Data fitting to the model and ANOVA
Considering models 1 to 3, the values coefficients of determination (i.e. 0.93, 0.86 and 0.87 for PbR, PbO and PbC, respectively) indicated a good fit between predicted values and the experimental data points. For a good model fit R2, should be more than 0.8. In general, the closer the R2 value is to 1.00 indicating the better fitting and more suitable model for the prediction of the response variables. In all the responses, differences between predicted R2 and adjusted R2 were less than 0.2 (Table 2), which indicates reasonable agreement between regression coefficients. According to the Table, the AP ratios for all the responses are considerably greater than 4, which describe good model discrimination. Normally the ratio greater than 4 is desirable, for the models to be used effectively. In all the responses, in comparison with the other models, the linear model (for PbC) showing higher PRESS value (the smaller the PRESS value, the better the model’s predictive ability). The low SD and CV values indicate the high precision and reliability of the experiments. According to Table 2, SD values were 0.30, 0.41 and 0.21, whereas CV values were 3.96, 5.66 and 3.50 for PbR, PbO and PbC, respectively. As a general rule, CV should not be higher than ten percent. The CV values calculated in this study were much lower than the limit, indicating high precision of the conducted experiments. The low values of CV showed that the variabilities between the predicted and observed values are low and were indicative of high reliability of the experiments (Apul et al. 2012; Abu Amr et al. 2014; Qiu et al. 2014; Kumar and Bishnoi 2017; Mourabet et al. 2017).
Considering the above mentioned points, all the selected models have high R2 value, significant F-value, a non-significant lack-of-fit p-value, desirable AP values and low SD and CV. The results confirm that the responses can be predicted with high reliability. Hence, the models can be applied for predictive purposes.
Effects of factors on the responses
Effects of main variables
Significance of the effects
Table 1 can be used to determine which factors significantly affect each response (Montgomery 2012). According to the table, factors biosorbent dosage, initial concentration and pH were significant for all the responses (p<0.05), whereas biosorbent size was not significant for any of the responses. The contact time and temperature were significant only for PbR and PbC, respectively. The salinity was significant for the response variables PbR and PbC.
Order of the effects
The relative effects of significant factors on the responses were determined by evaluating the p-values and F-ratios (Table 1). The parameter with the lowest p-value and the highest F-ratio shows the greatest impact on the response variables (Zar 2010). As mentioned previously, the relative influences of the parameters on the response variables may also be deduced from the perturbation plots (Fig. 2). In a perturbation plot, when the variable produces a steep slope or curvature, then the response variable is sensitive to that parameter, while a relatively flat line shows insensitivity to change in that particular variable (Anderson and Whitcomb 2016; Myers et al. 2016). Furthermore, the relative effects can also be distinguished by comparing the coefficients of the factors in the regression models. To evaluate the relative effect of the independent variables, the coefficients calculated in the regression equations (1 to 3) can be directly compared (Anderson and Whitcomb 2007). Description of the order of effects of the studied factors on each of the three response variables are provided separately below:
PbR: The initial concentration had the highest F-ratio and the lowest p-value (301.13 and <0.0001, respectively). Hence, this factor had the greatest effect on PbR, followed by biosorbent dosage, pH, salinity and contact time (Table 1). The similar results can be obtained from Fig. 2-a. The perturbation plot clearly shows that of the five significant independent variables, biosorbent dosage and initial concentration affect the value of PbR more than the others. With regards to the values of the model coefficients in Equation 1, a similar decreasing order of effects (with the relevant coefficients) was also observed as follows: initial concentration (1.07), biosorbent dosage (0.7235), pH (0.6499), salinity (0.2312) and contact time (0.1768).
PbO: According to Table 1, the factor of initial concentration had the highest F-ratio and the lowest p-value (169.00 and <0.0001, respectively). Hence, this factor produced the highest effect on the response, followed by biosorbent dosage and pH. Fig. 2-b clearly shows that initial concentration has the main and the major effect on PbO followed by biosorbent dosage and pH, which have the medium and low effects on the response, respectively. Considering the regression coefficients in Equation 2, among the three significant factors, the highest and lowest effects on PbO were for initial concentration and pH, respectively.
PbC: From the perturbation plot (Fig. 2-c), the following sequence of relative effects of the factors on PbC can be inferred: initial concentration > salinity > pH > biosorbent dosage > temperature. The steep slopes in opposite directions for initial concentration and salinity are quite clear. The significantly lower slopes for pH, biosorbent dosage and temperature show less sensitivity of PbC to changes in these factors. These results are consistent with those presented in Table 1 and Equation 3. As can be seen, the factors initial concentration and salinity indicated the highest F-ratios and the regression coefficients and the lowest p-values.
Positive and negative effects
The regression equations (Equation 1 to 3) as well as the perturbation plots (Fig. 2) were used to investigate whether the effect of each factor on the responses is positive or negative. In the equations, negative and positive signs before each term show antagonistic and synergistic effects on the response, respectively (El-Gendy et al., 2014). The significant negative and positive effects on each response variable are described below:
PbR: As can be seen in Fig. 2-a and Equation 1, the increase in the factors initial concentration and contact time has positive effect on PbR. On the other hand, the increase in biosorbent dosage has negative effect on PbR. It can be noticed from the figure that the factors pH and salinity have also the same effect, but less strong.
PbO: It is observed from Fig. 2-b and Equation 2 that the PbO increases with increasing initial concentration and decreasing biosorbent dosage and pH.
PbC: Fig. 2-c and Equation 3 depict that with increase in salinity and temperature reduction in PbC was observed (negative effect), while factors initial concentration, pH and biosorbent dosage have a positive effect on the response variable.
In order to simplify the comparisons, all of the above-mentioned descriptions about the significance of the effects, the relative effects of significant factors, and the positive or negative effects are summarized in the Table 6.
Table 6. Order of the significant effects of factors on the responses. Minus and plus signs indicate negative and positive, respectively.
Factors
|
Responses
|
C+
|
>
|
G-
|
>
|
F-
|
>
|
A-
|
>
|
E+
|
PbR
|
|
|
|
|
F-
|
>
|
A-
|
>
|
E+
|
PbO
|
D-
|
>
|
A+
|
>
|
F+
|
>
|
G-
|
>
|
E+
|
PbC
|
PbR: Concentration of Pb adsorbed by scales of Rutilus kutum; PbO: Concentration of Pb adsorbed by scales of Oncorhynchus mykiss; PbC: Concentration of Pb adsorbed by shells of Cerastoderma glaucum; A: Biosorbent dosage; C: Contact time; D: Temperature; E: Initial concentration; F: pH; G: Salinity.
Interaction between influencing factors
The response surface and contour plots (Fig. 3) are very useful to see the interaction effects of the parameters on the response variables. In general, the shape of the contour plot indicates the natures and extents of the interactions between parameters. A circular contour plot shows that the mutual interactions between corresponding variables are insignificant. In contrast, elliptical or distorted plots are evidence of significant interactions (Li et al. 2008; Montgomery 2012; Hou et al. 2016; Wong et al. 2017). For each response variable, only the significant interactions (based on Table 1) are shown and described separately below. The elliptical contour shapes in the figures confirm that all the mutual interactions are significant.
PbR: Fig. 3-a depicts that with increase in pH reduction in PbR was observed, but it was observed that with increment in biosorbent dosage, PbR was increased. The maximum PbR (7655 ppm) occurred at biosorbent dosage of 0.1 g/L and pH 5.5, while the PbR (493 ppm) was minimal at biosorbent dosage of 0.3 g/L and pH 7. Fig. 3-b shows that with lower pH and salinity, higher PbR was observed. The PbR was maximal (3009 ppm) at salinity of 0.2 ppt and pH 5.5, while the minimum PbR (493 ppm) was observed at salinity of 0.2 ppt and pH 7. At higher initial concentration and lower temperature, higher PbR was observed (Fig. 3-c). The maximum PbR (8376 ppm) occurred at initial concentration of 100 ppm and temperature of 20 ºC, while the PbR (414 ppm) was minimum at initial concentration of 30 ppm and temperature of 30 ºC.
According to Equation 1, all the three mentioned mutual interactions had negative effects on the PbR.
PbO: With regards to Fig. 3-d, the maximum PbO occurred at low salinity and high initial concentration. The PbO was maximal (5355 ppm) at initial concentration of 100 ppm and salinity of 0.2 ppt, while the minimum PbO (310 ppm) was observed at initial concentration and salinity of 30 ppm and 0.2 ppt, respectively.
According to Equation 2, interaction between salinity and initial concentration had negative effects on the PbO.
Possible causes of the effects
With regards to the results presented in Tables 1 and 6 and Fig. 2, the following explanations can be given about the causes of observed significant effects and comparison with similar studies, separately:
Initial concentration
The initial concentration had a positive significant effect on all the three dependent variables. The similar findings were also reported on lead biosorption by fish scales and bivalve shells (Zayadi and Othman 2013a; Ayodele and Adekola 2016). This could be arisen from the fact that the initial concentration actually plays the role of the driving force required to control the resistance of the mass transfer of metal ions between the aqueous phase and the surface of the sorbents, so higher initial concentrations of metal ions may increase their adsorption. Moreover, with increasing initial Pb ions concentration, higher interaction between the metal ions and the biosorbents, and consequently enhancing the availability of the binding sites on the surface of the biosorbents, could be expected (Mandal et al. 2016; Sun et al. 2016; Bulut et al. 2018).
Biosorbent dosage
It seems that in the present study, the selected range of the biosorbents dosage for the response variables PbR and PbO is higher than the equilibrium levels (i.e. maximum adsorption capacity of the biosorbents with a certain absorbate), while in terms of PbC the reverse trend could be observed. Therefore, higher uptake at low biosorbent concentrations for PbR and PbO could be due to availability of lower number of Pb ions per unit mass of the biosorbents. It can also be relevant to aggregation of the biosorbents particles at higher concentrations, thereby lead to a decline in the surface area of adsorbent and also an increase in the diffusion path length (Dileepa Chathuranga et al. 2014; Ayodele and Adekola 2016). The trend observed for PbC is possibly due to the availability of more functional groups (adsorption sites) along with the increase of the biosorbent dosage. Similar findings had been reported by other studies as well (Osu and Odoemelam 2010; Maghri et al. 2012; Al-Saeedi et al. 2019).
pH
Generally, the pH of a solution is one of the most effective environmental parameters for adsorption of heavy metal ions because it might affect strongly the degree of ionization and adsorption sites on the sorbent surface during the biosorption process (Abbar et al. 2017; Bulut et al. 2018; Al-Saeedi et al., 2019). In the present research, as was largely expectable, pH was found to be one of the important parameters affecting the adsorption of Pb by the studied biosorbents (Table 2).
The findings of several similar researches on the influences of a wider range of pH (3 to 7) on the metal sorption process of various biosorbents show that in most cases, with the gradual increment of pH, the following specific trends can be observed: a) strong positive influences: with an increase in pH, there is an increase in ligands with negative charges which results in increase binding of positively charged ions such as Pb2+ via the mechanism of ion exchange (Jimoh et al. 2012; Zabochnicka-Świątek and Krzywonos 2014; Varshini and Das 2015), b) slight positive influences: at higher pH, the reduction in adsorption is possibly due to the abundance of OH- ions, causing increased hindrance to diffusion of organics contributing to the metal ions. The main reason for the small increment in metal removal may be that the adsorption sites are no more influenced by the pH change (Jimoh et al. 2012; Abbar et al. 2017), c) negative influences: some more increase in pH usually leads to precipitation of the hydroxide form of the metals ions; therefore true adsorption would not be feasible; thus a decline in the percentage of metal ions removal could be observed (Ayodele and Adekola 2016; Shahzad et al. 2017).
The results of the present study in terms of scales of the two fish species are consistent with those obtained by some other researchers, e.g. El-Sheikh and Sweileh (2008); Bajić et al. (2013) and Zayadi and Othman (2013a) that explained the Pb biosorption capacity of fish scales decreases gradually with increasing pH value of the solution, in the pH range approximately similar to our study. The possible reason for this trend is explained above (regarding the negative effect of pH). The observed reverse trend in terms of PbC could be attributed to the fact that the interaction between the functional groups of the biosorbent and the heavy metal ions is dependent upon nature of the surface of biosorbent and chemistry of the biosorbate solution, which in turn depends on the pH of the solution (Harikishore Kumar Reddya et al. 2010; Chiban et al. 2016). For this reason, in the current study, the maximum biosorption for the scales of both fish species (PbR and PbO) occurred similarly at pH 5.5, while that for the bivalve shells (PbC) was found at pH 7.
Salinity
PbR and PbC were significantly and negatively affected by the salinity, but no significant effect could be observed on PbO. Generally, salinity is an important parameter in the biosorption process because the existence of the electrolyte ions in an aqueous environment will cause changes in adsorbate activities, and sorbent surface charge by electrostatic (Coulomb) force (Zhu et al. 2016). So far, no previous study has been performed on the influence of this parameter on biosorption of metals using mollusk shells and fish scales. However, according to the results of some researches in which other adosorbents have been applied, it can be inferred that the increase in the biosorptive capacity with decreaing salinity is likely because of the fact that at lower sodium-to-lead ratios, the less competition for binding sites between sodium and lead ions could be occurred, and vice versa (Green-Ruiz et al. 2008; Park et al. 2014).
Contact time
PbR was the only response variable that significantly affected by the contact time. The observed positive effect was also reported by Zayadi and Othman (2013a), who found that an increase in contact time leads to increase in Pb removal from aqueous environment by fish scales as biosorbent. This implies that initially, the biosorbent contains a higher number of binding sites for the binding of Pb. In the studies that the range of contact time was wider compared to that of current research, after a lapse of some time, depending on the biosorbent and the solution environment, the number of unoccupied sites decreased and gradually became saturated (Ayodele and Adekola 2016; Shahzad et al. 2017; Achieng et al. 2019).
Temperature
Of the three response variables, only PbC was significantly affected by temperature. This negative effect has also been observed in some other studies concerning the use of mollusk shells as biosorbents for metals removal from aqueous solutions (e.g. Shahzad et al. 2017; Weerasooriyagedra and Anand Kumar 2018). Since, it is believed that sorption reactions are normally exothermic and, therefore, the decrease in biosorption capacity at higher temperatures likely occurs due to damage to the active binding sites in the biosorbent (Yahaya and Don 2014; Shahzad et al. 2017). It is noticeable that, based on the results of various related studies, the effect of temperature on the biosorption process shows different and contradictory behaviors (El-Naggar et al. 2018). The positive effect of temperature on the process, which has been observed in most similar studies, could be attributed to either higher affinity of sites for heavy metal ions or many more binding sites being available on the relevant particle surface at higher temperatures (Ayodele and Adekola 2016; Xu et al. 2019).
Characterization of the biosorbents
FT-IR analysis
Various functional groups play a significant role in the adsorption processes of metal ions as well as the sorption potential of adsorbents. The number and type of functional groups located on the surface of different sorbents affect the adsorption mechanisms (Sooksawat et al. 2017; Gupta et al. 2020). The functional groups actually provide sites for the effective adsorption of heavy metals on the adsorbent surface, and their adsorption potential can be influenced by a relatively wide range of factors (Muthulakshmi Andal et al. 2016). The results of this study showed that the three investigated biosorbents consist of a variety of functional groups capable of binding heavy metal ions.The complexation of functional groups with Pb2+ changes their chemical environment and thus leads to shifts or disappearance of the peaks in the FTIR spectra. In other words, the peak shifts and disappearances observed after the adsorption can be considered strong evidence for adsorption of Pb2+on the surface of the biosorbents (Zhu and Li 2015; Usman et al. 2019). The presence of similar functional groups as well as their shifts after Pb adsorption on the surface of different aquatics-based biosorbents were also reported by some other researchers (Prabu et al. 2012; Bajić et al. 2013; Zayadi and Othman 2013a; Othman et al. 2015; Ayodele and Adekola 2016; Shahzad et al. 2017; Ighalo and Eletta 2020).
XRF analysis
The XRF results (Table 4) showed that the chemical composition of O. mykiss and R. kutum scales was dominated by CaO and P2O5, whereas the contents of the other elements were rather low. In the case of C. glaucum shells, calcium oxide was also the main constituent, but P2O5was not detectable. These findings are in concordance with the results reported by several other researchers who analyzed the chemical composition of other aquatics-based sorbents (Maghri et al. 2012; Mustakimah et al. 2012; Zayadi and Othman 2013a; Othman et al. 2015; Ayodele and Adekola 2016; Xu et al. 2019). It was observed that after Pb adsorption, ion percentage of other elements was decreased. In this case other elements may be involved in ion exchange process with the lead ions.
SEM and EDX analysis
The micrographs reveal the uneven, heterogeneous and slightly rough surface of the adsorbents (especially in the case of C. glaucum shell), which may serve as transport and attachment sites for metal ions (Lam et al. 2016; Mendoza-Castillo et al. 2016). Generally, the differences in adsorption capacity of different types of biosorbents depend on a number of factors, among which the surface morphology, composition and porosity are especially important (Homagai et al. 2010; Parida et al. 2017; Neves et al. 2018). Therefore, the observed differences in the surface microstructures of the three biosorbents (Fig. 5-a) can be effective in their different adsorption capacities. The observed significant changes in the morphological characteristics of the biosorbents and some precipitation on their surfaces after the adsorption (Fig. 5-b) are evidence of the potential of the biosorbents for adsorption and removal of the metal ions from aqueous solutions (Park et al. 2007). The results of several other studies have also shown that the surface morphology of the adsorbents of aquatic origin has changed after the adsorption of some heavy metals (e.g. Villanueva-Espinosa et al. 2001; Prabu et al. 2012; Muthulakshmi Andal et al. 2016; Yousefi et al. 2016; Xu et al. 2019, Dulla et al. 2020, El-Naggar et al. 2021). Generally, the emergence of post-biosorption peaks that characterize Pb (Fig. 6) indicates the binding of the metal ions to the sorbents surfaces. Hence, with regards to the results of EDX analysis, there are strong and logical reasons for adsorption of Pb ions on the investigated biosorbents (Bansal et al. 2014). The concentration of the adsorbed elements is directly related to the height of the corresponding EDX peaks (Hayeeye et al. 2018). The SEM analyses showed the existence of two regions, i.e., dark and white areas. The dark region is mainly composed of proteins containing large amounts of carbon, oxygen, and sulfur, whereas the white region is mainly consists of inorganic components, including high amounts of calcium and phosphorus (Villanueva-Espinosa et al. 2001; Zayadi and Othman 2013a). The difference between the two regions can also be deduced from the elemental composition of the adsorbent surface, as shown in the insets of the figures. With regards to Fig. 6, in the case of C. glaucum shells and O. mykiss scales, the greater Pb adsorption was detected in the white region, whereas in the case of R. kutum scales the darker area showed the higher adsorption capacity. The visible post-adsorption changes of the weight percentages of calcium, phosphorus, carbon, oxygen and sulfur after the sorption revealed that ion exchange seems to be the most important mechanism affecting the bioabsorption process of metal ions, which can occur through various functional groups located on the biosorbents’ surfaces (Kizilkaya et al. 2010; Bilal et al. 2018). Therefore, the different adsorption values observed in the white and dark regions are probably mainly caused by the differences in number and type of the functional groups, microstructure, surface morphology and chemical nature of the sorbents (Ramrakhiani et al. 2016; De Freitas et al. 2019; Jin et al. 2020).
Sorption isotherms
According to values of regression coefficients (R2), the Langmuir isotherm showed the best fitted values for R. kutum scale (R2= 0.9934) and C. glaucum shell (R2 = 0.9845) (Table 5 and Fig. 7). Therefore, it can be opined that the two biosorbents may have homogeneous surfaces and monolayer adsorption (Deng and Chen, 2019). The separation factor (RL) values were between 0 and 1, indicating a favorable adsorption of Pb onto the two biosorbents (Badi et al. 2018). While the Frenudlich model was suitable for the equilibrium isotherm of O. mykiss scale (R2= 0.9860). Contrary to Langmuir isotherm, the Freundlich isotherm is applicable to heterogeneous surfaces and multilayer adsorption (Djahed et al. 2016).
Comparison with other aquatics-based biosorbents
The biosorption capacities of the three studied biosorbents in the present study in comparison with those of other biosorbents reported is shown in Table 7. These data show that the sorption capacities of the three biosorbents are comparable to those of other sorbents reported in the literature (within the range of 0.86 and 248.00 mg/g for fish scales and brown seaweed, respectively).
In general, It should be noted that direct comparison of sorption capacities of different biosorbents listed in the table is difficult due to: a) the sorbents have been investigated under various preparation and test conditions (including contact time, pH, particle size, metal concentration range, temperature, mixing rate and ….), b) the methods of pre-treatment and preparation of the biosorbents are not similar in different investigations, and c) the techniques for determining maximum adsorption capacity (e.g. BBD-designed experiments, Langmuir Isotherm, Freundlich Isotherm, Pseudo-second order kinetics) have been different in various studies. The first and second points show the factors that may play an important role in increasing the adsorption capacity of sorbents for a given heavy metal (Nadeem et al. 2008a; Fomina and Gadd 2014), and the third point indicates the difference in calculation methods.
With regards to Table 7, a comparison of the three biosorbents studied in the present study shows that the ascending order of the sorption capacity is: the shells of C. glaucum, scales of O. mykiss and scales of R. kutum. Given that the preparation methods and experimental conditions were the same for all three sorbents, these differences in adsorption capacities are probably mostly arose from the differences in surface area, morphology and functional groups (Shrestha et al. 2016; Naik et al. 2017). On the other side, according to Regine et al. (2000) the role of the functional groups in biosorption of a given metal by a certain biosorbent is related to several factors, including accessibility of the reactive sites, the number of the sites in the biosorbent, chemical state of the sites (i.e. availability), and affinity between the sites and the particular metal.
Table 7. The comparison of biosorption capacity for lead with various biosorbents.
Biosorbent
|
Max sorption capacity
(mg/g)
|
Reference
|
Fish scales (Rutilus kutum)
|
24.26
|
Present study
|
Fish scales (Oncorhynchus mykiss)
|
14.39
|
Present study
|
Bivalve mollusk shell (Cerastoderma glaucum(
|
1.29
|
Present study
|
Fish scales (Labeo rohita)
|
196.80
|
Nadeem et al. 2008b
|
Fish scales (Genyonemus lineatus)
|
0.86
|
Onwordi et al. 2019
|
Fish scales (Cyprinus carpio)
|
62.5
|
Bajić et al. 2013
|
Fish fins (Catla catla)
|
3.00
|
Gupta et al. 2017
|
Bivalve mollusk shell (Anodontoides ferussacianus)
|
155.04
|
Shahzad et al. 2017
|
Cockle shell
|
24.66
|
Ayodele and Adekola 2016
|
Freshwater snail shell (Melanoides tuberculate)
|
0.59
|
Castañeda et al. 2012
|
Chitin of shrimp (Solenocera melantho)
|
7.00
|
Forutan et al. 2016
|
Marine brown algae (Cystoseira stricta)
|
64.5
|
Iddou et al. 2011
|
Green seaweed (Ulva lactuca)
|
2.25
|
Sari and Tuzen 2008
|
Brown seaweed (Cystoseira baccata)
|
124.00
|
Lodeiro et al. 2006
|
Brown seaweed (Laminaria japonica)
|
248.00
|
Luo et al. 2006
|
Aquatic plant (Hydrilla verticillata)
|
2.14
|
Dileepa Chathuranga et al. 2014
|
Aquatic plant (Myriophyllum spicatum)
|
55.12
|
Yan et al. 2010
|