Development and Optimization of a Dispersive Liquid–Liquid Microextraction Method for the Simultaneous Determination of Multiclass Pesticides in Environmental Waters

ABSTRACT

The widespread use of pesticides in modern agriculture necessitates sensitive and sustainable methods to monitor their residues in environmental waters. This study reports the development and optimization of a dispersive liquid–liquid microextraction (DLLME) method combined with high performance liquid chromatography with diode array detector (HPLC–DAD) for the simultaneous determination of multiclass pesticides (metalaxyl, benalaxyl, chlorpyrifos, endrin, 4,4′‐DDT and bifenthrin). Key extraction variables—including the type and volume of extraction/disperser solvents, pH, salt addition and vortex speed/time—were systematically evaluated. The optimized method employed tetrachloroethylene as the extraction solvent, acetonitrile as the disperser, pH 7, 3% w/v NaCl, a vortex speed of 1200 rpm and an extraction time of 80 s. Under these conditions, excellent enrichment factors, recoveries (87%–108%) and precision (intradays: 2.8%–8.6%; interday: 4.2%–8.6%) were achieved. The correlation coefficients (r ²) exceeded 0.9977, and the limits of detection ranged from 0.3 to 1.3 µg/L. Compared to conventional extraction techniques, the proposed DLLME method provides faster analysis and uses less solvent. This approach provides a robust, sensitive and environmentally friendly alternative for monitoring multiclass pesticide residues in diverse water matrices.

1. Introduction

Modern agriculture relies heavily on agrochemicals—particularly pesticides—to control insects, fungi, bacteria, weeds, nematodes, rodents and other pests, thereby improving crop yields and quality [1]. However, their extensive use poses risks to non‐target organisms, ecosystems and human health. Owing to their widespread application, pesticide residues are now commonly detected in both water [2] and soil [3]. The transport of pesticides from point or nonpoint sources into surface and groundwater depends on several factors, such as water solubility, log K _ow and pKa values. Highly water‐soluble pesticides tend to leach from soil and run off into environmental waters, especially groundwater and surface water, necessitating continuous monitoring of their concentrations. The European Union Directive 98/83/EC of 3 November 1998 has established a maximum tolerable concentration of 0.1 µg/L for each pesticide and 0.5 µg/L for total pesticides in drinking water [4].

Pesticides can be broadly classified into organochlorine pesticides (OCPs), organophosphorus pesticides (OPPs), carbamates and pyrethroids. OCPs are highly lipophilic, environmentally persistent and bioaccumulative, leading to their ban in many countries despite often being detected only at low concentrations in water. They were largely replaced by OPPs, which degrade more readily but are acutely toxic to humans and non‐target organisms. Carbamates, also acetylcholinesterase inhibitors, degrade faster than OCPs yet still pose toxicological risks. More recently, pyrethroids have gained prominence owing to their selective insecticidal activity and perceived safety for humans, though they remain a concern for aquatic ecosystems [5].

The reliable detection of such chemically diverse pesticides in environmental matrices is analytically challenging. Sample preparation is particularly complex due to the wide range of polarities, solubilities and volatilities of compounds in environmental matrices. Conventional extraction techniques, including liquid–liquid extraction (LLE) [6] and solid‐phase extraction (SPE) [7], have been widely applied but suffer from major drawbacks: they are labour‐intensive and require large volumes of hazardous organic solvents. Miniaturized techniques, such as single‐drop microextraction (SDME) [8] and hollow‐fibre liquid‐phase microextraction (HF‐LPME) were developed to reduce solvent usage. However, SDME is hindered by poor drop stability and reproducibility, whereas HF‐LPME requires long extraction times and still suffers from limited precision.

Dispersive liquid–liquid microextraction (DLLME) was introduced to overcome these limitations [9].

DLLME offers rapid equilibrium, high enrichment factors, minimal solvent consumption and reduced cost [10]. Over the past decade, DLLME has advanced significantly through the incorporation of green solvents, such as natural deep eutectic solvents (NADESs) and ionic liquids (IL), and the application of design‐of‐experiments strategies to enhance reproducibility and analytical performance [11, 12]. For example, NADES‐based DLLME has achieved excellent recoveries and ultra‐low detection limits (ng/L range) [12], while IL‐DLLME has proven effective for multiclass pesticide extraction in environmental waters [2].

In this study, we developed an optimized DLLME method coupled with high performance liquid chromatography with diode array detector (HPLC–DAD) for the simultaneous determination of six pesticides with markedly different physicochemical properties, ranging from highly polar (metalaxyl) to strongly hydrophobic (bifenthrin, 4,4′‐DDT). Unlike many earlier approaches that targeted structurally similar analytes, this method enables multiclass pesticide monitoring within a single procedure. The use of readily available, low‐cost solvents (tetrachloroethylene and acetonitrile) enhances accessibility and practicality, while systematic optimization of key variables—including solvent selection, pH, salt addition and vortex speed/time—improved reproducibility and precision. This study, therefore, represents a step forward in developing sensitive, robust and sustainable strategies for pesticide monitoring in environmental water samples.

2. Materials and Methods

2.1. Chemicals and Reagents

Analytical standards (Pestanal grade > 98.5% purity) of benalaxyl, bifenthrin, chlorpyrifos, 4,4′‐DDT, endrin and metalaxyl were obtained from Sigma‐Aldrich (Seelze, Germany). The chemical structures, solubility data in water and log K _ow values of the target pesticides are given in Table 1. Acetonitrile, methanol, tetrahydrofuran and acetone were obtained from Sigma‐Aldrich (Steinheim, Germany). The extraction solvents, that is, chloroform, 1,2‐dichloroethane and tetrachloroethylene, were purchased from Sigma‐Aldrich (Steinheim, Germany). Sodium hydroxide and hydrochloric acid were purchased from Merck (Darmstadt, Germany). Sodium chloride (NaCl) was purchased from Sigma‐Aldrich (Steinheim, Germany). Ultrahigh purity (UHP) water (18.2 MΩ cm) resistivity was used throughout the study (Billerica, MA, USA).

TABLE 1.

Chemical structure, solubility in water and log K _ow values of the target analytes.

Substance	Structure	Solubility in water at 20°C/mg L−1	log K ow [42]
Metalaxyl		8400	1.75
Benalaxyl		28.6	3.54
Chlorpyrifos		1.05	4.7
Endrin		0.24	3.2
4,4′‐DDT		0.006	6.91
Bifenthrin		0.001	6.6

2.2. Instrumentation

Chromatographic separation was carried out with a well‐appointed Agilent 1260 high‐performance liquid chromatography series (Agilent Technologies, Waldbronn, Germany) coupled with a diode array detector (DAD). ChemStation software (version 1.9.0) was used for data acquisition and processing. A vortex mixer (Velp, Scientifica, Italy) and centrifuge (Thermo Electron Corporation, Massachusetts, USA) were used to enhance the extraction and separate the extractant from the sediment, respectively.

2.3. Chromatographic Conditions

The separation of multiclass pesticides was achieved via HPLC with an Xterra MS C₁₈ (3.5 µm, 4.6 mm × 150 mm) column (Ireland). Water and acetonitrile (16:84, v/v) were used as the mobile phase to separate a mixture of target analytes at a flow rate of 1.2 mL/min in isocratic mode. The injection volume was 5 µL, and the column temperature was maintained at 40°C. The detection wavelengths were 210 nm for metalaxyl, benalaxyl and 245 nm for chlorpyrifos, endrin, 4,4′‐DDT and bifenthrin.

2.4. Standard Preparation and Calibration

Stock standard solutions (1000 mg/L) were prepared separately by dissolving an accurately weighed amount in acetonitrile and storing it in a refrigerator at 4°C. Intermediate standard solutions of 20 mg/L were also prepared by diluting individual stock solutions with an appropriate volume of acetonitrile. Working standard solutions were freshly prepared at the time of run by diluting the intermediate standard solutions with UHP water to the required concentrations.

Matrix‐matched calibration curves were prepared by spiking tap water samples at 10 concentrations, with a mixture of target analytes in a concentration range of 5–1000 µg/L. The calibration standards were then extracted in six replicates (𝑛 = 6) via the DLLME procedure and subsequently analysed under optimum chromatographic conditions. Each replicate was injected in duplicate. The calibration curves were used to determine the concentrations of the target analytes in real samples.

2.5. Water Samples

Five different types of water samples were purposively collected: tap water (I) and tap water (II) samples were collected from the University of South Africa (Florida, South Africa) and the Muckleneuk campus (Pretoria, South Africa), respectively. A river water sample was collected from the Suikerbosrant River (Gauteng, South Africa). The location of the river sampling site was 26°45′20.6″ S 28°01′20.2″ E. A groundwater sample was collected from a private homestead located within 300 m of the University of South Africa (Florida, South Africa). The location of the groundwater sampling site was 26°09′33.1″ S 27°54′06.8″ E. Stream water was collected from Florida, which is located within 600 m of the University of South Africa (Florida, South Africa). The location of the stream water sampling site was 26°09′47.2″ S 27°54′23.3″ E. The water samples were collected in amber glass bottles. The samples were filtered through Whatman filter paper and 0.45 µm nylon filters and then stored in a refrigerator at 4°C until analysis.

2.6. DLLME Procedure

Approximately 5 mL of a water sample containing 0.15 g of NaCl (i.e., 3%, w/v) at pH 7 was introduced into a 15‐mL conical‐bottom polyethylene centrifuge test tube, spiked with appropriate concentrations (50 µg/L) of benalaxyl, chlorpyrifos, endrin, DDT and bifenthrin and 100 µg/L of metalaxyl, and then allowed to stabilize for a certain period of time. A mixture of acetonitrile (1000 µL) and tetrachloroethylene (50 µL) was subsequently rapidly added into the sample centrifuge tube via a syringe to make a cloudy solution comprising very small droplets of tetrachloroethylene disseminated in the sample mixture. The mixture was vortexed for 80 s at 1200 rpm, followed by centrifugation at 4000 rpm for 5 min, which resulted in the sedimentation of the C₂Cl₄ phase. The sediment phase was quantitatively shifted to a 400 µL vial, which was housed in a 1.5 mL autosampler vial. The organic solvent was evaporated to dryness under a moderate stream of nitrogen, and the residue was dissolved in 25 µL of acetonitrile. The reconstituted residue was then vortexed for 30 s at 800 rpm. Finally, 5.0 µL of the extract was used for analysis. A graphic diagram of the major steps of the procedure is shown in Figure 1.

FIGURE 1.

Major steps of the procedure.

3. Results and Discussion

3.1. Optimization of the DLLME Procedure

The major parameters affecting the enrichment factor in the DLLME procedure, including the type of extraction and disperser solvent, the volume of extraction and dispersive solvents, the sample pH, the addition of salt, the effect of the rotational speed of the vortex agitator and the effect of the vortex agitation time, were evaluated. Each extraction was conducted in six replicates by spiking UHP water with 50 µg/L benalaxyl, chlorpyrifos, endrin, DDT, bifenthrin or 100 µg/L metalaxyl.

3.1.1. Choice of Extraction Solvent

High density, low polarity and low volatility are essential factors in selecting a suitable organic solvent that serves as an extraction solvent in DLLME [13]. Three high‐density organic solvents, namely, tetrachloroethylene (density of 1.62 g/L; water solubility of 0.17 g/L), chloroform (density of 1.49 g/L; water solubility of 8.1 g/L), 1,2‐dichloroethane (density of 1.26 g/L; water solubility of 8.5 g/L1) and a mixture of tetrachloroethylene and chloroform, were examined. Among the solvents considered, the most polar solvent, i.e., 1,2‐dichloroethane, failed to form any sediment phase at a 50 µL volume of extraction solvent of 50 μ. There was no phase separation for 1,2‐dichloroethane, owing to the higher solubility of 1,2‐dichloroethane in acetone. Thus, the extraction solvent, together with acetone, must have dissolved in the aqueous phase; hence, only one phase was visible, and the analytes could not be extracted. As shown in Figure 2, tetrachloroethylene yielded the best enrichment factors (96‐296), especially compared with chloroform and mixtures. This could be related to its lower water solubility and higher density. Similar findings were reported by Tay and See [14], who highlighted that high‐density chlorinated solvents such as tetrachloroethylene and carbon tetrachloride continue to provide superior extraction recoveries in classical DLLME setups due to their effective phase separation and hydrophobic character. However, recent studies increasingly emphasize the environmental and safety drawbacks of such solvents and have explored alternative ′green′ options. For instance, Silva et al. [15] and Negussie et al. [2] demonstrated that hydrophobic deep eutectic solvents (DES) and ILs can achieve comparable enrichment factors while reducing solvent toxicity and volatility.

FIGURE 2.

Choice of extraction solvent; extraction situation: sample volume, 5.0 mL of ultrapure water; (A) chloroform, (B) tetrachloroethylene, (C) chloroform + tetrachloroethylene; extraction solvent volume, 50 µL; acetone volume, 800 µL; rotation speed of the vortex agitator, 1200 rpm; vortex agitation time, 20 s; centrifugation time, 300 s at 4000 rpm, $n=6$.

Metaxayl was not well extracted in all the solvent systems, with a high enrichment factor of 13.3 observed when chloroform (a relatively polar solvent) was used. Such low enrichment factors for this particular analyte could be attributed to its relatively high solubility in water (8400 mg/L) or low octanol/water partition coefficient (1.75) (Table 1). Poor partitioning efficiency for highly water‐soluble analytes, such as polar pesticides, in conventional DLLME systems utilizing classical chlorinated solvents has been documented [16].To improve the extraction ability of relatively polar analytes such as metalaxyl, a mixture of chloroform and tetrachloroethylene (1:1) was used. This resulted in a slight improvement in the extraction of metalaxyl compared to tetrachloroethylene alone. This observation aligns with the findings of Khongsiri et al. [17], who reported that solvent mixtures or the use of low‐density aromatic solvents, such as anisole, enhanced the recovery of polar compounds in low‐density solvent DLLME. On the basis of these results, tetrachloroethylene was selected to optimize the remaining parameters.

3.1.2. Effect of the Volume of Extraction Solvent

The volume of tetrachloroethylene was investigated in the range of 20–60 µL in 10 µL intervals, although the other experimental parameters were not varied. Figure 3 shows a gradual increase in the enrichment factor with increasing volume of tetrachloroethylene up to 50 µL. The enrichment factors were lower at a higher extraction volume of 60 µL. At very high volumes of extraction solvent, the disperser solvent does not completely disperse the extraction solvent into the aqueous phase. Similarly, lower extraction solvent volumes meant that the organic extractant may have failed to be distributed evenly in the aqueous phase, thus also resulting in a low enrichment factor. Tankiewicz and Biziuk [18] investigated method development using tetrachloroethylene and other halogenated extraction solvents at varying volumes (10, 20, 40 and 60 µL). Their results indicated that an extraction solvent volume of 40 µL yielded optimal performance, with enrichment efficiency increasing proportionally with solvent volume up to this point. However, further increases to 60 µL resulted in dilution effects and reduced dispersion of the extraction phase. Subsequent experiments were then performed using 50 µL of tetrachloroethylene as the optimum volume.

FIGURE 3.

Choice of extraction solvent volume; extraction conditions: sample volume, 5.0 mL of ultrapure water; extraction solvent (C₂Cl₄); dispersive solvent (acetone) volume, 800 µL; vortex agitator speed, 1200 rpm; vortex agitation time, 20 s; centrifugation time, 300 s at 4000 rpm; $n=6$.

3.1.3. Choice of Disperser Solvent

For the DLLME method, the solubility of the disperser solvent in both the organic extractant and the aqueous phase is the most crucial criterion for selecting the disperser solvent. This allows for an increased contact surface area, which is required for the efficient transfer of target compounds from the aqueous phase to the organic solvent. Acetone, acetonitrile, tetrahydrofuran and methanol were investigated as potential disperser solvents, and 50 µL of tetrachloroethylene (extraction solvent) was used. The enrichment factors obtained are shown in Figure 4. The results show that all the solvents generally performed well. However, acetonitrile provided optimal dispersion and enrichment (9.9–290.9), and for this reason, it was selected as the disperser solvent for subsequent studies. Like this study, acetonitrile yielded the highest recoveries (> 75.0%) for all pesticides during quantification in beef samples [19]. In another study, Rezaee et al. [20] tested several dispersers, such as methanol, ethanol, acetone and acetonitrile and they found that acetonitrile gave higher recoveries. Acetonitrile is polar aprotic, miscible with water and also miscible with many organic extraction solvents. This allows the extraction solvent to be finely dispersed into the aqueous phase when acetonitrile is used as disperser [1, 20].

FIGURE 4.

Selection of disperser solvent; extraction conditions: sample volume, 5.0 mL of ultrapure water; extraction solvent (C₂Cl₄) volume, 50 µL; dispersive solvent volume, 800 µL; rotation speed of the vortex agitator, 1200 rpm; vortex agitation time, 20 s; centrifugation time, 300 s at 4000 rpm; $n=6$.

3.1.4. Effect of the Volume of Disperser Solvent

The disperser volume significantly affects the dissemination of the extraction solvent in the aqueous phase and has an impact on the volume of the sedimented phase and the enrichment factor. To acquire an appropriate volume of disperser solvent, different volumes of acetonitrile (400–1200 µL) containing 50 µL of tetrachloroethylene were investigated. Initially, the enrichment factors of all the compounds of interest increased as the volume of disperser solvent increased to 1000 µL and then decreased at high volumes (Figure 5). At 400 µL of acetonitrile, the gloomy suspension of the droplets was not dense, so the organic solvent could not be spread well into the aqueous phase; consequently, the enrichment factors of the target analytes were low. Similarly, at higher volumes of acetonitrile, the enrichment factor decreased because of the increased miscibility of the compound of interest in the water phase. Thus, 1000 µL of acetonitrile was chosen as the optimum volume since it provided a more stable cloudy solution.

FIGURE 5.

Effect of the volume of disperser solvent. Extraction conditions: sample volume, 5.0 mL ultrapure water; extraction solvent (C₂Cl₄) volume, 50 µL; dispersive solvent (acetonitrile); rotation speed of the vortex agitator, 1200 rpm; vortex agitation time, 20 s; centrifugation time, 300 s at 4000 rpm; $n=6$.

This behaviour aligns well with recent findings reported in the literature, which demonstrate that the enrichment factor generally increases with the disperser solvent volume up to an optimum value, after which it declines due to dilution or enhanced miscibility effects. Li et al. [21] observed a similar trend when using acetonitrile as the disperser solvent, where the enrichment factor increased up to 1.0 mL before decreasing at higher volumes. Likewise, Caleb et al. [22] identified an optimal volume of 1250 µL of acetonitrile for effective dispersion, beyond which extraction efficiency declined as a result of excessive miscibility. More recently, Makwakwa et al. [23] reported that 1439 µL of acetonitrile yielded the highest enrichment efficiency in a tetrachloroethylene‐based DLLME system, further confirming that the disperser volume must be carefully optimized to achieve maximal extraction performance. Collectively, these studies are consistent with the present results, reinforcing that excessive disperser solvent volumes compromise extraction efficiency by reducing phase separation and analyte partitioning.

3.1.5. Effect of pH

In the present study, the extractions were accomplished at various pH values ranging from 3 to 9. The pH value was set by introducing 0.05 M HCl or 0.05 M NaOH in spiked UHP water. The pH of the sample solution should be adjusted such that the target analytes are in molecular form, and consequently, their solubility in the aqueous phase is reduced, thus increasing their transferability into the organic extractant. As indicated in Figure 6, better EFs for all target analytes were obtained at pH 7. Under these conditions, the target analytes likely exist in their neutral form and have a greater tendency to partition into the extractant, which is consistent with previous DLLME studies reporting that neutral pH generally enhances extraction efficiency for mixed pesticide [18, 24, 25]. However, when the pH was greater than 7, the EFs of most target analytes slightly decreased. This may be attributed to the hydrolysis of the target analytes, such as chlorpyrifos, in alkaline solutions [26]. Since the EFs were highest at pH 7 (8.4–348.5), pH 7 was chosen as the best value for the following optimization experiments.

FIGURE 6.

Effect of pH extraction conditions: sample volume, 5.0 mL of ultrapure water; extraction solvent (C₂Cl₄) volume, 50 µL; dispersive solvent (acetonitrile) volume, 1000 µL; rotation speed of the vortex agitator, 1200 rpm; vortex agitation time, 20 s; centrifugation time, 300 s at 4000 rpm $n=6$.

3.1.6. Effect of the Vortex Speed

The agitation speed has a role in increasing the surface area of contact between the aqueous phase and the tiny dispersed droplets of the extraction solvent to obtain a larger octanol–water interfacial area. Various speeds ranging from 0 to 2400 rpm were investigated, and the results are shown in Figure 7. A gradual increase in the enrichment factor was observed when the rotational speed of the vortex increased from 0 to 1200 rpm. This increase may be due to the increase in the movement of target analytes from the aqueous phase into the extraction solvent [27]. Above 1200 rpm, the EFs of most of the analytes decreased. However, Kakalejčíková et al. [28] reported maximum fluorescence intensity for target analytes at 2000 rpm. The optimum speed of the vortex agitator of 1200 rpm was then used for all subsequent experiments.

FIGURE 7.

Effect of the vortex speed; extraction conditions: sample volume, 5.0 mL ultrapure water; extraction solvent (C₂Cl₄) volume, 50 µL; dispersive solvent (acetonitrile) volume, 1000 µL; vortex agitation time, 20 s; centrifugation time, 300 s at 4000 rpm; pH 7, $n=6$.

3.1.7. Effect of Vortex Time

The vortex affects mass transfer processes and thus impacts the enrichment factor. In the present study, the effect of the vortex time was investigated in the range of 20–200 s at a rotational speed of 1200 rpm. Figure 8 shows an insignificant surge in the enrichment factors of most analytes with increasing vortex time from 20–80 s. This finding revealed that vortex mixing could improve the movement of target analytes from the aqueous phase to the extraction solvent and that the equilibrium state could therefore be attained within a few seconds. The effect of vortex time variation on the EFs was insignificant beyond 80 s. Vortex flows play a crucial role in enhancing mass transfer by inducing intense mixing, increasing interfacial area and generating turbulence. These hydrodynamic effects significantly accelerate mass transfer between phases. In liquid–liquid systems, vortex‐induced motion intensifies velocity gradients and turbulent kinetic energy, thereby improving interfacial contact and facilitating more efficient mass transfer processes [29]. Comparable trends have been reported in recent studies. Farajzadeh et al. [30] demonstrated that optimal vortex durations facilitate rapid attainment of equilibrium during the extraction of aliphatic amines, with further increases resulting in only marginal improvements in recovery. Similarly, vortex times as brief as 20 s were sufficient for effective mass transfer in systems employing in situ DESs, highlighting the limited benefits of extended vortexing [31]. Thus, 80 s was chosen for further experiments.

FIGURE 8.

Effect of vortex time; extraction conditions: sample volume, 5.0 mL of ultrapure water; extraction solvent (C₂Cl₄) volume, 50 µL; dispersive solvent (acetonitrile); rotation speed of the vortex agitator, 1200 rpm; centrifugation time, 300 s at 4000 rpm; pH 7, $n=6$.

3.1.8. Effect of Salt Addition

In most cases, the addition of salt reduces the solubility of target analytes in aqueous samples and improves their partition to the extraction solvent. In this experiment, various amounts of NaCl (0–0.45 g) were added to the sample solution at intervals of 0.15 g. As revealed in Figure 9, the enrichment factors of most analytes increased to 0.15 g NaCl. Notably, the enrichment factors for analytes, which are normally highly soluble in water, such as metalaxyl and benalaxyl, continued to increase to 0.45 g (i.e., 9% w/v) NaCl. Adding NaCl to aqueous solutions decreases the solubility of many analytes, promoting their transfer into an organic phase or onto a sorbent, thereby increasing enrichment factors up to a certain concentration [32]. At higher NaCl concentrations, the solution's viscosity increases, which can slow down the mass transfer of analytes and reduce extraction efficiency, causing enrichment factors to plateau or even decrease [12]. Similar effects of salt addition have been reported in recent studies. Farajzadeh et al. [30] observed enhanced extraction efficiency of amines with NaCl addition up to 7.5% (w/v). Boughanem et al. [33] demonstrated improved extraction efficiency in complex matrices using salting‐out techniques. As a result, 0.15 g of sodium chloride was selected for the extraction process.

FIGURE 9.

Effect of salt addition; extraction conditions: sample volume, 5.0 mL of ultrapure water; extraction solvent (C₂Cl₄) volume, 50 µL; dispersive solvent (acetonitrile); rotation speed of the vortex agitator, 1200 rpm; vortex agitation time, 80 s; centrifugation time, 300 s at 4000 rpm; pH 7, $n=6$.

3.2. Method Validation

The major validation parameters, such as the linear range, repeatability, reproducibility, limit of detection (LOD), limit of quantification (LOQ) and recovery, were examined via the developed method in spiked tap water samples.

3.2.1. Analytical Performance Features

Matrix‐matched calibration standards were prepared in which the desired concentrations of the mixture of pesticide standards were spiked into tap water before the DLLME procedure was applied. Calibration curves were constructed at 10 concentrations ranging from 5–1000 µg/L. Each level was extracted in six replicates. Good linearity for the six pesticides, with coefficients of determination (r ²) ranging between 0.9977 and 0.9991, was obtained over a wide concentration range. These results indicated that the method could be used for the quantitative determination of multiclass pesticides over a wide range of concentrations. The method LOD and LOQ, set as the smallest target analyte concentrations, were 3 and 10 times the standard deviation and mean concentration, respectively. The performance features of the developed method in the tap water sample are shown in Table 2. The method LODs and LOQs for the target analytes ranged from 0.3–1.3 and 0.9–3.7 µg/L, respectively. Metalaxyl had the highest LOD, probably due to weak absorbance at both wavelengths used in this study.

TABLE 2.

Analytical performance characteristics of the proposed DLLME and HPLC–DAD methods.

Analyte	Calibration curve	Linear range µg/L ($n=6$)	r 2	LOD µg/L ($n=10$)	LOQ µg/L ($n=10$)
Metalaxyl	y = 0.2955x + 0.3508	10–800	0.9980	1.3	3.7
Benalaxyl	y = 2.7797x + 0.6145	5–600	0.9983	0.3	0.9
Chlorpyrifos	y = 0.1428x + 0.0196	5–1000	0.9982	1.0	3.0
Endrin	y = 0.1132x + 0.015	5–1000	0.9989	0.9	2.6
DDT	y = 0.7356x + 0.5421	5–800	0.9977	0.3	0.9
Bifenthrin	y = 0.8929x + 0.4492	5–800	0.9991	0.4	1.2

3.2.2. Precision Study

The precision of the developed DLLME‐HPLC method was assessed by performing replicate analysis of the spiked samples. Precision is expressed in terms of intraday and interday precision. The intraday precision of the method was evaluated by extracting spiked tap water samples at three concentrations: 40, 100 and 400 µg/L. The extraction and analysis of samples were carried out three times on the same day and at 8‐h intervals. Each sample was extracted in six replicates and injected in duplicate on the same day under the same experimental conditions. Similarly, interday precision was investigated by extracting six replicate tap water samples and injecting them in duplicate at each of the three concentrations employed for the repeatability studies. The interday precision study was performed for 5 days at 2‐day intervals. Table 3 shows the results for both the intra‐ and interday precisions of the method, which are expressed as percentages of the relative standard deviations. Under optimum conditions, the intraday and interday precisions of the developed method were 2.8%–8.6% and 4.2%–8.6%, respectively.

TABLE 3.

Intra‐ and inter‐day precision of the proposed method (RSD, %) for spiked tap water samples.

	Intraday precision (%RSD, $n=18$)			Interday precision (%RSD, $n=30$)
Analyte	Level 1	Level 2	Level 3	Level 1	Level 2	Level 3
Metalaxyl	4.8	6.5	8.5	7.7	7.1	6.3
Benalaxyl	3.2	5.8	5.9	5.2	8.2	4.2
Chlorpyrifos	8.6	7.5	7.5	7.5	7.0	7.1
Endrin	8.4	4.4	7.2	6.9	8.6	8.1
DDT	4.1	6.6	7.4	6.6	7.4	7.1
Bifenthrin	2.8	7.7	7.5	6.5	7.5	5.2

Note: Level 1: 80 µg/L for metalaxyl; and 40 µg/L for benalaxyl, chlorpyrifos, endrin, 4, 4′‐DDT and bifenthrin. Level 2: 200 µg/L for metalaxyl; and 100 µg/L for benalaxyl, chlorpyrifos, endrin, 4, 4′‐DDT and bifenthrin. Level 3: 800 µg/L for metalaxyl; and 400 µg/L benalaxyl, chlorpyrifos, endrin, 4, 4′‐DDT and bifenthrin.

3.2.3. Recovery

The accuracy and applicability of the proposed method were validated by extracting target analytes from different sources of water samples, such as tap, ground and river water. Each sample was spiked with a mixture of pesticides at three different concentrations (40, 100 and 400 µg/L) and extracted in six replicates via the optimized DLLME procedure. Each of these extracts was injected into the HPLC system in duplicate. The recoveries of the target analytes from tap water (I), tap water (II), ground water and river water were 88%–108%, 88%–103%, 87%–100% and 87%–106%, respectively (Table 4), with RSDs ≤ 7.7%, which showed that the proposed method was consistent and could be used for the determination of trace pesticides in water samples. Moreover, the results coincide with the tolerable recovery range of 70%–120%, which is well known by the European Commission for pesticide residue analysis [34]. Moreover, the developed method was utilized with stream water, and the target analytes were not detected in the stream water.

TABLE 4.

Recoveries (%R) of pesticides determined in different water samples via the proposed method.

Analyte	Tap water I (%RSD, n $=6)$			Tap water II (%RSD, n $=6)$			River water (%RSD, n $=6)$			Ground water (%RSD, n $=6)$
	Level 1	Level 2	Level 3	Level 1	Level 2	Level 3	Level 1	Level 2	Level 3	Level 1	Level 2	Level 3
Metalaxyl	101 (4.8)	107 (6.5)	108 (6.6)	101 (3.2)	99 (4.2)	97 (6.9)	92 (3.7)	94 (2.7)	91 (3.5)	99 (4.6)	101 (7.0)	98 (7.5)
Benalaxyl	95 (3.2)	104 (6.1)	102 (5.9)	97 (6.0)	100 (5.1)	103 (6.1)	93 (6.5)	96 (1.4)	96 (2.6)	99 (5.2)	106 (2.2)	103 (6.4)
Chlorpyrifos	106 (6.1)	103 (7.2)	100 (7.5)	94 (6.7)	88 (4.6)	93 (1.5)	100 (6.8)	93 (5.3)	91 (4.2)	100 (4.7)	100 (4.4)	89 (5.6)
Endrin	88 (4.8)	93 (6.9)	100 (7.2)	96 (4.8)	89 (3.3)	89 (3.8)	88 (5.5)	87 (4.2)	90 (4.0)	87 (6.4)	89 (7.3)	91 (5.5)
DDT	92 (4.1)	97 (6.6)	98 (7.4)	93 (4.1)	92 (5.6)	100 (3.6)	88 (6.2)	90 (3.0)	93 (3.6)	92 (5.0)	93 (5.4)	101 (5.7)
Bifenthrin	97 (2.8)	99 (7.7)	103 (7.5)	92 (5.2)	92 (6.7)	97 (5.8)	89 (5.1)	88 (3.7)	88 (2.5)	88 (4.9)	93 (4.5)	98 (4.9)

Level 1: 80 µg/L for metalaxyl; and 40 µg/L for benalaxyl, chlorpyrifos, endrin, 4, 4′‐DDT and bifenthrin. Level 2: 200 µg/L for metalaxyl; and 100 µg/L for benalaxyl, chlorpyrifos, endrin, 4, 4′‐DDT and bifenthrin. Level 3: 800 µg/L for metalaxyl; and 400 µg/L benalaxyl, chlorpyrifos, endrin, 4, 4′‐DDT and bifenthrin.

3.2.4. Selectivity

Selectivity was assessed by comparing the chromatograms of target analytes extracted from spiked and blank (unspiked) extracts of river water samples at concentrations of 200 µg/L for metalaxyl and 100 µg/L for benalaxyl, chlorpyrifos, endrin, 4,4′‐DDT and bifenthrin. The representative chromatograms of the river water samples are shown in Figures 10 and 11. Interfering peaks were observed at 210 nm for 4,4′‐DDT (retention time of 3.4 min), at 245 nm for metalaxyl (retention time of 1.5 min) and benalaxyl (retention time of 1.9 min), which made it impossible to determine all analytes at an individual wavelength. However, since a DAD was used, which provides the capability of simultaneous detection at different wavelengths, this was not seen as a limitation of the method. Therefore, the use of different wavelengths improved the selectivity of the method.

FIGURE 10.

Chromatograms of the analytes extracted from spiked river water compared with unspiked river water via the DLLME procedure at 210 nm. (A) Chromatogram of a blank river water sample. (B) Chromatogram of river water spiked with benalaxyl, chlorpyrifos, endrin, 4,4′‐DDT and bifenthrin at 100 µg/L and metalaxyl at 200 µg/L. Peak identification: (1) Metalaxyl (2) benalaxyl (3) chlorpyrifos (4) endrin (5) 4,4′‐DDT (6) bifenthrin.

FIGURE 11.

Chromatograms of the analytes extracted from spiked river water compared with unspiked river water via the DLLME procedure at 245 nm. (A) Chromatogram of a blank river water sample. (B) Chromatogram of river water spiked with benalaxyl, chlorpyrifos, endrin, 4,4′‐DDT and bifenthrin at 100 µg/L and metalaxyl at 200 µg/L. Peak identification: (1) Metalaxyl (2) benalaxyl (3) chlorpyrifos (4) endrin (5) 4,4′‐DDT (6) bifenthrin.

3.3. Comparison of the Proposed Method With Other Methods

The current proposed and validated DLLME method was compared with methods reported in the literature in terms of extraction time, precision (RSD), volume of extraction solvent and LOD (see Table 5). The LODs for the present method were comparable to those reported for DLLME GC/MS [12] and liquid–liquid extraction (DLLME HPLC–DAD) [2]. Recent studies have reported similar or improved performance using green solvents, including DES‐DLLME and IL‐DLLME, which achieved LODs in the range of 0.02–0.1 µg/L with %RSD between 2.8–5.0, extraction times as short as 90–100 s, and minimal solvent volumes of 0.08–0.10 mL [35, 36]. However, these values were lower than those reported for SPE with a GC electron capture detector (SPE GC‐ECD) [37] and with an HPLC diode array detector (SPE HPLC–DAD) [38]. The extraction time of the current method was generally shorter. As expected, the DLLME method uses microliter volumes, making it a greener technique. The RSDs of the developed method are comparable to those reported for other accepted methods. All these experimental findings show that the developed method takes a short amount of time, is environmentally benign, is reproducible, is comparatively sensitive and is suitable for the preconcentration of multiclass pesticides from environmental water samples.

TABLE 5.

Comparison of the performance of the developed method with other reported methods for the extraction and determination of target analyte residues in water.

Method	Sample	Analyte	LOD /µg/L	%RSD	Extraction solvent volume/mL	Extraction time/s	Refs.
NADES‐DLLME‐HPLC‐DAD	Water	Multiclass pesticides	0.05–0.1	3.5–5.0	0.10	100	[12]
IL‐DLLME‐HPLC‐DAD	Water	Multiclass pesticides	0.02–0.05	2.8–4.2	0.08	90	[2]
DLLME‐HPLC‐UV	Water	DDT	0.32	4.1	0.05	120	[39]
IPA‐LLE‐HPLC‐DAD	Water	Chlorpyrifos	1.4	2.2	1.50	600	[40]
Air‐assisted IL‐DLLME	Water	Triazoles	0.65–1.83	0.92–5.99	NA	NA	[41]
SPE‐HPLC‐DAD	Water	Metalaxyl	35	3	10	NA	[38]
DLLME HPLC‐DAD	Water	Metalaxyl	1.3	4.8
		Chlorpyrifos	1.0	7.0	0.05	80	Present work
		DDT	0.4	4.1

Note: NA = Not available or not reported.

Compared with SPE and other extraction techniques, our DLLME method offers faster extraction, minimal solvent use and strong green credentials. However, the use of green solvents such as NADESs or ILs could further reduce toxicity and increase sensitivity, potentially pushing detection to the ng/L scale (as reported in recent literature) [2, 12].

4. Conclusions

A DLLME‐HPLC‐DAD method was successfully developed and validated for the simultaneous extraction and determination of six multiclass pesticides in water samples. The systematic optimization of extraction parameters resulted in high enrichment factors, excellent recoveries, low detection limits and good reproducibility. Compared with conventional LLE and SPE methods, DLLME offers significant advantages in terms of speed, solvent economy and environmental sustainability. The method proved applicable to diverse environmental waters, including tap, ground and river sources, with recoveries meeting international guidelines. This work demonstrates the potential of DLLME as a practical and green approach for the routine monitoring of pesticide residues. Further integration of greener solvents such as NADESs or ILs could increase sensitivity and sustainability, advancing pesticide residue analysis toward ultratrace detection.

Author Contributions

Bezuayehu Tadesse Negussie: investigation, data curation, formal analysis (equal), methodology (lead), validation, writing – original draft. Simiso Dube: conceptualization (equal), methodology, resources (equal), writing – review and editing (equal). Nindi Mathew Muzi: conceptualization (equal), methodology, resources (equal), supervision, writing – review and editing (equal). Asmamaw Tesfaw: formal analysis (equal), visualization, writing – review and editing (equal).

Conflicts of Interest

The authors declare no conflicts of interest.

Negussie B. T., Dube S., Muzi N. M., and Tesfaw A., “Development and Optimization of a Dispersive Liquid–Liquid Microextraction Method for the Simultaneous Determination of Multiclass Pesticides in Environmental Waters.” Analytical Science Advances 6, no. 2 (2025): e70050. 10.1002/ansa.70050

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.