Utilization of Multivariate Optimization for Preconcentration and Determination of Lead in Different Water and Food Samples Using Functionalized Activated Carbon

Abstract

In this study, apricot-pit-based activated carbon was functionalized and used as a sorbent for the preconcentration of lead (Pb²⁺) in different water and food samples. The activated sorbent was modified with ethylenediaminetetraacetic acid (EDTA) to enhance its selectivity for the efficient removal of Pb²⁺ ions. The modified activated sorbent was characterized using FTIR, an SEM, BET, and TGA. The column adsorption method was used to study the adsorption capacity of synthesized and modified activated carbon and analyzed using atomic absorption spectrophotometry. A multivariate procedure, i.e., Plackett–Burman design (PBD) and central composite design (CCD), was studied for optimizing the adsorption process, which allows the optimization of multiple variables at the same time. An interference study was conducted to ascertain the selectivity of the developed method. The developed method was validated by assessing certified reference materials and additional standards for Pb²⁺ detection in real samples. To assess the precision of the proposed procedure, repeatability (RSD_r) and reproducibility (RSD_R) were calculated, which were determined to be <3.0 (n = 7) and <7.5 (n = 15), respectively. The obtained results revealed that the modified AC is a suitable and efficient sorbent for the preconcentration of Pb²⁺ in real water and food samples.

1. Introduction

Contamination due to heavy metals has become a serious problem, which is increasing globally and threatening the environment [1]. The rapid increase in manufacturing industries, urbanization, agricultural activities, petroleum drilling, etc., results in higher toxicity, which leads to several environmental problems [2]. Heavy metals are non-biodegradable and adversely affect both plants and animals, as well as human life. However, they are useful for various physiological and biochemical functions in living bodies [3]. Among the various heavy metals, lead (Pb) is considered a notable environmental pollutant [4]. Lead is a major component of various oxides, sulfides, and carbonates and thus may pollute water sources [5]. The precise estimation of heavy metals in water bodies and food items is a modern necessity. To assess the accurate concentration of these heavy metals, several methods have been used, including adsorption, ion exchange, filtration, evaporation, coagulation, precipitation, ultrafiltration, and flocculation [6]. Among these methods, adsorption is frequently considered, owing to its effectiveness, low cost, easy application, and ability to eliminate various contaminants effortlessly. The rest of the above methods are not preferred as they are time-consuming and less ecologically friendly. Therefore, the adsorption process is considered a productive and possible method for decontamination of these toxic metals [7]. Carbonaceous materials are used as adsorbents, including carbon nanotubes, graphene oxide, activated carbon, and biochar. These carbonaceous compounds have been utilized for the removal of heavy metals from wastewater due to their high adsorption capacity. The most used adsorbent is activated carbon, which is used for the treatment of wastewater [8,9,10]. Activated carbon is actually an allotropic form of carbon having a microcrystalline structure with a highly porous surface, providing a larger surface area that contributes to efficient adsorption [11,12]. Activated carbon derived from apricot pits is one of the widely used sorbents due to its properties such as high porosity, large surface area, high purity, and good adsorption performance for metal ions [12,13]. Activated carbon may be surface-modified using various methods such as nitrogenation or sulfuration, oxidation, ammonification, and ozonation in order to remove inorganic (heavy metals) and organic pollutants [14,15]. A number of ligands have also been used to improve the surface functionalities of activated carbon to enhance its adsorption capacity [16], and different ligands add different characteristics to the surface of activated carbon [17]. These modified surfaces improve the selectivity of active sites and can be easily used for the removal of toxic heavy metals and other impurities [18]. The adsorption capacity is increased with the process of chelation between introduced ligands on the surface. The surface polarity of activated carbon is enhanced by introducing nitrogen and/or sulfur complexes on the surface of carbon-based material, thus showing specific interaction with polar impurities [19]. Surface modification with ligands does not have the problem of leaching into the solution. These activated carbons can be used several times after treatment of the surface for further adsorption [20]. EDTA is frequently used as a complexing or chelating agent which contains four carboxyl groups and two amine groups. It reacts with heavy metals, forms stable complexes owing to its strong metal complexing properties, and remains stable after regeneration. Therefore, EDTA-modified adsorbents are widely used for the elimination of heavy metals such as Pb²⁺ [21,22]. Chemometric tools such as the design of experiments are highly beneficial and reduce the number of measurements, thus lowering the cost of experimentation and time required for the experiment [23]. One of the widely used methods for multivariate optimization is the response surface method (RSM), which is a statistical and mathematical procedure that is used to develop, improve, and optimize the processes [24]. This method is used for studying variables, their effect on each other, and finding the optimum condition for sorption. The central composite design (CCD), Box–Behnken design (BBD), and Doehlert design (DD) are also used for multivariate optimization [25].

In this study, apricot-pit-based activated carbon was synthesized and functionalized prior to its use as a sorbent for the preconcentration of Pb²⁺. Multivariate optimization was used to optimize the experimental variables such as pH, temperature, sorbent amount, sample volume, sample flow rate, and eluent volume. A column-based solid phase extraction procedure was used to preconcentrate Pb²⁺ in water and food samples followed by its spectrometric determination.

2. Experiment

2.1. Apparatus

A HI-2210 pH meter, Hanna instruments, UK, was used to measure pH. An D307032366 analytical balance was used for weighing chemicals. A Elmasonic (model E-30-H) sonicator was used for sonication purposes, while a magnetic stirrer (model F203A0160, VELP Scientifica, Usmate Velate, Italy) was utilized for stirring. A PH030A drying oven was used for drying purposes. Carbonization was performed using a muffle furnace (GLMB + PD/IND, Carbolite Barnford, Sheffield, UK). A Perkin Elmer Analyst 700 flame atomic absorption spectrophotometer was used for the spectrophotometric determination of Pb²⁺. FTIR (Perkin Elmer UATR) was used to identify the surface functionality of activated carbon. An SEM (model JSM5910, JEOL, Tokyo, Japan) was used to analyze the surface morphology of the activated carbon. For BET analysis, an automated gas adsorption analyzer, AUTOSORB-1 (Quantachrome Instruments, Boynton Beach, FL, USA), was used. TGA was carried out using TGA-50, manufactured by SHIMADZU Japan, to measure the thermal stability of the material.

2.2. Reagents and Solutions

The reagents used in the protocol for Pb²⁺ removal via the adsorption process were of analytical grade. Ethylenediaminetetraacetic acid (EDTA), potassium hydroxide (KOH), sodium hydroxide (NaOH), hydrochloric acid (HCl), lead nitrate solution (Pb(NO₃)₂), and methanol were purchased from Daejung chemicals, Seoul, Republic of Korea, and were used without further purification. A standard solution of 1000 mg L⁻¹ of Pb²⁺ was obtained from Merck KGaA, Darmstadt, Germany. EDTA-modified activated carbon was prepared in a ratio of 1:1. Syringe filters (Millipore Corporation, Bedford, MA, USA) with 0.45 μm pore size were used for column adsorption studies.

2.3. Synthesis of Activated Carbon

2.3.1. Preparation of Apricot-Pit-Based Activated Carbon

Apricot-pit-based activated carbon was prepared using apricot pits as the raw material. The raw material was washed with deionized water and then dried in an oven for several hours to remove the moisture. The dried mass was ground and sieved into fractions with an average particle size of 1.0 mm. The finely crushed and sieved material was impregnated with KOH in a ratio of 1:3 and left to dry at 100 °C for approximately 12 h. The dried mixture was carbonized in a muffle furnace with a programmed temperature in an inert atmosphere with a constant N₂ flow rate of 300 mL min⁻¹. The temperature was increased stepwise, starting at 200 °C and going up to 900 °C, with a ramp/hold time of 5 min for each step. To ensure the complete carbonization, the material was left in the furnace for 2 h. Finally, the product was neutralized to a pH of approximately 7 using 0.1 M HCl. The activated carbon sample was washed several times with distilled water, dried in an oven, and then stored in a desiccator.

2.3.2. Surface Modification of Activated Carbon

To enhance the surface functionalities, the as-prepared activated carbon was loaded with EDTA in a ratio of 1:1 (w/w). A total of 1.0 g of activated carbon was added to 1.0 g of EDTA (in 25 mL aqueous solution) and sonicated for a few minutes. The ligand, along with the activated carbon, was stirred and heated at 80 °C for 24 h. The sample was then cooled and kept at room temperature. The resultant sample was washed with distilled water multiple times until the pH approached 7 and dried in an oven at 80 °C for 12 h.

2.4. Sample Collection

Tap water and wastewater samples were collected from COMSATS University Islamabad, Abbottabad Campus. These samples were filtered to remove all impurities, such as suspended particles, using a 0.45 μm pore size membrane filter and then stored in a pre-treated water sampling bottle. Wheat flour and milk powder samples were purchased from the local market. One gram of each sample was weighed and digested with 10 mL of a 3:1 (v/v) mixture of concentrated HNO₃ and H₂O₂. This mixture was heated on a hot plate for 2 h until a semi-dried mass was observed. Subsequently, 8 mL of 1 M HNO₃ was added, and the mass was dissolved to obtain a clear solution. The resultant samples were stored in a refrigerator until analysis.

2.5. Microextraction Procedure

Column solid-phase extraction was performed by passing 20 mL of a 10 ppm Pb²⁺ solution through 40 mg of the adsorbent. The sample’s pH was adjusted to 6, and the flow rate was set to 10 mL min⁻¹. The sample temperature was maintained at 46 °C by heating in a water bath. Using the same flow rate, 5 mL of 0.1 M HCl solution was passed through the sorbent to desorb the analyte. Immediately after elution, the sorbent was washed with deionized water in triplicate to remove any remaining analyte from the sample. Finally, the eluted solutions were analyzed using a flame atomic absorption spectrophotometer to determine the Pb²⁺ concentration.

2.6. Adsorption Studies

To determine the sorbent’s adsorption capacity, 100 mL of a Pb solution with an initial concentration of 150 µg mL⁻¹ (C_i) was mixed with 100 mg of surface-modified sorbent in a 100 mL Erlenmeyer flask. The flask was shaken at 200 rpm using a mechanical shaker at room temperature for 24 h. After shaking, the aqueous phase was separated, and the equilibrium concentration of Pb²⁺ (C_e) was determined using FAAS. The adsorption capacity of the sorbent (q) was calculated using the following equation.

q = V(C_i − C_e)/W

(1)

where V represents the volume of the solution in milliliters (mL), and W is the sorbent amount. A comparison of the adsorption capacity of the modified sorbent with other reported materials is presented in Table 1.

Langmuir and Freundlich adsorption isotherms were used to describe the equilibrium adsorption characteristics. Equation (2) represents the Langmuir isotherm, which was subsequently transformed into its linear form, as shown in Equation (3), to determine the adsorption parameters.

$$q_e=\frac{q_{max}{K}_L{C}_e}{1+K_L{C}_e}$$

(2)

$$\frac{1}{q_e}=\frac{1}{K_L{q}_{max}}\times \frac{1}{C_e}+\frac{1}{q_{max}}$$

(3)

In this context, q_max represents the maximum adsorption capacity (mg/g), while K_L (L/mg) is the Langmuir isotherm constant, providing insights into the binding affinity between Pb²⁺ and CA@EDTA. The separation factor (R_L) was calculated using Equation (4), where R_L, a dimensionless Langmuir constant, indicates the favorability of adsorption, ranging from favorable (0 < R_L > 1) to unfavorable (R_L > 1), linear (R_L = 1), or irreversible (R_L = 0).

$$R_L=\frac{1}{1+C_i\times K_L}$$

(4)

Equation (5) represents the Freundlich isotherm, while its linear representation is shown in Equation (6).

$$q_e=K_{f }{C_e}^{\frac{1}{2}}$$

(5)

$$\log q_e=\log K_f+\frac{1}{n}\log C_e$$

(6)

In this case, K_f serves as the Freundlich constant, quantifying adsorption capacity, while 1/n elucidates the adsorption process, indicating whether it is favorable (0.1 < 1/n < 0.5) or unfavorable (1/n > 2).

2.7. Kinetic Study

The adsorption rates of Pb²⁺ on CA@EDTA was examined using pseudo-1st-order and pseudo-2nd-order kinetic models. Equation (7) illustrates the pseudo-1st-order model, with q_t representing the adsorption capacity (mg/g) at time “t”, and K₁ (min⁻¹) represents the equilibrium rate constant.

ln (q_e − q_t) = ln q_e − K₁t

(7)

The pseudo-2nd-order model is presented in Equation (8), where K₂ (g mg⁻¹ min⁻¹) represents the equilibrium rate constant. To determine the most appropriate isotherm and kinetic model for the adsorption process, the linear regression coefficients (R²) are used for prediction.

$$\frac{t}{q_e}=\frac{1}{K_2{q^2}_e}+\frac{1}{q_e}$$

(8)

The proposed procedure has been compared with adsorption techniques reported in the literature in terms of adsorption capacity (see Table 1). Isotherm and kinetic parameters for the adsorption are provided in Table 2. Figure 1 illustrates Langmuir’s and Freundlich’s isotherm plots, along with pseudo-second and pseudo-first-order plots, demonstrating the adsorption of Pb²⁺.

2.8. Multivariate Experimental Design for Pb²⁺ Analysis

Several parameters were selected for study and optimization through a multivariate optimization strategy. To achieve this, two software packages, Minitab 17.1 and STATISTICA 8, were utilized to develop multivariate experimental designs (response surface method—RSM) for modeling, optimizing, and analyzing the results.

The response surface method (RSM) is a statistical and mathematical procedure employed for developing, improving, and optimizing processes. It is a multivariate approach that involves the simultaneous analysis of multiple variables used to optimize processes and obtain the best possible results. This method is utilized to investigate both dependent and independent variables and their mutual effects to determine the optimal conditions for sorption.

In essence, the RSM relies on polynomial equations to describe the patterns within the sets of experimental data obtained. The most used types of RSM are the Plackett–Burman design (PBD) and the central composite design (CCD).

2.8.1. Plackett–Burman Design

Chemometric tools were employed to optimize analytical procedures for the determination of Pb²⁺. The Plackett–Burman design (PBD) was used to identify the significant parameters, as well as the higher (+) and lower (−) levels that could influence the effectiveness of the developed technique, as depicted in Table 3. This experimental design offers the significant advantage of reducing the number of experiments required for optimization compared to univariate procedures. For the developed method, a two-level PBD was implemented, consisting of 24 experiments with 6 factors, and the empirical data were evaluated using Minitab 17.1 and STATISTICA 8 software.

The fitted quadratic response model is expressed in Equation (9):

$$y=\beta o+{\sum }_{i=1}^k{\beta }_i{x}_i+{\sum }_{i=1}^k{\sum }_{j=1}^k{\beta }_i{x}_i{x}_j+{\sum }_{i=1}^k{\beta }_{ii}{x}_i^2+\mathsf{\epsilon }$$

(9)

Here “y” is the predicted response and regression coefficients for intercept, linear, quadratic, and interaction terms, which are shown as β_o, β_i, β_ij, and β_ii, respectively. Random error is expressed as “ε”, while x_i and x_j are coded values of independent factors.

The significant parameters were identified using the standardized Pareto chart, as illustrated in Figure 2, which was generated with the assistance of Minitab 17.1. This chart reveals the factors that have the potential to impact the efficiency of the developed technique.

In the chart, the horizontal bars represent the absolute magnitude of the factors, while the vertical bars extending beyond the red lines signify the significant factors that have an influence on the recovery of the analyte and the optimization of the process (with a significance level of p ≤ 0.05). The Pareto chart provides insights into how these parameters interact with each other.

2.8.2. Central Composite Design

The significant parameters were optimized through the execution of 24 new experiments, each conducted at two levels: low (−) and high (+). These experiments were designed to determine the optimum conditions for maximizing the recovery of the analyte. The parameters under investigation included pH, temperature, sorbent amount, and sample volume. The results of these optimization experiments, along with the corresponding recoveries, are presented in Table 4.

3. Results and Discussion

3.1. Characterization Study of Adsorbent

Apricot-pit-based activated carbon was synthesized, modified, and characterized for the preconcentration of Pb²⁺ in both wastewater and food samples. A multivariate optimization strategy was employed to assess all the experimental variables that could potentially impact the effectiveness of the developed method.

3.1.1. FTIR Analysis

Fourier transform infrared spectroscopy (FTIR) analysis was conducted to evaluate the surface functionality of the activated carbon and the modified activated carbon. The FTIR spectra, as depicted in Figure 3, revealed the stretching and bending vibration bands of various functional groups. For activated carbon (AC), peaks were observed at 3383 cm⁻¹ and 1749 cm⁻¹, which were attributed to the stretching vibrations of –OH and C=O groups. In the FTIR spectrum for EDTA-functionalized AC, peaks at 1671 cm⁻¹ and 1207 cm⁻¹ corresponded to the stretching of C=O and –NH, respectively. The slight shift in the position of the C=O peak and the appearance of a prominent peak for –NH indicate the successful functionalization of AC with EDTA.

3.1.2. SEM Analysis

The surface morphology of both non-modified and modified activated carbon was analyzed using a scanning electron microscope (SEM) from JEOL, Japan, operated at 30 kV with a maximum resolving power of 2.3 nm. The SEM images displayed in Figure 4 reveal that particles are uniformly distributed, indicating the high porosity of both the activated carbon and the EDTA-modified activated carbon.

Notably, it was observed that the porosity of the EDTA-modified activated carbon remained the same as that of the unmodified activated carbon. This observation suggests that the functionalization of the activated carbon does not have an adverse impact on its porosity. Instead, it enhances the adsorption capacity due to the incorporation of additional functional groups.

3.1.3. N₂ Adsorption Isotherm in Activated Carbon

To evaluate the surface area, 200 mg of AC@EDTA was subjected to degassing at 150 °C for 3 h before determining adsorption–desorption isotherms of nitrogen (N₂) at a temperature of −196 °C. An automated gas adsorption analyzer, AUTOSORB-1 from Quanta Chrome Instruments, USA, was employed for this purpose. The specific surface area was calculated using the BET (Brunauer, Emmett, and Teller) method, while the Dubinin–Radushkevich (DR) equation was used to determine the micropore volume. The pore volume was directly estimated by measuring the volume of N₂ at the highest relative pressure (P/P^o = 0.99) [26,27]. The N₂ adsorption isotherms of AC@EDTA exhibited type I isotherms, indicating that the sorbent possesses a microporous nature (as shown in Figure 5). In the low-relative-pressure region, there was a rapid increase in the volume of N₂ adsorbed, signifying that N₂ is primarily adsorbed within the microporous regions [28]. Type I isotherms are characterized by convex-shaped curves, and their platform appears to be horizontal, where the adsorption and desorption isotherms intersect directly at P/P^o = 1 [29,30]. Overall, AC@EDTA exhibited a substantial volume of micropores.

BET analysis was conducted to determine the specific surface area of AC@EDTA, which was found to be 587.5 m² g⁻¹. Additionally, a total pore volume of 0.289 cm³ g⁻¹ with an average pore size diameter of 29.3 Å was observed.

Thermogravimetric analysis was conducted using a thermogravimetric analyzer, specifically the TGA-50 model manufactured by SHIMADZU, Japan. This analysis aimed to evaluate the thermal stability of activated carbon loaded with EDTA within the temperature range of 40 °C to 600 °C.

In Figure 6, the percentage weight loss of EDTA-modified activated carbon is depicted. The weight loss occurring in the temperature range of 100 °C to 300 °C can be attributed to the decomposition of carboxylic acid groups and the loss of -OH groups. Beyond 400 °C, the active sites of EDTA, such as amine and carboxylic acid, were completely decomposed, leading to a gradual decrease in the percentage weight of the composite. This analysis provides insights into the thermal behavior and stability of modified activated carbon under increasing temperatures.

3.2. Optimization of Experimental Parameters

The optimization of various experimental parameters was undertaken to enhance the recovery of the analyte. In analytical procedures, one of the most crucial steps is optimizing all possible experimental variables. Consequently, a multivariate methodology was employed to systematically optimize the experimental parameters for the preconcentration of Pb²⁺.

These parameters encompassed pH, temperature, sorbent amount, sample volume, sample flow rate, and eluent volume, each of which played a significant role in the preconcentration process. The optimization of these parameters is essential for achieving the best possible results and ensuring the accuracy and efficiency of the analytical method.

3.2.1. Design of Experiments

The design of experiments, including techniques like the Plackett–Burman design (PBD) and central composite design (CCD), was employed to identify significant parameters and explore their interactions. A two-level PBD was carried out, involving 24 experiments and six factors that could potentially influence the effectiveness of the developed technique, as shown in Table 3.

Based on the standardized Pareto chart (Figure 2), four significant parameters were identified, which had a substantial impact on the percentage recovery of Pb²⁺. These parameters were pH, temperature, sorbent amount, and sample volume. They were subsequently optimized using the central composite design (CCD) by conducting a new set of 24 experiments. The results of these experiments are presented in Table 4, which displays the percentage recoveries of the analyte at low (−), high (+), and optimum levels of the significant experimental parameters.

It is worth noting that in experiments 1 and 24, the maximum recovery was achieved when all the parameters were set to their optimal levels. Deviating from these optimal values in experiments 16–23, where all parameters were at their optimum level except one, either higher or lower, resulted in a significant decrease in the recovery of the analyte. This emphasizes the importance of carefully optimizing and controlling these parameters to achieve the best analytical results.

Table 1. Comparison of adsorption capacity of EDTA-modified activated carbon (CA@EDTA) to reported sorbents.

Adsorbent	Adsorption Capacity (mg g−1)	Reference
Sawdust	3.2	[31]
Hazelnut shells	13.0	[32]
Raw rice husks	16.5	[33]
Eichhornia	16.6	[34]
Coconut shells	17.1	[35]
Peach pits	17.5	[35]
Olive stones	18.3	[35]
Calcite	19.9	[36]
Apricot kernel shells	22.8	[37]
Almond shells	22.7	[35]
Ceiba pentandra hulls	25.5	[38]
Tamarind wood	43.0	[39]
Bamboo AC	45.4	[40]
Alternanthera philoxeroides	53.7	[41]
Commercial AC	64.2	[42]
Date bead	76.9	[41]
Eucalyptus camalbulensis Dehn	113.9	[43]
Date pits AC	115.8	[42]
BAC@SiO2-EDTA	123.4	[40]
Cassava peels	5.8	[44]
Banana peels	57.1	[45]
CA@EDTA	36.17	Present study

Table 1. Comparison of adsorption capacity of EDTA-modified activated carbon (CA@EDTA) to reported sorbents.

Adsorbent	Adsorption Capacity (mg g−1)	Reference
Sawdust	3.2	[31]
Hazelnut shells	13.0	[32]
Raw rice husks	16.5	[33]
Eichhornia	16.6	[34]
Coconut shells	17.1	[35]
Peach pits	17.5	[35]
Olive stones	18.3	[35]
Calcite	19.9	[36]
Apricot kernel shells	22.8	[37]
Almond shells	22.7	[35]
Ceiba pentandra hulls	25.5	[38]
Tamarind wood	43.0	[39]
Bamboo AC	45.4	[40]
Alternanthera philoxeroides	53.7	[41]
Commercial AC	64.2	[42]
Date bead	76.9	[41]
Eucalyptus camalbulensis Dehn	113.9	[43]
Date pits AC	115.8	[42]
BAC@SiO2-EDTA	123.4	[40]
Cassava peels	5.8	[44]
Banana peels	57.1	[45]
CA@EDTA	36.17	Present study

Table 2. Isotherm and kinetic parameters for the adsorption of Pb²⁺ by CA@EDTA.

(a) Isotherm Parameters
Type of Isotherm	Parameter
Langmuir	qmax (mg/g)	109.05
	KL (L/mg)	0.03
	RL	0.39
	R2	0.9996
Freundlich	Kf	6.17
	1/n	0.52
	R2	0.9202
(b) Kinetics Parameters
Order of reaction	Parameter
	qe,exp (mg/g)	36.17
Pseudo-first-order	qe,cal (mg/g)	1.36
	K1 (min−1)	−9.2 × 10−4
	R2	0.53
Pseudo-second-order	qe,cal (mg/g)	35.17
	K2 (g mg−1 min−1)	0.08
	R2	0.9998

Table 2. Isotherm and kinetic parameters for the adsorption of Pb²⁺ by CA@EDTA.

(a) Isotherm Parameters
Type of Isotherm	Parameter
Langmuir	qmax (mg/g)	109.05
	KL (L/mg)	0.03
	RL	0.39
	R2	0.9996
Freundlich	Kf	6.17
	1/n	0.52
	R2	0.9202
(b) Kinetics Parameters
Order of reaction	Parameter
	qe,exp (mg/g)	36.17
Pseudo-first-order	qe,cal (mg/g)	1.36
	K1 (min−1)	−9.2 × 10−4
	R2	0.53
Pseudo-second-order	qe,cal (mg/g)	35.17
	K2 (g mg−1 min−1)	0.08
	R2	0.9998

Table 3. (a). Factors and levels used in factorial design for Pb²⁺ analysis. (b) Multivariate design matrix and the results of % R (n = 6).

(a) Factors and levels used in factorial design for Pb2+ analysis.
Factor			ID	Unit	Lower -	Higher +	Optimum a
pH			A	-	4	8	6
Temperature			B	°C	30	60	46
Sorbent amount			C	mg	40	60	40
Sample volume			D	mL	10	20	17
Sample flow rate			E	mL min−1	5	10	10 b
Eluent volume			F	mL	5	10	5 b
(b) Multivariate design matrix and the result of % R (n = 6).
S. No	A	B	C	D	E	F	% R
1	-	+	+	-	+	-	15.1 ± 1.9
2	+	-	-	+	+	-	15.8 ± 2.4
3	-	-	+	+	-	+	17.0 ± 3.1
4	-	+	-	-	+	+	16.7 ± 2.0
5	-	+	+	-	-	+	17.4 ± 3.4
6	-	+	-	+	-	-	31.9 ± 1.2
7	-	-	-	-	+	-	13.7 ± 0.9
8	-	-	+	-	+	-	9.1 ± 2.1
9	+	+	+	+	+	-	57.7 ± 0.9
10	+	-	+	+	+	+	70.0 ± 1.7
11	+	+	-	+	-	+	44.4 ± 3.2
12	-	+	-	+	+	+	25.1 ± 4.0
13	-	-	-	-	-	-	12.4 ± 3.1
14	-	+	+	+	+	+	22.7 ± 2.1
15	+	-	+	-	+	+	95.1 ± 2.5
16	+	-	-	+	+	-	99.8 ± 1.2
17	-	-	+	+	-	-	19.1 ± 2.4
18	-	-	-	+	-	+	20.8 ± 4.0
19	+	+	+	-	-	-	83.4 ± 3.1
20	+	-	+	-	-	+	90.3 ± 3.3
21	+	-	-	-	-	+	92.0 ± 1.8
22	+	+	-	-	-	-	67.1 ± 3.1
23	+	+	-	-	+	+	79.7 ± 2.3
24	+	+	+	+	-	-	87.3 ± 2.0

Notes: ^- Lower level of factors; ⁺ Higher level of factors; ^a Optimum values for significant factors; ^b Insignificant factors with convenient values.

Table 3. (a). Factors and levels used in factorial design for Pb²⁺ analysis. (b) Multivariate design matrix and the results of % R (n = 6).

(a) Factors and levels used in factorial design for Pb2+ analysis.
Factor			ID	Unit	Lower -	Higher +	Optimum a
pH			A	-	4	8	6
Temperature			B	°C	30	60	46
Sorbent amount			C	mg	40	60	40
Sample volume			D	mL	10	20	17
Sample flow rate			E	mL min−1	5	10	10 b
Eluent volume			F	mL	5	10	5 b
(b) Multivariate design matrix and the result of % R (n = 6).
S. No	A	B	C	D	E	F	% R
1	-	+	+	-	+	-	15.1 ± 1.9
2	+	-	-	+	+	-	15.8 ± 2.4
3	-	-	+	+	-	+	17.0 ± 3.1
4	-	+	-	-	+	+	16.7 ± 2.0
5	-	+	+	-	-	+	17.4 ± 3.4
6	-	+	-	+	-	-	31.9 ± 1.2
7	-	-	-	-	+	-	13.7 ± 0.9
8	-	-	+	-	+	-	9.1 ± 2.1
9	+	+	+	+	+	-	57.7 ± 0.9
10	+	-	+	+	+	+	70.0 ± 1.7
11	+	+	-	+	-	+	44.4 ± 3.2
12	-	+	-	+	+	+	25.1 ± 4.0
13	-	-	-	-	-	-	12.4 ± 3.1
14	-	+	+	+	+	+	22.7 ± 2.1
15	+	-	+	-	+	+	95.1 ± 2.5
16	+	-	-	+	+	-	99.8 ± 1.2
17	-	-	+	+	-	-	19.1 ± 2.4
18	-	-	-	+	-	+	20.8 ± 4.0
19	+	+	+	-	-	-	83.4 ± 3.1
20	+	-	+	-	-	+	90.3 ± 3.3
21	+	-	-	-	-	+	92.0 ± 1.8
22	+	+	-	-	-	-	67.1 ± 3.1
23	+	+	-	-	+	+	79.7 ± 2.3
24	+	+	+	+	-	-	87.3 ± 2.0

Notes: ^- Lower level of factors; ⁺ Higher level of factors; ^a Optimum values for significant factors; ^b Insignificant factors with convenient values.

Table 4. Central composite design for the set of factors A, B, C, and D.

S. No.	A	B	C	D	% R
1	aAo	bBo	cCo	dDo	99.4 ± 0.7
2	-	-	-	-	14.1 ± 0.7
3	+	-	-	-	20.5 ± 2.1
4	-	+	-	-	60.3 ± 1.7
5	+	+	-	-	12.0 ± 3.1
6	-	-	+	-	20.1 ± 1.9
7	+	-	+	-	55.3 ± 2.7
8	-	+	+	-	56.7 ± 0.9
9	+	+	+	-	45.4 ± 1.1
10	-	-	-	+	15.1 ± 0.7
11	+	-	-	+	44.4 ± 1.0
12	-	+	-	+	33.5 ± 2.2
13	+	+	-	+	46.0 ± 0.9
14	-	-	+	+	60.8 ± 4.1
15	+	-	+	+	86.2 ± 2.1
16	-aA	bB	cC	dD	28.2 ± 2.2
17	+aA	bB	cC	dD	37.6 ± 0.9
18	aA	-bB	cC	dD	16.7 ± 4.1
19	aA	+bB	cC	dD	4.8 ± 3.0
20	aA	bB	-cC	dD	69.6 ± 1.5
21	aA	bB	+cC	dD	39.4 ± 2.2
22	aA	bB	cC	-dD	65.0 ± 1.4
23	aA	bB	cC	+dD	7.9 ± 0.8
24	aAo	bBo	cCo	dDo	99.2 ± 1.1

Notes: -_aA: lowest level (1), +_aA: highest level (14), _aA^o: optimum level (6); -_bB: lowest level (5 °C), +_bB: highest level (90 °C), _bB^o: optimum level (46 °C); -_cC: lowest level (5 mg), +_cC: highest level (50 mg), _cC^o: optimum level (40 mg); -_dD: lowest level (5 mL min⁻¹), +_dD: highest level (50 mL min⁻¹), _dD^o: optimum level (17 mL min⁻¹).

Table 4. Central composite design for the set of factors A, B, C, and D.

S. No.	A	B	C	D	% R
1	aAo	bBo	cCo	dDo	99.4 ± 0.7
2	-	-	-	-	14.1 ± 0.7
3	+	-	-	-	20.5 ± 2.1
4	-	+	-	-	60.3 ± 1.7
5	+	+	-	-	12.0 ± 3.1
6	-	-	+	-	20.1 ± 1.9
7	+	-	+	-	55.3 ± 2.7
8	-	+	+	-	56.7 ± 0.9
9	+	+	+	-	45.4 ± 1.1
10	-	-	-	+	15.1 ± 0.7
11	+	-	-	+	44.4 ± 1.0
12	-	+	-	+	33.5 ± 2.2
13	+	+	-	+	46.0 ± 0.9
14	-	-	+	+	60.8 ± 4.1
15	+	-	+	+	86.2 ± 2.1
16	-aA	bB	cC	dD	28.2 ± 2.2
17	+aA	bB	cC	dD	37.6 ± 0.9
18	aA	-bB	cC	dD	16.7 ± 4.1
19	aA	+bB	cC	dD	4.8 ± 3.0
20	aA	bB	-cC	dD	69.6 ± 1.5
21	aA	bB	+cC	dD	39.4 ± 2.2
22	aA	bB	cC	-dD	65.0 ± 1.4
23	aA	bB	cC	+dD	7.9 ± 0.8
24	aAo	bBo	cCo	dDo	99.2 ± 1.1

Notes: -_aA: lowest level (1), +_aA: highest level (14), _aA^o: optimum level (6); -_bB: lowest level (5 °C), +_bB: highest level (90 °C), _bB^o: optimum level (46 °C); -_cC: lowest level (5 mg), +_cC: highest level (50 mg), _cC^o: optimum level (40 mg); -_dD: lowest level (5 mL min⁻¹), +_dD: highest level (50 mL min⁻¹), _dD^o: optimum level (17 mL min⁻¹).

3.2.2. Response Surface Methodology

Three-dimensional response surface plots were generated using the data obtained from the central composite design (CCD). These plots were created for each pair of significant parameters, such as A/B, C/B, D/A, and C/A, with the aim of determining the conditions that would yield the maximum recovery for Pb²⁺.

Figure 7 displays the graphs derived from the 3D surface plots for each parameter pair. Subsequently, quadratic equations were formulated for each set, resulting in Equations (10a–d). These optimum conditions, as determined from the response surface analysis, were then applied to real samples for validation, ensuring that the developed method performs effectively and accurately in practical applications.

A/B = 0.05 − 0.02x + 0.001y + 0.0004x² + 0.0003xy − 1.6 × 10⁻⁵y²

(10a)

C/B = −0.15 + 0.01x + 0.005y − 0.0002x² + 4.8 × 10⁻⁶xy − 5.4 × 10⁻⁵y²

(10b)

D/A = 0.14 − 0.02x − 0.009y + 0.0009x² + 0.0009xy + 2.0 × 10⁻⁴y²

(10c)

C/A = −0.01 − 0.01x + 0.003y + 0.0004x² + 0.0002xy − 3.7 × 10⁻⁵y²

(10d)

The impact of pH on Pb²⁺ recovery was investigated across a range of 4 to 8 to determine the optimum pH value at which maximum recovery could be achieved. pH plays a significant role in affecting the adsorption efficiency of the sorbent, as indicated in Table 4, where all the parameters were set at their optimum levels except for pH, resulting in a decrease in sorbent efficiency. At lower pH values, protonation of the active sites on the sorbent’s surface occurs, reducing the binding of cationic Pb²⁺ to the sorbent. This is because low pH leads to a lower electrostatic attraction between the sorbent and Pb²⁺ ions, thus decreasing the adsorption capacity. This observation is supported by the results of experiment 16, where the pH was at its lowest level.

Conversely, at higher pH values, the adsorption capacity for Pb²⁺ decreases due to the deprotonation of sorbent active sites and the formation of Pb(OH)₂ precipitates in the solution, resulting in lower recoveries. Experiment 17 demonstrates the response to higher pH values, where the lowest recoveries were observed. In conclusion, the highest sorption efficiency for Pb²⁺ was achieved at a pH of 6, highlighting the importance of optimizing and controlling the pH parameter to maximize recovery during the preconcentration process.

Temperature is a crucial factor that significantly influences the adsorption efficiency of the sorbent. The effect of temperature on Pb²⁺ recovery was explored within the range of 30 to 60 °C. Generally, the adsorption capacity of the sorbent tends to increase with an increase in temperature. This occurs because higher temperatures can disrupt the intermolecular bonds within the active sites of the sorbent, resulting in a higher adsorption capacity.

As observed in the experiments, increasing the temperature from a lower value to the optimum level (46 °C) led to an increase in the recovery, which is evident when comparing experiments 1 and 24 to experiment 18 (Table 4). This increase in recovery is attributed to a decrease in the intermolecular distance and reduced viscosity of the sample solution at higher temperatures.

However, it is important to note that temperatures above 46 °C can render the adsorption process less feasible. This is because at very high temperatures, the analyte may leach back into the aqueous layer, causing a gradual decrease in the percentage recovery. Therefore, optimizing and maintaining the temperature at around 46 °C is crucial for achieving the best recovery of Pb²⁺ during the preconcentration process.

Sample volume is indeed a critical factor in determining the extraction efficiency of an analyte. In this study, sample volume was examined within the range of 10 to 20 mL. The results indicated that the maximum recoveries were achieved at the optimal sample volume of 17 mL, which can be observed in Table 4 (experiments 1 and 24). When the sample volume was deviated from this optimal level (e.g., experiments 20 and 21), there was a significant decrease in the recoveries. This demonstrates the importance of precisely controlling the sample volume to obtain the best results.

Similarly, the sorbent amount is another crucial factor affecting the recovery of Pb²⁺. This study examined the effect of sorbent amount within the range of 40 to 60 mg. The findings revealed that an increase in the amount of sorbent led to enhanced adsorption capacity. This is because a higher sorbent amount provides a larger surface area and more available active sites for the metal ion. As a result, the optimum value for sorbent amount was determined to be 40 mg, at which the maximum adsorption occurred. Careful control of the sorbent amount is essential to optimizing the recovery of Pb²⁺ in the preconcentration process.

3.3. Interference Study

The effectiveness of the proposed method was assessed in the presence of several coexisting ions to determine its selectivity for the determination of Pb²⁺. An interference study was conducted, and the coexisting ions were present at relatively high concentration levels. The concentrations of all these coexisting ions are shown in

Table 5. Effect of interfering ions on Pb²⁺ analytical response (n = 6).

Coexisting Ions	Concentration (mg L−1)
Na+, K+, Cl−	>10,000
Mg2+, Ca2+, Fe3+,	6000
Al3+, Fe2+, Co2+, Ni2+, Cd2+, Zn2+,	5000
Cr3+, PO43−, SO42−	2000
F−, CO32−	500

. Despite the presence of these interfering ions, the results demonstrated that 95% of Pb²⁺ was recovered. This indicates that the presence of these interfering ions had no significant impact on the percentage recovery of Pb²⁺. As a result, it can be confidently concluded that the proposed method is suitable and efficient for the determination of Pb²⁺ even in the presence of these coexisting ions. The method exhibits good selectivity for Pb²⁺ determination.

3.4. Analytical Figures of Merit

Analytical figures of merit were assessed to ensure the precision and accuracy of the proposed method. The limit of detection (LOD) and quantification (LOQ) were calculated as 3 times the standard deviation (SD) divided by the slope of the calibration graph (m), resulting in values of 0.25 and 0.84 µg L⁻¹, respectively. The standard deviation was determined by running 10 blank samples, and “m” represents the slope of the calibration curve. A linear response of absorbance was observed within the concentration range of 0.84–1000 µg L⁻¹, with an R² value of 0.996, indicating a strong linear relationship.

Precision was assessed in terms of repeatability (RSD_r) and reproducibility (RSD_R), expressed as the percentage relative standard deviation (%RSD). Recoveries of spiked samples were evaluated on the same day and over five alternate days to determine RSDr (intra-assay precision) and RSDR (between-day precision), respectively.

Table 6. Analytical figures of merit for Pb²⁺ detection.

Analytical Parameters
Linear range (µg L−1)	0.84–1000
Correlation coefficient	0.996
Enhancement factor	130
Extraction recovery (%)	95
Calibration equation	y = 0.002x
LOD (µg L−1)	0.25
LOQ (µg L−1)	0.84
RSDr (n = 6)	<3.0
RSDR (n = 10)	<7.5

presents these figures for the proposed extraction procedure.

3.5. Validation of the Proposed Method

Before the newly developed analytical procedures can be applied for routine analysis, it is highly recommended to undergo a validation process. The primary objective of this validation is to assess the trueness or accuracy of the method concerning systematic errors. The goal is to ensure that the developed techniques are free from systematic errors. To evaluate the feasibility of the developed method, the standard addition method was employed for validation under the optimum conditions. This validation process aimed to determine the presence of Pb²⁺ in real samples.

In this process, real samples were collected and prepared for analysis. Known quantities of the analyte were deliberately added to the samples to determine Pb²⁺ using the proposed extraction procedure, followed by atomic absorption spectrophotometry. The efficiency and accuracy of the proposed procedure were further confirmed through the analysis of a certified reference drinking water sample, ERM-CA011 (as shown in

Table 7. Validation and uncertainty results of proposed technique (n = 7).

Sample	Added Amount (mg)	Found Amount (mg)	% R †
Tap water	0	BDL	-
	0.20	0.19	99.0 ± 0.78
	0.40	0.39	97.8 ± 0.93
	0.80	0.79	98.5 ± 1.01
	1.60	1.54	96.4 ± 1.12
Wastewater	0	0.01	-
	0.20	0.20	96.5 ± 2.4
	0.40	0.41	99.2 ± 0.6
	0.80	0.79	98.1 ± 1.0
	1.60	1.48	91.6 ± 2.7
Milk powder	0	BDL	-
	0.20	0.20	101.0 ± 1.4
	0.40	0.39	97.2 ± 1.8
	0.80	0.78	99.9 ± 1.4
	1.60	1.47	91.6 ± 2.1
Wheat flour	0	BDL	-
	0.20	0.19	98.5 ± 0.9
	0.40	0.39	97.8 ± 1.5
	0.80	0.79	98.5 ± 1.1
	1.60	1.54	96.4 ± 3.1
Certified reference drinking water (ERM-CA011a) (µg L−1)
Certified concentration	Found concentration		% Recovery
24.5 *	24.3 ± 0.7		99.2 ± 2.9

Notes: BDL: Below detection limit; * Standard deviation is not available; ^† % Recovery = [$\raisebox{1ex}{$Observed value$}\!\left/ \!\raisebox{-1ex}{$Certified value$}\right.$] × 100.

). This comprehensive validation process is crucial to ensuring the reliability and accuracy of the developed method when applied to real-world samples.

4. Conclusions

In recent research, activated carbon derived from apricot pits was synthesized and modified to enable the preconcentration of Pb²⁺ from both water and food samples. Notably, the BET surface area of the modified carbon, CA@EDTA, was found to be significantly high at 587.5 m² g⁻¹. This surface area falls within the range of values typically seen in commercially available activated carbons, which often exceed 500 m² g⁻¹. This characteristic indicates the material’s potential for efficient adsorption.

Furthermore, the functionalized sorbent demonstrated resilience and sustainability by retaining its adsorption capabilities even after undergoing more than 150 cycles of adsorption and desorption. As a result, apricot-pit-based activated carbon modified with EDTA emerges as a strong candidate for use in the microextraction of trace-level inorganic pollutants. Its robustness, high surface area, and sustained adsorption capacity make it a promising choice for applications involving the extraction and concentration of such pollutants.