Application of statistical design of experiments for optimization of As(V) biosorption by immobilized bacterial biomass

Ecological Engineering 86 (2016) 13–23

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Application of statistical design of experiments for optimization of As(V) biosorption by immobilized bacterial biomass Anindita Banerjee, Priyabrata Sarkar ∗ , Suchetana Banerjee Department of Polymer Science and Technology, University of Calcutta, 92, A.P.C. Road, Kolkata 700009, India

a r t i c l e

i n f o

Article history: Received 16 October 2014 Received in revised form 19 September 2015 Accepted 7 October 2015 Keywords: Arsenic Biosorption Immobilization Plackett–Burman design Response surface methodology

a b s t r a c t The present investigation demonstrated the effectiveness of immobilized Pseudomonas alcaligenes (RJBB) and Pseudomonas resinovorans (RJB-3) strains in the biosorption of As(V) from aqueous solution. Plackett–Burman and response surface methodology (RSM) based on Box–Behnken experimental design were applied to evaluate the interactive effect of most significant variables: pH, initial As(V) ion concentration and mixing speed (rpm). ANOVA with a high correlation coefficient (R2 > 0.99) and lower “Prob > F” value (<0.05) validated the second order polynomial model for the biosorption process. Pseudo-second order rate kinetics suggested chemisorption to be the rate limiting step. EDX (energy-dispersive X-ray spectroscopy) analysis confirmed the presence of As(V) in both the strains and FTIR (Fourier transform infrared spectroscopy) indicated the possible involvement of functional groups ( NH, OH) in the binding process. The results obtained for the adsorptive removal of As(V) by the selected biosorbent suggested its possible application in large scale treatment of As(V) contaminated water bodies. © 2015 Elsevier B.V. All rights reserved.

1. Introduction Arsenic (As), a highly toxic metalloid has been a major contaminant of drinking water due to its mobilization through several natural processes such as leaching, biological and anthropogenic activities, burning of fossil fuels and use of arsenic containing herbicides and pesticides (Wang and Zhao, 2009). The maximum concentration level of arsenic (MCL), i.e., permissible limit of arsenic in drinking water is 10 ␮g L−1 as per the US Environmental Protection Agency (Saqib et al., 2013). Contamination at higher levels causes serious health problems including cardiovascular, pulmonary, neurological and carcinogenic disorders (Saqib et al., 2013). Thus retention and mobility of arsenic in surface and ground water are of great concern due to their toxicity towards the environment. Arsenic occurs in various oxidation states but the most predominant forms that are abundant in natural waters are the two inorganic forms as oxyanions of arsenate (As(V)) and arsenite (As(III)). Although inorganic forms of As are most common in natural waters, it may also occur in organic forms such as monomethyl arsonate (MMAV ), dimethylarsinate (DMAV ) (Yan et al., 2010). The organic forms are mostly prevalent in surface waters that are significantly affected by industrial effluence (Smedley and Kinniburgh,

∗ Corresponding author. E-mail address: [email protected] (P. Sarkar). http://dx.doi.org/10.1016/j.ecoleng.2015.10.015 0925-8574/© 2015 Elsevier B.V. All rights reserved.

2001). Several studies reported that among the two forms of inorganic forms As(V) predominates over As(III) in majority of water systems. As for example, As is chiefly dominated by As(V) in oxic seawaters (Pettine et al., 1992). Although, due to variable salinity and redox the proportions of arsenic relatively vary in estuarine water yet, they As(V) predominates over As(III) (Abdullah et al., 1995). As(V) also prevails in several river water (Pettine et al., 1992); however, considerable seasonal disparity in the absolute concentration and speciation has been reported. In groundwaters, the ratio of As(III) to As(V) varies extremely due to huge deviations in the redox conditions of the aquifer and redox gradients. Speciation of inorganic As is an important phenomenon while considering remediation studies, since it is greatly species dependent. Redox potential and pH are the two most significant factors that controls As speciation (Jiang et al., 2012). In oxidizing conditions, As(V) exist as H3 AsO4 , H2 AsO4 − , HAsO4 2− , and AsO4 3− at a particular pH range. H2 AsO4 − predominates at low pH (pH 2–6.9) while HAsO4 2− occurs at high pH (7–11). H3 AsO4 and AsO4 3− are generally present in highly acidic and alkaline conditions, respectively. In contrast, As(III) species viz. H3 AsO3 , H2 AsO3 − , and HAsO3 2− are prevalent under reducing conditions, with H3 AsO3 being the most common occurring at pH below 9.2 (Jiang et al., 2012). Several studies have demonstrated that arsenic removal could be achieved by various techniques, namely oxidation/ precipitation, Fe-electrocoagulation/co-precipitation, alum coagulation/precipitation, lime softening, reverse osmosis followed

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by nanofiltration, ion-exchange resin, polymer ligand exchange, coagulation–microfiltration, etc. (Tuzen et al., 2009). Most of these methods suffer from certain drawbacks, such as high operational as well as capital cost and the disposal of the residual metal sludge (Lièvremont et al., 2009). Biosorption using microbial biomass has been considered as a potential technology for removal of toxic heavy metals from contaminated water (Giri et al., 2011; Pokhrel and Viraraghavan, 2008). It depends on the ability of the microbes to mobilize, immobilize or reduce arsenic through biomethylation, sorption, redox reactions. Microbially mediated oxidation/reduction reactions involving organic C, Fe, S, and Mn are the basic mechanisms that affect mobility of arsenic. Microbes have the ability to mobilize As that are absorbed by the Fe3+ or Mn4+ oxyhydroxides by redox reactions (Gadd, 2010). Microbes have evolved certain mechanisms to utilize arsenic oxyanions, either for anaerobic respiration [as an electron acceptor, e.g., As(V)], or to support chemoautotrophic CO2 fixation into cell carbon [as an electron donor, e.g., As(III)] (Rhine et al., 2006; Silver and Phung, 2005; Tuzen et al., 2009). Other advantages of the biosorption technique are the reduced operating cost, improved affinity for specific metals of interest, regeneration of biomass for reuse, generation of less toxic sludge and reduced time (Giri et al., 2011). However commercial application of these organisms has been obstructed due to lower density, lesser mechanical strength and smaller particle size leading to difficulty in separation of biomass from aqueous phase (Volesky, 2003). Biomass immobilization within a suitable matrix can overcome these problems by providing mechanical strength, ideal size, rigidity, porous characteristics to the biomaterial and greater opportunities for recovery and reusability (Bishnoi et al., 2007). Application of statistical experimental design for the biosorption process results in reduced process variability, improved product yields, closer verification of the output response to desired target, reduction in time and overall costs (González Bermúdez et al., 2012). The statistical designs, such as the Plackett–Burman (PBD) and Box–Behnken based on response surface methodology (RSM) are new effective techniques for optimization of the suitable operational parameters (S¸ahan et al., 2010). PBD is quite significant in screening of the most influential factors followed by RSM that explores the relationship between a response and a set of design variables (Box and Draper, 1987; Ferreira et al., 2007). RSM based on Box and Behnken experimental design is a particular set of statistical design of experiment that minimizes the number of combination of variable and maintains proper precision of the predicted response (Lahlali et al., 2007). Its application is quite significant in industrial research, particularly in conditions where a large number of operating parameters influence the system feature. The Plackett–Burman and response surface methods are quite effective in investigating the process of biosorption compared to single parameter optimization (Wang and Liu, 2008). The biosorption process with microorganisms is usually associated with intracellular transport, surface binding mechanisms or extracellular precipitation (Das et al., 2012a). Among various microorganisms, bacteria especially Pseudomonas strain developed a wide range of mechanisms to tolerate the stress conditions and acclimatize for survival requirements (Arivalagan et al., 2014). These strains have been reported to tolerate very high concentration of As(V) and developed mechanism to tolerate the toxic effects of the metalloid (Zahoor and Rehman, 2009). Therefore, it is quiet necessary to investigate the mechanism of biosorption in order to make the process more suitable in treatment of contaminated water bodies (Das et al., 2012). In the present study, Plackett–Burman experimental design was applied to screen the suitable operational parameters followed by RSM based on Box–Behnken design matrix for the optimization of biosorption process using immobilized bacterial biomass

viz. Pseudomonas alcaligenes RJB-B and Pseudomonas resinovorans RJB-3 wherein the interactive effect of the most significant operating variables: pH, initial As(V) concentration and mixing speed were evaluated for removal of As(V) from aqueous solution. Moreover, the kinetics and mechanism of As(V) adsorption based on FTIR (Fourier transform infrared spectroscopy) and EDX (Energydispersive X-ray spectroscopy) analysis were also investigated. Although there are several reports on optimization of process parameters based on Plackett–Burman and RSM, but to best of our knowledge application of Plackett–Burman along with RSM based on Box–Behnken design for optimization of arsenate removal from aqueous system using immobilized bacterial biomass is not yet reported. 2. Materials and methods 2.1. Preparation of stock solutions Sodium arsenate (Na2 HAsO4 ·7H2 O) used in this study was purchased from Sigma Aldrich, USA. A stock solution of As(V) (3 mg L−1 ) was prepared by dissolving appropriate amount of sodium arsenate in deionized water and pH was adjusted using 0.1 (M) HCl and 0.1 (M) NaOH. All the reagents used were of analytical grade and used as received. 2.2. Biomass preparation The strains viz. RJB-B and RJB-3 were previously isolated from soil crude source in our laboratory (Banerjee et al., 2011). The biomass were grown and maintained in Luria-Bertani medium of composition: Tryptone (1%); Yeast Extract (0.5%) and NaCl (0.5%) at pH 7.0 at 37 ◦ C and 120 rpm. Viable cells were harvested by centrifugation at 5500 rpm for 10 min, washed with Milli Q water, lyophilized and stored at −20 ◦ C for further use. 2.3. Immobilization of bacteria Sodium alginate (3%, w/v) was used as the polymeric matrix for immobilization of the bacterial biomass. Alginate, i.e., salts of alginic acid was preferred over other materials due to natural origin, mechanical and chemical stability biodegradability, hydrophilic properties, presence of carboxylic groups (Bayramo˘glu et al., 2006; Tsekova et al., 2010). Sodium alginate was well mixed with the bacterial biomass and dispersed into 0.1 M CaCl2 ·2H2 O solution at a regulated flow rate so that spherical beads encapsulating the bacteria were produced. The beads were washed twice with deionised water and stored in normal saline at 4 ◦ C for further use. 2.4. Spectrophotometric detection of As(V) As(V) concentration was determined spectrophotometrically using Cecil UV–Vis spectrophotometer (Dhar et al., 2004). The method used is based on reaction of dissolved As(V) with a colour reagent consisting of 10.8% l-ascorbic acid, 3% ammonium molybdate, 0.56% antimony potassium tartrate and 13.98% sulphuric acid at a ratio of 2:2:1:5 that forms a blue coloured arseno-molybdate complex which is measured spectrophotometrically at an O.D. of 850 nm. The amount of As(V) uptake by the biomass was calculated from the following mass balance equation: Q =

(Ci − Cf )V W

(1)

where Q is the specific uptake capacity in ␮g g−1 ; Ci and Cf are the initial and final concentrations before and after adsorption (␮g L−1 ); V is the volume of the solution (L) and W is the amount of the adsorbent (g).

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This method of As(V) detection is quite significant and rapid with a detection limit of 10 ␮g L−1 . 2.5. Adsorption isotherm

(2)

where Ce is the equilibrium concentration (␮g L−1 ), qe is the specific uptake capacity of the biosorbent (␮g g−1 ), qm is the amount of As(V) per unit mass of the biosorbent (␮g g−1 ) for formation of an absolute monolayer of the adsorbate on the surface of the adsorbent. The adsorption on different active sites present on the heterogeneous surfaces is represented by the Freundlich isotherm with different energy and the corresponding linearized model is given below (Gadd, 2009): log qe = log KF +

1 log Ce n

(3)

where KF is the Freundlich constant that specify the capacity of the biosorbent capacity (␮g g−1 ) and 1/n is the heterogeneity factor. 2.6. Uptake kinetics Kinetics of As(V) adsorption was investigated at statistically optimized initial metal ion concentration of 1000 ␮g g−1 . The sorption kinetics of As(V) was studied using Lagergren’s pseudo first order (Eq. (4)) and pseudo second order (Eq. (5)) rate models. The pseudo first order equation describes the rate of change of solute uptake to be proportional to the difference in the saturation concentration and the amount of solid uptake (Lagergren, 1898): log(qe − qt ) =

log qe − k1 t 2.303

(4)

where qe is the amount of As(V) adsorbed at equilibrium (␮g g−1 ), qt is the As(V) adsorbed (␮g g−1 ) at time t, k1 is the first order rate constant (min−1 ) and t = time (min). Although pseudo-first order equation provides a good description, it may not be applicable to several observations, where pseudo-second order kinetics proved appropriate. The latter one was considered the rate-limiting step as the formation of chemisorptive bond involving sharing or exchange of electrons between the adsorbate and the adsorbent. The generalized equation is given as follows (Ho and Ofomaja, 2006): t 1 t = + qt qe k2 q2e

performed in order to confirm the presence of arsenic in both the Pseudomonas species. 3. Results and discussion

Analysis of the experimental data for describing the stoichiometric interaction between the adsorbate and the adsorbent was carried out based on two adsorption isotherm models viz. Langmuir and Freundlich models. The Langmuir model describes adsorption as a formation of monolayer on the uniform surface of the biosorbent. The linearized form is represented as (Gadd, 2009): Ce 1 Ce = + qe qm bqm

15

(5)

where qt is the As(V) adsorbed (␮g g−1 ) at time t, k2 = pseudo second order rate constant (g mg−1 min−1 ), and qe is the amount of As(V) adsorbed at equilibrium (␮g g−1 ). 2.7. Analysis Investigation of the main functional groups involved in the binding process was carried out using FTIR spectroscopy. The transmission spectra were recorded using Nicolet-Magma 750 FTIR Spectrophotometer over the range of 400–4000 cm−1 . Energy dispersive X-ray analysis (Inca Penta FETx3, Oxford Instruments, UK) of the lyophilized pristine and As(V) adsorbed bacterial beads were

3.1. Effect of individual parameter (pH, dose of biosorbent, initial As(V) concentration) Solution pH greatly influences metal speciation, sequestration and mobility (Hamza et al., 2010). The effect of pH on adsorption of As(V) by the selected bacterial biomass viz. RJB-3 and RJB-B was studied over a pH range 2–8 at 37 ◦ C and 120 rpm. The initial concentration of As(V) was 1000 ␮g L−1 prepared from as stock solution of 3000 ␮g L−1 Adsorption of As(V) was found to be most favourable at pH 4.0 of but decreased with increasing pH (Fig. 1a). This could be due to the properties of both the metalloid and biomass (Yan et al., 2010). At low pH (pH 4.0) the negatively charged arsenate ions could bind with the positively charged amino and hydroxyl group of the biomass. But at pH below 4.0 the binding capacity was low due to weak electrostatic interaction between surface of the biomass and the As(V) ions (Yan et al., 2010). Further, at low pH As(V) is known to appear as H3 AsO4 . This is a neutral species that predominates at lower pH. It is quite unlikely for the neutral species to bind to the positively charged functional groups on the biomass surface, as a result, the uptake capacity of the biosorbent decreases. Binding of As(V) was not much favoured at higher pH due to competition between OH− and As(V) ions for the positively charged binding sites of the cell surface thereby decreasing the uptake capacity (Singh et al., 2011). The effect of the dose of Ca-alginate encapsulated bacterial beads on the specific uptake capacity was studied at different biomass dosage ranging from 0.9 g L−1 to 9.0 g L−1 other parameters remaining same (Fig. 1b). As the concentration of bacterial beads increased the As(V) ions gained better access to the active binding sites. But, with further increase in biosorbent concentration the q value did not increase due to decrease in the total amount of available arsenate in the solution thereby reaching equilibrium above 4.5 g L−1 of biosorbent concentration. Since, immobilized bacterial was used, agglomeration of the biomass could be avoided resulting in better uptake capacity. The effect of initial arsenate ion concentration was studied in the range of 500–3000 ␮g L−1 other conditions remaining same (Fig. 1c). With the increase in As(V) concentration the specific uptake capacity increased but gradually reached equilibrium at higher concentrations (>1000 ␮g L−1 ). This was due to the increase in mass transfer driving force of arsenate between the aqueous phase and sorbent phase resulting in the increase in biosorption (Han et al., 2007). However, at higher concentrations active binding sites got saturated, leading to an equilibrium value. Further study on the effect of concentration of the metalloid on biosorption process was carried on in detail based on statistical design of experiments. 3.2. Adsorption Isotherm Adsorption isotherm is a prerequisite for understanding any solute–solvent interaction (Das et al., 2012). Two adsorption isotherm models, viz. Langmuir and Freundlich were applied for analyzing the obtained experimental data. The linear plot between Ce and Ce /qe is represented in Fig. 2. The coefficients of determination (R2 ) were found to be 0.99 thereby indicating the process to follow Langmuir isotherm model. The values of the parameters were calculated from the slope and intercept of the plots was 0.04. Moreover, Langmuir isotherm was analyzed based on separation factor RL . Several studies reported that RL > 1 indicates unfavourable

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Fig. 1. (a) Effect of pH on uptake of As(V) by RJB-B and RJB-3, (b) effect of biomass dose on uptake of As(V) by RJB-B and RJB-3, and (c) effect of initial As(V) concentration on specific uptake capacity of RJB-B and RJB-3.

adsorption, adsorption is favourable if RL lies between 0 and 1, adsorption is irreversible when RL = 0 while linear adsorption occurs when RL = 1 (Gupta and Babu, 2009). The RL value obtained (0.04) in the present study confirms the favourable adsorption process for As(V) adsorption. The linear plot was drawn between log Ce vs. log qe to further analyze the experimental data with Freundlich isotherm model. The coefficient of correlation (R2 = 0.85) obtained was relatively lower than the Langmuir isotherm. Therefore, it could be concluded that the biosorption capacity of the selected biomass represented acceptable fit to Langmuir model but not to the Freundlich isotherm thereby indicating homogenous distribution of the active binding sites on the biomass surface suggesting monolayer adsorption. 3.3. Kinetics of biosorption The rate of uptake of metal ions is an important aspect in judging the efficiency of any biosorbents for suitable practical applications. The rate of adsorption of As(V) by the selected strains was very rapid for the initial 10 min irrespective of arsenate ion concentration, but gradually slowed down to reach equilibrium within 180 min. During the initial stage, a large number of active binding sites were available for As(V) adsorption. But, gradually the vacant sites started getting saturated resulting in deeper penetration of As(V) ions for the access to the active intraparticular sites encountering greater resistance. This slowed down the rate of sorption during the later period (Rahaman et al., 2008). The kinetic

rate constants obtained from second order pseudo kinetic models was represented in Table 1. The applicability of a particular rate equation for the present adsorption system could be judged by the condition of best fit. The linear plot of t/qt vs. t (Fig. 3) showed better fitness of data to pseudo second order rate model. Values of regression coefficient for RJB-B and RJB-3 further proved the validity of this model to explain biosorption of As(V) by biosorbent (Salinas et al., 2000). 3.4. Multi parameter optimization by Plackett–Burman and Box–Behnken designs 3.4.1. Plackett–Burman design Multivariable processes that include numerous potentially effective parameters, analyzing the process with an initial screening design prior to optimization is necessary (El-Helow et al., 2000). Plackett–Burman design of experiment was used to demonstrate the relative importance of various factors for the optimization of As(V) biosorption. Initial screening of the Table 1 Kinetic parameters obtained from pseudo-second order rate model for As(V) sorption onto the bacterial biomass. Adsorbent

K2ads (g ␮g−1 min)

R2

RJB-B RJB-3

0.00025 0.00035

0.997 0.980

A. Banerjee et al. / Ecological Engineering 86 (2016) 13–23

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Table 2 Response to Plackett–Burman design of 8 variables and 12 trials. Response (␮g g−1 )

Variables ◦

Run

Concentration (␮g L−1 )

Temperature ( C)

Time (min)

rpm per min

Biomass dose

Matrix concentration (%)

Sol. vol. (m)

pH

D1

RJB-B

RJB-3

1 2 3 4 5 6 7 8 9 10 11 12

1500 1500 500 500 500 1500 500 1500 1500 1500 500 500

30 37 30 30 37 30 37 37 37 30 37 30

60 300 300 60 300 300 60 60 300 60 60 300

120 120 120 100 100 100 120 120 100 100 100 120

0.5 0.1 0.1 0.1 0.5 0.1 0.5 0.1 0.5 0.5 0.1 0.5

5 3 3 3 5 5 3 5 3 3 5 5

50 50 50 20 50 20 20 20 20 50 50 20

5 7 7 5 7 7 7 5 5 7 5 7

−1 −1 1 −1 −1 −1 −1 1 1 1 1 1

241.10 380 180.50 130 125.91 233 164.50 283.51 180.65 300 53.35 185.95

175.50 285 130.30 80.50 65.75 156.50 123.90 190 110.31 215.95 39.95 140.10

significant parameters affecting the process of biosorption of As(V) among the tested ones viz. concentration of metal ions, matrix concentration, mixing speed, time of contact, temperature, dose of the biosorbent, pH and solution volume was performed using Plackett–Burman design (PBD) experimental design (Table 2). The observed responses obtained from PBD experiment showed that As(V) concentration, pH and mixing speed (rpm) were the most significant variable that affected the uptake capacity of bacterial strains. On the basis of the

Fig. 2. Adsorption isotherm (a) Langmuir and (b) Freundlich for As(V) biosorption onto the selected biosorbent.

different combination of the chosen parameters, RJB-B and RJB3 were found to be quite efficient in the uptake of As(V) from aqueous solutions. Plackett–Burman experimental design examined the significant factors that might affect the general microbial metabolism and specifically promote or reduce As(V) biosorption. 3.4.2. Response surface methodology and statistical analysis Response surface methodology (RSM) is a statistical method that is applied to and to improve the accuracy of the optimizing process. The main objective of RSM is to optimize the output variable (response) which is influenced by various independent input variables (Sarkar and Majumdar, 2011). The three most significant factors screened from Plackett–Burman design that affected the uptake of As(V) were further examined thoroughly by applying RSM based on Box–Behnken design matrix for both the strains viz. RJB-B and RJB-3. A series of experiments were conducted as per the Box–Behnken design to explore different combination of parameters and for evaluating the combined effect of these factors. The experiments were designed after selecting the range of factors (high and low) as represented in Table 3. The quadratic equation predicting the optimal point for metal adsorption was achieved following Box–Behnken experimental design and input variables. The empirical relationships between the response and the independent variables in the coded unit based on

Fig. 3. Pseudo second order kinetic model of RJB-B and RJB-3.

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Table 3 Experimental variables and the levels for Box–Behnken design. Variable code

Name of variable

A B C

pH Concentration Mixing speed

Levels −1

0

+1

3 500 60

4 1000 90

5 1500 120

the experimental results were described by the following second order polynomial equation: R1 = 405.36 − 3.10A + 79.48B + 48.06C + 3.23AB + 17.64AC + 11.59BC − 187.92A2 − 61.99B2 − 31.72C 2

RJB-B

(6)

R1 = 357.02 − 2.27A + 76.35B + 47.11C − 2.61AB + 16.72AC + 5.76BC − 173.48A2 − 59.77B2 − 25.98C 2

RJB-3

(7)

To evaluate the significance of the effect of variables, analysis of variance (ANOVA) was determined. The results of the ANOVA for the quadratic equation through P-value and F-statistics were presented in Table 4. The ANOVA suggested that the equation and the actual relationship between the response and the significant variables represented by the equation were adequate for both the strains. Higher values of F and R and lower standard error were considered as the criteria for satisfactory fit. It is predicted that ‘Prob > F’ values lesser than 0.05 statistically signifies the model. For the removal of As(V) by RJB-B and RJB-3, the F-values as obtained from ANOVA were 15.56 and 11.55, respectively, implying that most of the variation in the response could be explained by the regression equation and that the model was significant. The parameters that might express the goodness of fit of the proposed second order polynomial models are the coefficient of correlation R2 , adequate precision and adjusted-R2 . The measure of the number of variations of the experimental factors, i.e., R2 around the line of regression was found to be 0.97 and 0.99 for RJBB and RJB-3 respectively. The above stated results indicated that the quadratic model provided an excellent explanation for the relationship between the independent variables and the corresponding response. The adequate precision collates the average error with the predicted data at the design points. A ratio greater than 4 is recommended for a significant model. Precision value of 13.03, 11.50 for RJB-B and RJB-3 respectively provided an adequate signal, validating the model to navigate the design space. Application of diagnostic plots was provided by the Design Expert 7.0. Software, such as normal % probability plot of the internally studentized residual, as well as the actual vs. predicted value plot, judges the adequacy of the model. The data were analyzed to check the correlation between the experimental and predicted adsorption capacities

(Y, ␮g g−1 ), as represented in Fig. 4a and b. The experimental values were the measured response data from the runs designed by the Box–Behnken model, while the predicted values were obtained by calculation from the quadratic equation. The data points on the plot were reasonably distributed near to the straight line indicating a good relationship between the experimental and predicted values of the response, and that the underlying assumptions of the above analysis were appropriate. Fig. 4c and d demonstrated the normal % probability plot of the internally studentized residual for As(V) adsorption by alginate encapsulated bacterial beads that determined whether the residuals follow a normal distribution, in which case the input data will follow a straight line. The interactive behaviour of two independent factors was represented in 3D surface plots. Factors providing interaction and quadratic terms with the highest absolute coefficients in the fitted model were selected for response surface plot axes to describe the curvature of the surfaces. The response is mapped against two experimental parameters, third being constant at its central level. Fig. 5a and b depicted the interactive effect of pH and initial As(V) concentration of RJB-3 and RJB-B respectively. Solution pH is a crucial parameter affecting the process of biosorption (Singh et al., 2011). The adsorption of As(V) was maximum at pH 4.0. This could be attributed to the properties of both biosorbent and arsenic species. The cell wall structure of the selected bacterial biomass of RJB-3 and RJB-B comprising of functional groups viz. primary and secondary amines, carboxyl and hydroxyl groups do not disintegrate in acidic environment as compared to other biomasses (Yan et al., 2010). However besides the properties of functional groups, arsenic speciation is also a determining factor in the process of biosorption. The interaction between the positively charged functional groups on the biomass surface and negatively charged As(V) was most favourable at pH 4.0. Below this pH the uptake capacity decreased, in spite of the presence of positively charged biomass surface and negatively charged arsenate ions due to the lack of strong electrostatic interaction. As pH increases (above pH 4.0), the biomass becomes negatively charged due to ionization of acidic groups such as SO3 group on the surface or increase in hydroxyl ion concentration. The adsorption capacity decreases due to repulsion between the anions and negatively charged biomass surface. Further, with increase in the competing OH ions in the solution that might occupy the available binding sites on the cell surface arsenic biosorption decreases. Similar findings for the effect of pH on biosorption of As(V) using different kind of biosorbents were reported by several researchers (Rahaman et al., 2008). On the other hand metal uptake increased with increasing initial As(V) concentration with the most optimum being 1000 ␮g L−1 . At lower concentration levels, the uptake capacity decreased due to the decrease in diffusion and mass transfer coefficient of arsenic species. With the increasing initial concentrations, the mass transfer driving force of the arsenic between the biosorbent phase and aqueous solution increased, resulting in

Table 4 ANOVA for response surface reduced quadratic model. Organism

Source

F value

P-value P > F

RJB-B

Model Residual fit Lack of fit Pure error R2 2 Radj

245,905.9 10,179.72 9737.78 441.94 0.98 0.97

9 7 6 1

22,623.31 1454.25 1622.96 441.94

15.56

0.0008

Significant

3.67

0.3795

Not significant

Model Residual fit Lack of fit Pure error R2 2 Radj

224,852.5 12,195.92 11,755.17 440.75 0.99 0.98

9 7 6 1

20,129.75 1742.27 1959.20 440.75

11.55

0.0005

Significant

RJB-3

Sum of squares

Df

Mean square

4.45

Not significant

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Fig. 4. Correlation of experimental and predicted uptake capacities of (a) RJB-B and (b) RJB-3 and normal % probability plot of the studentized residual for As(V) adsorption on (c) RJB-3 and (d) RJB-B.

higher uptake of As(V) (Rahaman et al., 2008), leaving behind the unabsorbed arsenic ions in solution due to the saturation of active binding sites, thereby lowering the removal efficiency. At concentration above 1000 ␮g L−1 equilibrium is reached due to saturation of available active binding sites. Similar results were reported with various other adsorbent for the biosorption of As(V) (Yan et al., 2010). Since shaking consumes energy affecting the adsorption efficiency, it is important to determine the optimal mixing speed that must be applied during wastewater treatment (Salinas et al., 2000). As depicted in Fig. 5c and d the maximum removal efficiencies for the uptake of As(V) were obtained at 120 rpm at an optimized pH. This could probably be due to the decrease in boundary layer thickness surrounding the adsorbent that results from mixing intensity. Due to shaking at a particular mixing speed (rpm), the adsorbent particles moved around quite rapidly in the solution, and this increased the concentration of As(V) near the surface of the biosorbent, possibly to a level nearer to the bulk concentration. As the diffusion of the sorbate to the boundary layer between the sorbent particles and the surrounding solution increases with

increased shaking, this might increase the external mass transfer speed of the As(V) thereby reaching equilibrium more rapidly. However, when the mixing speed rose above 120 rpm, diffusion speed decreased. This may be due to the provision of sufficient additional energy with high shaking speed to separate newly formed bonds between the metalloid and the biosorbent. Similarly, at an optimized pH 3D surface plot demonstrating the interactive effect of concentration of As(V) and mixing speed was represented in Fig. 5e and f in RJB3 and RJB-B respectively. With RSM the operational condition of As(V) was optimized at: pH 4.0, concentration of As(V) solution 1000 ␮g L−1 and mixing speed 120 rpm with maximum uptake capacity of 465 ␮g g−1 and 425 ␮g g−1 in RJB-B and RJB-3 respectively. Therefore, it was observed that the q value was enhanced to quite a great extent as compared to the unoptimized conditions (253 ␮g g−1 and 215 ␮g g−1 in RJB-B and RJB-3 respectively). A comparative study of maximum biosorption of As(V) was presented in Table 5. The data demonstrated that the biosorbents RJB-B and RJB-3 used in the present study is quite efficient in uptake of As(V) and are quite environment friendly and cost effective.

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A. Banerjee et al. / Ecological Engineering 86 (2016) 13–23

(a)

410

(b)

440

322.5

345

235

R1

R1

250

147.5

155

60

60

1500.00

1500 .00

5.00

1250 .00 1000 .00 3.50 500.00

4.00 750.00

B: conc

A: pH

3.00

4.50

1000.00

4.00 750.00

B: conc

5.00

1250.00

4.50

3.50 500.00

A: pH

3.00

(d) 470

(c) 430

382 .5

345

295

R1

R1

260

175

207.5

90

120

120 .00 4.50 90.00

C: mixing speed

(e)

120 .00

5.00 105.00

5.00 105.00

4.00 75.00

3.50 60.00

3.00

4.50 90.00

A: pH

C: mixing speed

(f)

470

4.00 75.00

3.50 60.00

3.00

A: pH

430

347.5

397.5 265

R1

R1

325

182.5

252.5 100

180 120.00

120.00 105.00

1500 .00 105.00

150 0.00

90.00

1250 .00 90.00

C: mixing speed

1000 .00 75.00

750 .00 60.00

500 .00

1250 .00

C: mixing speed

1000 .00 75.00

750.00 60.00

500 .00

B: conc

B: conc

Fig. 5. Response surface plot showing the interactive effect of (a and b) pH and As(V) concentration, (c and d) mixing speed and pH and (e and f) concentration and mixing speed of RJB-3 and RJB-B respectively.

Table 5 Comparative study of biosorption of As(V) by RJB-B and RJB-3 with various other biosorbents. Organisms

Maximum uptake capacities (␮g g−1 )

References

Acidithiobacillus ferrooxidans BY-3 Rice polish (an agricultural residue) Dry water hyacinth plant leaf Pseudomonas alcaligenes (RJB-B) Pseudomonas resinovorans (RJB-3)

222.64 147.05 344 465 425

Yan et al. (2010) Ranjan et al. (2009) Mohan and Pittman (2007) Present study Present study

A. Banerjee et al. / Ecological Engineering 86 (2016) 13–23 80

A 1420 1018 1428 1041 1636

70

Transmittance (%)

60

1626

B

3422 3420

C

50

1442 1031

D 40

577 1648 1029 1439

30

574 20

3411

1647

10

3397 0 4000

3500

3000

2500

2000

1500

1000

500

-1

Wave number (cm ) Fig. 6. FTIR spectra of (a and b) pristine and (c and d) As(V) treated RJB-B and RJB-3 biomass.

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carboxylate anions of alginate. The bands around 1031 cm−1 and 1041 cm−1 was assigned to the CN stretching of the protein units (Salinas et al., 2000). After As(V) adsorption, change in the frequencies was observed from 3420 cm−1 to 3397 cm−1 and 3422 cm−1 to 3411 cm−1 in RJB-B and RJB-3 respectively. A close examination of spectra revealed shift of C O band from 1636 cm−1 to 1647 cm−1 which could be attributed to the electrostatic interaction of arsenic ions to adsorption sites. The shifting of bands was also observed from 1428 cm−1 to 1439 cm−1 in RJB-B denoting the involvement of CH2 in the binding process. Similarly the peaks shifted from 1626 cm−1 to 1648 cm−1 and 1420–1442 cm−1 in RJB-3. The peaks at 1031 cm−1 and 1041 cm−1 also shifted to 1029 cm−1 and 1031 cm−1 in RJB-B and RJB-3 respectively indicating the involvement of the primary amines in the binding process. Peaks at 577 cm−1 and 574 cm−1 appeared in As(V) treated biomass in addition to control in both RJB-B and RJB-3 respectively. These two peaks represented the inorganic arsenic species in the biomass (Prasad et al., 2013). The FTIR spectra of the pristine and As(V) loaded biomass of both the strains exhibited significant information regarding the involvement of functional groups, especially the hydroxyl ( OH), carboxyl ( COOH) and amines ( NH2 ) in the binding of As(V) ions.

3.5. FTIR analysis 3.6. SEM and EDAX analysis FTIR-spectra of pristine and As(V) loaded P. alcaligenes (RJB-B) and P. resinovorans (RJB-3) were analyzed to identify the functional groups involved in the binding process (Fig. 6). The bands observed at 3422 cm−1 and 3420 cm−1 of pristine RJB-B and RJB3 respectively were indicative of the presence of OH and NH groups on the cell surface (Argun et al., 2007). The peaks at 1636 cm−1 and 1626 cm−1 in RJB-B and RJB-3 respectively corresponded to C O stretching vibrations (Das and Guha, 2007) of amide I and those at 1428 cm−1 and 1420 cm−1 represented CH2 bending (Kiran and Thanasekaran, 2011). The stretching band 1636 cm−1 and 1626 cm−1 were also due to the presence of

The surface morphology of the pristine and As(V) adsorbed biosorbent was analyzed with scanning electron microscopy (SEM). SEM micrographs of the pristine biomass (Fig. 7a) represented rough, irregular, rough, and porous surface morphology, indicating high surface area of the biosorbent. Whereas, the surface morphology of the As(V) treated biomass (Fig. 7b) clearly represented a smooth surface morphology thereby signifying that the process of biosorption is a surface phenomenon (Hasan et al., 2009). The adsorption of As(V) by the selected strains was further determined by EDX (Fig. 7a–d), since X-ray analysis provides

Fig. 7. Scanning electron microscopy of pristine and As(V) loaded biomass of RJB-3 (a and b) and RJB-B (c and d), EDAX spectra of pristine and As(V) loaded biomass of RJB-B (e and f), and RJB-3(g and h).

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A. Banerjee et al. / Ecological Engineering 86 (2016) 13–23

Fig. 7. (Continued ).

significant information on the structural and the electronic state of an element. The presence of arsenic was observed in both RJB-B and RJB-3 thereby confirming the significant role of the selected biomass in the process of biosorption. Uptake of As(V) mainly took place due to binding of negatively charged arsenate ions by positively charged amino group on the cell wall of bacteria (Wei et al., 2011), sequestration by cysteine rich peptides and oxidation and reduction of As ions (Sarı and Tuzen, 2009). 4. Conclusion The results demonstrated effectiveness of statistical experimental designs in optimization of the biosorption process. Maximum As(V) uptake in RJB-B and RJB-3 were 465 ␮g g−1 and 425 ␮g g−1 under optimized conditions (pH 4.0, 1000 ␮g L−1 concentration, 120 rpm shaking speed). Higher correlation coefficient predicted better agreement between experimental and predicted values. Pseudo-second order rate model suggested chemisorption as the rate limiting step. Biosorption mechanism was demonstrated by FTIR and EDAX analysis. Statistical optimization proved to be an effective approach for modelling adsorption of arsenic and can be applied at a large-scale treatment of arsenic contaminated water bodies. Acknowledgements The authors gratefully acknowledge Sensor Hub, Central Glass and Ceramic Research Institute, Kolkata for providing necessary research facilities and CSIR, New Delhi for providing financial assistance. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecoleng.2015.10. 015. References Abdullah, M., Shiyu, Z., Mosgren, K., 1995. Arsenic and selenium species in the oxic and anoxic waters of the Oslofjord, Norway. Mar. Pollut. Bull. 31 (1), 116–126.

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