Environmental Technology & Innovation 4 (2015) 82–91

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Environmental Technology & Innovation

journal homepage: www.elsevier.com/locate/eti

Biosorption of cadmium from aqueous solution by shell dust of thefreshwater snail Lymnaea luteolaAsif Hossain a, Satya Ranjan Bhattacharyya b, Gautam Aditya a,c,∗

a Department of Zoology, The University of Burdwan, Golapbag, Burdwan 713104, Indiab Centre for Nanomaterials and Surface Physics, Saha Institute of Nuclear Physics, 1/AF Bidhan Nagar, Kolkata 700 064, Indiac Department of Zoology, University of Calcutta, 35 Ballygunge Circular Road, Kolkata 700 019, India

h i g h l i g h t s

• Biosorption of cadmiumby shell dustof L. luteola (LSD) was evaluatedunder laboratory condition.

• Biosorption maxima of LSD was20.00 mg/g, which fitted to Lang-muir and Freundlich equations.

• Biosorption kinetics fitted topseudo-second order and Lagergrenmodels.

• FTIR and EDX analyses indicate ionexchange involving different func-tional groups.

• LSD can be considered as low costbiosorbent for cadmium removal.

g r a p h i c a l a b s t r a c t

a r t i c l e i n f o

Article history:Received 15 December 2014Received in revised form30 April 2015Accepted 7 May 2015Available online 19 May 2015

Keywords:BiosorptionCadmiumShell dustANNIsothermKinetic

a b s t r a c t

An assessment of the metal biosorption potential of shell dust of the freshwater snail (Lymnaea luteola)was made using cadmium as model metal. Artificial Neural Network (ANN) model was constructed forportraying the biosorption process under varied conditions. Under optimal conditions, the shell dust of L.luteola (LSD) exhibited a biosorption capacity of 20.00mg g−1 at pH 6. The adsorption data at equilibriumfitted significantlymore to Langmuir (R2

= 0.997) than Freundlich equation (R2= 0.790). The kinetics for

the biosorption process followed the pseudo-second order model (R2= 0.998) better than the Lagergren

model (R2= 0.835). Confirmation of cadmium adsorption by LSD was further revealed through EDX

studies, and the experimental results fitted well with the predicted pattern of biosorption based on ANNmodel (R2

= 0.924). It appears that shell dust of the snail L. luteola (LSD), a waste biomaterial, can be usedas a low cost and environment friendly biosorbent for cadmium removal from aqueous solution.

© 2015 Elsevier B.V. All rights reserved.

1. Introduction

Heavy metal contamination affects aquatic ecosystem functions and biodiversity conservation. A cumulative increase in the heavymetal content in successive trophic levels destabilizes aquatic community structure along with decline in species richness. The entry ofheavy metals in aquatic ecosystems is linked to the generation of various byproducts or wastes from metal refineries, battery and paint

∗ Corresponding author at: Department of Zoology, The University of Burdwan, Golapbag, Burdwan 713104, India. Tel.: +91 3422656566; fax: +91 342 253045.E-mail addresses: asifhossain.bu@gmail.com (A. Hossain), satyar.bhattacharyya@saha.ac.in (S.R. Bhattacharyya), gautamaditya2001@gmail.com (G. Aditya).

http://dx.doi.org/10.1016/j.eti.2015.05.0012352-1864/© 2015 Elsevier B.V. All rights reserved.

A. Hossain et al. / Environmental Technology & Innovation 4 (2015) 82–91 83

industries, and mining of natural deposits. Cadmium is one of the common heavy metal causing pollution of natural water bodies, whicheventually affects human and wild life health (Du et al., 2011; Chakravarty et al., 2010). Owing to longer residence time and ability toform non-biodegradable compounds, removal of heavy metal from contaminated sites is given priority to restore ecosystem functionsand stability. Adsorption based techniques using low cost adsorbent of biological origin provides a feasible alternative to the conventionalmethods heavy metal removal from freshwater (Liu et al., 2009; Gifford et al., 2006). Application of microorganisms (Hetzer et al., 2006),hydrophytes (Sinha et al., 2007) and different animals (Jana and Das, 1997) as metal hyper accumulator has empirically been tested inremoval of heavy metal from aquatic systems. Uncontrolled application of live specimens in removal of heavy metals could alleviate bi-ological invasion, parasitic infestation or secondary pollution that possibly will hinder the ecosystem functioning. In contrast, biologicalmaterial derived from organisms (Du et al., 2011; Liu et al., 2009; Vijyaraghavan et al., 2006; Dahiya et al., 2008; Pena-Rodriguez et al.,2010) seems to be a suitable better alternative to the use of whole organisms. This forms the basis for search of materials of biologicalorigin for metal bioremediation, which would be environmentally suitable and economically sustainable.

In view of exploration of biological material for metal removal, the present study aimed at evaluation of biosorbent capacity of the shelldust of freshwater disease transmitting snail Lymnaea luteola (Lamarck, 1822) using cadmium as a model metal. A common inhabitant ofpond, bogs, sewage drains and agro-ecosystems, L. luteola is a prolific breeder and serves as intermediate host of helminth parasites causingdisease in human and domestic animals (Roy and Raut, 1994; Aziz and Raut, 1996; Subba Rao, 1989). The flesh of L. luteola is used as a cheapanimal protein source for poultry industry and prawn culture (Jahan et al., 1998). Following death, the shells of the snail L. luteola remainas a waste material for natural decay and addition to the nutrient pool of the ecosystem. The shells of freshwater snails and mussels arecomposed of 95%–99% calcite and/or aragonite (forms of calcium carbonate) oriented in a matrix of proteins (0.1%–5%), and act as ceramicequivalent with excellent strength and toughness (Kaplan, 1998; Boro et al., 2012). Shells of L. luteola can be considered as a source of cal-cium carbonate, which can act as a potential cost-effective biosorbent for removal of heavy metals (Du et al., 2011; Pena-Rodriguez et al.,2010). Thus the biosorption efficiency of the waste shells of L. luteola (LSD) was assessed using initial pH, adsorbent dosage, contact time,and cadmium concentration as explanatory variables. An Artificial Neural Network (ANN) model was used to portray the highly nonlinearand complex relationship between cadmium biosorption and the explanatory variables (Maghsoudi et al., 2015). Considering biosorptionas a complex process influenced by multiple factors, ANN model can be opted to provide an overview of the relative importance of theinput parameters that drive the sorption process (Celekli and Geyik, 2011; Ahmad et al., 2014; Witek-Krowiak et al., 2014; Celekli et al.,2013). In the present context, the metal absorption efficacy of the shell dust of L. luteola (LSD) is being portrayed through multilayer per-ceptron ANN model, apart from description of the equilibrium and kinetic models. The compilation of the results is expected to providean insight to the biosorption capability of LSD and suggest their prospective use in metal bioremediation.

2. Materials and methods

2.1. Preparation of the material

The snails (Lymnaea luteola) were collected from local agricultural fields and ponds in and around the campus of The University ofBurdwan, Golapbag, Burdwan, India, using hand nets. The collected specimenswere kept in aquaria until natural death. The tissue portionswere discarded by boiling in water followed by wash in distilled water. The shells were sundried for two days and kept in oven at 50 °Cfor the next two days for complete dryness.

The dried shells were pulverized in mortar and pestles to fine granules, and sieved through 500µm and subsequently through 200µmcloth mesh. Granules of two different sizes were available, viz., 500–200 µm and ≤ 200 µm. Initial experiments showed that the largersized granules adsorbed less amount of cadmiumand thuswere not studied further. The fine granules (≤ 200µm) used in the experimentswas featured by an ash content of 51.56%, the water retention capacity of 773.2 mg g−1 and surface area of 23.58 m2 g−1.

2.2. Preparation of the metal solution

A stock solution of 1000 mg L−1 was prepared using cadmium chloride in double distilled water and working solutions were preparedby appropriate dilution. The pH of the solutions were adjusted by adding HNO3 (0.1 N) and NaOH (0.1 N). All the inorganic chemicals thathave been used in these experiments were purchased from Merck India Ltd., India.

2.3. Experimental procedure

The batch sorption experiments were performed in 250 mL Erlenmeyer’s flask that contained 100 mL solution of a particular cadmiumion concentration at required pH and requisite amount of snail shell dust (LSD). The flasks were sealed with wax paper and placed in ashaking incubator (Lab Companion, SI-300R, India) at 150 rpm under 30 °C±1 S.E. temperature for the required time period. After shakingfor a particular time period, the solutions of the flaskswere centrifuged at 2000 rpmand the supernatantwere taken for estimation ofmetalconcentration by atomic absorption spectroscopy (GBC Avanta 1.3, India). The influences of pH of the solution on biosorption equilibriumwere studied with changing pH of the solution in range between 2 and 7. The effect of contact times between solution and the LSD weremonitored by varying it at five levels between 10 and 80 min. For equilibrium studies, the metal ion concentrations were used in a variedconcentration of 25–1000 mg L−1 and for optimum biosorption study the LSD biomass were varied between 200 and 1000 mg L−1. Theamount of cadmium ion adsorbed on the LSD was estimated (Sharma et al., 2011) following the equation:

qe = (co − ce)v/m (1)

where, qe = amount of metal adsorbed, v = volume of solution, m = mass of adsorbent, co = initial concentration of the solution andce = equilibrium concentration of the solution.

84 A. Hossain et al. / Environmental Technology & Innovation 4 (2015) 82–91

2.4. Analyses of FT-IR absorbance spectra of LSD

To determine the functional groups responsible, if any, for the biosorption of cadmium by LSD, IR spectra of protonated or Cd2+ loadedLSDs were recorded in a (Perkin-Elmer FTIR, Model RX1) FT-IR spectroscopy. Samples of 100mg KBr disks contains 1% of the finely groundpowder of protonated or Cd2+ loaded LSDs were prepared less than 24 h before recording.

2.5. The scanning electron micrograph (SEM) study

Raw andmetal adsorbed LSDs were dried and prepared for scanning electronmicroscopic studies. The samples were attached with thestubs by both side cello tapes and gold plated in a sputter coater before use in the SEM. Electron acceleration potential of 20 kV was usedfor the microscopic observations. Photographs were taken in a HITACHI S530 Scanning Electron Microscope at required magnification.

2.6. Energy dispersive x-ray (EDX) analysis

For EDX analyses the samples were studied by field emission gun based Scanning Electron Microscope with Energy-dispersive X-rayanalysis (SEM/EDX) (Quanta model by FEI Co., Netherlands) for the morphological as well as for the presence of elemental information onthe samples. EDX spectra were taken from the area corresponding to the SEM image shown in the in-set. The SEM studies were performedat 30 kV in the low vacuum mode. The EDX spectra were taken in the ‘region mode’ with the bombardment of energetic electrons forduration of electron of 100 s.

2.7. Equilibrium modeling

The adsorption equilibriums were studied for the estimation of maximum cadmium biosorption by the LSD. For the equilibrium studythe experiments were performed at different initial cadmium ion concentration (25–1000 mg L−1). Langmuir and Freundlich adsorptionmodels were used in describing the equilibrium between adsorbed cadmium ions on the LSD (qe) and in solution (ce) at a particulartemperature. The parameters of the Langmuir equation (Langmuir, 1918) were determined from a linear form of the following equation:

ce/qe = (ce/a) + 1/ab (2)

where, a = maximum amount of metal ions/unit mass of adsorbent to form a monolayer, b = equilibrium constant. The linear plot ofce/qe against ce indicates the applicability of the Langmuir modeling in the experiment.

Freundlich equation (Freundlich, 1906) is described as below:

qe = kf c1/ne (3)

where, kf and 1/n are the Freundlich constants indicating the adsorption capacity and intensity respectively. The linear plot of log qeagainst log ce indicates the applicability of the Langmuir modeling in the experiment.

2.8. Kinetic model

The pseudo-first ordermodel in adsorptionwas proposed by Lagergren. It describes the rate of adsorption is proportional to the numberof unoccupied binding sites of the biosorbent. This model works well in the region where the biosorption process occurs quickly (Witek-Krowiak et al., 2011). Lagergren equation (Lagergren and Sven, 1898) or pseudo-first order reaction is mathematically expressed as:

dq/dt = kl(qe − qt) (4)

where, qe = amount of adsorbed metal ion on biosorbent at equilibrium, qt = amount of adsorbed metal ion on biosorbent at time ‘t ’,kl = Lagergren constant.

Integrating the above equation and transforming to log scale:

log(qe − qt) = log qe − kl(t/2.303). (5)

A linear plot of log(qe − qt) against time indicates whether this kinetic model is applicable or not for biosorption process. The pseudo-firstorder model for prediction of biosorption is not suitable for a long period of adsorption process (Ho and McKay, 1999) and the pseudo-second order equation in this case as described by Ho is:

dqt/dt = k2(qe − qt)2 (6)

or

qt = qe[(qek2t)/(1 + qek2t)] (7)

where, qe = amount of adsorbed metal ion on biosorbent at equilibrium, qt = amount of adsorbed metal ion on biosorbent at time ‘t ’,and k2 = second order rate constant (g mg−1 min−1). A linear plot of t/q vs. t indicates whether this model of biosorption is applicablefor this case or not.

2.9. Artificial Neural Network (ANN) model

An Artificial Neural Network (ANN) is a nonlinear mathematical model that is inspired by the structural as well as functional aspectof neuron in analysis of a group of input data as function of output data and to predict the behavior of the given system (Cavas et al.,

A. Hossain et al. / Environmental Technology & Innovation 4 (2015) 82–91 85

2011; Yetilmezsoy and Demirel, 2008). A three layered ANN model based on multilayer perceptron used in the analyses of the complexrelationships in finding the pattern of experimental data. A multilayer perceptron is a supervised learning technique in comparing outputresponses to the desired responses by adjusting the weights of the input signals and remind it, so that in the next time of data feed theoutput response will be much closer to the desired one when the same input signals are provided (Shanmugaprakash and Sivakumar,2013).

The weight of an input signal, here treated as neuron, is calculated by following equation:

Wi =

nj=1

wijxj (8)

where, wij = corresponding weight of connection between each neuron ‘j’ in input layer and each neuron ‘i’ at hidden layer, xj = value ofinput signal ‘j’ at input layer (Celekli et al., 2013).

For a particular function, the output is predicted from following equation:

yk =

(wi + bj) (9)

where, wi = sum weight of each neuron and bj = bias i.e., constant weight of a neuron representing the generalization error (Celekli andGeyik, 2011).

The layers used here, are (a) input layer, (b) output layer and all other intermediate units as a whole (c) hidden layer(s). The first layerreceives the signal from external sources and the signals are immediately weighted here and the weighted information are then sent tohidden layer for processing (Ozdermir et al., 2011) that finally gives the output based on the sum of the weighted values of the input data.In predicting the biosorption of Cadmium (output), the combine effect of the variables—(i) pH, (ii) Cd2+ concentration, (iii) biosorbent doseand (iv) contact time were used in the analysis. Sixty nine experimental sets, obtained from batch sorption studies were used in training,validation and testing the Artificial Neural Network model in SPSS R⃝ 18 version.

3. Results

3.1. Effect of pH of the solution

The biosorption procedure was observed over a range of pH 2 to 7. The sorption procedure seemed to be affected by the pH of themedium in twoways—metal solubility and total charge of the functional groups of the biosorbent. The optimumpH, atwhich the procedureshows highest adsorption, was estimated. At high pH, that is at alkaline condition, precipitation of themetal takes place and at low pH dueto high protonation metal sorption capacity decreases. The experiment was carried out on 100 mL solution having 100 mg L−1 Cd2+ and100 mg of the LSD at 30 °C in reference to varying pH of the solution. As indicated in Fig. 1(a), the pH dependent adsorption of the metalion by LSD as the metal sorption was negligible at pH 2 and it increased with the increase in pH and at pH 4, it shows highest adsorption.The metal sorption declined slightly and remained even with further increase in the pH.

3.2. Effect of initial metal ion concentration

Increase in metal ion concentration lead to a corresponding increase in metal adsorption by LSD. In this study, seven different dosescadmium ion concentrations ranging between 25 and 1000 mg L−1 were considered. At 25 mg L−1 of initial cadmium ion concentration,lowest adsorption was observed; with a steep increment at 200 mg L−1 and thereafter the sorption process remained stable as a functionof Cd2+ ion increment (Fig. 1(b)).

3.3. Influence of the biosorbent dose

An increase in the amount of LSD in the metal solution yielded a corresponding increment in cadmium biosorption potential. Possiblythe increase in the amount of biosorbent increased the availability of free binding sites or exchanging groups facilitating cadmiumadsorption from the solution. Keeping the cadmium ion concentration constant (100 mg L−1), an increase in the biosorbent amount from200 to 1000 mg L−1, resulted in corresponding increase in metal ion adsorption (Fig. 1(c)).

3.4. Effect of contact time

The sorption potential of the LSD over time was monitored for time intervals of 10, 20, 40, 60, and 80 min by using 100 mL of100 mg L−1Cd2+ at pH 6 (Fig. 1(d)). At the beginning, metal adsorption was less due to more binding sites remaining free when treatedfor the short period of time and increased rapidly as the treatment time increased. It showed lowest adsorption when treated for 10 minwhich increased over time to saturate at about 40 min. The variation in uptake of the cadmium ions with time were used in fitting thekinetic models.

3.5. FT-IR study

In order to study the mechanism of cadmium removal and the main functional groups responsible for Cd2+ binding the FT-IR spectrumof the LSD were performed. IR spectra of protonated and cadmium loaded LSD are shown in Fig. 2(a) and (b), respectively. A peak at3432 cm−1 is indicating the presence of hydroxyl (–OH) groups. Strong band at 2926 cm−1 is due to C–H stretching frequency and peakat 1639 cm−1 is due to –C=O stretching mode of the primary and secondary amides (Reddy et al., 2010). Weak band at 1474 cm−1 isattributed to aromatic C=C and the strong band at 1083 cm−1 is due to C–O stretching of alcoholic groups (Blazquez et al., 2011). The FTIRof metal loaded LSD shows that distinct shift of some band as well as change in intensity informs some ion exchange behavior of the LSD.

86 A. Hossain et al. / Environmental Technology & Innovation 4 (2015) 82–91

a b

c d

Fig. 1. (a) Effect of pH of the solution on biosorption of cadmium on LSD at 30 °C, initial cadmium ion concentration of 100 mg L−1 , contact time of 60 min and biosorbentdose of 1 g/100 ml solution. (b) Effect of initial cadmium ion concentration of solution on biosorption on LSD at 30 °C, pH 6, contact time of 60 min and biosorbent dose of1 g/ 100 ml solution. (c) Effect of time on biosorption of cadmium on LSD at 30 °C, pH 6, initial cadmium ion concentration of 100 mg L−1 , and biosorbent dose of 1 g/100 mlsolution. (d) Effect of biosorbent dose on biosorption of cadmium on LSD at 30 °C, pH 6, initial cadmium ion concentration of 100 mg L−1 , contact time of 60 min.

Table 1Elemental profile ofMSDbefore (B) and after (A) treatmentwith Cadmiummetal as obtained from EDX study.

Element (Wt %) LymnaealuteolaB A

C 41.03 49.67O 38.49 35.42Al – 0.53Si 2.16 0.79S – 0.37Cd – 1.26Cl 0.42 –Ca 15.87 11.96Fe 1.74 –Co 0.29 –

Total 100 100

3.6. Energy dispersive x-ray (EDX) analysis

The surface structure of the dust provides a large unadsorbed surface area for the cadmium ion of the solution. The elemental profile ofthe shell dust before and after the treatment of the Cadmium solution was estimated using the EDX analysis (Table 1). In case of cadmiumsorption by CaCO3 compound generally follows surface precipitation due to similar ionic radii (Prieto et al., 2003) of divalent calcium andcadmium. The prominent peaks in the EDX spectra correspond to CKα, OKα, AlKα, SiKα, PKα, CaKα, CaKβ FeKα, etc. in the untreated LSDand in addition to it a CdLα peak in treated LSD (Fig. 2(c) and (d)). Both the treated and untreated LSD shows strong peaks correspondingto calcium.

3.7. Adsorption isotherm

The biosorption isotherm is important in waste water treatment as it implies estimation of biosorption capacity of the adsorbent. Thelinear representations of Langmuir and Freundlich isotherm of cadmium adsorption at 30 °C are given in Fig. 3(a) and (b), respectively.The correlation coefficient and constants obtained from the equations are presented in Table 2. The coefficient of determination is highin Langmuir equation (y = 0.050x + 1.084; R2

= 0.997) contrast to Freundlich equation (y = 0.316x + 0.459; R2= 0.790).

It indicates Langmuir model is more suitable for describing the biosorption equilibrium of Cd2+ on the mussel shell dust. High qmaxvalue (20.00 mg g−1) from Langmuir equation is more close to experimental data obtained from atomic absorption spectrophotometer(18.55 mg g−1). The value of b (0.046) from Table 2 indicates that the adsorption isotherm follows Langmuir isotherm model.

A. Hossain et al. / Environmental Technology & Innovation 4 (2015) 82–91 87

21.6

20

18

16

14

12

10

8

6

43.0

4400.0 4000 3000 2000 1500 1000 400.0

%T

%T

cm-1

cm-1

4400.0 4000 3000 2000 1500 1000 400.0

16.0

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1.0

keV0.90 1.80 2.70 3.60 4.50 5.40 6.30 7.20 8.10

keV0.90 1.80 2.70 3.60 4.50 5.40 6.30 7.20 8.10

a

b

c

d

Fig. 2. FTIR absorbance spectra of snail shell dust for LSD before (a) and after (b) biosorption of cadmium and EDX analyses for elemental composition in the snail shell dustfor LSD before (c) and after (d) biosorption of cadmium clearly showing the peaks for cadmium, in the treated shell dust, indicating the adsorption of the metal on the shelldust.

3.8. Biosorption kinetics

The kinetic model is necessary for determination of optimal condition of the biosorption process. For the evaluation of differences insorption process the kinetics of metal uptake were described by pseudo first order and pseudo second order model (Ho andMcKay, 1999).The linear plots obtained for pseudo first order and pseudo second order model at 100 mg L−1 initial Cd2+ concentration, pH 6 and at

88 A. Hossain et al. / Environmental Technology & Innovation 4 (2015) 82–91

a b

c d

Fig. 3. Langmuir isotherm plot (a) and Freundlich isotherm plot (b) for biosorption of cadmium on LSD (pH 6, temperature 30 °C, biosorbent = 1 g, cadmium ionconcentration = 100 mg g−1) and Pseudo-first order (c) and pseudo-second order (d) plot for biosorption of cadmium on LSD (pH-6, temperature 30 °C, biosorbent = 1 g,cadmium ion concentration = 100 mg g−1).

Table 2Coefficients of the Langmuir and Freundlich isotherm models for cadmium biosorption by LSD.

Snail species Langmuir constant Freundlich constantqmax (mg g−1) b (L mg−1) R2 n kF R2

L. luteola 20.00 0.046 0.997 3.165 2.877 0.790

Table 3Comparison of rate constants and equilibrium metal uptake for cadmium binding by LSD at pH6 and initial metal ion concentration of100 mg L−1 .

Snail species qeexp (mg g−1) Pseudo-first order model Pseudo-second order modelk1 (min−1) qe cal. (mg g−1) R2 k2 (g mg−1 min−1) qe cal. (mg g−1) R2

L. luteola 18.55 0.018 3.775 0.835 0.018 9.433 0.998

studied temperature are shown in Fig. 3(c) and (d) respectively. The rate constants, expected metal uptake and correlation coefficientshave been described in Table 3. For the pseudo first order model the coefficient of determination (y = 0.577 − 0.008x; R2

= 0.835)obtained from Fig. 3(d) and Table 3 is high and the calculated metal uptake with this model is very low (3.775 mg g−1) than the expectedmetal uptake (18.55 mg g−1). The pseudo second order reaction in biosorption is based on the sorption capacity on the solid phase. Asportrayed in Fig. 3(d) and Table 3, the coefficient of determination in the pseudo second order reaction (y = 0.106x+ 0.631; R2

= 0.999)is high and the calculated metal adsorption (9.33 mg g−1) is much nearer to the expected value (18.55 mg g−1). These suggest that thebiosorption process comply with the pseudo second order model.

3.9. Optimization results through ANN model

The topology of the ANN model developed in the study comprised of three layers as illustrated in Fig. 4(a). The input layer has fourunits namely initial pH regime, initial Cd2+ concentration, initial biomass dose and contact time. The output layer has one unit as theamount of Cd2+ adsorbed on the shell dusts i.e., the biosorption capacity and the hidden layer has 1 unit (Table 4). The regression curveof the output (predicted adsorption) and the corresponding target (observed) of the artificial network is given in Fig. 4(b). The coefficientof determination of the equation is high (R2

= 0.924), justifying the ANN model of the system fits well with the data set. The weight ofthe neurons were used in analysis of the relative contribution of each of the input variables on the biosorption process i.e., on output layer(Khataee and Kasiri, 2010; Khataee et al., 2011; Yang et al., 2011).

4. Discussion

It appears from the results that the shell dust of the disease transmitting freshwater snail Lymnaea luteola (LSD) bear the ability toremove substantial amount of cadmium from the medium, depending upon the concentration of the metal, amount of biosorbent, pH

A. Hossain et al. / Environmental Technology & Innovation 4 (2015) 82–91 89

a

b

Fig. 4. The architecture (a) of the multilayer perceptron artificial neural network (ANN) model used in the study along with the regression line (b) confirming the linearityof predicted and observed cadmium removal by the LSD.

of the medium and interaction time. The equilibrium models and biosorption kinetics of cadmium ions indicate that under optimumconditions (pH 6, biosorbent dose of 1 g, Cd2+ concentration of 100 mg L−1 and 60 min time period) the maximum biosorption capacityby the LSD is 20.00 mg g−1. The biosorbent ability of the LSD is comparable to similar biosorbents of biological origin (Table 5). While thematerials of plant origin like rice husk, rice straw, Ficus carica leaves and alike (Table 5) show ability to adsorb cadmium, the potential riskof leaching of lignin, tannin and othermetabolites cannot be ruled out thatmay interferewith the aquatic food chain and disrupt ecosystemfunctions. Compared to these materials, LSD is chiefly made up of calcium carbonate interspersed in a fine protein matrix, which upondegradation is less likely to yield any unwanted compounds. Instead the degradation of calcium carbonate may enable maintenance of thenutrient load of the aquatic ecosystems. In addition, the calcium of LSD will facilitate adsorption of cadmium because of the similarities inionic radii that enhance ion exchange (Du et al., 2011).

In view of metal removal from lentic and lotic ecosystems, use of materials of biological origin is preferred to reduce the chances ofaddition of contaminants and recalcitrant compounds that may alter ecosystem functions. Although materials like leaves of Ficus (Zhenget al., 2010) and mushroom (Azouaou et al., 2008) have shown ability to absorb cadmium to a greater extent, the cost associated issignificantly higher compared to waste shells of animal origin. Besides, the mushrooms have high food value. In a similar way, use ofmicroorganisms in heavy metal removal from aquatic system does not yield benefits due to limitations in desorption and continuous flowsorption (Gifford et al., 2006). Empirical evidences suggest that animal wastes like egg shells, crab and mussel shells are comparatively

90 A. Hossain et al. / Environmental Technology & Innovation 4 (2015) 82–91

Table 4Basic information of the artificial neural network used in describing the biosorptionof cadmium by LSD.

Network information

Input layer Covariates 1 pH2 Biomass3 Cd.conc4 Time

Number of unitsa 4Rescaling method forcovariates

Normalized

Hidden layer(s) Number of hidden layers 1Number of units in hiddenlayer 1a

1

Activation function Hyperbolic tangentOutput layer Dependent variables 1 Removal

Number of units 1Rescaling method for scaledependents

Standardized

Activation function IdentityError function Sum of squares

a Excluding the bias unit.

Table 5Comparative data of biosorption capacities (qmax—maximum metal uptakecapacity) for cadmium by different biosorbent.

Adsorbent qmax (mg/g) Reference

Raw corn stalk 3.39 Zheng et al. (2010)Olive waste 6.55 Azouaou et al. (2008)Brewer’s yeast 10.17 Cui et al. (2010)Corncob 4.73 Ramos et al. (2005)Rice Straw 13.9 Ding et al. (2012)Ficus leaves 30.30 Farhan et al. (2013)Wheat bran 15.71 Nouri et al. (2007)Castor seed hull 6.98 Sen et al. (2010)Walnut tree sawdust 5.76 Yasemin and Zek (2007)Bamboo charcoal 12.08 Wang et al. (2010)Mushrooms 34.96 Vimala and Das (2009)Coconut copra meal 4.99 Ho and Ofomaja (2006)Aspergillus niger biomass 10.14 Amini et al. (2009)Lymnaea shell dust 20.00 Present study

more efficient for metal removal in terms of availability in nature and cost benefit ratio (Du et al., 2011; Vijyaraghavan et al., 2006; Dahiyaet al., 2008). Consistent with the observation on egg shells and crab shells, the present study demonstrate that little processing of theshells of a snail can make an efficient biomaterial for adsorption of cadmium from aquatic system. Use of the LSD can be promoted forremoval of cadmium from aquatic systemwhile restoring ecosystem functions in situations where L. luteola is a disease transmitting snail(Roy and Raut, 1994; Aziz and Raut, 1996; Subba Rao, 1989). Although metal desorption and extraction from LSD needs to be evaluatedthrough further studies, the present study is a pioneer effort to establish that shell dust of a common snail L. luteola can be used in cadmiumremoval from aquatic ecosystem.

5. Conclusion

The results of present study indicate that at pH 6, the maximum biosorption capacity of LSD is 18.55 mg g−1. The main functionalgroup responsible for chelation are OH, C=O, C=C and C–C, as supported by FTIR analysis. The isotherm model follows Langmuir model(R2

= 0.996) better than Freundlich model (R2= 0.969). The biosorption process conformed to the pseudo second order (R2

= 0.999)kinetics better than the pseudo first order (R2

= 0.833) kinetics. A three layer artificial neural network (ANN) of multilayer perceptronmodel successfully portrayed biosorption of cadmium ion on the LSD with high correlation coefficient (R2

= 0.961) between predictedand observed removal. LSD may prove as efficient, low cost and environment friendly biosorbent for cadmium bioremediation.

Acknowledgments

The authors acknowledge the comments of two anonymous reviewers that enhanced the manuscript in its present form. The authorsare thankful to the respective Heads, Department of Zoology, Department of Chemistry and Department of Environmental Science, TheUniversity of Burdwan, Burdwan, India and the Head, Department of Zoology, University of Calcutta, for the facilities provided includingDST-FIST. The authors acknowledge The Director, SINP, Kolkata for providing the facilities to carry out a part of this work. AH thankfullyacknowledges the financial assistance provided Council of Scientific and Industrial Research (CSIR), New Delhi, India (Sanction no. 20-12/2009(ii)EU-IV dated 31-5-2010).

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