Introduction

One of the industrial by-products threatening the environment worldwide today is pesticides residues. Reports have shown that the presence of pesticide in the environment and the threat they pose to wild life and mankind have generated great concern in the past 20 years. The excessive and non-judicial use of pesticide and insecticide in crop farms, orchards, fields and forest lands contaminate the surface and groundwater. This contamination arises from surface runoff, leaching, wind erosion, deposition from aerial applications, industrial discharges and various other sources (Salman et al. 2011). It is not surprising, therefore, that small amounts of these chemicals have been isolated from many phases of the environment, including water supplies. The harmful influence of pesticides on human health and environment has resulted in the imposition of stringent legislation on drinking water quality in many countries (Derylo-Marczewska et al. 2010).

Carbaryl (1-naphthyl methyl carbamate) is one of the most widely used insecticides today. The IRED cited a study by the US Geological Survey’s National Water Quality Assessment program that reported carbaryl as the second most frequently found insecticide in water. At present, there is increased concern about the environmental fate and toxicity of carbaryl because of its high toxicity to both humans and wildlife.

There are several procedures available for pesticides removal from water which includes photocatalytic degradation (Ugurlu and Karaoglu 2011; Gong et al. 2011), ultrasound combined with photo-Fenton treatment (Katsumata et al. 2011), advanced oxidation processes (Zhou et al. 2011), aerobic degradation (Rajashekara Murthy and Manonmani 2007), electrodialysis membranes (Banasiak et al. 2011), ozonation (Maldonado et al. 2006) and adsorption (Al-Muhtaseb et al. 2011). Among these, adsorption is effective and economical (Ahmad et al. 2009). Various adsorbents are used for the removal of pesticides in water samples including activated carbon (Ohno et al. 2008; Castro et al. 2009), straw (Akhtar et al. 2007), lignocellulosic substrate from agro industry (Bakouri et al. 2009), bagasse fly ash (Traub-Eberhard et al. 1995), coal fly ash (Singh 2009) and charcoal from agro waste (Sudhakar and Dikshit 1999) and bark (Boudesocque et al. 2008). Biosorption is relatively new and one of the effective alternative methods for the removal of pesticides in contaminated water samples. In addition to scientific preference, economic considerations also play an important role in the selection of appropriate biomass for pollution control. Thus, profound research attention is now focused on cost-effective, eco-friendly and easily available adsorbent, particularly of biological origin (Chatterjee et al. 2010). In recent years, various natural adsorbents such as agricultural wastes including Rhizopus oryzae biomass (Chatterjee et al. 2010), bagasse fly ash (Gupta et al. 2002), thermally treated egg shell (Elwakeel and Yousif 2010) and waste jute fiber carbon (Senthilkumaar et al. 2010) have been tried to achieve effective removal of pesticides. The search for an appropriate and inexpensive biomass is a continuing process. The most effective and optimized utilization of a biomass demands a detailed understanding of the binding mechanism.

Pistia stratiotes is a floating macrophyte. It is a genus of aquatic plant in the arum family, Araceae. The single species it comprises, P. stratiotes, is often called water cabbage, water lettuce, Nile cabbage or shellflower. The living aquatic macrophyte P. stratiotes has been extensively used in bioaccumulation processes (Sune et al. 2007), phytoremediation (Skinner et al. 2007) and for removal of Cr(VI) ions (Das et al. 2013), which makes the study using dead biomass very important. So, this biomass can also be utilized to remove carbaryl from aqueous solutions which formed the motivation of this study.

Adsorption isotherms were established to explain the solute and solvent interaction mechanism. Various kinetic models were used to determine the rate and order of the adsorption process. To optimize the process parameters for the biosorption process, a central composite design (CCD) in response surface methodology (RSM) by Design Expert Version 7.0.3 [Stat-Ease, Inc. (2009), USA] is used. When compared with other similar adsorption studies, the novelty of using RSM in this work is in reducing the number of experiments, studying the effect of factor interactions and developing a statistical mathematical model equation, to identify optimum conditions where high uptake capacity is obtained for the removal of carbaryl using P. stratiotes biomass, to ensure high uptake capacity at low adsorbent dosage and to reduce time consumption with high carbaryl concentration.

Materials and methods

Chemicals

A fresh 0.03 % (w v−1) methanolic solution of 4-nitrobenzene diazonium fluoborate (Sigma Aldrich) A.R. (99.9 % purity) was used as the main reagent. An analytical standard pure sample of carbaryl was obtained by the recrystallisation of a technical-grade sample supplied by Bayer. The solvent used for the extraction was Optima-grade methanol. Stock solutions were made in A.R. grade CaCl2 and A.R. grade NaOH and subsequent dilutions were made in methanol. All standard solutions were kept at room temperature for further use.

Adsorbent collection and preparation

P. stratiotes, a floating macrophyte, was collected from the surrounding area of University of Burdwan, West Bengal, India. The macrophyte was washed several times with distilled water and then it was initially sun dried for 7 days followed by drying in a hot air oven at 343 ± 1 K for 2 days. The dried material was crushed and sieved to give a fraction of 250 mesh screen. The material was washed thoroughly with deionised water to remove any adhering dust that might be present in the material, then dried and stored in sterile, closed glass bottles and used as an adsorbent. The physicochemical properties of the adsorbent are summarized in Table 1. Characterization of the adsorbent has been previously reported (Das et al. 2013).

Table 1 Physicochemical characteristics of adsorbent
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Batch adsorption procedure

The spectrophotometric determination of carbaryl was done by the method of Stansbury and Miskus (1964). 5 mL of 0.5(N) NaOH solution was added to 5 mL of aliquot taken in a 20 mL volumetric flask followed by the addition of 5 mL of 0.03 % (w v−1) methanolic solution of p-nitrobenzene diazonium fluoborate. The mixture was then diluted to 20 mL with methanol. After 20 min, the absorbance of the greenish-blue color of the resulting solution was measured at 590 nm using UV–VIS spectrophotometer (Systronics, Vis double beem Spectro 1203).

The influence of pH (2.0–8.0), initial carbaryl concentration (5, 10, 15, 20 mg L−1), contact time (1, 5, 10, 20, 30, 60 min) and adsorbent dose (0.05, 0.1, 0.5, 0.8 g per 100 ml) were evaluated during the present study. Samples were collected from the flasks at predetermined time intervals for analyzing the residual carbaryl concentration in the solution. The amount of carbaryl ions adsorbed in milligram per gram was determined by using the following mass balance equation:

$$q_{\text{e}} = \frac{{(C_{\text{i}} - C_{\text{e}} )V}}{m}$$
(1)

where Ci and Ce are carbaryl concentrations (mg L−1) before and after biosorption, respectively, V is the volume of adsorbate in liter and m is the weight of the adsorbent in grams. The percentage of removal of carbaryl ions was calculated from the following equation:

$${\text{Removal (}}\% ) { } = \frac{{(C_{\text{i}} - C_{\text{e}} )}}{{C_{\text{i}} }} \times 100.$$
(2)

The biosorption experiments were performed in triplicate, and mean values were used in the data analysis. The control experiments were performed without the addition of adsorbent which confirmed that the biosorption of carbaryl on the walls of flasks were negligible.

Design of experiment

Central composite design (CCD)

CCD is a statistical method based on the multivariate nonlinear model that has been widely used for the optimization of process variables of biosorption and also to determine the regression model equations and operating conditions from the appropriate experiments (Kalavathy et al. 2009; Arulkumar et al. 2011). It is also useful in studying the interactions of the various parameters affecting the process. The CCD was applied in this present study to determine the optimum process variables for biosorption of carbaryl using P. stratiotes biomass. The CCD was used for fitting a second-order model which requires only a minimum number of experiments for modeling (Ahmad et al. 2009). The CCD consists of 2n factorial runs (coded to the usual ± notation) with 2n axial runs (±α, 0, 0, …, 0), (0, ±α, 0, 0, …, 0), …, (0, 0, …, ±α) and nc center runs (6 replicates, 0, 0, 0, …, 0). The number of factors n increases the number of runs for a complete replicate of the design which is given in Eq. (3)

$$N \, = \, 2^{n} + \, 2n \, + \, n_{\text{c}} .$$
(3)

Basically, the optimization process involves three major steps: (1) performing the statistically designed experiments, (2) estimating the coefficients in a mathematical model and (3) predicting the response and checking the adequacy of the model (Sahu et al. 2009; Kumar et al. 2009). An empirical model was developed to correlate the response to the biosorption process and is based on second-order quadratic model for removal of carbaryl using P. stratiotes biomass) as given by Eq. 4 to analyze the effect of parameter interactions

$$Y = \beta_{0} + \mathop \sum \limits_{i = 1}^{k} \beta_{i} x_{i} + \mathop \sum \limits_{i = 1}^{k} \mathop \sum \limits_{j = 1}^{k} \beta_{ij} x_{i} x_{j} + \mathop \sum \limits_{i = 1}^{k} \beta_{ii} x_{ii}^{2} + \varepsilon$$
(4)

where Y is the response variable; β0 is the intercept; β i , β ij and β ii are coefficients of the linear effect, double interactions; x i, x j are the independent variables or factors and ε is the error (Table 2).

Table 2 Variables and levels considered for the adsorption of carbaryl onto P. stratiotes biomass
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Results and discussion

Central composite design analysis

Generally, a system or process with several variables is likely to be driven primarily by some principal factors and low-order interactions. In the present work, only two-way interactions were investigated. Linear, two-factor interaction (2FI), quadratic and cubic models were used to analyze the experimental data to obtain the appropriate regression equations. To determine the adequacy of the models depicting the removal of carbaryl by P. stratiotes biomass, two different tests, i.e., the sequential model sum of squares and the model summary statistics, were conducted. The corresponding results are tabulated in Tables 3 and 4. The fitness of the model was determined by R2 and its statistical significance was evaluated by an F test (Peng et al. 2002). The higher the value of R2, the better is the model. The results from the sequential model indicated that the 2FI model did not provide a good description of the experimental data. R2 is a measure of the reduction amount in the variability of the response obtained by the independent factor variables in the model. Furthermore, the quadratic model had maximum “predicted R2” and “adjusted R2” values indicating that the quadratic model provided an excellent explanation for the relationship between the independent variables and the corresponding response. “Adequate precision” measures the signal-to-noise ratio and a ratio >4 is desirable. It is clear that only the “adequate precision” value of the quadratic model indicated an adequate signal, validating the viewpoint mentioned above. Consequently, the quadratic model could be used to navigate the design space and for this reason it was selected as the most appropriate model for further analysis.

Table 3 Model summary statistics for carbaryl adsorption onto P. stratiotes biomass
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Table 4 Adequacy of the model tested
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The optimization of biosorption conditions

In the first step of the study, the effects of operating variables such as initial carbaryl concentration, pH, adsorbent dose and contact time on carbaryl capacity of P. stratiotes biomass were investigated using RSM according to CCD. In the second step, the main objective was to select the initial pH and time to achieve the optimal carbaryl biosorption capacity of the biosorbent. The batch runs were conducted in CCD-designed experiments to visualize the effects of independent factors on responses and the results along with the experimental conditions. Figure 3 shows the response surface plots described by the regression model and drawn to illustrate relationships between factors on carbaryl biosorption capacity on P. stratiotes biomass under the sets of conditions and treatment levels tested (Table 3). The experimental results were evaluated and the approximating function of carbaryl biosorption capacity of biosorbent was obtained in Eq. (5) by utilizing Design Expert software. Equation (5) could be used in predicting the response for the given values of initial carbaryl concentration, pH, adsorbent dose and time. Approximating equation of the fitted response surface was also checked for the model adequacy using analysis of variance (ANOVA) results and the diagnostic plots of Figs. 1, 2, 3 and 4. The experimental and predicted values of removal of carbaryl are given in Table 4

$$\begin{aligned} Y = & +\, 65.15 - 1.57 \times X_{1} - 23.73 \times X_{2} + 13.95 \times X_{3} - 2.26 \times X_{4} \\ + 2.45 \times X_{1} \times X_{2} + 2.55 \times X_{1} \times X_{3} - 1.40 \times X_{1} \times X_{4} - 9.20 \times X_{2} \times X_{3} + 3.10 \times X_{2} \times X_{4} \, \\ + 4.40 \times X_{3} \times X_{4} - 3.76 \times X_{1}^{2} - 1.43 \times X_{2}^{2} - 10.52 \times X_{3}^{2} -2.08 \times X_{4}^{2} . \\ \end{aligned}$$
(5)
Fig. 1
figure 1

Normal plots of residuals vs internally studentized residuals

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Fig. 2
figure 2

Comparison between the actual values and the predicted values of RSM model for adsorption of carbaryl

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Fig. 3
figure 3

Plot of Cook’s distance vs run number

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Fig. 4
figure 4

Plot of leverage vs run

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In Eq. (5), Y is the response of carbaryl biosorption capacity of biosorbent at the end of biosorption time. X1, X2, X3 and X4 correspond to independent variables of initial carbaryl concentration, pH, adsorbent dose and contact time, respectively. ANOVA results of this quadratic model presented in Table 5 indicate that it could be used to navigate the design space. It is probably the most useful technique in the field of statistical inference. As can be seen in Table 5, the model F value for carbaryl biosorption capacity of biosorbent was evaluated as 155.36. This value indicated that the quadratic model was significant. The values of R2 pred. (0.9707) and R2 adj. (0.9868) for carbaryl biosorption capacity of P. stratiotes biomass well satisfied the model. “Adequate precision” ratio of 40.483 indicates an adequate signal for the model to be used to navigate the design space. The P values <0.0500 indicate that the model terms are significant, whereas the values >0.1000 are not significant.

Fig. 5
figure 5

Response surface plots showing the effect of independent variables on carbaryl adsorption onto P. stratiotes biomass

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Table 5 Experimental design matrix and responses for the adsorption of carbaryl onto P. stratiotes biomass
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By constructing a normal probability plot of the residuals, a check was made for the normality assumption, as given in Fig. 1. The residuals show how well the model satisfies the assumptions of the ANOVA where the studentized residuals measure the number of standard deviations separating the actual and predicted values (Montgomery 1996; Myers 2002; Korbahti and Rauf 2008) The normality assumption was satisfied as the residual plot approximated along a straight line (Table 6).

Table 6 Analysis of variance (ANOVA) for percentage removal of carbaryl onto P. stratiotes biomass
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The actual and the predicted carbaryl biosorption capacity of P. stratiotes biomass is shown in Fig. 2. The actual values are the measured response data for a particular run, and the predicted values are evaluated using the approximating functions generated for the models (Montgomery 1996; Myers 2002; Korbahti and Rauf 2008).

Figure 3 shows Cook’s distance plot; according to this plot there were no points that were potentially powerful due to their location in the factor. So we conclude that the empirical model is adequate to describe the carbaryl biosorption capacity on P. stratiotes biomass.

Optimal conditions for the carbaryl biosorption process

Figure 5 shows the pH and biomass dose interaction that had the most significant effect on carbaryl removal. At pH 2.01 and biomass dose of 0.72 g, the model-predicted carbaryl removal efficiency by biomass is close to 100 %. At these predicted optimal conditions, P. stratiotes biomass achieved a carbaryl biosorption capacity of 3.1 mg g−1 (initial concentration 15.57 mg L−1 and contact time 30 min). Figure 5 shows the removal of carbaryl as a function of biomass dose. Increase in biomass dose increased the percentage removal of carbaryl. The optimal biomass dose is 0.72 g. Nevertheless, carbaryl removal decrease at biomass dose higher than the optimal value may be due to biomass agglomeration. A similar effect was reported on carbaryl adsorption by soils (Chattoraj et al. 2013) and lead biosorption on Aspergillus terreus biomass (Cerino-Cordova et al. 2011).

Adsorption isotherms

Various isotherm equations like Freundlich, Langmuir and Temkin were used to describe the equilibrium characteristics of biosorption of carbaryl onto P. stratiotes biomass. The linearized form of isotherms (Babu and Gupta 2008) and their constants are given in Table 7. The experimental data obtained at equilibrium was fitted satisfactorily with Freundlich isotherm and is shown in Fig. 6. The value of the Freundlich constant, KF represents the degree of adsorption. The magnitude of n gives a measure of favorability of biosorption. Values of n between 1 and 10 represent a favorable biosorption process. For the present study, the value of n also presents the same trend (Table 7) representing a beneficial biosorption process. The Freundlich isotherm reveals the multilayer adsorption.

Table 7 Summary of parameters for various isotherm models
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Fig. 6
figure 6

Freundlich isotherm for adsorption of carbaryl onto P. stratiotes biomass

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Adsorption kinetics

The pseudo-first-order and pseudo-second-order kinetic models were tested to investigate the rate of biosorption of carbaryl on P. stratiotes biomass. The linearized form of biosorption kinetics (Guo et al. 2002) and their constants are presented in Table 8. The plot of t/q versus t shows a linear relationship which confirmed that the biosorption of carbaryl on P. stratiotes biomass followed the pseudo-second-order reaction and is shown in Fig. 7. The values of qs and k2 can be determined from the slope and intercept of the plot. It is clear that the pseudo-second-order kinetic model showed excellent linearity with high correlation coefficient (R2 > 0.99) at all the studied concentrations in comparison to the first-order kinetic model (Table 8). In addition, the calculated qs values also agree with the experimental data in the case of pseudo-second-order kinetic model.

Table 8 Summary of parameters for various kinetic models
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Fig. 7
figure 7

Pseudo-second-order kinetics for adsorption of carbaryl onto P. stratiotes biomass

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Desorption studies

To assess the reusability of carbaryl-loaded P. stratiotes biomass, desorption experiments were carried out. The effect of the strength of the desorbing solution (NaOH) on the recovery of carbaryl is shown in Fig. 8. It is evident from the above figure that when the strength of the desorbing solution increased from 0.2 to 2.5 M, carbaryl desorption percentage increased from 19 to 91 %. Thus a significant amount of carbaryl is being desorbed, which shows that the P. stratiotes biomass can be effectively reused after desorption.

Fig. 8
figure 8

Effect of NaOH concentration on carbaryl desorption

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Comparison of the present study with other adsorbents

Comparison of adsorption capacity (3.1 mg g−1) observed in this study with other adsorption capacities in the literature is given in Table 9.

Table 9 Adsorption capacity of various adsorbents as reported in literature
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Conclusions

Carbaryl removal from aqueous solutions by P. stratiotes biomass was studied with a CCD. According to the ANOVA analysis, carbaryl removal is highly affected by biomass dose, pH and the interactions between pH with the other factors (biomass dose, initial concentration and time). The hierarchical quadratic model represents adequately the response surface space based on the adjusted determination coefficient (R2 adj = 0.9868) and the adequate precision ratio (40.483). By using this model, at the optimal conditions (pH 2.1, 0.72 g of biomass, 30 min contact time and 15.57 mg L−1 of carbaryl concentration), the predicted removal efficiency achieved near 100 % of carbaryl from aqueous solutions when using P. stratiotes biomass. Finally, the reported results in this research demonstrate the feasibility of employing P. stratiotes biomass as a low-cost biosorbent for carbaryl removal.