Optimisation and Modelling of Pb(II) and Cu(II) Biosorption onto Red Algae (Gracilaria changii) by Using Response Surface Methodology

The removal of Pb(II) and Cu(II) ions by using marine red macroalgae (Gracilaria changii) as a biosorbent material was evaluated through the batch equilibrium technique. The effect of solution pH on the removal of metal ions was investigated within the range of 2–7. The response surface methodology (RSM) technique involving central composite design (CCD) was utilised to optimise the three main sorption parameters, namely initial metal ion concentration, contact time, and biosorbent dosage, to achieve maximum ion removal. The models’ adequacy of response was verified by ANOVA. The optimum conditions for removal of Pb(II) and Cu(II) were as follows: pH values of 4.5 and 5, initial concentrations of 40 mg/L, contact times of 115 and 45 min, and biosorbent dosage of 1 g/L, at which the maximum removal percentages were 96.3% and 44.77%, respectively. The results of the adsorption isotherm study showed that the data fitted well with the Langmuir’s model for Pb(II) and Cu(II). The results of the adsorption kinetic study showed that the data fitted well with the pseudo-second order model for Pb(II) and Cu(II). In conclusion, red alga biomass exhibits great potential as an efficient low-cost sorbent for removal of metal ions.


Introduction
Heavy metal-containing water is a severe pollution issue that considerably affects the environment and animal and human health. Heavy metals can accumulate in the food chain and cause major human health problems if not managed and treated properly. Kim et al. classified heavy metals into two groups based on their toxicity: essential and non-essential heavy metals [1]. Essential heavy metals including Cu, Fe, Co, and Zn are relatively less toxic at low concentration levels. They can act as cofactors in different biochemical and physiological functions in living organisms; however, they become toxic if they exceed the threshold level [2]. On the other hand, non-essential heavy metals including Pb, Cd, Hg, and As are highly toxic and can be lethal even at low concentrations. Heavy metals severely affect the human nervous system and can cause headache, hypertension, fatigue, anaemia, and even cancer [3,4]. Lead and Cu(II) are listed on the United States Environmental Protection Agency (USEPA) priority control pollutants list due to their persistence and irreversible toxic characteristics [1]. Rapid industrialisation increases the levels of heavy metal contamination in the environment [5,6]. Heavy metals are discharged from various industries such as those involving petroleum, mining, and electroplating (Table 1). If untreated, heavy metals can accumulate in the receiving environment and directly or indirectly enter the food chain [2,7,8]. According to the USEPA, the allowable levels of Pb(II) and Cu(II) in drinking water are 0.015 and 1.3 mg/L, respectively [9].
The dried algae were ground using a mill (FRITSCH cutting mill combination PULVERISETTE 25/19) (FRITSCH, Pittsboro, North Carolina, NC, USA). The obtained powder was sieved within the range of 0.15-0.30 mm and stored in airtight bottle.

Alga Biomass Characterisation
Field-emission scanning electron microscopy with energy-dispersive X-ray (FESEM-EDX) spectroscopy (Carl-Zeiss AG, Oberkochen, Germany) was used to study the surface morphology of the biosorbent and its component. The biosorbent was subjected to analysis by the instrument before and after the biosorption of Pb(II) and Cu(II).
Fourier transform infrared (FTIR) spectroscopy (Perkin -Elmer, Waltham, Massachusetts, MA, USA) was used to detect changes in the vibration frequency in the biosorbent before and after biosorption. Infrared spectra were recorded within the range of 550-4000 cm −1 . The spectral data were processed using Essential FTIR v3.5 (Operant LLC, Madison, Wisconsin, WI, USA).

Preparation of Synthetic Solution
Stock solutions of metals (1000 mg/L) were prepared by dissolving 1.598 g of Pb(NO 3 ) 2 or 3.802 g of Cu(NO 3 ) 2 ·3H 2 O in 1 L of distilled water. The stock solutions were diluted to obtain different concentrations.

Batch Biosorption Experiment
All adsorption tests were carried out in 250 mL conical flasks containing 100 mL of each Pb(II) and Cu(II) solution. The tests were conducted by varying four factors, namely, pH (2-7), initial concentration (10,40,70,100, and 130 mg/L), contact time (10,45,80,115 and 150 min), and biomass dosage (0.1, 0.4, 0.7, 1, and 1.3 g/L), on metal biosorption. All the samples were agitated at 180 rpm at room temperature in orbital shaker (Protech model 722). Samples were collected at the predetermined interval and filtered through filter paper (Whatman No.1, 0.45 µm). The filtrates were examined for metal ion concentration by using flame atomic absorption spectrophotometer (AAS) model (Agilent 200 series AA systems, 240FS AA, Agilent technologies, Santa Clara, California, CA, USA). A similar procedure proposed by Lingamdinne was conducted for removal of metal ions [38]. The experiments were conducted in triplicate to increase the precision and minimise error. Removal percentage was calculated using Equation (1) [39]: where C 0 and C f are the initial and final metal ion concentrations, respectively.

Effect of Solution pH
The effect of solution pH on the removal of metal ions was investigated by varying the pH within 2-7 while maintaining the ion concentration, contact time and dosage at 100 mg/L, 120 min and 1 g/L, respectively. The solution pH was adjusted by adding NaOH and HCl solutions. After shaking, the samples were filtered and examined using AAS.

Experimental Design Using RSM
RSM was used to design the experiment to model and evaluate the effects of three independent variables (A: ion initial concentration, B: contact time and C: biosorbent dosage) on the response (R: metal removal percentage). Full factorial CCD was implemented. The CCD for the three independent variables was based on six axial points, eight factorial points and six replicates at the central point. The selected factors were varied at five levels (−α, −1, 0, 1, +α). The number of experiment runs was calculated based on Equation (2): (2) where N is the number of runs, K is the number of factors to be tested, and C is the number of experiments conducted at the centre. The lower and higher limits of each factor are shown in Table 2.
The experiment data matrix was determined using State-Ease design expert v10.0.1. The obtained model was statistically analysed using analysis of variance (ANOVA). The interactions between variables were studied using surface contour plots.

Adsorption Isotherms
Adsorption isotherm models were utilised to examine the adsorption behaviour of metal ions on the adsorbent. Langmuir and Freundlich isotherms are the most commonly used in exploring the adsorption behaviour of metal ions. The Langmuir model assumes that the adsorption is a monolayer type and occurs on a homogeneous surface with a finite number of active sites, regardless of the existence of mutual interaction between the adsorbent molecules [40]. The linearized Langmuir equation can be written in the following form: where C e (mg/L) is the equilibrium concentration, and q e (mg/g) is the amount of the adsorbed ion; q m (mg/g) and K L (L/mg) are the maximum adsorption capacity and rate of adsorption (Langmuir constant), respectively. One of the essential characteristics of Langmuir isotherm modelling is the term of equilibrium dimensionless parameter (R L ), which can be calculated using Equation (4): where K L (L/mg) and C 0 (mg/L) are the Langmuir constant and initial ion concentration, respectively. The R L value determines whether the adsorption is unfavourable (R L > 1), favourable (0 < R L < 1), linear (R L = 1), and irreversible (R L = 0) [41]. The Freundlich model presumes that molecules are adsorbed on the heterogeneous surfaces based on different energy sites. The model considers the mutual interaction between adsorbate molecules. The linear form of the Freundlich equation can be used in the following form: where K F [(mg/g)/(L/mg) 1/n ] and 1/n are Freundlich constants related to capacity and favourability of the adsorption, respectively. The values of 1/n within 0 and 1 indicate favourable adsorption. n represents the intensity of adsorption.

Adsorption Kinetics
Adsorption kinetics models were utilised to examine to study the effect of contact time between adsorbent and adsorbate. The adsorption kinetics can strongly constrain the use of some adsorbents [42]. Small adsorption kinetics leads to longer adsorption process time which can cause the adsorption to be inadequate [42]. In this study. Four different kinetics models were investigated, namely the pseudo-first-order, pseudo-second order, the Elovich, and the intra-particle diffusion kinetic models, and were used to test the experimental data.
The pseudo-first -order model is suitable for low concentration of adsorbate [43]. The linearized form on this model can be expressed as follows: where q e and q t is the metal uptake of G. changii at equilibrium and at time t, respectively, and k 1 is the pseudo-first order model constant.
The linearized form of pseudo-second-order model can be expressed as follows [42]: where q e and q t is the metal uptake of G. changii at equilibrium and at time t, respectively, and K 2 is the pseudo-second order model constant.
The Weber-Morris diffusion kinetic model was applied to the experimental data using the following form: where q t is the metal uptake of G. changii at time t (mg/g) and k i is the diffusion rate constant (mg/g min 0.5 ). The Elovich kinetic model simplified equation can be written in the following form [44]: where α and β are the initial adsorption rage (mg/g min) and desorption constant (g/mg), respectively, and q t is the metal uptake of G. changii at time t (mg/g).

FESEM-EDX Analysis
The surface morphology and elemental composition of the biomass before and after the biosorption were investigated using FESEM-EDX. Figure 1 shows the surface morphology of red algae before and after the biosorption of Pb(II) and Cu(II). The morphological characterization of G. changii after the biosorption of Pb(II) and Cu(II) was significantly different from those before biosorption. The pores on the biomass surface became smooth after the biosorption. This change can be explained by accumulation of metal ions on the surface by electrostatic attraction effect, which has been reported previously by [6,45].
The EDX analysis showed the biosorbent surface elemental composition before and after the biosorption. The system was calibrated using CaCO 3 (C), SiO 2 (Si), MAD-10 Feldspar (K), FeS 2 (S), Wollastonite (Ca), KCl (K), MgO (Mg), Mn (Mn), PbF 2 , and Cu as standards. The Pb-Mα peak was recorded at 2.342 KeV overlapping with S-Kα peak at 2.304, the Pb-L peak at 10.55 was also recorded. The Cu-Kα and Cu-Lα peaks were recorded at 8.04 and 0.93 KeV, respectively. The presence of Pb(II) (wt% = 7.83%) and Cu(II) (wt% = 2.94%) on the surface of the biomass after biosorption confirmed the attachment of the metal ions on the biosorbent surface. It was observed that amount of Mn, Mg, and K decreased or disappeared after the biosorption process. This change demonstrates a possible ion exchange has occurred during the biosorption of Pb(II) and Cu(II). Similar observations were reported by [46][47][48]. Elemental mapping was carried out to show the spatial distribution of each element detected in EDX. All elements were recorded from their K α line, except Pb, which was recorded from its Lα line (Figures S1-S3).

FTIR Analysis
The functional groups on the biosorbent surface and their interaction during the biosorption process of Pb (II) and Cu (II) were investigated through FTIR spectrum analysis. Figure 2 shows the peaks corresponding to the functional groups of red algae before and after the biosorption of metal ions. The broad and strong peak at 3281.88 cm −1 was assigned to the overlapping of O-H and N-H stretching, indicating the existence of hydroxy stretching and carboxylic groups on the surface on algae [22,49]. The peak at 2928.35 cm −1 was attributed to the C-H stretching vibration of aliphatic groups [6,50]. The peaks at 1636.14 and 1545.21 cm −1 represented the presence of C=C stretching [51]. The peak found at 1413.01 cm −1 was assigned to the C-H bonding [50]. The peaks observed at 1243.88 cm −1 was attributed to the C-O stretching [45]. The sulfoxide band was assigned to the peak observed at 1030.15 cm −1 [52,53]. The numerous characteristic peaks observed on the surface indicate the

FTIR Analysis
The functional groups on the biosorbent surface and their interaction during the biosorption process of Pb(II) and Cu(II) were investigated through FTIR spectrum analysis. Figure 2 shows the peaks corresponding to the functional groups of red algae before and after the biosorption of metal ions. The broad and strong peak at 3281.88 cm −1 was assigned to the overlapping of O-H and N-H stretching, indicating the existence of hydroxy stretching and carboxylic groups on the surface on algae [22,49]. The peak at 2928.35 cm −1 was attributed to the C-H stretching vibration of aliphatic groups [6,50].
The peaks at 1636.14 and 1545.21 cm −1 represented the presence of C=C stretching [51]. The peak found at 1413.01 cm −1 was assigned to the C-H bonding [50]. The peaks observed at 1243.88 cm −1 was attributed to the C-O stretching [45]. The sulfoxide band was assigned to the peak observed at 1030.15 cm −1 [52,53]. The numerous characteristic peaks observed on the surface indicate the complex nature of the biosorbent. The changes in vibration peaks are summarised in Table 3. The changes in the vibrational frequency of the functional groups after the biosorption of Pb(II) and Cu(II) indicated the involvement of these groups in the biosorption [6,54]. The difference in ion biosorption can be attributed to the different ions' affinity to for the functional groups [45]. (II) indicated the involvement of these groups in the biosorption [6,54]. The difference in ion biosorption can be attributed to the different ions' affinity to for the functional groups [45].

Effect of Solution pH
Solution pH is a crucial factor that affects the biosorption of heavy metals. pH affects the metal ion chemistry in solution and the surface chemistry of the biosorbent [55,56]. The results obtained at pH values higher than 5.5 were ignored due to metal precipitation that formed metal hydroxides. These observations are similar to those reported by Sheng et al. [57] and Jalali et al. [58]. Therefore, the high removal percentage of over 5.5 is mainly due to the incorporating role of metal precipitation and adsorption [59]. A low removal percentage was observed at low pH. This finding might be due to proton H + competition with the metals' cations for the adsorption sites [60]. The increase in the removal percentage at pH higher than 5.5 could be attributed to the precipitation of metal hydroxide [58]. The maximum removal percentages were 60% and 22% at pH 4.5 and 5 for Pb (II) and Cu (II),

Effect of Solution pH
Solution pH is a crucial factor that affects the biosorption of heavy metals. pH affects the metal ion chemistry in solution and the surface chemistry of the biosorbent [55,56]. The results obtained at pH values higher than 5.5 were ignored due to metal precipitation that formed metal hydroxides. These observations are similar to those reported by Sheng et al. [57] and Jalali et al. [58]. Therefore, the high removal percentage of over 5.5 is mainly due to the incorporating role of metal precipitation and adsorption [59]. A low removal percentage was observed at low pH. This finding might be due to proton H + competition with the metals' cations for the adsorption sites [60]. The increase in the removal percentage at pH higher than 5.5 could be attributed to the precipitation of metal hydroxide [58]. The maximum removal percentages were 60% and 22% at pH 4.5 and 5 for Pb(II) and Cu(II), respectively. Similar results were obtained for Pb(II) and Cu(II) removal by using different types of macroalgae, including Gracilaria corticate [58], Gracilaria canaliculate [58], Spirogyra spp. [61], Cladophora spp. [61], and Gelidium sesquipedale [62]. Figure 3 shows the effect of solution pH on the removal percentages of Pb(II) and Cu(II).

Model Development for Pb(II) and Cu(II) Removal
A CCD matrix was used to investigate the interaction effects of three important factors, including metal ion concentration, contact time, and biosorbent dosage, on the removal of Pb(II) and Cu(II). The experimental design and the responses are shown in Table 4. Based on the obtained results, the following polynomial equations for Pb(II) and Cu(II) removal were developed: where A, B, and C are the coded values for the selected influential parameters, namely, metal ion concentration, contact time and adsorbent dosage, respectively. Table 5 shows the ANOVA for the polynomial equations and the corresponding regression coefficients for the removal models of Pb(II) and Cu(II). The significance of the obtained models can be determined based on the p-value, the correlation of determination (R 2 ), and the results of the lack of fit test [36]. Both models showed p-values less than 0.0001, suggesting their significance. The lack of fit test showed the variation of responses around the fitted model. The insignificant lack of fit indicated that the model did not fit the data well. The model fit the real data better for Cu(II) than for Pb(II). The values of R 2 and adjusted R 2 were higher than 0.99 for Cu(II), whereas those for Pb(II) were lower at 0.97 and 0.95, respectively. The coefficient of variance (CV) refers to the ratio of standard deviation to mean. The CV values were 7.9 and 3.26 for Pb(II) and Cu(II) models, respectively. A model is reproducible if the CV value is less than 10 [63]. Adequate precision (AP) is defined as the ratio of signal to noise [64]. An AP ratio higher than 4 is desired. The AP values of the models were 22.135 and 80.572 for Pb(II) and Cu(II), respectively, which showed a good signal and indicated that the models can be used to navigate the design space.

Effect of Factors on the Removal Percentages of Pb(II) and Cu(II)
Three-dimensional surface plots present the effects and interactions of independent variables, namely, ion concentration, contact time, and biosorbent dosage, on the removal percentages of Pb(II) ( Figure 4) and Cu(II) ( Figure 5) as the responses. The interaction of initial ion concentration and biosorbent dosage shown in Figures 4b and 5b indicates the significant influence of both factors on the removal of Pb(II) and Cu(II). For both adsorbates, the removal percentages increased with increasing biosorbent dosage. This result was due to the presence of additional active sites and large biosorbent surface area that is readily available for adsorption [65]. The removal percentage was reduced by increasing the initial ion concentration. This finding might be due to the limited active sites on the biosorbent surface at high adsorbate concentrations [66]. The removal percentage was increased slightly by increasing the residence time from 40 min to 115 min. These results confirmed that the initial adsorption rate was very rapid due to the availability of large surface area and the presence of unused sites on the biosorbent surface [38]. The slowing down of ion removal might be due to the difficulty of reaching the remaining vacant sites. Repulsive forces can also be a factor in this case. The efficiency of different macroalga species in metal ion adsorption has been reported [54,67,68]. The optimum values of the operation variables and the predicted maximum responses are presented in Table 6. The maximum removal percentages for Pb(II) and Cu(II) were predicted to be 91% and 44%, respectively. Verification experiments were conducted under the optimum conditions, and the removal percentages were 96% and 44% for Pb(II) and Cu(II), respectively; these findings are similar to the predicted values and indicated the suitability and accuracy of the suggested models. The diagnostic plots of the predicted versus actual values for Pb(II) and Cu(II) removal percentages are presented in Figure 6. The metal uptakes obtained under the optimum conditions were 38.52 and 17.9 mg/g for Pb(II) and Cu(II), respectively. Table 7 lists the comparison of metal uptake of Pb(II) and Cu(II) onto various types of algae.

Adsorption Isotherm Study
The sorption equilibrium data were tested with the Langmuir and Freundlich isotherms. The model parameters for each isotherm and their respected correlation coefficients (R 2 ) are shown in Table 8. Figure 7 illustrates the Freundlich and Langmuir adsorption isotherms of ions on the biosorbent. The Langmuir isotherm best fitted for Pb (II) and Cu (II) on the red algae, with R 2 greater

Adsorption Isotherm Study
The sorption equilibrium data were tested with the Langmuir and Freundlich isotherms. The model parameters for each isotherm and their respected correlation coefficients (R 2 ) are shown in Table 8. Figure 7 illustrates the Freundlich and Langmuir adsorption isotherms of ions on the biosorbent. The Langmuir isotherm best fitted for Pb(II) and Cu(II) on the red algae, with R 2 greater than 0.99. This fitting indicated that the sorption of both metal ions had a mono layer coverage on the sorbent surface [54]. The maximum sorption capacity was higher for Pb(II) than that for Cu(II). The K L value of Pb(II) was higher than that of Cu(II), indicating the higher affinity of the former to the bonding sites on the alga surface. The values of R L were 0.056 and 0.108, confirming the favourable sorption of Pb(II) and Cu(II) onto the alga biomass [70].

Adsorption Kinetic Study
In this study, four different kinetics models were investigated, namely the pseudo-first order, the pseudo-second order, and the Elovich and the intra-particle diffusion kinetic models, to test the experimental data. The parameters for each kinetic model and its respected correlation coefficients (R 2 ) are shown in Table 9. Plots of the four kinetic models are shown in Figure 8. The experimental results fitted well with the pseudo-second order model, with the correlation of the coefficient close

Adsorption Kinetic Study
In this study, four different kinetics models were investigated, namely the pseudo-first order, the pseudo-second order, and the Elovich and the intra-particle diffusion kinetic models, to test the experimental data. The parameters for each kinetic model and its respected correlation coefficients (R 2 ) are shown in Table 9. Plots of the four kinetic models are shown in Figure 8. The experimental results fitted well with the pseudo-second order model, with the correlation of the coefficient close to unity. Table 9. The pseudo-first order, pseudo-second order, and Elovich and intra-particle diffusion kinetic parameters for biosorption of Pb(II) and Cu(II) onto G. changii.

Model Equation Parameters Pb(II) Cu(II)
Pseudo-first order log(q e − q t ) = log(q e ) − k 1 t q e (mg/g) 14   . Linear fitting of biosorption of Pb (II) and Cu (II) by G. changii to the pseudo-first order, pseudo-second order, and Elovich and intra-particle diffusion kinetic models.

Conclusions
Batch sorption experiments were conducted using red algae as a biosorbent for Pb (II) and Cu (II) ions. The effect of solution pH on the removal percentage was studied. The optimum solution pH values were determined as 4.5 and 5 for Pb (II) and Cu (II), respectively. In addition, RSM was applied to determine the optimum values of initial ion concentration, contact time, and biosorbent dosage to maximise the responses. The optimum conditions for removal of Pb (II) and Cu (II) included the following: initial concentration of 40 mg/L, contact time of 115 and 45 min, and adsorbent dosage of 1 g/L, under which 96.3% and 44.77% removal percentages were achieved, respectively. The Figure 8. Linear fitting of biosorption of Pb(II) and Cu(II) by G. changii to the pseudo-first order, pseudo-second order, and Elovich and intra-particle diffusion kinetic models.

Conclusions
Batch sorption experiments were conducted using red algae as a biosorbent for Pb(II) and Cu(II) ions. The effect of solution pH on the removal percentage was studied. The optimum solution pH values were determined as 4.5 and 5 for Pb(II) and Cu(II), respectively. In addition, RSM was applied to determine the optimum values of initial ion concentration, contact time, and biosorbent dosage to maximise the responses. The optimum conditions for removal of Pb(II) and Cu(II) included the following: initial concentration of 40 mg/L, contact time of 115 and 45 min, and adsorbent dosage of 1 g/L, under which 96.3% and 44.77% removal percentages were achieved, respectively. The adsorption isotherms best fitted the Langmuir model. The adsorption kinetics best fitted the pseudo-second order model. Hence, red alga biomass exhibits huge potential as a cheap and locally available biosorbent for removal of metal ions from industrial wastewater.