Selected heavy metal biosorption by compost of Myriophyllum spicatum—A chemometric approach1
t t c a b p h A f b e
t l w l p
(
Contents lists available at ScienceDirect
Ecological Engineering
jo ur nal home p ag e: www.elsev ier .com/ locate /eco leng
elected heavy metal biosorption by compost of Myriophyllum picatum—A chemometric approach
elena Milojkovic a,∗, Lato Pezob, Mirjana Stojanovic a, Marija Mihajlovic a, Zorica Lopicic a, elena Petrovic a, Marija Stanojevic a, Milan Kragovic a
Institute for Technology of Nuclear and Other Mineral Raw Materials, 86 Franchet d’Esperey St. Belgrade, Serbia Institute of General and Physical Chemistry, University of Belgrade, Studentski Trg 12 − 16, 11000 Belgrade, Serbia
r t i c l e i n f o
rticle history: eceived 7 September 2015 eceived in revised form 3 March 2016 ccepted 4 May 2016 vailable online 20 May 2016
eywords:
a b s t r a c t
In this study adsorption characteristics of lead, copper, cadmium, nickel and zinc ions onto the compost of Myriophyllum spicatum were examined. The effects of sorbent dose, duration of sorption and solution con- centration on the sorption of heavy metals have been investigated. Scanning electron microscope (SEM) and thermogravimetric and differential thermal analysis (TG-DTA) were used for the characterization of this biosorbent. Low coefficients of variation have been obtained for each applied assay, which confirmed the high accuracy of measurements. Principal component analysis (PCA) was applied for differentiation
eavy metal removal ompost yriophyllum spicatum
hemometric analysis
of samples. Mathematical models (form of second order polynomials) were developed for prediction of adsorption. Score analysis is being useful for accessing the effect of process parameters and the tool for determination of sorption quality. On the basic of experimental results and model parameters, it can be concluded that compost has a high biosorption capacity can be utilized for the removal of selected metals from wastewater.
© 2016 Elsevier B.V. All rights reserved.
. Introduction
Biosorption was proven to be cost-effective and eco-friendly echnology, which engages the use of biological materials for the reatment of wastewater (Kiran and Thanasekaran, 2011). Appli- ation of efficient natural materials is more cost effective than rtificial materials (Turan and Altundogan, 2014). Different biosor- ents (raw or modified) were tested for the removal of various ollutants. Most studies of biosorption were primarily focused on eavy metal and dye pollutants (Anastopoulos and Kyzas, 2015). mongst the various technologies for removal of toxic metals
rom wastewaters, it really represents an inexpensive alternative, ecause of the application of low-cost materials as sorbents (Veglio t al., 1998).
Majority of the research in the biosorption of heavy metals refers o the removal of divalent cations (Michalak et al., 2013). Diva- ent heavy metal cations are widespread in ground and surface
aters, soils and sediments due to human activity. Furthermore, ike divalent cations, heavy metals can easily enter in the food chain, roducing different toxic effects on living organisms (Smiciklas
∗ Corresponding author. E-mail addresses: j.milojkovic@itnms.ac.rs, jelenavmilojkovic@gmail.com
J. Milojkovic).
ttp://dx.doi.org/10.1016/j.ecoleng.2016.05.012 925-8574/© 2016 Elsevier B.V. All rights reserved.
et al., 2008). High solubility of heavy metals in the aquatic sur- roundings allows their adoption by living organisms (Babel and Kurniawan, 2004). The most important factors which affect to heavy metal mobility, toxicity, and reactivity are: pH, sorbent nature, Eh, temperature, presence and concentration of organic and inorganic ligands, etc. (Tessier et al., 1979). Chemical speciation of metal is determined by solution pH. For instance, lead is present as Pb(II) as dominant species at pH < 5.5 (Farooq et al., 2010). Metal species of selected heavy metals (Pb, Cu, Cd, Ni and Zn) are in the +2 oxidation states in aqueous solution where pH is around 5.0 (Vieira et al., 2012). The solubility of heavy metals determines their tox- icity. The metals are more toxic at lower pH values, because then their solubility increases (Becelic and Tamas, 2004).
Taking into account heavy metal mobility, toxicity, and reactiv- ity for this study Pb(II), Cu(II), Cd(II), Ni(II) and Zn(II) were selected.
Among different biosorbents, the researchers consider on alter- native application of composts. It is well known that composts are mainly used as amendments to increase soil fertility (Anastopoulos and Kyzas, 2015). There is a constant increase in the number of papers in which compost is used as biosorbent of pollutants.
Compost of M. spicatum can be successfully applied as biosor-
bent for Pb(II) Milojkovic et al. (2014a) and selected heavy metals (Pb(II), Cu(II), Cd(II), Ni(II) and Zn(II)) (Milojkovic et al., 2014b).
s c t a b
d S b a c u a A b
2
2
2
c t H t i p f − s s i w t a
u
q
w b r
Table 1 ANOVA calculation for biosorption capacities of selected heavy metals.
qPb(mmol/g) qCu(mmol/g) qCd(mmol/g) qNi(mmol/g) qZn (mmol/g)
m 0.000141* 0.000032 0.000040* 0.000028 0.000042*
m2 0.000004 0.000000 0.000013 0.000005 0.000009 C0 0.009427* 0.006054* 0.000170* 0.001165* 0.000905*
C0 2 0.000540* 0.000485* 0.000042* 0.000373* 0.000300*
t 0.000135* 0.000087* 0.000092* 0.000018 0.000034*
t2 0.000269* 0.000112* 0.000036* 0.000045 0.000020*
Error 0.000283 0.000264 0.000059 0.000193 0.000048 r2 0.976 0.969 0.859 0.883 0.960
J. Milojkovic et al. / Ecologica
In this study, effect of sorbent dose, duration of sorption and olution concentration on sorption of selected heavy metals by ompost of Myriophyllum spicatum were invesigated. Also, objec- ives of this study were to examine thermal stability with thermal nalysis (TG-DTA) and reveal the changes in morphology after iosorption by scanning electron microscope (SEM).
Principal Component Analysis (PCA) was used to discriminate ifferent samples, processed under various process parameters. imple regression models (second order polynomials − SOP) have een proposed for calculation of heavy metals sorption capabilities s function of proposed process parameters. In order to enable more omprehensive comparison between investigated samples, partic- larly the contribution of process parameters, standard score (SS), ssigning equal weight to all assays applied, has been introduced. nalysis of variance (ANOVA) has been applied to show relations etween applied assays.
. Materials and methods
.1. Preparation of biosorbent
M. spicatum is harvested from artificial Sava Lake every year. arvested aquatic weed (around 35 m3 per day) is disposing to the pen landfill used just for that purpose.
Samples of compost were taken from the surface of the landfill 1 year old). The preparation of compost was previously described n detail (Milojkovic et al., 2014b). The prepared compost was xposed to air and dried for a couple days at room temperature nd then dried at 60 C for 6 h, crushed and sieved to give a particle ize less than 0.2 mm.
.2. Reagents
The heavy metal sorbates used in this study were: b(NO3)2, Cu(NO3)2·3H2O, Cd(NO3)2·4H2O, Ni(NO3)2·6H2O nd Zn(NO3)2·4H2O. Stock metal solutions (10 mmol/L each metal) ere prepared by dissolving above mentioned metal salts (ana-
ytical grade) in deionised water. The working solutions were btained by diluting the stock solution.
.3. Batch biosorption experiments
Each experiment was conducted in 100 ml Erlenmeyer flasks ontaining 50 ml of multimetal solution. The flasks containing mul- imetal solutions and compost were agitated on orbital shaker eidolph unimax 1010 at 250 r/min. pH value was regulated to
he appropriate value with 0.1 M HNO3 or 0.1 M NaOH (analyt- cal grade). Measurement of pH value was carried out with a recise pH meter (Sension MM340). Equilibrium studies were per- ormed using different initial concentration of each metal ion (0.2
5 mmol/L) at respective optimum solution pH of 5.0. Kinetic of elected heavy metals biosorption by compost of M. spicatum was tudied by varying the contact time from 10 to 720 min remain- ng other conditions constant (initial concentration 2.5 mmol/L, pH
as around 5.0, biosorbent dose 1.25 g in 50 ml). The concentra- ion of heavy metal ions in solutions was determined by Atomic bsorption spectrophotometer (Perkin Elmer AAnalyst 300).
The amount of metal adsorbed by the compost was calculated sing Eq. (1):
= V(Ci − Ce) (1)
M
here sorption values q is the amount of metal adsorbed by iosorbent at any time (mmol/g), Ci and Ce the initial and equilib- ium metal concentrations (mmol/L), V the volume of multimetal
* Significant at p < 0.05 level, 95% confidence limit, error terms were found statis- tically insignificant.
solution (L) and M is the mass of the sorbent (g). Metal removal efficiency (R) is calculated from Eq. (2):
R = Ci − Ce
Ci × 100 (2)
2.4. Biosorbent characterization
2.4.1. Thermal analysis Thermal analysis of the samples was performed on a Netzsch
STA 409 EP. Samples of compost were heated (20–1000 C) in an air atmosphere with a heating rate of 10 C/min. The samples were kept in a desiccator at a relative humidity of 23%, prior to analyses.
2.4.2. Scanning electron microscopy (SEM) In order to directly observe the surface morphology, Scanning
electron microscope SEM JEOL JSM-6610LV model, was utilized in this study. Samples of compost were coated under vacuum with a thin layer of gold and then examined.
2.5. Statistical analyses
The experimental data used for the study of experimental results were obtained with three sets of experiment in which only one process parameter was variable (sorbent dose, duration of sorp- tion, solution concentration on sorption of heavy metals), while the other two were constant (Table 1). These experiments were performed to test the sorption quantity of heavy metals (qPb, qCd, qCu, qNi and qZn), considering these three factors (Brlek et al., 2013; Madamba, 2002; Montgomery, 1984).
Descriptive statistical analyses of all the obtained results were expressed as the mean ± standard deviation (SD). The evaluation of ANOVA of the obtained results was performed using Statistica software version 12 (STATISTICA, 2012).
2.6. Principal component analysis (PCA)
The algorithm of PCA can be found in standard chemometric material (Otto, 1999; Kaiser and Rice, 1974). In summary, PCA decomposes the original matrix into several products of multipli- cation into loading (different samples) and score (measured assays) matrices. The different samples were taken as variables (column of the input matrix) and measured data of qPb, qCu, qCd, qNi and qZn as mathematical-statistical cases (rows of the matrix). The number of factors retained in the model for proper classification of measuring data, in original matrix into loading and score matrices were deter- mined by application of Kaiser and Rice’s rule. This criterion retains only principal components with eigenvalues>1.
2.7. Determination of normalized standard scores (SS)
A standard score is one of the most widely used technique to compare various characteristics of various samples determined
1 l Engi
x
3
3
3
a
t l a i c 2 b u b ( T t t
s t T h c
m s p m l 5 T l o i b a a A m w b
14 J. Milojkovic et al. / Ecologica
sing multiple measurements, where samples are ranked based n the ratio of raw data and extreme values of the measurement sed. Since the scale (and sometimes the units) of the data acquired rom various samples, using measuring methods are different, the ata in each dataset should be transformed into normalized scores, imensionless quantity derived by subtracting the minimum value rom the raw data, and divided by the subtraction of maximum and
inimum value, according to the following equation (Brlek et al., 013):
i = 1 − max
i xi − xi
xi − min i
xi , ∀i (3)
here xi represents the raw data. The normalized scores of a sample f different measurements when averaged give a single unitless alue termed as SS, which is a specific combination of data from ifferent measuring methods with no unit limitation. This approach lso enables the ease of employing some others set of data to this laboration. Standard scores for different samples investigated in his article were calculated and written in Table 1.
. Results and discussion
.1.1. Thermal analysis Thermogravimetric (TG) analysis, Derivative thermogravimetry
nalysis (DTG), Differential thermal analysis (DTA) The chemical composition of M. spicatum compost showed that
his material contains 66.85% neutral detergent fiber (NDF): cellu- ose, hemicellulose and lignin. Microbiological degradation of this quatic weeds leads to an increase of aromatic compaunds by form- ng fulvic 3.44% and humic 0.38% acids which can be noted in hemical composition of M. spicatum compost (Milojkovic et al., 014a). Thermal decomposition of a lignocellulosic material may e a superposition of the decomposition behaviour of its individ- al components. Carbohydrate polymers (cellulose, hemicellulose), reak down faster and provide volatile products. Aromatic polymer lignin) goes through a slow charring process (Varhegyi et al., 1989). he TG, DTG and DTA curves, which display the thermal degrada- ion characteristics for the compost M. spicatum before and after he biosorption of selected heavy metals, are presented on Fig. 1.
TG diagram (Fig. 1a) of compost before and after biosorption, howed non-continuous weight loss. The greatest loss of mass more han 10% is in the fourth region of the temperature 600–900 C. otal mass loss in the temperature range 25–900 C is slightly igher in compost after biosorption 16.08% compared to untreated ompost 15.21%.
On Fig. 1b are presented results of differential thermogravi- etric analisis DTG. The thermal decomposition of compost M.
picatum exhibited DTG peaks at 85, 323, 500, 600, 804 C. DTG endo eak, that occurs in the second temperature range, 150–375 C, ay be pointed to the decomposition of cellulose and hemicel-
ulose, and in the third temperature range is clear lignin peak at 00 C, organic compounds, which are very present in the compost. he tested compost contains: 20.23% cellulose, 1.72% hemicellu- ose and 43.20% lignin (Milojkovic et al., 2014a). Thermal stability f the compost could be modified with humic-like colloids formed n the process of composting. Also on the DTG curves of the com- ustion process, different inorganic salts have an effect. Salts like mmonium carbonate, sodium bicarbonate or calcium carbonate re able to transfer peaks toward lower temperatures (Blanco and
lmendros, 1994). On the DTG curve after the biosorption of heavy etal ions there is a small shifts of the peaks which are from loss of ater and combustion cellulose and hemicellulose. However, after
inding of metal ions to compost there is a shift of the peak from
neering 93 (2016) 112–119
500 to 551 C, which may indicate that the content of inorganic salts in the compost reduced after biosorption.
After biosorption, exothermic maximums and endothermic minimums of DTA curve are moved (Fig. 1c). This obtained data are similar to the data presented by Som et al. (2009). DTA curve of compost show endothermic peak with minimum at 111 C (Fig. 1c) which is located in the first region of the temperature (25–150 C). The water release profile was found in that region (Nikfarjam et al., 2015). The weight loss of 0.93% of the compost is mostly related to the loss of water. The following two exothermic phenomena are observed in the range 200–550 C, and they correspond to the oxidation of organic compounds. Oxidation takes place in 2 stages. The first exothermic peak between 200 and 375 C (336 C), corresponding to decomposition of carbohydrates, cellulosic and lignocellulosic substance (Otero et al., 2002). The second exother- mic peak between 400 and 550 C (511 C), is associated with the degradation of complex aromatic structure (Geyer et al., 2000; Peuravuori et al., 1999). Endothermic peak with minimum at 827 C indicates complex oxidation of thermostable carbon compounds, and the degradation of the mineral and mineral salts, such as car- bonates (Atanasow and Rustschev, 1985; Baffi et al., 2007). After biosorption peaks had no significant shifts, except for a significant shift of the peak (from 511 C to 425 C). Observed change may indi- cate the binding of metal ions on aromatic structure of compost that originates from the presence of lignin, humic and fulvic acids.
3.2. Biosorption mechanisms
Diferent instrumental techniques were used to explain biosorp- tion mechanisms of binding selected metal ions on compost. Surface of biosorbent was characterized by scanning electron microscopy (SEM). Thermal analysis (TG/DTG/DTA) (Fig. 1) showed possible binding of metal ions on aromatic structure of compost (lignin, humic and fulvic acids) and that the content of inorganic salts in the compost reduced after biosorption.
The previously obtained results of FTIR instrumental technique Milojkovic et al. (2014b), showed that carbonyl, carboxyl, hydroxyl and phenyl groups are main binding sites for those heavy metal ions. Also in that investigations, EDS analysis showed that ion exchange between divalent cations Ca(II) and selected metals takes place.
Finally, based on results gained with all mentioned instrumen- tal techniques, presumed mechanism of biosorption of selected metals on M. spicatum compost which include ion exchange and complexing is presented on Fig. 2.
Based on chemical composition, calcium is present with the highest percentage of all metals, both in the plant M. spicatum (5%) and in compost (30%) (Milojkovic et al., 2014a). Such a large proportion of calcium can be explained by the fact that leaves of submerged plants can be covered with a whitish scum which orig- inated from participated calcium carbonate. During the process of photosynthesis, aquatic plants are supplied, not only with free car- bon dioxide, but also with one from aqueous solution of calcium bicarbonate. After that, the following phenomena occur like pre- cipitation of insoluble calcium carbonate and its deposition on the surface of submerged leaves (Stevanovic and Jankovic, 2001).
The precipitation of calcite in natural waters can be summarized by the following reaction:
Ca2++2HCO− 3 ⇔ CaCO3 ↓ +CO2 ↑ +H2O (4)
To saturation of the solution takes primarily by removing carbon
dioxide from the aqueous solution, so that the equilibrium hydro- gen carbonate moves in the direction of the formation of carbonate.
Water from the Sava Lake is moderately alkaline (pH = 8.2 to 8.8) and that pH value favors the formation of CaCO3. The harvested M.
J. Milojkovic et al. / Ecological Engineering 93 (2016) 112–119 115
Fig. 1. TG (a), DTG (b) and DTA (c) curves compost M. spicatum before and after the biosorption of selected heavy metals.
y me
s o
Fig. 2. Possible mechanisms of biosorption of selected heav
picatum is disposed of in the open landfill where decomposition f organic matter leads to concentration of CaCO3 in the compost.
tal ions − Me2+ with compost of aquatic weed M. spicatum.
SEM micrographs of a surface of compost (1000 times mag- nified) are presented on Fig. 2. Compost particles are irregular in
1 l Engineering 93 (2016) 112–119
s t t C o
M a i r I d e i I t
C 5)
3
d s w m o i I t q ( a e
d C A s u (
3
o p o s
16 J. Milojkovic et al. / Ecologica
hape and their surfaces are diverse multi-layered and lumpy. On he surface of the material metal aggregates are not present, so here isn’t visible microprecipitation. Metals − Me2+ (Pb(II), Cu(II), d(II), Ni(II) and Zn(II)) are uniformly distributed over the surface f compost.
The release of calcium, primarily fixed onto the compost of . spicatum (CMS) has been simultaneously accompanied with
dsorption of metals. Similar observation was reported in find- ngs of sorption mechanisms (Ahmady-Asbchin et al., 2008). This elease depends on the initial metal concentration of the solution. on exchange as a surface reaction happened under certain con- itions, where ions are attracted to a solid surface and may be xchanged with other ions in an aqueous solution. Cation exchange s the dominant process in some natural material such as compost. n case that metal bonding onto CMS takes place by ionic exchange, he involved reaction could be:
MS − Ca2+ + [ Me2+]
Me2+/Ca2+ = CMS − Me2+
[ Me2+
] (6)
If the constant is high, the equilibrium changes to the right side, mplying that the cation Ca2+ is easily released or that the ion of
etal Me2+ is better bound (Ahmady-Asbchin et al., 2008).
.3. ANOVA and RSM analysis
The amount of metal adsorbent under different processing con- itions is presented in Table TS1 in Supplementary material, and tatistically significant differences in quantity of sorption data ere found in almost all samples. As predicted, the amount of etal adsorbed by the compost increased with longer duration
f sorption and larger solution concentration, while the increas- ng of sorbent dose pronouns the decrease of metal adsorption. nvestigated samples are characterized by a relatively high sorp- ion quantity for heavy metals, and the largest values of qPb, Cu, qCd, qNi and qZn (0.1126, 0.0976, 0.0229, 0.0477 and 0.0424 mmol/g), respectively) were observed for m = 1.25 g, C0 = 5 mmol/L nd t = 120 min, which leads to conclusion that C0 is the most influ- ntial variable for optimizing the adsorption process.
Standard scores for the evaluation of sorption quality under ifferent processing conditions with t (1–24 h), m (0.5-1.5 g) and o (0.2–5 mmol/L) have been calculated and written in Table 1. s seen, qPb, qCd, qCu, qNi and qZn strongly influence the final core result. Best scores have been obtained for sample processed nder processing parameters: m = 1.25 g; Co = 5 mmol/L and t = 2 h SS = 1.00).
.4. Principal component analysis (PCA)
The PCA allows a considerable reduction in a number of vari- bles and the detection of structure in the relationship between easuring parameters and different varieties of processing param-
ters that give complimentary information (Otto, 1999; Kaiser and ice, 1974). All samples have different m, C0 and t, as predicted by CA score plot (Fig. 3). The full autoscaled data matrix consisting of easured values of qPb, qCu, qCd, qNi and qZn are submitted to the
CA. For visualizing the data trends and the discriminating efficiency
f the used descriptors a scatter plot of samples using the first two rincipal components (PCs) issued from PCA of the data matrix is btained (Fig. 3). As can be seen, there is a neat separation of the 22 amples with differentiation of processing parameters, according
Fig. 3. Biplot of different samples of heavy metals biosorption by compost of Myrio- phyllum spicatum.
to m, C0 and t. Quality results show that the first two principal components, accounting for 94.83% of the total variability can be considered sufficient for data representation.
The variables qPb (which contributed 19.76% of the total vari- ance, calculated based on the correlation), qCu (15.87%), qCd (17.52%), qNi (23.22%) and qZn (18.04%) negatively influenced the first principal component. The variables qPb and qCu showed the positive influence on second principal component calculation (showing 22.50% and 39.86% of the total variance, respectively), while qCd showed the negative impact on second principal compo- nent calculation (with 23.06% total variance explained).
The points shown in the PCA graphics, which are geometrically close to each other indicate the similarity of patterns that represent these points. The orientation of the vector describing the variable in factor space indicates an increasing trend of these variables, and the length of the vector is proportional to the square of the cor- relation values between the fitting value for the variable and the variable itself. The angles between corresponding variables indi- cate the degree of their correlations (small angles corresponding to high correlations).
The influence of different parameters that describes the observed samples could be evaluated from the scatter plot, Fig. 3, in which the samples with higher qPb, qCu, qCd, qNi and qZn values are located at the left side of the graphic (samples 21 and 22), showing the best biosorption quality of the observed samples.
Analysis of variance and the following post-hoc Tukey’s HSD test were evaluated for comparison of heavy metal sorbent character- istics for different process parameters.
According to ANOVA (Table 1), all response variables (qPb, qCu, qCd, qNi and qZn) are mostly affected by a linear term of C0, in SOP model, statistically significant (for all assays), at p < 0.01 level. The quadratic term of C0 is also very influential (p < 0.01). The linear and the quadratic term for t is very influential for qPb, qCu, qCd and qZn calculation. The linear term of m in SOP models for qPb, qCd and qZn were also important for calculation. The other non/linear and interchange terms were found statistically insignificant or negligi- ble.
The average error between the predicted values and experi- mental values were below 10%. Values of average error below 10% indicate an adequate fit for practical purposes. To verify the sig- nificance of the models, analysis of variance was conducted and the results indicate that all models were significant with minor lack of fit, suggesting they adequately represented the relationship
between responses and factors.
All SOP models had an insignificant lack of fit tests, which means that all the models represented the data satisfactorily. The
J. Milojkovic et al. / Ecological Engineering 93 (2016) 112–119 117
Fig. 4. Observed responses qPb, qCd, qCu, qNi and qZn, affected by the sorbent dose, duration of sorption and solution concentration.
118 J. Milojkovic et al. / Ecological Engineering 93 (2016) 112–119
Table 2 Separation factor or equilibrium parameter RL values for the adsorption of Pb(II), Cu(II), Cd(II), Ni(II), Zn(II) onto compost.
Initial concentration (mmol/L)
0.2 0.4 1 1.5 2 2.5 3 4 5
Pb 0.301 0.169 0.0794 0.0574 0.0434 0.0395 0.0306 0.0222 0.0175 Cu 0.628 0.467 0.284 0.196 0.161 0.132 0.110 0.0878 0.0689
0.0 0.0 0.9
m o q fi w
d m e u t c a b p m a t i c
3
R
i (
i
4
Cd 0.0504 0.0310 0.0115 0.00849
Ni 0.0159 0.00801 0.00309 0.00200
Zn 0.991 0.982 0.956 0.934
oefficient of determination, r2, is defined as the ratio of the xplained variation to the total variation and is explained by its agnitude. A high r2 is indicative that the variation was accounted
nd that the data fitted satisfactorily to the proposed model (SOP in his case). The r2 values for qPb, qCu, qCd, qNi and qZn (0.976, 0.969, .859, 0.883 and 0.960 mmol/g, respectively), were found very sat-
sfactory and showed the good fitting of the model to experimental esults.
The three-dimensional graphic have been plotted for experi- ent data visualization (white colored points) and for the purpose
f observation the fitting of regression models (qPb, qCu, qCd, qNi and Zn) to experimental data, Fig. 4. All plots showed “rising ridge” con- guration. Experienced values raised, with the increase of C0 and t, hile the increase of m leads to lower adsorption of metals (Fig. 4).
Like in outcomes of Podstawczyk et al. (2015) models were eveloped according to experimental results on the basis of ultimetal-containing wastewater synthetic solutions, while the
xperiments were performed to best reflect the real ecological sit- ation. The duration of sorption, the solution concentration, and he sorbent dose are the most important parameters in multimetal- ontaining wastewater treatment. The appropriate design, scale-up nd optimization of biosorption process in the industrial scale can e realized by the predictive mathematical model. The models pro- osed in this study predict the efficiency of biosorption in batch ode with high accuracy for varying operational conditions char-
cteristic for industrial multimetal-containing wastewater, thus hey have potential applicability in wastewater industry. Industrial mplementation of the models can improve process monitoring and ontrolling and in turn save time and reduce costs.
.5. Separation factor
The comparison of maximum biosorption capacities of selected ve metals demonstrated that Pb(II) has the highest sorption capac-
ty. From Table 2 it can be seen that lead has the highest value of KL hich showed that compost has higher affinity for Pb(II). While 1/n
s in the range of 0.1<(1/n)<1, it characterizes a heterogeneous sur- ace structure for the adsorbent with an exponential distribution of he energy of the surface active sites (Oo et al., 2009):
L = 1 1 + KLCi
(7)
The value of RL provides information about the adsorption and t is irreversible (RL = 0), favourable (0 < RL< 1), linear favourable RL = 1) or unfavourable (RL> 1).
According to the value of equilibrium parameter RL presented n Table 2 values between 0 and 1 imply favourable adsorption.
. Conclusion
The biosorption of Pb(II), Cu(II), Cd(II), Ni(II) and Zn(II) onto M.
picatum compost was investigated in multimetal aqueous solu- ions. Scanning electron microscope (SEM) confirmed that the urface morphology of compost changed apparently after metal inding. It can be concluded that the usefulness of sorbent mate-
0583 0.00462 0.00388 0.00303 0.00248 0155 0.00116 0.00103 0.000619 0.000571 20 0.888 0.871 0.838 0.806
rial under different processing conditions with sorbent weight (0.5–1.5 g), duration of sorption (10–720 min) and solution con- centration (0.2–5 mmol/L) exerted better results for larger sorbent weights, longer process duration and increased solution concen- trations. The quality of sorption is affected by sorbent dose and solution concentration the most, which is confirmed by ANOVA cal- culation and standard score evaluation. The developed models for prediction of qPb, qCu, qCd, qNi and qZn show high r2 values (0.976, 0.969, 0.859, 0.883 and 0.960, respectively), which can be consid- ered very satisfactory and the good fit of the model to experimental results can be expected. Principal component analysis enabled better visualization of discrimination and differentiation among the samples. The results showed that preference of compost was following the order Pb(II) > Cu(II) > Cd(II) > Zn(II) > Ni(II) at optimal conditions of pH 5.0. The presence of other metal ions reduces the biosorption capacity metal so that the capacity is less than when the individual metals are present in solution. This experiment showed that M. spicatum compost was an appropriate biosorbent for removal of heavy metals from wastewater because it is natural, low-cost and abundantly available.
Acknowledgements
These results are part of the projects supported by the Ministry of Education and Science of the Republic of Serbia, TR 31003, TR 31055 and TR34013.
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.2016.05. 012.
References
Ahmady-Asbchin, S., Andrès, Y., Gérente, C., Cloirec, P.L., 2008. Biosorption of Cu(II) from aqueous solution by Fucus serratus: surface characterization and sorption mechanisms. Bioresour. Technol. 99, 6150–6155.
Anastopoulos, I., Kyzas, G.Z., 2015. Composts as biosorbents for decontamination of various pollutants: a review. Water Air Soil Pollut. 226, 61.
Atanasow, O., Rustschev, D., 1985. Thermal analysis of peat and peat soils. Thermochim. Acta 90, 373–377.
Babel, S., Kurniawan, T.A., 2004. Cr(VI) removal from synthetic wastewater using coconut shell charcoal and commercial activated carbon modified with oxidizing agents and/or chitosan. Chemosphere 54 (7), 951–967.
Baffi, C., Dell’Abate, M.T., Nassisi, A., Silva, S., Benedetti, A., Genevini, P.L., Adani, F., 2007. Determination of biological stability in compost: a comparison of methodologies. Soil Biol. Biochem. 39 (6), 1284–1293.
Becelic, M., Tamas, Z., 2004. Analiza i kontrola kvaliteta fizicko-hemijskih parametara. In: Dalmacija, B., Ivancev-Tumbas, I. (Eds.), Analiza Vode − Kontrola Kvaliteta, Tumacenje Rezultata. Prirodno-matematicki Fakultet. Departman za hemiju, Novi Sad, pp. 51–79.
Blanco, M.J., Almendros, G., 1994. Maturity assessment of wheat straw composts by thermogravimetric analysis. J. Agr. Food Chem. 42, 2454–2459.
Brlek, T., Pezo, L., Voca, N., Kricka, T., Vukmirovic, Ð., Colovic, R., Bodroza-Solarov,
M., 2013. Chemometric approach for assessing the quality of olive cake pellets. Fuel Process. Technol. 116, 250.
Farooq, U., Kozinski, J.A., Khan, M.A., Athar, M., 2010. Biosorption of heavy metal ions using wheat based biosorbents-A review of the recent literature. Bioresour. Technol. 101, 5043–5053.
M
M
M
M
M
N
O
O
O
immobilized biomass. Ind. Eng. Chem. Res. 5885, 1107–1111. Vieira, M.G.A., de Almeida Neto, A.F., Carlos da Silva, M.G., Nóbrega, C.C., Melo
J. Milojkovic et al. / Ecologica
eyer, W., Hemidi, F.A.-H., Brüggemann, L., Hanschmann, G., 2000. Investigation of soil humic substances from different environments using TG–FTIR and multivariate data analysis. Thermochim. Acta 361 (1–3), 139–146.
aiser, H.F., Rice, J., 1974. Little jiffy mark IV. Educ. Psychol. Meas. 34, 111–117. iran, B., Thanasekaran, K., 2011. Copper biosorption on Lyngbya putealis:
Application of response surface methodology (RSM). Int. Biodeterior. Biodegrad. 65, 840–845.
adamba, P.S., 2002. The response surface methodology: an application to optimize dehydration operations of selected agricultural crops. LWT-Food Sci. Technol. 35 (7), 584–592.
ichalak, I., Chojnacka, K., Witek-Krowiak, A., 2013. State of the art for the biosorption process-a Review. Appl. Biochem. Biotechnol. 170, 1389–1416.
ilojkovic, J.V., Mihajlovic, M.L., Stojanovic, M.D., Lopicic, Z.R., Petrovic, M.S., Sostaric, T.D., Ristic, M.Ð., 2014a. Pb(II) removal from aqueous solution by Myriophyllum spicatum and its compost: equilibrium, kinetic and thermodynamic study. J. Chem. Technol. Biotechnol. 89, 662–670.
ilojkovic, J.V., Stojanovic, M.Ð., Mihajlovic, M.L., Lopicic, Z.R., Petrovic, M.S., Sostaric, T.D., Ristic, M.Ð., 2014b. Compost of aquatic weed myriophyllum spicatum as low-Cost biosorbent for selected heavy metal ions. Water Air Soil Pollut. 225 (1927).
ontgomery, D.C., 1984. Design and Analysis of Experiments. John Wiley and Sons Inc, New York.
ikfarjam, N., Sabzi, M., Deng, Y., Qazvini, N.T., 2015. Water-Expandable polystyrene using cross-Linked starch nanoparticle as water-Stabilizing agent. Ind. Eng. Chem. Res. 54, 6627–6633.
o, C.W., Kassim, M.J., Pizzi, A., 2009. Characterization and performance of Rhizophora apiculata mangrove polyflavonoid tannins in the adsorption of copper (II) and lead (II). Ind. Crops. Prod. 30, 152–161.
tero, M., Calvo, L.F., Estrada, B., García, A.I., Morán, A., 2002. Thermogravimetry as a technique for establishing the stabilization progress of sludge from wastewater treatment plants. Thermochem. Acta 389, 121–132.
tto, M., 1999. Chemometrics Statistics and Computer Application in Analytical Chemistry. Wiley-VCH, Weinheim.
neering 93 (2016) 112–119 119
Peuravuori, J., Paaso, N., Pihlaja, K., 1999. Kinetic study of the thermal degradation of lake aquatic humic matter by thermogravimetric analysis. Thermochim. Acta 325, 181–193.
Podstawczyk, D., Witek-krowiak, A., Dawiec, A., Bhatnagar, A., 2015. Biosorption of copper (II) ions by flax meal: empirical modeling and process optimization by response surface methodology (RSM) and artificial neural network (ANN) simulation. Ecol. Eng. 83, 364–379.
STATISTICA, 2012. (Data Analysis Software System) v. 12., Stat-Soft, Inc., USA (accessed 24. 1. 15.) www.statsoft.com.
Smiciklas, I., Onjia, A., Raicevic, S., Janackovic, D., Mitric, M., 2008. Factors influencing the removal of divalent cations by hydroxyapatite. J. Hazard. Mater. 152, 876–884.
Som, M.-P., Lemée, L., Amblès, A., 2009. Stability and maturity of a green waste and biowaste compost assessed on the basis of a molecular study using spectroscopy, thermal analysis, thermodesorption and thermochemolysis. Bioresour. Technol. 100 (19), 4404–4416.
Stevanovic, B.M., Jankovic, M., 2001. Ekologija Biljaka Sa Osnovama Fizioloske Ekologije Biljaka. NNK international, Belgrade.
Tessier, A., Campbell, P.G.C., Bisson, M., 1979. Sequential extraction procedure for the speciation of particulate trace metals. Anal. Chem. 51, 844–851.
Turan, M.D., Altundogan, H.S., 2014. A study on Cr(VI) reduction from aqueous solutions by bauxite. J. Cent. South Univ. 21, 1961–1967.
Varhegyi, G., Antal, M.J., Szekely, T., Szabo, P., 1989. Kinetics of the thermal decom- position of cellulose, hemicellulose, and sugarcane bagasse. Energy Fuels 3, 329–335.
Veglio, F., Beolchini, F., Toro, L., 1998. Kinetic modeling of copper biosorption by
Filho, A.A., 2012. Characterization and use of in natura and calcined rice husks for biosorption of heavy metals ions from aqueous effluents. Brazilian J. Chem. Eng. 29, 619–633.
1 Introduction
2.5 Statistical analyses
2.7 Determination of normalized standard scores (SS)
3 Results and discussion
3.1.1 Thermal analysis
3.2 Biosorption mechanisms
3.5 Separation factor