A Dynamic Multiple Reaction Monitoring Analytical Method for the Determination of Fungicide Residues in Drinking Water

Abstract

The extensive use of fungicides causes their continuous release into the environment through spraying, soil seepage, leaching, and runoff. It has been observed that their residues can be found in foods and a variety of environmental compartments, such as wastewater, lakes, rivers, sediments, drinking water sources (groundwater and surface water), treated water, and drinking water. A sensitive GC-MS/MS, using dynamic multiple reaction monitoring, an analytical method was developed to determine 10 fungicides (azoxystrobin, boscalid, captan, cyproconazole, cyprodinil, hexaconazole, metalaxyl, myclobutanil, paclobutrazol, and prochloraz) in drinking water. A solid-phase extraction method for sample preparations and validations was performed according to SANTE 2019 guidelines. All fungicides demonstrated mild or medium matrix effects (ME) ranging from 40.1% to 11.2%. Their recoveries ranged between 60% and 110%. The limits of detection were equal to or higher than 0.01 μg/L. The method was employed on 18 drinking water samples collected from public taps in Northern Evros, Greece, distributed in six sampling sites. Azoxystrobin, boscalid, cyproconazole, cypronidil, metalaxyl, and paclobutrazol mean concentrations did not surpass the allowable limit of 0.1 μg/L set by EU in any sampling site. Hexaconazole mean concentrations were higher than 0.1 μg/L in one sampling site, while prochloraz mean concentration showed limit exceedances in all sampling sites. Captan was not detected in any sampling site, and myclobutanil mean concentrations demonstrated exceedances of the permissible limit in four sampling sites. The presence of fungicide residues in the studied area is mainly due to the occasional point-sources pollution and preferential flow. Additionally, through the use of water, the risk of pesticides to human health was assessed for two different age groups. The sum of the hazard quotient values in each of the studied drinking water was less than unity. Consequently, the acute risk assessment procedure regards the examined drinking water as safe. Nevertheless, as prochloraz carcinogenic risk values were higher than the safe limit suggested by USEPA for both age groups, the existence of prochloraz residues raises concerns about chronic toxicity.

1. Introduction

The penetration and seepage of surface water replenish the groundwater reserves of the planet. However, concerns are developed when the surface water that requires filtering or percolating presents high levels of pollutants. These contaminants pollute groundwater and degrade its quality. Agricultural practices are responsible for the significant infiltration of contaminants into groundwater. Numerous agricultural pollutants, including pesticides, are presented in abundance in agroecosystems. Herbicides comprise 49% of all pesticide applications worldwide, while fungicides and insecticides each represent 27% and 19%, respectively [1]. Pesticides in drinking water constitute a concern on a global scale. As a result, maximum residue limits are being lowered by environmental legislation around the world. The European Union (EU) sets a maximum cumulative concentration for all pesticides in drinking water of 0.5 μg/L, with a maximum concentration of 0.1 μg/L for each pesticide [2,3].

Modern agriculture relies significantly on fungicides, a category of chemical compounds used primarily to prevent fungal diseases in crops. Their use is anticipated to rise steadily as a result of global population rise and increasing fungus resistance. Fungicides have a variety of uses apart from agriculture, including in paints, building materials, and urban environments [4]. The extensive use of fungicides causes their continuous release into the environment through spraying, soil seepage, leaching, and runoff. Fruits, vegetables, cereals, and numerous environmental compartments, including wastewater lakes, rivers, sediments, drinking water sources (surface water and groundwater), treated water, and drinking water, have all been reported to contain pesticide residues. Consequently, they could be hazardous to both ecosystems and human health [5,6].

Compared to insecticides and herbicides, fungicides’ risks attracted less attention. Fungicides can be hazardous to non-target organisms since their action modes are not limited to fungus. The ecosystem and human health may be negatively impacted by agricultural usage. For instance, it has been demonstrated that several strobilurin fungicides harm zebrafish embryonic development and cause immunotoxicity and oxidative stress [6]. Additionally, it has been reported that several azole fungicides have endocrine-disrupting properties [6]. Fungicides can reach humans by food, skin contact, and inhalation. Due to fungicides’ medium-to-high water solubility and frequent detection, people can consume fungicides regularly by drinking water, too [7].

Target hazard quotient approaches are notably used in research to determine pesticide threats. Human health risk assessments are carried out by the US Environmental Protection Agency (USEPA) under some published standards and policies, such as the exposure factors handbook, recommendations for carcinogen risk assessment, and broad guidelines for completing overall exposure and risk assessment [8,9]. Chronic exposure to legacy pesticides over the secure limit can have detrimental consequences and cause cancer or non-cancer risks to humans, with the severity of these risks becoming higher in vulnerable populations such as the elderly, pregnant women, and children [9].

Solid-phase extraction (SPE) has gradually gained popularity recently as analytical techniques have developed. It employs cartridges or disks of various materials, which keep the active compounds contained in aquatic samples and then release them from the washing action using small amounts of appropriate solvents. SPE demands only a small quantity of sample and solvent, which decreases the processes involved in working samples, the extraction time, the risk to the handler, and the expense of waste removal. The efficiency is also increased since the extract is more appropriate for analysis utilizing gas chromatography (GC) or liquid chromatography (LC). Also, it provides satisfactory recoveries and repeatability [10].

Among various chromatographic systems, GC paired with mass spectrometry (MS) is one of the most sensitive methods and finds various applications. The application of GC-triple quadrupole mass spectrometry (GC-MS/MS) has increased with regard to quantitative validation in different matrices [11]. Due to its selectivity, GC-MS/MS is typically used in multiple reaction monitoring (MRM) mode for pesticide residue determinations [12]. A different approach to MRM is dynamic multiple reaction monitoring (DMRM), which overcomes the requirement to establish temporal segments based on retention time, the number of target species, and dwell duration for the chosen group of transitions. In fact, the software creates “virtual” time segments during analysis (as a timescale of constant motion during the analysis time), which improves peak form and separation and boosts sensitivity [13].

Analytical methods must include method validation to demonstrate their suitability for the intended use and to guarantee the trustworthiness of the results, which is often lacking in published studies. It is achieved by multiple assessments that enable the identification of significant technique performance features that are appropriate for its planned application: selectivity, limits of detection and quantitation, working range, sensitivity, trueness, precision, and measurement uncertainty [14].

Based on what we know from the literature, this investigation is the first effort to link the assessment of risks to human health with the detection of fungicide residues in drinking water in the Balkans. This is also the innovation of the work. Therefore, the objective of this investigation is to develop and validate a precise GC-MS/MS identification and quantification in the DMRM mode analytical method to monitor 10 fungicides in drinking water samples collected in Northern Greece, following SANTE 2019 guidelines [15]. An assessment of the long-term risks associated with drinking water pollution and its impact on human health was also performed.

2. Materials and Methods

2.1. Chemicals and Reagents

The fungicide analytical standards had the highest accessible purity (>97.5%) and were obtained from Dr. Ehrestrofer GmbH (Augsburg, Germany), Riedel-de-Haen, (Berlin, Germany) and Neochema GmbH, (Bodenheim, Germany). The HPLC grade ethyl acetate, hexane, and methanol used for SPE procedure and standard solutions preparation were obtained from Riedel de Haen (Seelze, Germany). LiChrolut^® EN Polymer-based SPE cartridges with 200 mg absorbent and 3 mL volume were bought from Merck (Darmstadt, Germany). Ethyl acetate was used as the solvent to create each standard stock solution at a concentration of 100 μg/L. Serial dilution was utilized to create a working standard mixture from the stock solutions. Until needed for analysis, stock and working solutions were kept at −20 °C.

2.2. Study Area Target Fungicides Selection and Sampling

The selection of the sampling area took into account the extensive cultivation of wheat, cotton, sunflowers, maize, beets, various vegetables, and various tree crops, as well as the extensive use of the target fungicides in Evros, Thrace District, North Greece. As a result, fungicide residues may enter into groundwater, which is the primary origin of drinking water in the studied area. Previously, Vryzas et al. [16,17] and Parlakidis et al. [3] investigated the leaching behavior of various herbicides in the examined area.

Table 1. Physicochemical characteristics of target fungicides [18].

Fungicide	Chemical Group	Molecular Weight (g/mol)	Soil Degradation DT50 (Field)	Dissociation Constant (pKa) at 25 °C	Water Solubility at 20 °C (mg/L)	Octanol–Water Partition Coefficient at 20 °C (LogKow)	Vapor Pressure at 20 °C (mPa)	Henry’s Law Constant at 25 °C (Pa m3/mol)
Azoxystrobin	strobilurin	403.4	180.7	-	6.7	2.5	1.10 × 10−7	7.4 × 10−9
Boscalid	carboxamide	343.21	254	-	4.6	2.96	7.2 × 10−4	5.2 × 10−5
Captan	phthalimide	300.61	3.7	-	5.2	2.5	0.41	3.0 × 10−4
Cyproconazole	triazole	291.78	129	-	93	3.09	0.026	5.0 × 10−5
Cyprodinil	anilinopyrimidine	225.29	45	4.44	13	4	0.51	6.6 × 10−3
Hexaconazole	triazole	314.21	225	2.3	18	3.9	0.018	3.3 × 10−4
Metalaxyl	anilide	279.33	14.1	0	8400	1.75	0.75	1.6 × 10−5
Myclobutanil	triazole	288.78	35.0	2.3	132	2.89	0.198	4.3 × 10−4
Paclobutrazol	triazole	293.8	29.5	-	22.9	3.11	1.9 × 10−3	2.4 × 10−5
Prochloraz	conazole	376.7	68.8	3.8	26.5	3.5	0.15	1.6 × 10−3

shows the physicochemical properties of the target fungicides.

To evaluate if the DMRM analytical method is applicable for the determination of fungicides in real samples, aquatic samples were collected from public water taps. The samples were collected in triplicate from six sampling locations (cities or villages) in the Ardas Valley, Greece’s third-largest agricultural area. Particularly, a fungicide residues monitoring study was carried out in villages Lepti, Neochori, Abelakia, Pimeniko, and Inoi and in the city of Orestiada (Figure 1). Sampling was conducted on 28 March 2023. The samples (volume of 1 L) in triplicate were placed in dark glass bottles, where the analytes could be protected by light. Also, in the case where we chose plastic bottles, there was the possibility that plastic and pesticides would interact. The physicochemical characteristics of water were measured in situ by a portable device (HQ30D Field Case, HACH-LANGE E.P.E., Athens, Greece). Afterward, they were transferred to the analytical laboratory by a cold chain in glass containers at 4 ± 2 °C and were kept in the dark at temperatures below −20 °C till analysis that was carried out in a week.

2.3. Sample Preparation and Instrumental Analysis

Pesticides were extracted by SPE, as reported by Parlakidis et al. [4] with minor adjustments, and instrumental analysis was performed by GC-MS/MS analysis. Aquatic samples of 1 L were extracted using cartridges that were preconditioned by supplementing 4 mL methanol, followed by 4 mL deionized water. Samples were obtained through cartridges at a flow rate of 5 mL/min. Fungicides were eluted with 5 mL ethyl acetate followed by 3 mL hexane. Then, samples were concentrated under a nitrogen stream at 50 °C. Residues were disintegrated with 1 mL of ethyl acetate and kept at −20 °C till analysis.

In this study, full scans for precursor and product ions selection, MRM and DMRM method development, and DMRM application on real samples for fungicides detection and quantification were performed utilizing an Agilent 8890 GC system equipped with a triple quadrupole mass spectrometer (Agilent 7000D, Palo Alto, CA, USA). The separateness of 10 fungicides was attained using a dual capillary column with dimensions of 15 m length, 250 i.d. μm, and 0.25 μm film thickness (Agilent 190905-431U1, Palo Alto, CA, USA). Therefore, the overall length of the two columns was 30 m. Carrier gas flow within first column transferred from stainless steel inlet to collision cell auxiliary. Within second column, carrier gas is transferred from collision cell auxiliary to mass selective detector. A sample aliquot (2 μL) was inserted into the injection port heated at 280 °C with spitless mode. The carrier gas (He) was regulated at a flow rate of 1 mL/min. The temperatures of the transfer line, ion source, and quadrupole were 280, 230, and 150 ℃, respectively. The oven temperature was initially 70 °C held for 2 min, ramped to 150 °C at 25 °C/min, afterward increased to 200 °C at 3 °C/min, and lastly ramped to 280 °C and maintained for 3 min. The electron ionization energy was regulated at 70 eV. The total run time was 41.37 min.

2.4. Method Validation

The method’s accuracy (recovery percentage), precision (relative standard deviation, RSD%), limits of detection (LODs), and limits of quantification (LOQs) were all validated following the European Commission’s SANTE/12682/2019 criteria [15]. By examining blank samples that had been spiked with standard solutions at three different concentrations, 0.01, 0.1, and 1 μg/L, the accuracy and precision were calculated. Five replicates were tested to determine repeatability and reproducibility, which were then represented as intra-day (the same day) and inter-day (three separate days) RSDs%, respectively. By examining matrix-matched calibration curves with spiked blank samples at concentrations of 0.01 0.05, 0.1, 0.25, and 1 mg/L, the linearity was proven. Standard deviations of response and slope were used to calculate LODs and LOQs, which are represented as follows [19]:

LOD = 3S

(1)

LOQ = 10S

(2)

where S stands for ten times the blank analysis’s standard deviation [19].

Furthermore, matrix-matched standards were applied to take into account the matrix effects (MEs), as stated in SANTE/12682/2019 [15]. Limitations with quantification accuracy were explored using matrix-matched calibration curves. The slopes of the calibration curves in ethyl acetate and the matrix-matched calibration curves in the extracts were compared to determine the matrix effects% (ME%) utilizing the equation below [20]:

$$\mathrm{ME}\%=\left(\frac{slope of the calibration curve in matrix}{slope of the calibration curve in solvent}\right)-1\times 100\%$$

(3)

2.5. Human Health Risk Assessment

A human health risk assessment was performed for target fungicides. A human health risk assessment of pesticides is able to reveal data regarding the potential and nature of their outcomes on the human population, according to Kim et al. [21]. In our situation, the route to humans was thought to be oral exposure through water consumption. Risk assessment was conducted based on the two age groups, adults and children, as well as the two categories of risk assessment, carcinogenic and non-carcinogenic. Studied drinking water is provided through public taps to the local population.

2.5.1. Chronic Daily Intake

The CDI displays the predicted pesticide intake per kilogram of body weight Equation (4):

$$\mathrm{C}\mathrm{D}\mathrm{I}\mathrm{i}=\frac{{\mathrm{D}}_{\mathrm{I}\mathrm{P}}\times {\mathrm{E}\mathrm{F}}_{\mathrm{i}}\times {\mathrm{E}\mathrm{D}}_{\mathrm{i}}}{{\mathrm{B}\mathrm{W}}_{\mathrm{i}}\times \mathrm{A}\mathrm{F}}$$

(4)

D_IP refers to average daily intake, EF refers to exposure regularity (365 days annually for both age groups), ED refers to exposure time (6 and 70 years for adults and children, correspondingly), BW refers to body weight (70 kg for adults and 20 kg for children), and AF refers to mean lifetime (2190 and 25550 days for children and adults, correspondingly). Equation (5) as used to determine the D_IP, recommended by Muhammad et al. [22], Ali et al. [23], and Parlakidis et al. [4]:

D_IP = C_i × IR_i

(5)

IR indicates the water intake rate (0.87 L/day for children and 1.41 L/day for adults), and C (μg/L) shows the mean concentration of fungicide residues.

2.5.2. Hazard Quotient (Non-Carcinogenic Risk Assessment)

The hazard quotient (HQ) was determined for triazole fungicides by dividing the CDI by the corresponding acute toxicity reference dose for each fungicide Equation (3).

HQ = CDIi/ARfD

(6)

where ARfD is the acute toxicity reference dose [4,24].

The ARfD values for fungicides were obtained from PPDB [18], except cyprodinil boscalid. Azoxystrobin and hexaconazole are not signalized with ARfD values. The exposed group of the population is under health risk when HQ values are equivalent to or greater than 1.

Equation (4) can be used to calculate the risk of several fungicide residues by summing the risk of a single fungicide [4]:

$$\mathrm{HQs}={\sum }_{\mathrm{i}=1}^n\mathrm{HQi}$$

(7)

2.5.3. Carcinogenic Risk Assessment

Equation (8) was used to calculate the carcinogenic risk (R) [4,21]

R = CDI × SF × ADAF

(8)

where ADAF is an age factor taking into account early life pesticide exposure (3 for children and 1 for adults), and SF is the cancer slope factor (mg/kg-day), which evaluates the potential that a single fungicide will pose cancer. Among the fungicides, only prochloraz has an available SF value, which was obtained from IRIS [25].

3. Results and Discussion

3.1. Development of the Multiple Reaction Monitoring Method for Fungicide Determination

The development of the DMRM method was performed as described by Lee et al. [26]. A full scan and product ion scan were carried out utilizing a GC-MS/MS system to create the MRM settings for the 10 fungicides used in this investigation. The mass spectrum was regulated to 10–550 m/z, and standard solutions of 0.1 and 1 μg/L were used to carry out a full scan. The analytes with the highest sensitiveness and selectivity were chosen as precursor ions, preferably with mass values that showed the highest abundance, based on the findings of the full scan. A product ion scan for various collision-induced dissociation (CID) energy levels (5–35 eV) was carried out using these chosen precursor ions. The highest-sensitivity product ions were chosen as quantification ions, while the second-highest-sensitivity ones were chosen as qualification ions. The monitored ions for each fungicide are listed in

Table 2. Fungicides precursor ions and quantification ions, quantification transitions and qualification transitions, and collision energy (CE) of transitions 1 and 2.

Fungicides	Precursor Ions	Product Ions	Quantification Transitions (1)	Qualification Transitions (2)	Retention Time	CE1 (V)	CE2 (V)
Azozystrobin	344.1	329 182.9 171.9	344.1 → 329	344.1 → 182.9	37.4	15	25
Boscalid	140 111.9	112 76 76	140 → 112	140 → 76	33.33	10	25
Captan	151 149	80 79 79.1	151 → 79	151 → 80	23	15	5
Cyproconazole	222 138.9	124.9 111 75	222 → 124.9	138.9 → 75	26.06	25	35
Cyprodinil	226.2 225.2 224.2	225.3 224.3 208.2	225.2 → 224.3	226.2 → 225.3	22.36	10	10
Hexaconazole	214 175	159 147 111	175 → 111	175 → 147	24.86	20	10
Metalaxyl	234 220	174.1 146.1 192.1	234 → 146.1	234 → 174.1	19.12	20	10
Myclobutanil	179 150	125.1 90 123	179 → 125.1	179 → 90	25.63	10	30
Paclobutrazol	236 125.1 214	167.1 125.1 89	236 → 125.1	236 → 167.1	24.04	10	10
Prochloraz	310 195.9 180	69.8 96.9 138	180 → 138	195.9 → 96.9	32.31	10	30

.

3.2. Separation Distribution, Peak Shape, and Sensitivity Using the Dynamic Multiple Reaction Monitoring Mode

A feature of MRM is the DMRM mode of MS/MS due to the advancements in peak form and identification sensitiveness. The software application MassHunter can automatically create the DMRM method or the time schedule of DMRM, depending on the retention time for each compound in a time opening. The retention time, time window, and data rate are used in the DMRM mode to automatically determine the dwell times of the compound transitions. The DMRM mode specifically enables tracking more transitions at a certain dwell time and data rate. To improve the data acquisition effectiveness of GC-MS/MS, the DMRM mode was used in this study for multi-target compound analysis [27].

With MassHunter software, each analyte has a chromatographic peak, which is recognized as its identification, and an accurate quantification requires at least 15–20 points across a chromatographic peak. Particularly, the data rate should be at least three points/s (or cycles/s) when the breadth of an inadequate chromatographic peak is 6 s. Therefore, while simultaneously analyzing ultra-multi-target compounds, the system scan tempo is a crucial execution factor. For each transition, the utilized GC-MS/MS system needs a minimum dwell time of 10 ms. This is performed to assure mass accuracy. Particularly, 10 analyte transitions at a given data rate of three cycles/s should be the greatest number that may be acquired during the synchronized time opening (synchronized MRM number). When the analytes are grouped in a constrained elution area, a peak overlap is typically anticipated for multi-target pesticide analyses. Therefore, to provide the needed scan tempo of the system, the synchronized MRM number should be limited.

Using retention times for the target compounds in an identification opening (Delta RT) to prevent analyte losing due to peak displacement and a continuous cycle of scanning period (to provide enough number of data points in all detected peaks, (i.e., >10), the software tool Mass Hunter version 10.1, “Dynamic MRM (DMRM)”, utilized in this investigation, impulsively constructed the tables of DMRM. The Delta RT was maintained at 0.6 min for the objective of this study [27,28]. Retention time, two transitions, and the DMRM made up the process utilized to identify fungicide residues. It was also achievable to isolate the 10 fungicides to acceptable attributes. Figure 2 displays the chromatograms of the fungicides with the chosen transitions for the analysis.

In the current study, according to the results of the MS scans performed at the beginning of the method development, active substances paclobutrazol and hexaconazole demonstrated identical precursor ion (214 m/z), similar retention periods and retention times (24.04 min and 24.84 min, respectively), but no product ion detected was common amongst them (Table 2). To avoid chromatogram overlapping and difficulties in identifying these individual components, the next two other precursor ions with the most satisfying sensitivity and peak height were detected and used for identification for each of these fungicides in the method developed.

3.3. Dynamic Multiple Reaction Monitoring Method Validation and Matrix Effect

The analytical method was confirmed valid using the values of numerous authentication factors, including linearity, accuracy, precision, LOD, and LOQ. With blank extracts of tap water, concentrations of 0.01, 0.05, 0.1, 0.25, and 1 mg/L were used to create linear calibration curves using the matrix-matched standard calibration method. The summary of results in

Table 3. Method validation data of 10 fungicides in drinking water.

Fungicides	Linearity (R2)	LOD (μg/L)	Recovery (%)			Repeatability (%RSD)			Reproducibility (%RSD)
			0.01	0.1	1	0.01	0.1	1	0.01	0.1	1
			(μg/L)			(μg/L)			(μg/L)
Azoxystrobin	0.9999	0.0018	98	83	89	16	14	17	16	12	10
Boscalid	0.9996	0.0014	89	85	80	15	11	13	15	17	13
Captan	0.9991	0.01	60	71	64	9	10	8	17	12	13
Cyproconazole	0.9999	0.0011	89	107	85	7	10	9	8	16	16
Cyprodinil	0.9998	0.0015	87	92	100	13	18	17	12	14	9
Hexaconazole	0.9998	0.0017	95	109	100	11	15	14	17	11	18
Metalaxyl	0.9994	0.0016	87	95	89	15	16	12	10	13	14
Myclobutanil	0.9992	0.0013	98	89	110	12	17	14	9	13	12
Paclobutrazol	0.9993	0.0019	107	93	100	13	11	17.6	12	10	10
Prochloraz	0.9997	0.0012	92	97	106	9	16	14	15	11	14

shows that the 10 fungicides all had acceptable coefficients of determination (R² ranges from 0.9991 to 0.9999). According to Equations (1) and (2), LOQ and LOD were obtained. The results can be regarded as pleasant because the obtained LOQ levels were adequate for monitoring pesticides at their maximum allowed level of 0.1 μg/L for drinking water set by the European Council [29]. Three levels of fortification (0.01, 0.1, and 1 μg/L) were used to determine the recovery of the 10 fungicides, which ranged between 60% and 109.8%. For captan, low recoveries were obtained, ranging from 60% to 71%. For all 10 fungicides, the RSDs for repeatability and reproducibility have been estimated to be <17% and 18%, respectively (Table 3). According to European criteria, all compounds demonstrated good accuracy and were within the allowable recovery range [30].

One of the key considerations when assessing a multi-residue approach for pesticide analysis is matrix effects (MEs). Matrix components that remain in samples after extraction have a significant impact on a calibration curve’s slope. MEs (%ME) were calculated in this study based on ion suppression and/or enhancement [19,31]. When the ME ranges from −20% to 20%, there is a not strong matrix effect. A great matrix effect occurs when the ME is 50% or higher, and a medium matrix impact occurs when the ME is between 20% and −50% or −50% and 20%.

As presented in Figure 3, all fungicides demonstrated mild or medium ion suppression and enhancement, with MEs ranging from 40.1% to 11.2%, with captan showing the highest ME and myclobutanil the lowest ME. Several factors, such as the sample preparation method, the sample matrix, and the physicochemical characteristics of the pesticide, affect ion suppression and/or enhancement through MEs [32]. Pesticides with a molecular weight of more than 400 g/mol are prone to matrix effects. These phenomena are comprehensible given that they stay at the GC inlet longer and postpone their volatilization, which may provide them more time to interact with the active sites of the GC inlet and result in significantly greater matrix effects [33]. In our study, among the targeted fungicides, azoxystrobin has the highest molecular weight (403.4 g/mol) and showed the second-highest ME (31.3%). The rest of the targeted fungicides have molecular weights lower than 400 g/mol and presented significantly lower ME (except captan). Also, a well-known issue with multi-residue analysis utilizing a GC system is the identification and quantification of pH-dependent pesticides (acids or bases), such as captan. Due to dissipation at high pH and high temperature during sample preparation and on the active sites of the GC system, these pesticides showed enhanced matrix effects [34].

3.4. Application on Real Samples

To assess the applicability of the developed analytical approach for the determination of fungicides in real samples, 18 drinking water samples were collected from public taps in villages Neochori, Abelakia, Lepti, Inoi, Pimeniko, and the city of Orestiada (Northern Evros, Greece) and monitoring was performed. The EU sets a maximum concentration for all pesticides of 0.5 μg/L, with a maximum concentration of 0.1 μg/L for each compound. As shown in

Table 4. Mean (n = 3) concentration (μg/L) of target fungicides and water physicochemical characteristics at 6 different sampling sites in the studied area.

Fungicides	Neochori	Abelakia	Lepti	Inoi	Pimeniko	Orestiada	GUS
Azoxystrobin	0.096 ± 0.08	0.097 ± 0.07	0.096 ± 0.08	0.096 ± 0.08	0.096 ± 0.08	0.097 ± 0.1	3.10
Boscalid	0.048 ± 0.06	0.050 ± 0.06	0.089 ± 0.08	0.047 ± 0.06	0.053 ± 0.04	0.051 ± 0.07	2.68
Captan	nd	nd	nd	nd	nd	nd	0.97
Cyproconazole	0.071 ± 0.04	0.069 ± 0.07	0.067 ± 0.05	0.067 ± 0.06	0.071 ± 0.09	0.068 ± 0.06	3.04
Cyprodinil	0.016 ± 0.01	0.024 ± 0.01	0.014 ± 0.01	0.014 ± 0.01	0.018 ± 0.01	0.016 ± 0.01	1.06
Hexaconazole	0.214 ± 0.04	0.063 ± 0.06	0.063 ± 0.03	0.063 ± 0.07	0.065 ± 0.03	0.090 ± 0.05	2.31
Metalaxyl	0.028 ± 0.02	0.029 ± 0.04	0.029 ±0.02	0.032 ± 0.02	0.030 ± 0.063	0.030 ± 0.02	2.06
Myclobutanil	1.617 ± 0.02	0.084 ± 0.02	nd	0.137 ± 0.04	0.963 ± 0.09	2.094 ± 0.16	1.99
Paclobutrazol	0.080 ± 0.05	0.082 ± 0.05	0.081 ± 0.05	0.081 ±0.05	0.081 ±0.08	0.081 ± 0.06	2.47
Prochloraz	0.125 ± 0.12	0.123 ± 0.09	0.124 ± 0.08	0.127 ± 0.1	0.124 ± 0.1	0.126 ± 0.11	1.55
Temperature (°C)	12.2 ± 0.01	12.5 ± 0.01	11.9 ± 0.01	12.6 ± 0.01	12.5 ± 0.01	11.8 ± 0.01
pH	8.13 ± 0.01	8.92 ± 0.01	8.2 5 ± 0.01	8.54 ± 0.01	8.33 ± 0.01	8.61 ± 0.01

GUS = Groundwater Ubiquity Score; nd = not detectable.

, the azoxystrobin, boscalid, cyproconazole, cypronidil, metalaxyl, and paclobutrazol mean concentrations did not surpass the allowable limit of 0.1 μg/L in any sampling site. Hexaconazole mean concentrations were greater than 0.1 μg/L in Neochori. Prochloraz mean concentration showed limit exceedances in all sampling sites. Captan was not detected in any sampling site. Myclobutanil demonstrated four exceedances of the permissible limit in Neochori, Pimeniko, Orestiada, and Inoi. Also, Table 4 demonstrates the water characteristics. The extensive use of the target fungicides in Northern Evros, as well as the large-scale farming of wheat, cotton, sunflowers, maize, beets, various vegetables, and various tree crops, were taken into consideration when choosing the sampling area.

Due to the low vapor pressure of the fungicides used in this study, losses because of volatilization were expected to be low [18,35]. The concentrations of the target compounds exceeded the drinking water quality standards, suggesting that tests conducted as part of the pesticide registration procedure are not always consistent with monitoring study findings. Less than 1% of pesticides applied are expected to approach the target pest, with the leftover spreading to different environmental parts, including groundwater bodies [36]. The determination of high or extremely high concentrations for myclobutanil (1.617 μg/L and 2.094 μg/L), hexaconazole (0.214 μg/L), and prochloraz (from 0.123 μg/L to 0.127 μg/L) indicates the occurrence of sporadic point sources pollution. Point source pollution primarily results from agricultural operations, including pesticide mixing and filling and the washing of spray equipment, malfunctioning equipment leaks, handling of remaining tank mix, inappropriate pesticide storage, and accidents [37]. Additionally, the frequency and severity of precipitation events have a significant impact on the amount of pesticides that end up in rivers and groundwater because these meteorological events are known to cause quick flow procedures such as surface runoff and preferential flow [38]. Regarding the lower detected concentrations of azozystrobin, boscalid, captan, cyproconazole, cyprodinil, metalaxyl, and paclobutrazol, despite their extensive usage, their low water solubility, minimal surface runoff, and modest-to-minimal persistence in soil due to biotic and abiotic dissipation may all contribute to the low concentrations found in drinking water [39].

As has been previously stated in the investigated region, the multiple applications of the examined pesticides may result in enhanced biodegradation, but the remaining amounts of bound residues gradually desorbed from the soil matrix to the soil water and were moved to groundwater through leaching [4]. Furthermore, a previous study revealed that preferential flow has a major impact on pesticide leaching in the area due to the low adsorption capacity of the herbicides atrazine and metolachlor in the soil profile. This is because contaminants can enter the saturated zone of the aquifer through preferential flow paths, such as the shrinkage of clay minerals, plant roots, and earthworms forming burrows, without passing through chromatographic flow within the unsaturated zone, thus evading degradation [16,17].

In parallel, the transportation of nonpoint-source pesticides from agricultural areas via riparian drainage canals is thought to be one of the primary factors contributing to natural water contamination. The canals used for irrigation and drainage in the Evros basin are combined, exactly as in other Greek farms. Water that has been drained is typically used for irrigation [40]. Additionally, in the studied area, irrigation is frequently performed using self-propelled sprinkler irrigation systems or basin irrigation systems. These kinds of irrigation systems supply large amounts of water at high pressures, which, when combined with rain, may increase the leaching process [4]. Moreover, basin irrigation schemes could facilitate pesticides to pass into groundwater, according to Nouma et al. [41].

An experimental index that links K_oc (soil adsorption coefficient) and pesticide half-life is known as the GUS (Groundwater Ubiquity Score). According to their potential for moving into groundwater, pesticides can be categorized using the GUS index [42]. Hyun et al. [43] used the GUS to evaluate the risk of specific pesticides in the soil of Jeju Island that could contaminate groundwater. If the pesticides’ GUS index was higher than 2.8, they were categorized as groundwater contaminants. In addition, in Hawaii’s soil, pesticide leaching was examined, and the movement of several pesticides was anticipated using the GUS index [44]. Therefore, among fungicides, the highest leaching is expected for azoxystrobin (GUS = 3.10). The detected concentrations of azoxystrobin almost reached the permissible limit of 0.1 μg/L, presenting concentrations of 0.96 and 0.97 μg/L. Cyprodinil, with a GUS index of 1.06, presented the lowest concentrations (0.014–0.024 μg/L), whereas captan (GUS = 0.97) was not detected.

The non-point sources contamination is another factor, which could affect the fungicides’ fate in the studied area. The migration of pesticides from broad areas across the watersheds and their final reaching to the water bodies gradually constitute the non-point sources of pollution. Agricultural fields are the origins of non-point sources of pesticides because runoff and erosion from those fields lead pesticides to steadily seep into the ground and surface water [5].

The concentrations of fungicides found in this study are in the same range or higher as those reported in samples detected in waters (drinking water, surface water, and groundwater) globally, proving that the problem of high concentrations of fungicide residues in water is widespread. Myclobutanil was one of the most frequently found fungicides, with concentrations greater than 0.2 μg/L in water samples of aquatic systems across Australia. Similar findings demonstrated that myclobutanil is frequently found in water and sediment in coastal sites in California, the United States of America, in lettuce-growing regions [45]. According to previously published research, the highest concentration levels of boscalid found in groundwater and surface water were 2.120 μg/L and 0.109 μg/L, respectively, in three major coastal estuaries in California, the United States of America. Boscalid was one of the most commonly found pesticides (in more than 90% of the samples), with the determined concentration being 36 μg/L [46]. Ganaie et al. [9] reported that the mean concentration of hexaconazole detected in drinking water was 0.108 μg/L in an Indian valley. Azoxystrobin environmental concentrations have been documented by Kunz et al. [47] up to 30 μg/L in Europe and 4.6 μg/L in America. In Chile, the concentration of cyprodinil in the aquatic environment reaches 0.2 μg/L when it rains and 0.33 μg/L in Switzerland [48]. The results of a study conducted in South Brazil showed that the maximum detected concentration of cyproconazole was 0.014 μg/L [49], while in our study, it reached a mean concentration of 0.071 μg/L. Regarding metalaxyl, a concentration of up to 400 μg/L was found in the USA [50]. Hussain et al. [51] estimated the environmental concentrations of paclobutrazol in groundwater to be equal to 4.2 µg/L and 0.119 µg/L in surface water during a toxicity test experiment. According to Kuzmanović et al. [52], prochloraz was found with a frequency detection of 42% in rivers in Spain. However, the researchers did not report the detected concentrations.

Previous studies carried out in Greece reported the target fungicide detection in natural waters. Papadakis et al. [53] performed a pesticide residue monitoring study in two river basins (Strymonas and Nestos) in Northern Greece, where the main crops were maize, cotton, and cereals. Twenty-nine fungicides were found at least once throughout a two-year period, while boscalid was found frequently (seven to ten times). For the fungicide azoxystrobin, extremely high concentrations were detected, reaching a maximum concentration of 0.819 μg/L. Kapsi et al. [34] quantified pesticides (including fungicides) in a river basin (Louros) in Northern Greece, too. Myclobutanil was detected with a maximum concentration of 0.063 μg/L. In the water of the lower catchment basin of the Acheloos River, Western Greece, among detected fungicides, cyproconazole presented a maximum concentration of 0.724 μg/L [54].

3.5. Human Health Risk Assessment

Table 5. Hazard quotient (HQ) and carcinogenic risk (R) indexes of fungicides, obtained from fungicide mean concentrations through drinking water consumption for adults (A) and children (C).

Fungicides		Neochori		Abelakia		Lepti		Inoi		Pimeniko		Orestiada
	Index	A	C	A	C	A	C	A	C	A	C	A	C
Azoxystrobin	HQ	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
Boscalid	HQ	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
Captan	HQ	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
	R	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
Cyproconazole	HQ	0.072	0.154	0.069	0.150	0.067	0.146	0.067	0.146	0.072	0.154	0.068	0.148
Cyprodinil	HQ	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
Hexaconazole	HQ	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a	n/a
Metalaxyl	HQ	0.001	0.019	0.001	0.005	0.001	0.005	0.001	0.005	0.001	0.006	0.001	0.008
Myclobutanil	HQ	0.105	0.227	0.005	0.012	n/a	0.000	0.009	0.019	0.063	0.135	0.136	0.294
Paclobutrazol	HQ	0.016	0.035	0.017	0.036	0.016	0.035	0.016	0.035	0.016	0.035	0.016	0.035
Prochloraz	HQ	0.101	0.218	0.099	0.214	0.100	0.216	0.102	0.221	0.100	0.216	0.102	0.219
	R	0.003	0.002	0.003	0.002	0.003	0.002	0.003	0.002	0.003	0.002	0.003	0.002
HQ Sum		0.295	0.652	0.192	0.417	0.185	0.402	0.196	0.427	0.252	0.546	0.324	0.704

n/a = not applicable.

displays the findings of the risk assessment for human health. Concerning non-carcinogenic risk assessment, ARfD values for cyproconazole, metalaxyl, myclobutanil, pachlobutrazol, and prochloraz were used. Regarding carcinogenic risk assessment, among the fungicides, only prochloraz has available SF values. However, captan was not detected, and thus, a risk assessment was not conducted. Due to their lower body weight and higher intake rates than adults, children were at a higher risk of both cancer and non-cancer than adults.

The exposed group of the population is under health risk when HQ values are equivalent to or greater than 1. For adults, the HQ values for mean fungicide concentration ranged from 0.001 to 0.136, while for children, they ranged from 0.002 to 0.294. Myclobutanil had the highest recorded HQ value (0.294), whereas metalaxyl had the lowest (0.001). The highest HQ values, 0.136 and 0.294 for adults and children, respectively, were found in the drinking water tap in Orestiada. In none of the investigated wells the sum of HQ values was equal to unity. The tap water in Orestiada was found to have the highest cumulative potential risk, with values for adults and children of 0.704 and 0.324, respectively. The tap water in Lepti showed the lowest possible risk, with HQ values less than 0.4. The carcinogenic risk assessment, however, revealed high R values. Prochloraz R results consistently exceeded the parametric value of 1 × 10⁻⁶ suggested by the USEPA [24] for both age groups, indicating that the local population is at risk for cancer.

4. Conclusions

A method for the precise identification and quantification of 10 fungicides using SPE and GC-MS/MS equipped with a triple quadrupole mass spectrometer was developed. The recommended method’s linearity, LOD, LOQ, accuracy, and precision were all validated, too. The devised method provided reliable identification and quantification of 10 target fungicides in drinking water samples from 6 different sampling locations in Northern Evros, according to the validation test and analytical results of real samples. However, captan presented the lowest recoveries, ranging from 60.2% to 71.4%, and a medium matrix effect equal to 40.1%. Nevertheless, specific consideration ought to be paid to captan detection. The results of the real samples showed that the presence of fungicide residues in drinking water could be risky for public health since exceedances of the allowed limit of 0.1 μg/L regulated by the EU were reported. Also, the contamination of drinking water with fungicides is mainly due to the occasional point-sources pollution and preferential flow. Therefore, extensive temporal and spatial distribution and human health risk assessment studies for target fungicides, including more emerging pollutants, are strongly recommended to be performed in the studied area.