Automotive

Biosorption of Toxic Heavy Metals on Sawdust

Description
The present investigation deals with the biosorption of Zn2‡, Cr3‡, and Pb2‡ in the liquid phase on the surface of zinc sequestering bacterium VMSDCM (accession no. HQ108109) cells immobilized on pineapple peel powder particles (the term sawdust has
Categories
Published
of 8
All materials on our website are shared by users. If you have any questions about copyright issues, please report us to resolve them. We are always happy to assist you.
Related Documents
Share
Transcript
  Vishal MishraSrinivas Tadepalli Department of Chemical Engineering,University of Petroleum and EnergyStudies, Dehradun, India Research Article Biosorption of Toxic Heavy Metals on Sawdust  The present investigation deals with the biosorption of Zn 2 þ , Cr 3 þ , and Pb 2 þ in theliquid phase on the surface of zinc sequestering bacterium VMSDCM (accessionno. HQ108109) cells immobilizedon pineapplepeelpowder particles (the term sawdusthas been used for immobilized microorganisms). In batch studies, the biosorption datadesign andregression model for predicting the relationship betweenthemetal ionandtheir biosorption capacity used were Minimum Run Res V Design and quantitative ioncharacter activity relationship (QICAR), respectively. The values of   q max  calculated by the Langmuirisotherm werefound to bein increasing order of Cr 3 þ < Zn 2 þ < Pb 2 þ . Theresults of the present investigation showed that the pH and contact time played animportant role in the removal of Zn 2 þ from the liquid phase. In case of Cr 3 þ , thesignificant independent parameters associated with metal ion removal were the initialconcentration of the metal ion and the pH. The important independent parameters forPb 2 þ removal were the temperature (°C) and pH, respectively. In QICAR modeling,among allsix physico-chemical characteristicsonly the ionization potential (eV) playeda negative role in the biosorption of metal ions. The rationale behind the inverserelationship between the removal of metal ions and eV was the variation of thecorresponding covalent index. The rest of the remaining five physico-chemicalproperties showed that regression models fitted quite well and were characterized by high values of the linear regression coefficient (  R 2 ), adjusted  R 2 , and low values of theerror function ( x 2 ). In the present work, it was concluded that the immobilized cells of sawdust can be a suitable alternative for the removal of heavy metal ions from theliquid phase. The initial concentration of all the metals was kept at 50mgL  1 each inthe liquid phase. The residual concentrations of Zn 2 þ , Cr 3 þ , and Pb 2 þ after the biosorption were 0.4, 1.2, and 0.2mgL  1 , respectively. Keywords:  Covalent index; Ionization potential; Langmuir isotherm; Minimum Run Res V Design;Quantitative ion character activity relationship (QICAR) Received:  December 17, 2013;  revised:  February 25, 2014;  accepted:  March 8, 2014 DOI:  10.1002/clen.201300934 1 Introduction  Tremendous development of industries and a growth in urbaniza-tion have led to increased pollution due to heavy metals in global water bodies. In the list of toxic substances provided by theComprehensive Environmental Response, Compensation and Liabil-ity Act 2011, zinc, chromium, and lead stand at the 75th, 18th, and2ndrank,respectively.ExcessiveintakeofZn 2 þ andchromiumleadsto irreversible gastro-intestinal disorder and cancer due to thecarcinogenic property of chromium [1]. Additionally, excessiveexposure to Pb 2 þ ion leads to behavioral and learning disability, vomiting, cramps, etc. [2] Previously, many metal ion remediationmethodologies have been practiced such as cementation, ionexchange, osmosis, reverse osmosis, hydroxide precipitation, andelectro coagulation–flocculation [3, 4]. Undoubtedly, these technol-ogies arequiteefficientbut theirusageatamass scaleinvolves hugecost investments and leads to the generation of secondary chemicalsludge. Furthermore, these technologies do not work below athreshold concentration of 100mgL  1 [5, 6]. Against the demerits of theabove-mentionedconventionalmetalionremediationprocesses, biosorption of toxic metal ions on the surface of biomass is a very inexpensive and eco-friendly technology. Biosorption of heavy metals is usually affected by various factors such as pH, tempera-ture, initial concentration of metal ions, biomass dose, agitationrate, contact time, etc. Moreover, various physico-chemical proper-ties of metal ions such as atomic weight, ionic radius, log  K  , ionicindex, covalence, etc., also play a significant role in the sorption of metal ions [7, 8]. A relatively large number of researchers haveperformed biosorption studies on the physical process conditionslike pH, temperature, etc., and on the mechanism of metal ion binding. But little research has been done to elucidate therelationship between metal ion characteristics and biosorption. Inthe past, a few investigators [9–11] have developed the quantitativeion character activity relationship (QICAR) to show the relation between metal ion binding tendencies and metal ion toxicity.Recently, QICAR has also been widely used to interpret the pattern Correspondence:  Dr. V. Mishra, Department of Chemical Engineering,UniversityofPetroleumandEnergyStudies,P.O.Bidholi,viaPremNagar,Dehradun 248007, India E-mail:  mishrdch@gmail.com  Abbreviations: AAS , atomic absorption spectrophotometry;  BET ,Brunauer–Emmett–Teller;  QICAR  , quantitative ion character activity relationship 1 © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com Clean – Soil, Air, Water 2014,  42  (9999), 1–8  of biosorption of metal ions with the maximum uptake capacity ( q max , mgg  1 ), as many functional groups like carbonyl, hydroxyl,and carboxyl groups play a significant role in metal ion complexa-tion and ion exchange [12]. Biosorption of heavy metals has gainedsignificant interest in the past few decades. With this motivation, inthe present work, sawdust (the term sawdust has been used forimmobilized microorganisms) has been used to remove Zn 2 þ , Cr 3 þ ,andPb 2 þ fromtheliquidphase.Thesetofexperimentswerefittedinan experimental factorial design matrix, which was meant to findout the significant factors involved in the biosorption of metal ions.In addition to this, the other objective of the present work was todecipher the relationship between the physico-chemical propertiesof metal ions with the maximum uptake capacity ( q max , mgg  1 ).Countries like Malaysia, Philippines, Thailand, and Brazil are the world’s largest producers of pineapple fruit. The pineapple peelcontributes nearly 30% of the total fruit weight and the peel powderparticleshavebeenusedtoadsorbpollutantsintheliquidphase[13]. 2 Materials and methods 2.1 Microbial cell growth Living biomass was grown in pre-sterilized flasks of 500mL capacity in Luria Bertaini media. 2.2 Procedure for immobilization withmicrobial cell 2.2.1 Preparation of biosorbent bed Pineapple peel powder particles of 0.5mm size were soaked in hot water at 60°C for 1h. These pineapple peel powder particles werethen dried at 80°C. Finally, the dried pineapple peel powderparticlesweresteamsterilizedat121°Cfor15minat103.42kPa[14]. 2.2.2 Immobilization of microorganism Mid log phase cells of zinc sequestering bacterium VMSDCM,accession no. HQ108109, were immobilized by dispensing apredetermined amount of pre-sterilized pineapple peel powderparticles (2000mg) on the 48-h old culture grown in 50mL LuriaBertaini medium. The flasks were incubated at 35°C for another48h. The accession number of the bacterium cells was provided by the National Center for Biotechnology Information, USA. 2.2.3 Characterization of sawdust  The scanning electron micrograph of the sawdust was performed at10kV voltage in a vacuum chamber together with the measurementof the Brunauer–Emmett–Teller (BET) surface area. This characteri-zation was performed to evaluate whether there is any adsorptioneffect by carrier particles. 2.3 Preparation of stock solution  Thestocksolutionsofpredeterminedconcentrations(1Mchromium,lead, and zinc) were prepared by dissolving the required amount of metal ion salt in deionized water. The residual volume of the stock solutionwasmadeupto1L.Thestocksolutionsofthemetalionwereprepared by dissolving PbCl 2   3H 2 O, ZnCl 2   5H 2 O, and CrCl 3   2H 2 O. The salts were of analytical grade (Merck, Mumbai, India). 2.4 Instrumentation  The pH of the stock and all experimental solutions were measured byadigitalpHmeter(Toshniwal,Ajmer,India).Theconcentrationof metal ions before and after the adsorption was determined by atomicabsorptionspectrophotometry(AAS)(GBCAvanta,Germany). The lamps used in AAS for the measurement of the metal ions werehollow cathodes of zinc, chromium, and lead working at 213.9,357.9, and 283.3nm, respectively. 2.5 Experimentation Pre-sterilized stoppered conical 500mL flasks containing 150mL of solutions of metal ions were agitated with sawdust till theattainment of equilibrium. The agitation of samples was performedin an incubator combined with a shaker (Orbitek Incubator, Ajmer,India). After the attainment of equilibrium time, the sample wasretrieved and analyzed through flame AAS. 2.6 Mathematical approach  The percentage adsorption of metal ions and uptake capacity (mgg  1 )ofadsorbentwerecalculatedbyEqs.(1)and(2),respectively: %  Adsorption  ¼  C  0    C  e C  0   100  ð 1 Þ Uptakecapacity  ¼  C  0    C  e  M  = V   ð 2 Þ  where  C  0 ,  C  e ,  M  , and  V   is the initial concentration of the metal ion,equilibrium concentration of the metal ion, mass of adsorbent (g)and volume of solution (L). All the experiments were repeated thriceand average values were used for curve-fitting. The Langmuir isotherm was used to identify the maximum bestpossible value of the uptake capacity. Equation (3) represents thelinear form of the Langmuir isotherm. The model is based upon theassumption that all the active sites present on the surface of adsorbent are energetically equal [15]. C  e q e ¼  C  e q max þ  1 q max b  ð 3 Þ  where  b  is the Langmuir constant (Lmg  1 ). The errors of theregression models were calculated through the Chi ( x 2 ) square test. The  x 2 square test is shown in Eq. (4): x 2 ¼ X  q e ð exp Þ   q e ð th Þ½  2 q e ð th Þ ð 4 Þ  The correlations used for surface area coverage and separationfactor have been shown in Eqs. (5) and (6):  R L  ¼  11  þ  bC  0    ð 5 Þ  where  R L  is the separation factor of metal ions. bC  0  ¼  u  1    u  ð 6 Þ  where  b  and  u   are Langmuir model constant (Lmg  1 ) and surfacearea coverage. 2 V. Mishra and S. Tadepalli © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com Clean – Soil, Air, Water 2014,  42  (9999), 1–8  2.7 Calculations In the present work, the factorial design of 2 k  1 has been used toidentify the significant factors. The independent variable has beenidentified in-between two boundary conditions of minima andmaxima. The minima and maxima of the boundary conditions aredesignated as positive (P) and negative (N). The modeling of batchtest runs was performed through physico-chemical variables. Thephysico-chemical variables were atomic number, Pauling ionicradius(Å),ionizationpotential(eV),electronegativity,atomicradius,and atomic weight. The analysis of variance (ANOVA), an inbuiltutility of xlstat package (version 7.5.2) with linear regressioncoefficient were used to calculate the linear regression coefficient(  R 2 ), adjusted  R 2 ,  x 2 ,  F  -value,  p -value, and the probability for analysisofgoodness-of-fitofthemaximumuptakecapacity, q max ,curves.Thelevel of data confidence was kept as 0.95. 2.8 BET surface area of biosorbent andimmobilized bacterium  The surface area of the pineapple peel powder particles and sawdust was measured by the BET simultaneous nitrogen sorption desorp-tion isotherm hysteresis method (Micrometrix, India). The flow rateof the gas was 20mLmin  1 . 2.9 Isolation and biochemical characterization ofbacterium cells  The detailed characterization of bacterium cells including Fouriertransformation IF spectra, scanning electron microscopy, and phasecontrast micrograph have been provided in some of our earlierpublications [16, 17]. The minimum inhibitory concentrations fortheisolatedbacteriumcellswere0.6mgL  1 forZn,0.3mgL  1 forPb,and 0.4mgL  1 for Cr. The minimum inhibitory concentrations of the bacterium cells were determined by performing calculationsthrough colony forming units (mL  1 ). 3 Results and discussion 3.1 Surface characterization of sawdust  Table 1 presents the surface characterization of sawdust. It becameevident from Tab. 1 that there was a significant difference betweenthe BET surface area of the pineapple peel powder particles andsawdust. These results led to the conclusion that in addition to thespecific surface area of the immobilized microorganisms, thesurface of pineapple peel powder particles played a significant rolein the biosorption of metal ions from the liquid phase.However, to confirmthe participation of enhancedsurface area of sawdust, a detailed analysis of surface area coverage of metal ionsand separation factor was performed and is given in Section 3.2. 3.2 Influence of enhanced surface area onbiosorption of toxic metal ions  The influence of enhanced surface area of sawdust on the biosorption of the toxic metal ions is shown in Fig. 1. Figure 1presentsthevalueofthesurfaceareacoverageandseparationfactoron the  y -axis and the  x -axis represents the pineapple peel powderparticles and sawdust.ItisclearfromFig.1thatthesurfaceareacoverageandseparationfactor of the metal ions was quite small in case of pineapple peelpowder particles. However, in case of sawdust higher values of surface area coverage and separation factor of metal ions wereobtained compared to pineapple peel powder particles. Higher values of surface area coverage and separation factor of metal ionsobtainedincaseofsawdustwereduetothehigher/enhancedsurfacearea offered to the metal ions during biosorption. 3.3 Derivation of regression models In the development of the regression models, the maximum andminimumlimitsoftheexperimentalvariableswerekeptconstantinall experiments. The maxima and minima of all the physicalparameterswereactuallyfinalizedonthebasisofbatchexperimentsperformed in the present work (results not shown) and only thelowestandhighestvaluesoftheindividualparametersusedinbatchexperiments is given in Tab. 2 (as maxima and minima).It is clear from the factorial design represented in Tabs. 3–5 thatunder different sets of experimental parameters, the response of the percentage removal of metals from the liquid phase showstremendousvariation.Atthismoment, itisdifficultto delineatetheextent of influence, individual or a combination of parameters, onthe removal of metal ions. Therefore, regression models (Eqs. (7)–(9)) were developed to generate the pattern in which experimental variables influence the removal of metal ions.Linear regression data modeling, an inbuilt utility of xlstatpackage (version 7.5.2) has been used in the present work to derivetheregressionmodelsintermsofvariousprocessparameterslikepH(  P  ), initial concentration of metal ions (  I  ), temperature ( T  ), biomassdose (  B ), contact time ( C  ), and agitation rate (  A ). These modelequations were used to predict the influence of process parameters(enlisted above) on the removal of Zn 2 þ , Cr 3 þ , and Pb 2 þ removal,respectively. The equations (Eqs. (7)–(9)) have been derived as linear Table 1.  BET surface area of raw pineapple peel powder particles andsawdust Biosorbent BET surface areaPineapple peel powder particles 14.55 (m 2 g  1 )Sawdust 23.12 (m 2 g  1 ) Figure 1.  Surface area coverage and separation factor of pineapple peelpowder particles and sawdust.Biosorption of Heavy Metal Ions 3 © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com Clean – Soil, Air, Water 2014,  42  (9999), 1–8  regression models using a factorial design of 2 k   1 . The confidencelevel of data was kept at 0.95 for all the metal ions. % Zn  ¼  58 : 21  þ  4 : 65  P     0 : 19 T     7 : 13    10  2  I   þ  0 : 16  B  þ  0 : 02  A þ  4 : 19 C   ð 7 Þ % Pb  ¼  63 : 08  þ  4 : 16  P   þ  4 : 82 T     1 : 89    10  3  I   þ  6 : 73    10  4  B þ  8 : 98    10  4  A  þ  0 : 01 C   ð 8 Þ % Cr  ¼  49 : 36  þ  3 : 33  P     7 : 70    10  3 T     9 : 15    10  3  B  þ  3 : 12  I    1 : 50    10  3  A  þ  9 : 20    10  3 C   ð 9 Þ Equations (7)–(9) were studied in terms of the numericalcoefficients associated with the parameters. The parametersassociatedwithsignificantnumericalcoefficientshavebeenmarkedin bold in these equations. The percent adsorption of metals ions was extrapolated as a function of the independent variables. Thecorresponding  p  and  F   values, the linear regression coefficient, andthe adjusted regression coefficients are shown in Tab. 6. It is evidentfrom Tab. 6 that the values of the linear regression coefficient (  R 2 )and the adjusted  R 2  were similar in magnitude. Furthermore, the value of the Probability was found to be  < 0.05 (Pr >  F  ), whichsignifies that the independent parameters were trustworthy. Additionally, the results of Eqs. (7)–(9) showed that the pH andcontact time played a very significant role in the removal of zincions. In case of chromium, the removal of the metal was preferably dependentuponthepHandinitialconcentrationofmetalions.Leadremoval was found to be more dependent upon the increase of pHand temperature. 3.4 Langmuir maximum uptake capacity  The Langmuir maximum uptake capacity ( q max ) with the corre-sponding values of the linear regression coefficient (  R 2 ) for the zinc,cadmium, and lead ions was calculated through Eq. (3) and theresults have been shown in Supporting Information Tab. S1. Themaximum uptake capacities (derived from the Langmuir model) forCr 3 þ , Zn 2 þ , and Pb 2 þ  were obtained as 0.86, 1.13, and 2.16mgg  1 ,respectively. As can be seen from Supporting Information Tab. S1,the relative order of the removal of metal ions and the decreasingorder of   q max  forthe metal ions was Pb 2 þ > Zn 2 þ > Cr 3 þ . The orderof removal of these metals was due to the difference in their electronegativities and atomic weights. Lead has the highest electronegativity (2.33) and ionic radii 1.18 (Å). However, the electronegativity and ionic radii of zinc and chromium are 1.81, 1.66, 0.75(Å) and 0.62 (Å), respectively. Therefore, in the present work, it wasconcludedthatthehighertheelectronegativity,atomicweight,andionic size, the better is the adsorption of the metal ions. Sengil andOzacar [18] also reported that the higher the atomic weight, electronegativity, electrode potential, and ionic size, the greater is theaffinity of the metal ions towards sorption.In addition to this, the equilibrium uptake capacities ( q e ) of thesawdust (derived from Eq. (2)) of Cr 3 þ , Zn 2 þ , and Pb 2 þ  were obtainedas0.55,0.89,and1.87mgg  1 ,respectively.Thevaluesofequilibriumuptake capacity of metal ions showed the decreasing trend of affinity of metal ions in the order of Pb 2 þ > Zn 2 þ > Cr 3 þ . TheequilibriumuptakecapacitiesofpineapplepeelpowderparticlesforCr 3 þ , Zn 2 þ , and Pb 2 þ  were diminutive compared to the equilibriumuptake capacities of sawdust. The equilibrium uptake capacities of pineapple peel powder particles for Cr 3 þ , Zn 2 þ , and Pb 2 þ  wereobtained as 0.14, 0.39, and 0.92mgg  1 , respectively. Table 2.  Symbols and range of experimental independent parameters Symbol Independents variable RangeN P T   Temperature (°C) 25 35  I   Initial metal ion concentration (mgL  1 ) 15 50  P   pH 1 7  B  Biomass dose (gL  1 ) 1 15  A  Agitation rate (rpm) 50 150 C   Contact time (h) 0.5 7.5 Table 3.  Details of factorial design based-experimental set-up for zinc pH Temperature (°C) Initial concentration (mgL  1 ) Biomass dose (gL  1 ) Agitation rate (rpm) Contact time (h) % Zn7 25 50 1 150 7.5 85.361 35 15 15 50 0.5 57.171 25 15 1 50 0.5 56.977 35 15 15 50 7.5 85.361 25 15 15 150 0.5 85.381 35 50 15 50 0.5 57.721 35 50 15 150 0.5 56.197 25 15 15 50 7.5 85.361 35 50 1 50 0.5 57.711 25 15 1 150 0.5 57.701 25 50 1 150 0.5 56.921 25 50 15 50 0.5 57.727 35 50 1 150 7.5 85.321 35 15 1 150 0.5 57.191 25 50 15 150 0.5 56.967 35 15 1 150 7.5 85.361 35 15 15 150 0.5 56.671 25 15 1 50 0.5 56.621 25 50 15 150 0.5 56.847 25 50 15 50 7.5 85.771 25 15 15 50 0.5 56.697 35 50 1 1 7.5 85.62 4 V. Mishra and S. Tadepalli © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com Clean – Soil, Air, Water 2014,  42  (9999), 1–8  In the present work, the rate of agitation was kept at 150rpm forallbiosorptionexperiments.At150rpm,nodesorptionofmetalionsoccurs from the surface of the sawdust. However, at  > 150 rpm,desorption of Cr 3 þ occurs. The percentages desorption for Cr 3 þ  wascalculated as 33% of the initial concentration of the metal ion(  16.5mgg  1 ). The equilibrium capacity of Cr 3 þ  was also reduced to0.31mgg  1 . However, no desorption of Zn 2 þ and Pb 2 þ occurred at > 150rpm. Therefore, the maximum limit of agitation was fixed at150rpm,inordertoavoiddesorptionofCr 3 þ .ThedesorptionofCr 3 þ at  > 150rpm was due the presence of weak physical forces of interaction between Cr 3 þ ion and sawdust. The presence of physicalforces of interaction (physiosorption) was confirmed by estimatingthe value of the energy of adsorption of Cr 3 þ , which was found to be8kcalmol  1 .TheenergiesofadsorptionofZn 2 þ andPb 2 þ  wereoftheorder of    27kcalmol  1 . 3.5 Ionic characteristics vs. maximum uptakecapacity  All the six physico-chemical ionic properties (Supporting Informa-tion Tab. S2) were evaluated as a function of the maximum uptakecapacity. The influence of ionic characteristics on metal ion uptakecapacityisshowninFigs.2–7.Figures2–7showthatwithanincreasein the atomic number, ionic radii, electronegativity, atomic radiiand atomic weight, there is a substantial increase in the uptake Table 4.  Details of factorial design based-experimental set-up for lead pH Temperature (°C) Initial concentration (mgL  1 ) Biomass dose (gL  1 ) Agitation rate (rpm) Contact time (h) % Pb7 35 15 15 50 0.5 92.231 25 50 1 150 0.5 67.331 25 15 1 150 7.5 67.847 35 50 15 50 0.5 92.361 25 50 1 150 7.5 67.721 25 15 15 50 0.5 67.697 35 50 1 50 7.5 92.421 25 50 15 150 7.5 67.227 35 15 1 50 0.5 92.117 35 50 1 50 0.5 92.191 25 15 15 150 7.5 67.127 35 50 15 50 0.5 92.177 35 50 15 150 0.5 92.337 35 15 15 150 7.5 92.677 35 15 1 150 7.5 92.347 35 15 1 50 7.5 92.331 25 50 15 50 7.5 67.181 25 15 1 150 0.5 67.131 25 15 1 50 7.5 67.197 35 50 1 150 7.5 92.161 25 50 15 50 0.5 67.081 25 50 1 50 0.5 67.16 Table 5.  Details of factorial design based-experimental set-up for chromium pH Temperature (°C) Initial concentration (mgL  1 ) Biomass dose (gL  1 ) Agitation rate (rpm) Contact time (h) % Cr7 25 50 15 50 0.5 92.231 35 15 1 150 0.5 67.331 25 15 1 150 7.5 67.847 35 50 15 50 0.5 92.361 35 15 1 150 7.5 67.721 25 15 15 50 0.5 67.697 35 50 1 50 7.5 92.421 35 15 15 150 7.5 67.227 25 50 1 50 0.5 92.117 35 50 1 50 0.5 92.191 25 15 15 150 7.5 67.127 35 50 15 50 0.5 92.177 35 50 15 150 0.5 92.337 25 50 15 150 7.5 92.677 25 50 1 150 7.5 92.347 25 50 1 50 7.5 92.331 35 15 15 50 7.5 67.181 25 15 1 150 0.5 67.131 25 15 1 50 7.5 67.197 35 50 1 150 7.5 92.161 35 15 15 50 0.5 67.081 35 15 1 50 0.5 67.16 Biosorption of Heavy Metal Ions 5 © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com Clean – Soil, Air, Water 2014,  42  (9999), 1–8
Search
Similar documents
Related Search
We Need Your Support
Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

Thanks to everyone for your continued support.

No, Thanks