Peterson

qe ¼KRPCf

1þ aRPCbf

KRP L/g [38]

aRP Lb

/mgb

b –

Toth qe ¼qmaxbTCf

1þ ðbTCf Þ1=nTh inT

qmax mg/g [39]

bT L/g

nT –

Sips qe ¼qmKeqCn

f

1þ KeqCnf

qmax mg/g [40]

Keq L/g

n –

D. Ova, B. Ovez / Journal of Environmental Chemical Engineering 1 (2013) 813–821816

and

SSE ¼ 1

N

XN

i¼1

ðqexp � qcalÞ2 (3)

where the subscripts ‘exp’ and ‘cal’ refer to the experimentaland calculated values, and N is the number of experimental data.Smaller SSE and RMSE values combined with a higher correlationcoefficient (R2) indicate better modeling [42].

Column mode sorption studies

The column packed with GAC was operated in the up flow modeat room temperature using a peristaltic pump with no effluentrecycle. The column was cleaned with dilute chromic acid andrinsed with distilled water before the experiments. Distilledwater was pumped in the up flow mode in order to remove anytrapped air from the bed. The effect of different parameters, suchas the influent 2,4-D concentration, the flow rate, the mass of theadsorbent, and the presence of different biological contami-nants, was investigated. When biological contaminants wereincluded, the microorganisms were loaded into the columnswith the effluent microorganism recycle. After loading thecolumn with the microorganisms, a 200 mg/L stock solution of2,4-D was then pumped at a flow rate of 0.4 mL/min. Thebreakthrough curves show the loading behavior of the 2,4-D thatis to be removed from the solution over the fixed bed, and isusually expressed in terms of the normalized concentration,which is defined as the ratio of the effluent pollutantconcentration to the influent pollutant concentration (C/C0) asa function of time or volume of the effluent (Veff) for a given bedheight [43]. The effluent volume (Veff) can be calculated usingthe following equation:

Veff ¼ Qttotal (4)

where ttotal and Q are the total flow time (min) and volumetricflow rate (mL/min), respectively. The area under the breakthroughcurve (A) obtained by integrating the adsorbed concentration(Cad; mg/L) versus t (min) plot can be used to find the total sorbedadsorbate quantity (qtotal; mg), which is, for a given feedconcentration and flow rate, calculated as:

qtotal ¼QA

1000¼ Q

1000

Z t¼ttotal

t¼0Cad dt (5)

The total amount of adsorbate sent to the column (mtotal) iscalculated using the following equation:

mtotal ¼C0Qttotal

1000(6)

The total percent of removed adsorbate (column performance)with respect to the flow volume can be also found from the ratio ofthe total sorbed quantity of adsorbate (qtotal) to the total amount ofadsorbate sent to the column (mtotal):

Total removal % ¼ qtotal

mtotal� 100 (7)

The equilibrium adsorbate uptake (qeq) in the column is definedas qtotal per gram of sorbent (X) at the end of the total flow time[43]:

qeq ¼qtotal

X(8)

The successful design of a column sorption process requiresprediction of the concentration–time profile or breakthroughcurve for the effluent and sorption capacity of the sorbent for theselected sorbate under a given set of operating conditions [44].Many models have been developed to predict the sorptionbreakthrough behavior with a high degree of accuracy. Showinga good fitting for all examined ranges of breakthrough curves, theBohart–Adams, Thomas, and Yoon–Nelson models were applied topredict different parameters of the column like service time(Bohart–Adams), adsorption capacity (Thomas), and time requiredfor 50% breakthrough (Yoon–Nelson). The Thomas model [45] isbased on the assumption that the process follows Langmuirkinetics of adsorption–desorption with no axial dispersion andfitting of the experimental data indicates that the external/internaldiffusion is not the limiting step. The Yoon–Nelson [46] wasderived based on the assumption that the rate of decrease in theprobability of adsorption for each adsorbate molecule is propor-tional to the probability of adsorbate adsorption and theprobability of adsorbate breakthrough on the adsorbent. TheBohart–Adams [47] was established based on the surface reactiontheory assuming that equilibrium is not instantaneous, so the rateof adsorption is proportional to both the residual capacity of theactivated carbon and the concentration of the sorbing species. Themodel equations and parameters of these three models weredetermined by using MATLAB Version 7.7.0 Curve Fitting Tool andare listed in Table 3.

Analysis of 2,4-dichlorophenoxyacetic acid

The concentration of 2,4-D in the solutions was determinedspectrophotometrically using a Jasco Model 7800 UV/VIS Spectro-photometer at lmax = 228 nm.

Results and discussion

Batch sorption studies

The adsorption characteristics of 2,4-D on GAC were investi-gated in the presence of various microorganisms and the relativeisotherms, drawn at 25 8C in comparison to the no microorganismcase, are presented in Fig. 4. The adsorption of 2,4-D in terms of themaximum adsorption capacity (mg/g) in the presence of thedifferent microorganisms was found to increase in the followingorder: LMG 17238 < G. verrucosa < the aquarium-isolated micro-organisms < S. platensis < no microorganism. Referring to Fig. 3,it can be concluded that the maximum adsorption capacityobtained in the absence of microorganisms cannot be reached ifmicroorganisms are present. In addition, because the LMG 17283

Table 3Continuous kinetic model equations and parameters.

Kinetic models Model equation Parameters Units Reference

Thomas lnC0

Ct� 1

� �¼ kThq0x

v� kThC0Veff

vkTh mL/mg�min [45]

q0 mg/g

Yoon–Nelson lnCt

C0 � Ct¼ kYNt � tkYN kYN L/min [46]

t min

Bohart–Adams lnCt

C0¼ kABC0t � kABN0

Z

U0kAB L/mg�min [47]

N0 mg/L[(Fig._3)TD$FIG]

Fig. 3. Equilibrium 2,4-D uptake on GAC at 25 8C in the presence of different

microorganisms in comparison to no microorganism.

Table 4Parameters of the sorption isotherms, including the correlation coefficients, for 2,4-D o

No

microorganism

Spirulina

platensis

Aquari

consor

Two-parameter models

Langmuir

qm (mg/g) 192.40 219.30 148.2

Ka (L/mg) 0.22 0.08 0.0

R2 0.99 0.99 0.9

Adjusted R2 0.99 0.98 0.9

RMSE 2.80 7.76 7.1

SSE 70.70 541.80 462.9

Freundlich

KF(mg/g)(L/mg)1/n 64.94 38.47 33.2

n 3.78 2.58 3.0

R2 0.95 0.93 0.9

Adjusted R2 0.95 0.93 0.9

RMSE 14.18 16.65 13.3

SSE 1810.00 2495.00 1601.0

Three-parameter isotherms

Redlich–Peterson

KRP (L/g) 42.58 11.21 8.3

aRP (Lb

/mgb

) 0.21 0.01 0.0

bRP 0.99 1.44 1.4

R2 0.99 0.99 0.9

Adjusted R2 0.99 0.99 0.9

RMSE 2.96 1.72 3.4

SSE 70.30 23.55 97.3

Sips

qmax (mg/g) 192.50 185.40 127.0

Keq (L/g) 0.22 0.03 0.0

n 0.99 1.54 1.7

R2 0.99 0.99 0.9

Adjusted R2 0.99 0.99 0.9

RMSE 2.97 3.37 3.4

SSE 70.68 90.99 96.2

Toth

qmax (mg/g) 192.50 175.80 121.7

bT (L/g) 0.23 0.06 0.0

nT 1.00 0.40 0.2

R2 0.99 0.99 0.9

Adjusted R2 0.99 0.99 0.9

RMSE 2.97 1.38 1.6

SSE 70.68 15.19 21.6

D. Ova, B. Ovez / Journal of Environmental Chemical Engineering 1 (2013) 813–821 817

pure bacteria culture has approximately 40 times smaller cellsthan the other bacteria and algae, it is able to better fill the mesoand micropores of the GAC, and thus the adsorption capacity in thepresence of LMG 17283 was observed to decrease. The resultsobtained for the nonlinear regressions using the MATLAB softwareand the modeling parameters of the 2,4-D sorption isotherms inthe presence of various microorganisms are listed in Table 4. It wasdetermined that, based on the correlation coefficients in the nomicroorganism case, the Langmuir, Redlich–Peterson, Sips, andToth models (R2 = 0.99) best fitted the adsorption data ascompared to the Freundlich model (R2 = 0.95). Comparison ofthe RMSE values calculated for the five models validates the resultthat four models, except Freundlich, fit reasonably well to theadsorption data for the examined concentration range of 2,4-Dsince a RSME value closer to zero indicates a fit that is more usefulfor prediction.

n GAC at 25 8C in the presence of various microorganisms.

um isolated

tium of microorganisms

Gracilaria

verrucosa

Acidovorax avenae subsp.

Avenae LMG 17238

0 101.40 558.10

9 0.04 1.00E�04

7 0.99 1.00

7 0.99 1.00

7 2.15 0.01

0 41.72 3.00E�04

3 13.44 0.05

7 2.42 1.00

1 0.96 1.00

0 0.96 1.00

4 5.17 4.00E�04

0 240.30 2.00E�06

3 3.30 0.31

1 0.01 4.87

1 1.34 3.00E�04

9 0.99 1.00

9 0.99 1.00

9 1.01 4.00E�04

3 8.17 2.00E�06

0 89.44 2946.00

2 0.03 2.80E�05

5 1.28 0.89

9 0.99 0.99

9 0.99 0.99

7 1.65 0.13

1 21.71 0.15

0 83.23 5908.00

6 0.04 1.00E�05

9 0.55 2.34

9 0.99 0.99

9 0.99 0.99

5 1.29 0.05

9 13.24 0.02

[(Fig._5)TD$FIG]

Fig. 5. Breakthrough curves for GAC obtained at different flow rates.

D. Ova, B. Ovez / Journal of Environmental Chemical Engineering 1 (2013) 813–821818

The relatively better fit of the experimental equilibrium data inthe Langmuir isotherm implies that the binding energy on theentire surface was uniform, the sorbed molecules do not interact/compete with each other, and a single surface reaction describe theadsorption mechanism where, on the other hand, the Freundlichisotherm describes the equilibrium on heterogeneous surfaces anddoes not assume monolayer capacity. A favorable adsorbent shouldhave a low Langmuir constant, Ka (0.10 L/mg) and high qm

(181.82 mg/g) values as discussed in [20]. The results of thisstudy, with Ka value of 0.22 L/mg and qm value of 192.40 mg/g,confirm that GAC is a favorable sorbent for 2,4-D molecules. Inaddition, the fact that the experimental qe values are lower thanthe calculated qm values obtained from the Langmuir isothermsuggests that GAC was not fully covered by the 2,4-D molecules[30,48].

With the Redlich–Peterson constant b, the Sips constant n, andthe Toth constant nT approaching unity, suggests that theisotherms are favoring Langmuir instead of Freundlich. Further,R2 values suggest that the Redlich–Peterson, Sips, Toth, andLangmuir overlapped the experimental data. Thus, the Langmuirisotherm is a special case of these three isotherms when constantsb, n, and nT are unified. This result of a better fit of the five models isalso valid for the case of biological contamination, but only as theconstants b, n, and nT deviated from unity and the theoreticaladsorption capacity (q) value decreases which supports the ideathat the presence of a biological species decreases the adsorptionefficiency of GAC.

Column sorption studies

The batch mode analysis is certainly not sufficient whiledesigning a treatment system for continuous operation. Fixed-bedcolumns do not operate under equilibrium conditions because ofthe short contact time. The other operational problems such as anuneven flow pattern in the column, recycling, and regenerationcannot be effectively studied in batch experiments which make itnecessary to analyze the sorbate–sorbent system using the columnmode. The column performance of the GAC for the adsorption of2,4-D was investigated by various operating parameters, such asthe influent 2,4-D concentration, the flow rate, the adsorbent mass,and the microorganism load. It was found that the larger the 2,4-Dinfluent concentration, the steeper the slope of the breakthroughcurve and the smaller the breakthrough time, as seen in Fig. 4. Thisresult demonstrates that changes in the concentration gradientaffect the saturation rate and breakthrough time, and thus thediffusion process is concentration dependent. The breakthrough

[(Fig._4)TD$FIG]

Fig. 4. Breakthrough curves for the adsorption of 2,4-D on GAC obtained at different

concentrations.

time to reach the saturation point increases significantly with adecrease in the flow rate, as shown in Fig. 5. At a low influent flowrate, the 2,4-D ions have more time to come in contact with theGAC, resulting in a higher removal of 2,4-D in the column. Theadsorption capacity of the bed will therefore reach the equilibriumvalue faster with a higher flow rate, which will likely have anegative effect on the mass transfer efficiency of the 2,4-D ions.Breakthrough occurred more rapidly with a smaller adsorbentmass (Fig. 6). Increasing the mass of the GAC caused an increase inthe breakthrough time. Thus, longer breakthrough times and largerbed capacities were obtained with bigger adsorbent masses. Forthe studies involving biological contamination inoculation, each ofthe microorganisms was initially loaded for 44 h, and then the 2,4-D solution (200 mg/L) was passed through the different columnsconsisting of 1 g of GAC each at a flow rate of 0.4 mL/min. Thebreakthrough curves for the adsorption of 2,4-D on GAC in thepresence of different microorganisms can be seen in Fig. 7.According to the results, the order of adsorption of 2,4-D in termsof the maximum adsorption capacity (mg/g) from the least to thegreatest is as follows: no microorganism < S. platensis < theaquarium-isolated group of microorganisms < G. verrucosa < LMGLMG 17238. Therefore, the results obtained for the continuoussystem are the opposite of those obtained for the batch systemusing the same microorganisms. In the batch method, thedisplaced competitive substances remain in the solution, andthus they may interact with the solid. In contrast, in continuousflow methods, which are closer to the dynamic natural conditionsin drinking water systems, there is a constant flow of the solution,

[(Fig._6)TD$FIG]

Fig. 6. Breakthrough curves for the adsorption of 2,4-D on GAC with different

adsorbent masses.

[(Fig._7)TD$FIG]

Fig. 7. Breakthrough curves for the adsorption of 2,4-D on GAC obtained in the

presence of different microorganisms.

D. Ova, B. Ovez / Journal of Environmental Chemical Engineering 1 (2013) 813–821 819

and the displaced substances are flushed out and thus do notcompete for adsorption. There is disagreement, however, as towhich system is more effective. Some studies have reported thatadsorption is higher for open-flow methods, such as the columnmode [49,50] while many other studies have shown that theadsorption is higher in batch systems [51–53]. There are severalpossible reasons for this paradoxical behavior: (1) the presence ofimmobile water in the column, which acts as a kinetic barrier, (2)the difference in the solid/solution ratio between the batch andcolumn systems, and (3) the underachievement of the chemicalequilibrium in the column, if the mean residence time issignificantly lower than the mean reaction time.

Application of continuous kinetic models

The column data was fitted to the Thomas, Yoon–Nelson, andBohart–Adams models, and the constants obtained from theseapplied models at different influent concentrations, flow rates, andadsorbent masses for the no microorganism case are listed inTable 5. As can be seen in Table 5, for the Thomas model, as the 2,4-D influent concentration increased, the values of the rate constant,kTh, decreased, while the values of the maximum bed adsorptioncapacity, q0, increased. Higher concentrations provide an opportu-nity for more pesticide ions to come in contact with the GAC, andthus the bed capacity increases. On the other hand, as the flow rateincreased, the values of kTh increased, while the values of q0

decreased. This behavior is an expected result, because an increasein the flow rate causes a decrease in the retention time, and

Table 5Comparison of the column parameters calculated for 2,4-D adsorption at different influ

inoculation.

C0 (mg/L) v (mL/min) Mass of

GAC (g)

Thomas model

kTh�10�6

(mL/min�mg)

q0 (mg/g) R2

50 0.4 1.0 10.00 172.85 0.99

100 0.2 1.0 6.00 198.58 0.99

0.3 1.0 6.00 242.79 0.98

0.4 0.75 7.00 207.26 0.96

1.00 7.00 241.03 0.94

1.25 5.00 274.30 0.93

1.50 5.00 247.69 0.94

150 0.4 0.75 6.00 229.02 0.95

1.00 4.67 253.37 0.85

1.25 4.00 306.32 0.85

1.50 3.33 355.46 0.95

therefore the pesticide ions do not have as much time to come incontact with the GAC, leading to a decrease in the bed capacity. Inaddition, as the mass of the adsorbent increased, the values of kTh

decreased, while the values of q0 increased. Thus, the maximumsorption capacity was observed at the maximum influentconcentration, minimum flow rate, and maximum bed height[54,55]. In the case of the Yoon–Nelson model, the values of therate constant, kYN, increased and the values of time required for50% adsorbate breakthrough, t, decreased when the influentconcentration and flow rate increased and the mass of the GACdecreased. The rate constant, kYN, increases with respect toconcentration due to the fact that increase in initial 2,4-Dconcentration increases the competition between the adsorbatemolecules for the adsorption sites, which ultimately results inincreased uptake rate. At high flow rate, the number of 2,4-Dmolecules passing through GAC is more, which increases the rate.At higher bed height, the adsorbate molecules have more time totravel through the column which results in the reduced adsorptionrate. It is known that kYN and the time (t) are inversely related, asexpected a maximum value for t was obtained for the maximumbed height with the lowest flow rate and concentration [43,56].The time (t) was found to be minimized with an influentconcentration of 150 mg/L, a flow rate of 0.4 mL/min, and 0.75 gof GAC and maximized with a 100 mg/L concentration, an 0.2 mL/min flow rate, and 1 g of GAC. So, Thomas and Yoon–Nelson modelsshowed good agreement under given sets of operating conditions.For the case of Bohart–Adams model, the values of the rateconstant, kAB, were influenced positively by an increase in the flowrate and negatively to some extent by an increase in the mass of theGAC, which indicated that the overall system kinetics aredominated by external mass transfer during the initial phases ofadsorption in the column. As was expected, the maximumadsorption capacity (N0) increased with increasing influentconcentration [43], and the maximum value was obtained usingthe highest mass of adsorbent with the lowest kAB value.

The same continuous models were also fitted to the columndata obtained for the adsorption of 2,4-D in the presence ofmicroorganisms from a 200 mg/L 2,4-D solution at a flow rate of0.4 mL/min, using 1 g of GAC. The calculated parametric coeffi-cients are shown in Table 6. According to the correlationcoefficients for these three kinetic models, the Thomas andYoon–Nelson kinetic models fit the experimental data better thanthe Bohart–Adams model. The kinetic constants for the Thomasand Yoon–Nelson models increased in the following order:S. platensis < no microorganism < the aquarium-isolated groupof microorganisms = G. verrucosa < LMG 17238; while the

ent concentrations, flow rates, and adsorbent masses without any microorganism

Yoon–Nelson model Bohart–Adams model

kYN�10�4

(L/min)

t (min) R2 kAB�10�6

(L/min�mg)

N0 (mg/L) R2

5.00 8643 0.99 6.00 133.19 0.90

6.00 9929 0.99 4.00 252.76 0.85

6.00 8093 0.98 4.00 226.48 0.87

6.00 4695 0.98 3.00 198.77 0.89

7.00 6026 0.94 4.00 209.58 0.80

5.00 8572 0.93 4.00 196.31 0.83

5.00 9288 0.94 4.00 196.69 0.87

9.00 2863 0.85 2.70 187.99 0.62

7.00 4306 0.85 2.67 219.37 0.60

6.00 6382 0.85 2.67 256.87 0.69

5.00 8886 0.95 2.67 272.16 0.84

Table 6Comparison of the column parameters calculated for 2,4-D adsorption at a 200 mg/L initial concentration, a 0.4 mL/min flow rate, and using 1 g of GAC in the presence of

microorganisms.

Type of microorganism Thomas model Yoon–Nelson model Bohart–Adams model

kTh�10�6

(mL/min�mg)

q0

(mg/g)

R2 kYN

(L/min)

t (min) R2 kAB�10�6

(L/min�mg)

N0

(mg/L)

R2

No microorganism 6.00 207.55 0.97 12.00 2599 0.98 3.00 133.19 0.90

Spirulina platensis 5.50 180.69 0.96 11.00 2259 0.96 2.50 186.95 0.79

Aquarium isolated consortium of microorganisms 7.50 126.49 0.98 15.00 1571 0.98 3.00 144.59 0.79

Gracilaria verrucosa 7.50 117.11 0.98 15.00 1464 0.98 3.00 137.92 0.77

Acidovorax avenae subsp. avenae LMG 17238 9.50 97.10 0.94 17.00 1418 0.95 4.50 107.61 0.79

D. Ova, B. Ovez / Journal of Environmental Chemical Engineering 1 (2013) 813–821820

maximum adsorption capacity together with the half saturationtime decreased in the following order: no microorganism > S.

platensis > the aquarium-isolated group of microorganisms > G.

verrucosa > LMG 17238. On the other hand, the constants for theBohart–Adams model varied dramatically because it is used todescribe the initial part of the breakthrough curve, so for the initialpart of the experiments, the Bohart–Adams model fits the data well,but after that stage this model deviates from the experimental data.Although the Bohart–Adams model provides a simple andcomprehensive approach to evaluating sorption-column tests, itsvalidity was limited to the range of conditions used.

Conclusion

The aim of this work was to explore the potential of GAC for theremoval of the chemical contaminant 2,4-D from aqueoussolutions in the presence of biological contaminants in both batchand continuous systems. Rather than linearization, a non-linearmethod is applied to obtain model parameters since the errordistribution does not get altered, as all the isotherm parameters arefixed in the same axis.

In the batch studies, the maximum sorption capacities (mg/g)exhibited by the GAC were found to be 5.9, 76.8, 124.0, 173.1, and177.6 for LMG 17238, G. verrucosa, an aquarium-isolated group ofmicroorganisms, S. platensis, and no microorganism, respectively.The Langmuir isotherm is a special case of Redlich–Peterson,Sips, and Toth equilibrium models when model constants b, n,and nT are unified and they describe the single step sorptionmechanism of 2,4-D onto GAC better than the Freundlichisotherm.

When the column performance for the adsorption study iscompared to the 2,4-D standards set by WHO and the EU DrinkingWater Directive, satisfactory results are obtained with minimumflow rate, minimum influent concentration, and maximumadsorbent mass operational parameters, but, the results obtainedin the presence of biological contaminants in the simulateddrinking water were not adequate compared to the standardvalues. Among the kinetic models, the Thomas and Yoon–Nelsonmodels showed a better fit rather than Bohart–Adams. Therefore,although GAC can find a wide application area in wastewatertreatment plants as a very effective adsorbing agent, the efficiencyof 2,4-D adsorption decreases in the presence of biologicalcontamination. To avoid microorganisms, various types ofmembrane separation may be preferred as a pretreatment step.A number of bioseparation goals can thus be achieved by usinghybrid approaches that combine adsorption and filtration pro-cesses for the separation and purification of biological molecules.Regarding the environmental, economical, and energy savingaspects, the role of appropriate technologies that reduce theecological load and enable the implementation of strategies for theselection of ecologically friendly processes with novel classes ofadsorbents is crucial.

Acknowledgment

This work has been carried out within the scope of the ResearchFoundation of Ege University.

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