Introduction

Industrial processes, such as mining, metal plating, and battery manufacturing, textile, plastics, electroplating, metallurgical processes, etc. (result in the release of heavy metals to aquatic ecosystems (Gabr et al. 2009; Joo et al. 2010). Heavy metals are toxic pollutants, which can accumulate in living tissues causing various diseases and disorders, heavy metals pollution represents an important problem with serious ecological and human health consequences (Abskharon et al. 2010; Hassan et al. 2008). Lead is among the most toxic heavy metal ions in the environment. It is a toxic metal that is harmful if inhaled or swallowed. Lead can threaten human life due to its toxicity, accumulation in food chains and persistence in nature (Gabr et al. 2009; Hassan et al. 2008). It may cause anemia, headache, chills, diarrhea and reduction in hemoglobin formation and can causes severe damage to kidneys, nervous system, reproductive system, liver and brain(Gupta and Rastogi 2008; Nadeem et al. 2006; Vilar et al. 2008). The main sources of lead are the manufacture of storage batteries, pigments, leaded glass, mining, metal electroplating, painting, coating, smelting, petrochemical, plumbing fuels, photographic materials, matches and explosives (Gabr et al. 2008; Gupta and Rastogi 2008). Untreated effluents from these industries have an adverse impact on the environment and aquatic life (Chakravarty et al. 2010; Gupta and Rastogi 2008). The current Environmental Protection Agency (EPA) standard for lead in wastewater and drinking water is 0.5 and 0.05 mg l−1, respectively (Gupta and Rastogi 2008). Removal of toxic heavy metals from water streams is one of the most important environmental issues. Due to its non-biodegradable, they must be removed from the polluted streams for the environmental quality standards to be met (Davis et al. 2003; Volesky 2007). Traditional methods for removal of heavy metals from aqueous solutions include adsorption, ion exchange, coagulation, inverse osmosis, chemical precipitation and membrane separation processes (Volesky 2007). These methods have several disadvantages like high operating costs, low selectivity, incomplete removal, and is not efficient for solution which contain low concentration of heavy metals (Gabr et al. 2008; Volesky 2007). Development of efficient and low-cost separation processes is therefore of utmost importance. Biosorption can be defined as the ability of biological materials (Algae, bacteria, yeast, fungi and plant biomasses) to accumulate heavy metals from wastewater through physico-chemical pathways of uptake, or as a property of certain types of inactive, non-living microbial biomass or plant biomass which bind and concentrate heavy metals from even very dilute aqueous solutions (Davis et al. 2003; Volesky 2007). Removal of heavy metal ions from polluted wastewaters by bacterial biomass has been studied extensively (Gabr et al. 2008, 2009; Hassan et al. 2009; Joo et al. 2010; Oh et al. 2009). Both living and non-living biomass of bacteria, fungi, and algae have been used in removing toxic metal ions (Gabr et al. 2009; Hassan et al. 2008; Morsy et al. 2011; Oh et al. 2009). Metal removal by microorganisms is a complex process in which depends on the chemistry of the metal ions, cell wall composition, types of microorganisms, cell physiology, and physicochemical factors such as pH, temperature, contact time, ionic strength, biomass concentration and metal concentration (Gabr et al. 2009; Morsy et al. 2011; Oh et al. 2009). Different functional groups located on the bacterial cell wall are known to be included in heavy metal biosorption. These include carboxyl, amine, hydroxyl, phosphate, and sulfhydryl groups. The mechanism of metal biosorption by bacterial biomass occurs through complexation, coordination, physical adsorption, chelation, ion exchange, inorganic precipitation and/or a combination of these processes (Akar et al. 2009; Davis et al. 2003; Volesky 2007). Response surface methodology (RSM) design is a collection of statistical and mathematical techniques which are useful for analyzing the effects of several independent variables on the response design and optimization process (Amini et al. 2008). RSM methodology is more practical as it arises from experimental methodology which includes interactive effects among the variables and, eventually, it depicts the overall effects of the parameters on the process (Amini et al. 2008).

The objective of this work is to explore the biosorption efficiency of lead ion using dead cells of Lactobacillus bulgaricus. Several experiments were carried out to study the effects of different variables: pH, biomass concentration and initial lead concentration, on the efficiency of the lead ion removal (%). To generate systematic experimental data for covering a wide range of operating conditions and to predict the maximum percent of lead removal, response surface methodology (RSM) based on central composite rotatable design (CCRD) was used as a systematic experimental design method.

Materials and methods

Microorganism and growth condition

Lactobacillus bulgaricus was supplied within research department of science and industry (Tehran. Iran). The microorganism was grown aerobically in agitated (100 rpm) and incubated at 30°C using nutrient medium containing 10 g/l yeast extract, 20 g/l glucose, 2 g/lK2HPO4, 0.2 g/l MgSO4 and 0.05 g/l MnSO4. The pH of the medium was adjusted to 6.5.

Preparation of microorganism for biosorption

After bacterial growth, then cell was harvested by centrifugation for 30 min at 5,000 rpm. The cell pellet was rinsed three times with sterile water, and it dried at the temperature of 60°C in oven for 24 h in order to improve the bisoorption efficiency.

Metal solution

The chemicals used for the study were analytical grades of lead nitrate (Pb(NO3)2. The initial pH of the working solutions was adjusted to 5.5 by addition of 0.1 N HNO3 or NaOH solutions, except the experiment examining the effect of pH. Fresh dilution used for each sorption study. The stock solution of Pb2+ (1 gl−1) was prepared by dissolving a weight quantity of Pb(NO3)2 in deionized water.

Methods of adsorption study

Batch biosorption studies were carried out by shaking the 100 ml flasks containing bacterial biosorbent (1 g/l) and 32.6 ppm of lead ion at 120 rpm at room temperature (30°C) for a period of contact time. To study effect of pH on metal biosorption a specific weight of bacterial biomass at 32 ppm of lead ion was conducted at different pHs ranging from 2.0 to 6.78. To study the effect of bacterial biomass on biosorption process a different weight ranging from 2.0 to 8.0 g/l was conducted at pH 5.5 and metal concentration 32.6 ppm. After biosorption equilibrium, samples were taken from the solutions, and were subsequently centrifuged at 10,000 rpm for 5 min. The metal ion concentration was measured by flame atomic absorption spectrophotometer (Philips, PU 9400, USA). Each determination was repeated 3 times and results given were the average value.

Scanning electron microscope (SEM)

Scanning electron microscope (SEM) was used to show the morphology of the dried biomass L. bulgaricus. The samples were dried by liquefied nitrogen, coated with gold and observed with a (Phillips XL30, Holland) microscope. Finally, image of the samples were taken under SEM at different magnification (Can et al. 2006).

Result and discussion

Experimental design

Central composite design (CCD), Box–Behnken design (BBD) and Doehlert designs (DD) are among the principal response surface methodology (RSM) used in experimental design (Amini et al. 2008; Göksungur et al. 2005; Montgomery and Wiley 2001; Schiewer and Volesky 1995; Tang and Xu 2002). The central composite design (CCD) is the most frequently used under response surface method (RSM) design (Can et al. 2006). The study carried out involved the employment of central composite design to optimize the biosorption process due to it’s suitability to fit quadratic surface which usually works well for process optimization.

This design consists of full factorial or fractional factorial design to determine effects of variables and interactions, an additional design, often a star design in which experimental points are at a distance α from its center which can be used to determine quadratic terms and central point to determine curvature of response. A central composite rotable design (CCRD) for three factors was employed for experimental design to provide data to model the effects of the independent variables, i.e., pH, biomass concentration (g/l) and initial lead concentration (ppm). The number of trials was based on the number of the design factors and was equal to 18 experiments (15 combinations with 3 replications). The chosen independent variables used in this study were coded according to Eq. 1.

$$ xi = \frac{{{\text{X}}i - {\text{X}}0}}{{\Updelta {\text{X}}}} $$
(1)

where x i is the dimensionless coded value of the ith independent variable, X 0 is the value of Xi at the center point and ΔX is the step change value. The basis of forming a polynomial equation is given in Eq. 2:

$$ {\text{Y}} = \beta_{0} + \sum\limits_{i = 1}^{k} {\beta_{i} {\text{X}}_{i} } + \sum\limits_{i = 1}^{k} {\beta_{ii} {\text{X}}_{i}^{2} } + \sum {\sum\limits_{i < j = 1}^{k} {\beta_{ij } {\text{X}}_{i} {\text{X}}_{j} } } + \varepsilon $$
(2)

where Y is the predicted response, x i , x j , …, xk are the input variables, which affect the response Y, x 2 i, x 2 j, …, x 2 k are the square effects, x i x j , and x j x k are the interaction effects, β0 is the intercept term, βi (i = 1, 2,…, k) is the linear effect, βii (i = 1, 2, …, k) is the squared effect, βij (i = 1, 2, …, k; j = 1, 2, …, k) is the interaction effect and ε is a random error (Amini et al. 2008).

Table 1 indicates a five-level-three-factor CCRD which has been employed in this optimization study; Each of the parameters was coded at five levels: −α, −1, 0, +1 and +α.

Table 1 Independent variable and levels used for CCRD in Pb biosorption process
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Meanwhile, the design matrix with their corresponding results as well as lead removal percentage (experimental and predicated) as the process response was presented in Table 2. Multiple regression analysis techniques included in the response surface methodology (RSM) were used to estimate the coefficients of the models. Regression coefficients of predicted quadratic polynomial model which is obtained by employing a least square technique for percent of Pb2+ ion removal process is shown in Table 3.

Table 2 CCRD arrangement and responses for biosorption process
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Table 3 Regression coefficients of predicted quadratic polynomial model for Pb ion removal (%s)
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Using the coefficients determined, the predicted model in terms of actual factors for lead ion removal (response) is:

$$ \begin{aligned} (Y)\,{\text{lead}}\,{\text{ion}}\,{\text{removal}}\,(\% ) & = + 22.55087 + 16.95385{\text{X}}_{1} - 4.31145{\text{X}}_{2} - 0.76736{\text{X}}_{3} + 0.81519{\text{X}}_{1} {\text{X}}_{2} + 5.991{\text{E}} \\ &\quad - 003{\text{X}}_{1} {\text{X}}_{3} + 0.11175{\text{X}}_{2} {\text{X}}_{3} - 1.0113{\text{X}}_{1}^{2} - 0.287{\text{X}}_{2}^{2} - 5.41549{\text{E}} - 004{\text{X}}_{3}^{2} \\ \end{aligned} $$
(3)

where Y is lead ion removal (response) in percentage, x1, x2 and x3 are the coded values of the tests variables, initial solution pH (x1), initial lead ion concentration (x2) in mg/l and L. bulgaricus dosage (x3) in g/l. The equation coefficients indicate that linear and quadratic terms of X1, linear and quadratic term of X2, linear term of X3 and interaction terms X1X2, X2X3 were found to be significant model terms. The regression model was found to be highly significant with a coefficient of determination 0.95. The illustration of the experimental data versus predicted values has been shown in Fig. 1 for lead ion removal (%). It can be seen that most of the points of experimental values lies close to the straight line which is the predicted values.

Fig. 1
figure 1

Actual versus predicted values for lead ion removal (%)

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The analysis of variance (ANOVA) for Eq. 2 is shown in Table 4. The F value, which is a ratio of the mean square due to regression to the mean square due to error, is converted into its corresponding p value. From statistical point of view, if p value < 0.05, then the model is statistically significant. As the p value decreases, it becomes less likely that the effect is due to chance, and more likely that there was a real cause. In this case, p value of model is 0.0003 and it is significant. The “lack of fit tests” compares residual error with “Pure Error” from replicated design points. Also, R2 = 0.95 for Eq. 2, indicates a very good fitting for the experimental data and predicted values.

Table 4 ANOVA for the lead ion removal (%) model
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Effect of parameter

Contours (Fig. 2a–c) were drawn at constant value of 36.22 ppm initial lead concentration (X3), 6.58 gr/l biomass concentration (X2) and pH amount (X1) = 6.78 respectively. The responses corresponding to the contour plots of second order predicted model indicated that for high pH, the percent of lead ion removal increases with increasing biomass concentration (Fig. 2a). For low pH, the effect of biomass concentration is low to some extent. Therefore, at higher pH, there seemed to be more effect of increase in biomass concentration than at lower pH. The contour plot of second order predicted model indicated that for every amount of pH, the percent of lead ion removal decreases with increasing of initial lead concentration (Fig. 2b). However, the interaction of X1X3 is not significant which can be inferred from figure. From Eq. 2 the coefficient of X1X3 is 5.98094 E−003 and it is very low. At any value of biomass concentration, there was decrease in percent of lead ion removal with increase in initial lead concentration (Fig. 2c).However, from the approximately linear contours, we can find that the quadratic terms don not have significant effect on response. Therefore, optimum point is located on boundaries and not among the variable ranges.

Fig. 2
figure 2

Contour plots describing the response surface for lead ion removal (%) as a function of each parameter pair

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Due to greater positive coefficients of pH—biomass concentration (X1X2) interaction than biomass concentration—initial lead concentration (X2X3) interaction, the term of X1X2 has more effect than other terms.

From contours, it can be concluded that increasing pH and biomass concentration parameters and decreasing initial lead concentration parameter cause increase in percent of lead ion removal. However, the value of effectiveness of each parameter is different from each other. Figure 3, indicates perturbation plot of three independent factors. For response surface designs, the perturbation plot shows how the response changes as each factor moves from the chosen reference point, with all other factors held constant at the reference value. In this case, at the center point, factors X1 (pH) produces a relatively high effect on response as it changes from the reference point. The higher amount of pH causes the higher amount of lead ion removal. Factor X2 has the same effect as factor X1 but it can be concluded that term X2 has less effect than term X1. The effect of term X3 is the inverse of both terms mentioned (Fig. 3).

Fig. 3
figure 3

The Perturbation plot of factors pH (X1), biomass concentration (X2) and initial lead concentration (X3)

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Response optimization

The objective of the experimental design was to optimize the process conditions for maximizing the percent of lead ion removal. The Nelder–Mead Simplex method was used to look for the optimal conditions in which each response variable achieved a maximum value. The variables that affect the efficiency of lead ion removal (%) and were discussed in our experimental design were chosen as the decision variables.

We define composite desirability function (D) that ranges from zero outside of the limits to one at the goal. The numerical optimization finds a point that maximizes the desirability function. Generally, increase in parameters pH (X1) and biomass concentration (X2) and decrease in initial lead ion concentration (X3) yield increase in efficiency of lead ion removal.

The ranges of decision variables used are as follows:

$$ \begin{aligned} &3.22 \le {\text{pH}} \le 6.78 \\& 2.4\,{\text{g/l}} \le {\text{weight}}\,{\text{of}}\,{\text{dried}}\,{\text{biomass}} \le 6.58\,{\text{g/l}} \\& 36.22\,{\text{ppm}} \le {\text{initial}}\,{\text{lead}}\,{\text{concentration}} \le 83.78\,{\text{ppm}} \hfill \\ \end{aligned} $$

In this case the maximum percent of lead ion removal was found at pH = 6.78, biomass concentration = 6.58 g/l, initial lead concentration = 36.22 ppm. This set gave the highest D at 0.95 and predicted percent of lead ion removal = 86.21% for optimized response. We define composite desirability function (D) that ranges from zero outside of the limits to one at the goal. The numerical optimization finds a point that maximizes the desirability function.

Figure 4 indicates three-dimensional display and contour plots for the composite desirability function of the interaction effect of X2X1 (Fig. 4a), X3X1(Fig. 4b), X3X2 (Fig. 4c) response surface. Figure 4a indicated that at low pH (X1) and biomass concentration (X2), the desirability (D) for maximum percent of lead ion removal is too low. For low X1 and high X2 and for high X1 and low X2, the desirability is low. Desirability of high X1 and low X2 is much higher than the desirability of low X1 and high X2. However, in order to obtain maximum percent of lead ion removal at high desirability, both terms (X1 and X2) should be high (Fig. 4a).

Fig. 4
figure 4

Three dimensional display and contour plots (X2 vs. X1)

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The effect of parameters X1X3 on maximization of lead ion removal is shown in Fig. 4b. At low pH and every arbitrary amount of initial lead concentration (X3), the desirability of maximum response is very low. Maximum desirability for maximum amount of response was found at high amount of X1 and low amount of X3. Lead ions removal showed to be very sensitive to the changes in the pH of the solution. The removal efficiency of L. bulgaricus increased when the pH of the solution increased from 2.8 to 6.78. The results showed that the maximum removal of lead ions was achieved at pH 6.78 while under highly acidic (pH 2.0) and moderate basic conditions (pH 8.0) low amount of lead removal was occurred. At highly acidic pH, the overall surface charge on the active sites became positive and metal cations and protons compete for binding sites on cell wall, which results in lower uptake of metal. Anyway, the change in the amount of initial lead concentration has no important significant effect on response (Fig. 4b). Figure 4c depicts the lead ion removal efficiency as a function of the L. bulgaricus dosage (X2) and the initial concentration of lead (X3) in the aqueous solution. Lead concentration showed a little effect while a remarkable effect of L. bulgaricus dosage is seen in Fig. 4c. It is inferred that high amount of X2 and low amount of X3 have positive effect on higher percent of lead ion removal. Low amount of X2 with any arbitrary amount of X3 has no significant effect on maximization of response. Generally, increase in parameters pH (X1) and biomass concentration (X2) and decrease in initial lead ion concentration (X3) yield increase in efficiency of lead ion removal. From Fig. 4, maximum removal efficiency (86.21%) was achieved at biomass concentration of 6.58 g/l and initial pH of 6.78, while lead concentration was 36.22 ppm. These optimal conditions can be used in wastewater for lead ion removal.

Scanning electron microscope

Figure 5 shows the SEM images of living and dried L. bulgaricus biomass before and after lead biosorption process. As the Fig. 5a shows the L. bulgaricus has the cylindrical shape so this can be concluded that it has high surface area. Figure 5b is the dried biomass before process, the surface layers exhibited micro-cavities and porous structure, indicated that the inner surface of the bioadsorbent is also seemed to have multilayered walls available for the adsorption process. The biomass after process has been shown in the Fig. 5c and d; they have smooth shape after adsorption of metal ions. Figure 5 also indicates that all the surface of dried biomass interacts with metal ions. Therefore, after biosorption all the surfaces of biomass are smooth (not sharp).

Fig. 5
figure 5

Lactobacillus bulgaricus a living, b before, c, d after biosorption process

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Conclusions

In this paper, the biosorption of Pb2+ by L. bulgaricus have been carried out. The response surface methodology involving central composite design and regression of analysis is used in finding the effect of the operating variables, namely, pH, biomass dosage, and initial lead concentration on the percent of lead ion removal. The second order polynomial equation model whose validity is agreed upon is estimated using ANOVA statistical testing.

The lead adsorption capacity increased with increase in pH from 2 to 6.78. The extent of lead removal was directly related to the dosage of the dried activated biomass in the solution. The lead removal efficiency increased from 44.86 to 79.33% with increase in adsorbent dosage from 2 to 8 g/L. Additionally, the best biosorption condition in CCRD design was found at pH = 6.78, biomass concentration = 6.58 g/l and initial lead concentration = 36.22 ppm that consequently had 86.21% lead ion removal. At pH lower than 2 (high acidic condition) and in alkaline condition, there is no significant biosorption. Based on these results, it can be concluded that L. bulgaricus is a very promising and low cost biosorbent for lead removal from waste water.