Presence of Pesticides and Transformation Products and Associated Risk Assessment in Groundwater of a Region with an Intensive Agricultural Activity

Abstract

The protection of natural resources, particularly groundwater, is essential for the sustainability of rural environments, especially when urban centers rely on this water for consumption. The objective of this study was to evaluate the occurrence, seasonal distribution, and associated risk of pesticide residues in groundwater in a region of intensive farming in the Duero river basin (Spain). A total of 40 pesticides and 7 degradation products were analyzed at 20 sampling points over four campaigns conducted in 2018. Overall, twenty-one compounds were detected, including three insecticides, three fungicides, ten herbicides, and five degradation products. Concentrations of eight compounds (one fungicide, five herbicides, and two degradation products) exceeded the limits established by the European Union (EU) for drinking water. Herbicides were the most frequently detected pesticides and were present at the highest concentrations (up to 3.416 μg L⁻¹) across all sampling campaigns. Metolachlor, prosulfocarb, metribuzin, and metolachlor degradation products (ethanesulfonic acid (ESA)– and oxanilic acid (OA)–metolachlor) were detected in concentrations over 1.0 µg L⁻¹. The sum of Toxic Units (∑Tu_i) showed that none of samples posed a high risk. None of compounds presented a high risk for the three aquatic organismstested; only prosulfocarb for algae and Daphnia magna; pendimethalin for algae and fish; and metribuzin, chlorotoluron, and desethyl-terbuthylazine (DETbz) for algae posed high risks.

1. Introduction

The main factor contributing to chemical pollution in aquatic ecosystems within agricultural areas is the extensive use of pesticides, which are necessary to control pests and protect crops. Pesticides applied in agriculture can reach water bodies through surface runoff or by vertical movement through the soil profile (leaching). Groundwater in areas of intensive agriculture is particularly vulnerable to pesticide contamination, a concern that becomes more serious when the water is intended for human consumption [1].

Sustainability is a central tenet of the European agricultural model, in which agricultural production serves as one of the main pillars [2]. This model relies, among other factors, on agrochemical inputs. The application of these compounds is driven by the need to ensure crop yields of sufficient quantity and quality to meet the population nutritional demands. Contemporary agricultural systems are largely oriented toward maximizing productivity and economic return. To achieve these goals, conventional agricultural practices often employ intensive agronomic interventions, frequently without adequate consideration of their long-term environmental or ecological consequences [3].

European legislation regulates the concentration of these compounds in water intended for human consumption through several directives to protect the quality of water for human consumption and groundwater from pollution and deterioration [4]. Revised Directive 2006/118/EC [5] establishes individual threshold for pesticides (0.1 μg L⁻¹) and a maximum total pesticide concentration (0.5 μg L⁻¹) permitted in water. Other European guidelines also consider the risks that pesticide residues pose to aquatic organisms [6,7]. Indicators such as Toxic Units (TUs) and Risk Quotients (RQs) for evaluating ecological and exposure effects of pesticides at different trophic levels have been identified as useful for assessing the biological risks of these compounds [8].

Sources of surface and groundwater contamination by pesticides from agricultural activities may be diffuse (such as inadequate application or the use of doses exceeding recommendations) or point sources (leaks from application equipment, spills or washing of application machinery). The impact of diffuse contamination depends on the extent of adsorption, mobility and degradation processes that pesticides undergo in the unsaturated soil zone. The agricultural sector accounts for approximately one-third of Europe’s total freshwater consumption, with even higher proportions in countries such as Spain. Agricultural activities affect not only the availability of water resources but also water quality, limiting its suitability for alternative uses. In several regions of Europe, contamination from agrochemicals—particularly pesticides and fertilizers—represents one of the primary sources of water quality degradation [9].

According to Eurostat (https://ec.europa.eu/eurostat/), pesticide sales in the EU reached approximately 333,000 tons in 2019 [10], representing a 6% decrease compared with 2018. The principal categories (fungicides, herbicides, and insecticides) accounted for about 86% of the total. In Spain, 75,397 tons of pesticides were sold, placing the country among the leading consumers in the EU, along with France, Germany, and Italy [11]. The use of such agrochemicals poses potential risks to soil and water systems, which may in turn affect human and animal health and contribute to biodiversity loss. Water is a fundamental component of agricultural systems and a key determinant of crop productivity. However, reliance on groundwater for human consumption becomes particularly concerning when leaching processes facilitate the transport of pesticides and fertilizers into aquifers [12].

Globally, the United States, China, and Spain have produced the largest number of studies on pesticide contamination in surface freshwater. Some economically less developed countries with intensive agricultural practices have also contributed to this research field. Among the pesticides evaluated, atrazine was the most frequently studied and detected compound up to 2021, and it remains one of the substances found at the highest concentrations in aquatic environments [13]. In recent years, several studies have reported the presence of pesticides and other substances in surface waters of different Spanish river basins, including the Ebro [8], Júcar and Turia [14], Júcar [15], Turia [16], Llobregat [17], Guadalquivir [18,19], and Miño [20], as well as in the Mediterranean coastal waters [21] and natural protected areas [22]. However, no studies have included the Duero river basin, and only a limited number of investigations have analyzed large datasets of groundwater samples [23,24,25,26].

The protection of natural resources, including soil and water, plays a fundamental role in the sustainability of rural environments. Groundwater preservation is of particular importance, especially considering that many urban centers in Spain rely on this resource. Although groundwater is generally less vulnerable than surface water, it is still subject to significant degradation risks, and its recovery is considerably more difficult once contaminated. According to the Duero Hydrographic Confederation (CHDuero, https://www.chduero.es/), the chemical quality of groundwater in the basin is generally good; however, in some areas, aquifers are overexploited, and exhibit low water levels [27].

In certain cases, pesticide concentrations exceeding the legal limits have been detected in drinking water supplied to some municipalities in the region. This is the case for S-metolachlor, which has been used in Spain since 1998 [28] and has been detected in both surface and ground water in several European countries, including Greece [29], Hungary [30], Poland [31] and Italy [32]. In recent years, groundwater contamination has been reported in the Castilla-León region of Spain, primarily associated with intensive potato and maize cultivation, where S-metolachlor is widely applied [27]. Moreover, the degradation products of S-metolachlor—ethane sulfonic acid (ESA-metolachlor)—and oxanilic acid (OA-metolachlor)—have been detected in groundwater due to their high mobility and persistence in the environment [33,34].

These findings support the need to evaluate groundwater levels of the main pesticides used in irrigated areas of the province of Salamanca, where potato and maize cultivation predominates, in order to obtain an environmental diagnosis of contamination by these compounds.

The objectives of this study were: (i) to monitor natural water bodies in an agricultural area dominated by potato and crop cultivation in order to determine the presence of pesticides and their transformation products in groundwater samples, and (ii) to assess the potential ecotoxicological risk to aquatic organisms (algae, Daphnia magna, and fish) by calculating Toxic Units (TUs) and Risk Quotients (RQs) in the Duero river basin (Spain). To the best of our knowledge, this is the first study in the region to simultaneously evaluate pesticide occurrence, their degradation products, and their associated ecotoxicological risks in an intensively impacted agricultural area.

2. Materials and Methods

2.1. The Study Area

The area selected is located in the northeast of the province of Salamanca, the region with the highest density of land dedicated to agricultural use in the province (>75%). It includes extensive potato, maize, cereal and beet crops, where different commercial formulations containing different pesticides are applied. Irrigated potato cultivation requires intensive pesticide use, particularly herbicides and insecticides, with several active principles recommended to manage the different diseases affecting these crops.

The groundwater in the study area belongs to the “Tierra del Vino” water body, as defined in the Hydrological Plan of the Duero River Basin District, approved in June 2013 [27]. It is classified as overexploited (“at risk of not achieving a satisfactory quantitative status”) due to the large number of wells and boreholes in the area. Irrigation water in this area is obtained from wells and boreholes and two irrigation canals. There are approximately 1000 wells and boreholes in the area. The water level is quite shallow, and most wells share similar characteristics, with an average depth of 10–15 m.

The aquifer lithology consists of continental detrital sediments of Tertiary age (sand-gravel assemblages, arkosic materials and local clay intercalations) resulting in moderate permeability. Groundwater recharge mainly takes place through rainfall infiltration at higher elevations and through surface water–groundwater interactions along the Tormes River and its tributaries (IGN, https://www.ign.es/) [35].

2.2. Sampling Network and Pesticides Selected

Twenty wells and boreholes were selected for groundwater sampling close to potato- and cereal-growing areas with different soil types (Figure 1). Most sampling points correspond to hand-dug wells of various depths, while 3 samples were taken from boreholes ranging from 17 to 160 m in depth. These water sources are mainly used for irrigation and drinking water supply, and one sample was collected from a public fountain (

Table 1. Characteristics and physicochemical parameters of the groundwater (GW) sampling points monitored in the sampling campaign of June.

Sampling Point	Water Depth (m)	Use of Water	pH	Conductivity (μS cm−1)	NO3− (mg L−1)	PO43− (mg L−1)	SO42− (mg L−1)
GW-1	5	Watering	6.92	1768	385	1.03	252.1
GW-2	17	Watering	6.88	615	152	5.66	51.57
GW-3	3	Watering	8.07	879	217	0.31	104.1
GW-4	5	Filling tanks	6.79	1294	214	0.32	72.79
GW-5	30	Supply	7.28	385	104	0.70	31.18
GW-6	5	Watering	7.20	447	118	0.68	37.73
GW-7	1	Watering	7.30	1892	407	3.78	217.2
GW-8	2	Watering	8.53	1258	345	2.46	162.9
GW-9	2	Watering	8.28	1109	264	0.65	141.8
GW-10	2	Watering	7.51	1450	134	0.78	116.8
GW-11	2	Watering	7.96	902	178	0.58	111.4
GW-12	6	Watering	7.97	196	54.0	2.69	19.46
GW-13	3	Watering	7.84	1261	273	1.44	123.4
GW-14	-	Watering	6.61	486	9.75	0.36	38.48
GW-15	-	Watering	7.20	447	118	0.68	37.73
GW-16	80	Spring	7.58	378	14.7	0.04	3.77
GW-17	160	Watering	7.48	299	15.9	0.10	8.78
GW-18	2	Watering	6.78	904	263	0.28	86.03
GW-19	17	Watering	7.07	479	141	0.79	42.08
GW-20	3	Watering	7.23	854	175	1.45	56.24

).

Four sampling campaigns were conducted in April (before the planting period), June (start of the potato campaign, and after herbicide application), July (intermediate period in the potato campaign, and after insecticide application), and October (after finishing the potato recollection) of 2018. It is important to note that the April and June sampling campaigns were carried out following heavy rainfall events during March and May of that year.

Water samples were collected manually or by pumping and transported to the laboratory in amber glass bottles in iceboxes. Samples were stored in the dark in a refrigerated chamber at 4 °C until analysis, after filtration through 0.45 μm membranes, following established procedures. Extracts were analyzed within two weeks of sample collection. The general characteristics of water samples are shown in Table 1.

Based on data provided by the local cooperative (Cantalpino, Spain) responsible for supplying phytosanitary products, together with information obtained from regional farmers, a total of 40 compounds were selected for analysis. These included 21 herbicides, 5 insecticides, 6 fungicides, and 8 known degradation products, chosen from among the substances most commonly used in the area in recent years. In addition, several banned pesticides previously detected in other regions were included to broaden the scope of the study and provide a more comprehensive assessment of environmental contamination. The selected compounds, representing multiple chemical classes (herbicides, fungicides, and insecticides), are listed in Table S1 (Supplementary Materials). Analytical standards for the target pesticides and selected degradation products (minimum purity > 98%) were obtained from Sigma Aldrich Química S.A. (Madrid, Spain) and Dr. Ehrenstorfer (Augsburg, Germany).

2.3. Analytical Determination of Pesticides and Inorganic Anions

Pesticide concentrations in the aqueous phase were determined in water samples previously pre-concentrated using a slightly modified version of the multi-residue method developed by Herrero-Hernández et al. [36], in order to incorporate newly applied pesticides specific to the study area. Solid-phase extraction (SPE) was performed using Oasis HLB cartridges as sorbents. A volume of 500 mL of water was pre-concentrated. Elution was performed with 4 mL of acetone and 4 mL of acetonitrile. The solvent was evaporated to dryness, and the residue was re-dissolved in 500 µL of methanol/water mixture (1:1) for analysis by LC-MS using a Waters system (Milford, MA, USA) equipped with an electrospray ionization (ESI) interface.

Quantification was performed by external calibration using matrix-matched standards, which were managed in a similar way to collected water samples. The proposed methodology was validated for each of the compounds by studying different analytical parameters such as the accuracy (average recovery), precision (reproducibility and repeatability) at the level of concentration established by EU legislation, the linearity parameters, and the limits of detection (LOD) and quantification (LOQ) of the complete method estimated as the analyte concentration with a signal-to-noise ratio of 3 and 10, respectively.

Detailed procedures for sample pre-concentration and analytical conditions are provided in the Supplementary Materials and summarized in Table S2.

2.4. Environmental Risk Assessment

Ecotoxicological risk assessment of detected compounds in water samples was conducted using Toxic Units (TUs), following European regulatory guidelines [6]. TUs were determined for at least three representative taxa covering different trophic levels: algae, Daphnia magna, and fish. The Toxic Unit for each compound (TUi) was calculated by dividing its measured environmental concentration (MECi) by its corresponding median effective concentration (EC50i) or median lethal concentration (LC50i), as follows: TUi (algae; Daphnia magna; fish) = MECi/EC50i or LC50i. The cumulative toxic pressure at each sampling site (TUsite) was then estimated by summing all individual TUi values for compounds detected at that location [8].

Risk Quotients (RQs) were also calculated to assess potential ecological risk, defined as the ratio between the measured environmental concentration (MEC) and the predicted no-effect concentration (PNEC): RQ = MEC/PNEC. To derive PNEC values, an assessment factor (AF) was applied to account for uncertainties in toxicity data, as recommended by the European Commission [6]. When chronic (long-term) toxicity data were available, a default AF of 100 was used, typically based on the lowest no-observed-effect concentration (NOEC). When only acute (short-term) data were available (i.e., EC50 or LC50 values), a more conservative AF of 1000 was applied [31]. This factor compensates for uncertainties inherent in extrapolating laboratory toxicity data to environmental conditions. Four risk categories were defined based on RQ values: minimal risk: RQ < 0.01, low risk: 0.01 ≤ RQ < 0.1, medium risk: 0.1 ≤ RQ < 1 and high risk: RQ ≥ 1 [37].

Acute toxicity endpoints included the 48 h EC50 for Daphnia magna, the 72 h EC50 for algae, and the 96 h LC50 for fish. Chronic toxicity endpoints were based on the 96 h NOEC for algae and the 21-day NOEC values for both Daphnia magna and fish. These data were obtained from the Pesticide Properties DataBase (PPDB) [28], which defines EC50 values according to Daphnia magna immobilization, algal growth inhibition (species not specified), and fish survival, typically in Oncorhynchus mykiss (rainbow trout).

2.5. Statistical Analysis

Principal component analysis (PCA) was used to extract valuable insights from the data, allowing for the examination of multivariate correlations among the concentrations of various pesticides. The Unscrambler^® X software (v 10.5, CAMO Software AS., Oslo, Norway, 2017) was used for the PCA. Prior to the analysis, the data were pretreated by row normalization. To prevent averaging based on a significant number of unquantified values and facilitate statistical analysis, concentrations below LOD were set to 0 (estimated as null), and those between LOD and LOQ were set to LOQ/2 when needed. This data transformation mitigated the impact of zero values while still incorporating low levels in the calculation of the average.

The potential connections among pesticide occurrences throughout all the sites were assessed by conducting pairwise correlations through Spearman’s rank test, setting a significance level of 0.01.

3. Results and Discussion

3.1. Characterization of Groundwater Samples and Pesticide Monitoring

Physicochemical parameters of water samples were analyzed, and the results from the June sampling campaign are shown in Table 1. The pH values were above 4.5, and conductivity values were below 2500 μS cm⁻¹ in all cases, indicating that the waters were neither aggressive nor encrusting [6]. Chloride concentrations were <250 mg L⁻¹ in all cases. Sulfate concentrations were generally below 250 mg L⁻¹, except for water 1, which slightly exceeded the legal limit established for water intended for human consumption [6]. Nitrate concentrations exceeded the regulatory limit of 50 mg L⁻¹ in most samples, with the exception of samples 2, 6, 8, 12, 14, 15, 16, and 17, which remained below the legal limit in some or all of the sampling campaigns [6].

Pesticide concentrations detected in groundwater, and percentage of positive samples with concentrations below and over 0.1 µg L⁻¹ and average and maximum concentrations for the pesticides detected in the samples analyzed through the four sampling campaigns are included in Figure 2 and

Table 2. Percentage of positive groundwater samples with concentrations below and over 0.1 μg L⁻¹ and average and maximum concentrations for the pesticides detected in the samples analyzed in the four sampling campaigns (April, June, July and October 2018).

Pesticide	April				June				July				October
	Positive Samples		Conc. (μg L−1)		Positive Samples		Conc. (μg L−1)		Positive Samples		Conc. (μg L−1)		Positive Samples		Conc. (μg L−1)
	C < 0.1	C > 0.1	Average ± SD	Cmax	C < 0.1	C > 0.1	Average ± SD	Cmax	C < 0.1	C > 0.1	Average ± SD	Cmax	C < 0.1	C > 0.1	Average ± SD	Cmax
Fungicides
Metalaxyl	3	-	0.020 ± 0.019	0.041	5	-	0.021 ± 0.018	0.051	7	1	0.030 ± 0.038	0.120	5	-	0.022 ± 0.019	0.056
CGA-92370	-	-	-	-	-	-	-	-	2	-	<LOQ	-	2	-	<LOQ	-
Tebuconazole	-	-	-	-	1	-	<LOQ	-	0	-	-	-	-	-	-	-
Dimethomorph	-	-	-	-	-	-	-	-	5	-	<LOQ	-	-	-	-	-
Insecticides
Thiamethoxam	-	-	-	-	1	-	0.019	0.019	1	-	0.075	0.075	2	-	0.041 ± 0.009	0.047
Imidacloprid	3	-	0.019 ± 0.006	0.023	3	-	0.040 ± 0.019	0.053	4	-	0.040 ± 0.029	0.077	2	-	0.034 ± 0.028	0.053
Chlorpyrifos	1	-	0.013	0.013	2	-	0.021 ± 0.006	0.025	2	-	0.022 ± 0.013	0.032	-	-	-	-
Herbicides
Pyrimidinol	-	-	-	-	-	-	-	-	-	-	-	-	4	-	0.020 ± 0.008	0.032
Atrazine	3	-	<LOQ	-	3	-	<LOQ	-	1	-	<LOQ	-	-	-	-	-
Chlorotoluron	-	-	-	-	-	-	-	-	-	-	-	-	9	2	0.049 ± 0.040	0.121
Terbuthylazine	4	-	<LOQ	-	1	-	<LOQ	-	3	-	<LOQ	-	3	-	0.064 ± 0.049	0.099
DETbz	-	-	-	-		-	-	-	-	-	-	-	2	-	0.068 ± 0.008	0.073
HTbz	1	-	<LOQ	-	4	-	<LOQ	-	3	-	<LOQ	-	-	-	-	-
Metolachlor	6	1	0.178 ± 0.410	1.108	3	6	0.220 ± 0.251	0.697	3	3	0.420 ± 0.820	2.086	4	3	0.137 ± 0.193	0.554
ESA-metolachlor	-	-	-	-	-	-	-	-	-	-	-	-	1	8	1.200 ± 1.063	3.416
OA-metolachlor	-	-	-	-	-	-	-	-	-	-	-	-	3	7	0.592 ± 0.807	2.694
Prosulfocarb	4	1	0.317 ± 0.669	1.514	4	5	0.314 ± 0.392	1.065	4	4	0.341 ± 0.632	1.879	-	-	-	-
Fluazifop-butyl	-	-	-	-	-	-	-	-	1	-	<LOQ	-	-	-	-	-
Pendimethalin	-	-	-	-	2	-	<LOQ	-	1	-	0.087	0.087	-	-	-	-
Metribuzin	4	5	0.385 ± 0.797	2.504	9	4	0.596 ± 0.132	0.170	5	-	<LOQ	-	-	-	-	-
Bentazone	-	-	-	-	-	-	-	-	-	-	-	-	2	1	0.113 ± 0.116	0.239

, respectively.

Although fungicides are the most widely sold compounds in Spain, only 3 out of 6 fungicides included and one of their degradation products were detected at least once. This result is consistent with the fact that fungicides are rarely used in potato or cereal crops. Only two samples (sample GW-4 and -5) presented two fungicides simultaneously, metalaxyl and dimethomorph, both at very low concentrations (<0.024 μg L⁻¹). Metalaxyl was detected in all sampling campaigns but exceeded 0.1 μg L⁻¹ only in sample GW-6 in July (0.120 μg L⁻¹). Its degradation product CGA-92370 was detected in several samples at very low concentrations (<0.011 μg L⁻¹), appearing simultaneously with metalaxyl only in GW-6 (July and October) and GW-13 (October).

Only three insecticides were detected in the study area: thiamethoxan and imidacloprid are widely used in potato crops, and chlorpyrifos, used in maize. All of them are currently banned in EU. Imidacloprid was detected in several samples across all campaigns (three in April and June, four in July and two in October) always below 0.1 μg L⁻¹ (Cmax 0.077 μg L⁻¹). Thiamethoxam, commonly used to control the Colorado potato beetle, was detected in three sampling campaigns (sample GW-8 in October and sample GW-13 in June, July and October). Chlorpyrifos was detected in three sampling campaigns, April, June and July with a Cmax of 0.032 μg L⁻¹ in four samples (GW-2 in July, GW-4 in April, GW-12 in June and GW-17 in June and July). Only two samples presented two insecticides simultaneously: GW-4 (imidachloprid and chlorpyrifos) and GW-13 (imidachloprid and tiamethoxam in July). These three insecticides have also been detected in groundwater and surface waters in other regions of Spain, sometimes at much higher concentrations, up to 10.44 µg L⁻¹ in groundwater from the Canary Islands [26], and in other countries, including Greece [29,38], India [39], Tunisia [40], Argentina [41] and Costa Rica [42].

Herbicides were the most ubiquitous pesticide group, with ten herbicides and four of their degradation products detected. Metolachlor, prosulfocarb, and metribuzin were the more frequently detected compounds, present in over 40% of the samples in one or more campaigns (Figure S1). These herbicides also showed the highest concentration in each campaign: metribuzin with Cmax 2.504 μg L⁻¹ in April, prosulfocarb with Cmax 1.065 μg L⁻¹ in June, metolachlor with Cmax 2.086 μg L⁻¹ in July and ESA-metolachlor with Cmax 3.416 μg L⁻¹ in October, representing the highest concentration observed in this study.

It is noteworthy that several of the pesticide concentrations detected in the analyzed groundwater samples exceed the maximum limits established by EU drinking water standards [4], in some cases by an order of magnitude. These exceedances underscore the potential implications for human health and the importance of continued monitoring. Highlighting these findings provides context for both regulatory compliance and the need for sustainable agricultural practices to minimize contamination of groundwater resources.

Special mention should be given to metolachlor and its degradation products. Metolachlor degrades primarily into polar metabolites, ESA-metolachlor and OA-metolachlor, which exhibit higher chemical stability and water solubility than the parent compound, increasing their mobility in the environment. Due to their lower affinity for soil organic matter and reduced potential for volatilization, ESA-metolachlor and OA-metolachlor are more persistent in aquatic systems [28]. Consequently, these metabolites may contribute to long-term contamination and should be included in environmental monitoring and risk assessment programs. These compounds were detected in all samples except GW-16, GW-17 and GW-19. In eleven sampling points, the maximum concentration corresponded to one of these compounds. This contamination may originate from prior soil residues of metolachlor or its continued application. Given the generally sandy soils in the area, leaching may facilitate the transport of these compounds into groundwater [1].

These findings align with previous market surveys in the same region, confirming that the most frequently detected compounds correspond to those most intensively applied in local agricultural practices. The presence of metolachlor and/or its degradation products has been previously reported in the study area [43], elsewhere in Spain [21,22,44], and in other countries including Germany [45,46], France [47], Greece [29,38], Italy [32], Poland [31], USA [48], Argentina [41] and China [49]. Concentrations of metolachlor and its degradation products observed in this study were generally higher than those reported in previous works, except in the studies by Jurado et al. [44] and one conducted in the USA [48], which reported comparable levels.

The Groundwater Ubiquity Score (GUS) index was used to evaluate the leaching potential of the detected pesticides into groundwater. This index allows compounds to be classified as leachable (GUS > 2.8), non-leachable (GUS < 1.8), or exhibiting intermediate behavior (1.8 < GUS < 2.8). Most pesticides detected at concentrations > 0.1 μg L⁻¹ showed GUS values ranging from medium (metribuzin, bentazone, chlorotoluron and metalaxyl) to high (metolachlor and its degradation products) only prosulfocarb presented a low GUS index [28]. Despite its low GUS index, prosulfocarb was consistently detected at concentrations above 0.1 μg/L. This may be explained by repeated or recent applications in the area, as well as preferential transport pathways such as macropore flow, soil fissures, or direct leaks from irrigation systems, which can facilitate rapid movement to groundwater and bypass normal sorption processes [50].

Other detected compounds were also classified as leachable, including the insecticides thiamethoxam and imidacloprid, and the herbicides pyrimidinol, atrazine, terbuthylazine and its degradation products. Fungicides such as metalaxyl, dimethomorph, and tebuconazole were classified as transition compounds [28]. These results suggest that the occurrence of certain pesticides in groundwater can be largely explained by their GUS classifications, highlighting the relevance of this index as a predictive tool for assessing pesticide mobility through the soil profile.

Five or more pesticides were detected in seven samples in one or more sampling campaigns (Figure 3). Samples GW-4, GW-18, GW-13 and GW-6 contained the highest number of pesticides, with nine, seven, six, and six compounds, respectively. Special mention should be given to sample GW-4 in which nine, seven, six and six pesticides were detected in October, July, June and April campaigns, respectively. No significant correlation was observed between the number of detected pesticides and groundwater depth. The occurrence of pesticides in deeper groundwater layers is likely attributable to the predominantly sandy or sandy loam soil textures characteristic of the study area, which facilitate vertical leaching [1].

As previously noted, herbicides were the most frequently detected class of pesticides, accounting for 74% of all detections (147 above the limit of detection), and exhibiting the highest cumulative concentration in the area (4.747 µg L⁻¹) (Figure 4). In contrast, fungicides were detected 31 times (15% of total detections), with concentrations up to 0.123 µg L⁻¹, while insecticides were the least frequently detected group, with 21 occurrences (11%) and the lowest cumulative concentration (0.086 µg L⁻¹) (Figure 4). These findings differ from those reported in other Spanish Designations of Origin (DO) regions, such as Rioja [24], where fungicides were the predominant pesticide group in both surface and groundwater, and Jumilla [36], where insecticides were the most commonly detected compounds. However, these results are consistent with studies conducted in several European countries, including Poland [31], France [47], Greece [29,38], England [51], and Germany [46].

3.2. Seasonal Evolution of Pesticide Residues in Natural Waters

The total concentration of pesticides (Figure 3), as well as the concentrations of fungicides, insecticides and herbicides (Figure 4a–c) was determined in groundwaters for each sampling period (April, June, July and October). The temporal evolution of total pesticide concentrations was evaluated to assess the chemical status of groundwater in accordance with the European Directive 2006/118/EC [5], which sets a threshold of 0.5 µg L⁻¹ for the sum of all pesticide concentrations in water intended for human consumption. The results showed that eleven samples (55%) exceeded this threshold in at least one sampling campaign. Four samples (GW-04, GW-08, GW-13 and GW-18) exceeded 0.5 μg L⁻¹ in two sampling campaigns.

By pesticide type, fungicides (Figure 4a) reached the highest total concentrations in July for most samples, reaching values > 0.12 μg L⁻¹. July was also the sampling campaign in which three of the four detected fungicides were present in the highest proportion of samples, with metalaxyl detected above 0.1 μg L⁻¹ in one sample (Figure S1c). Insecticides (Figure 4b) reached values above 0.08 μg L⁻¹, with smaller differences observed between June, July and October campaigns. Herbicides (Figure 4c) showed the highest total concentrations in October, with two samples (GW-4 and GW-20) exceeding 4.5 μg L⁻¹. This campaign also had the highest number of herbicides found in concentrations above 0.1 μg L⁻¹, with four of the eight herbicides exceeding this value (Figure S1d). Seasonal variations in pesticide concentrations may be attributed to several factors including crop-specific application patterns, climatic conditions, and the chemical stability of the compounds.

No significant statistical correlations were observed between nitrate concentration and the sum of herbicide and fungicide concentrations. Spearman’s rank correlation coefficient indicated a single statistically significant association was found (ρ = 0.2542; p = 0.0229) in the case of insecticides. Similarly, no significant correlations were found between sampling depth and the sum of herbicide, fungicide, or insecticide concentrations.

A multivariate analysis (PCA) was performed using the 80 available samples and their corresponding concentrations. The first two principal components explained 25% and 23% of the total data variance, respectively. Although the first two principal components together explain only about 50% of the total variance (approximately 25% each), this result is consistent with the substantial variability and multidimensional structure of the dataset. When observations are highly dispersed, the variance tends to be spread across several orthogonal directions, which limits the proportion captured by the first principal components. The relatively low percentage of explained variance does not reflect a weakness of the PCA itself, but rather the inherent complexity of the system under study. The resulting biplot (scores and loadings) is shown in Figure 5, where three sample groups with slight differences can be distinguished. Samples located in the upper-left region (negative values for the PC1 and positive values for the PC2) exhibited higher concentrations of metribuzin compared to the rest of the samples and mainly corresponded to the April and June campaigns. Samples with negative values for both components showed higher concentrations of metolachlor and prosulfocarb and were associated with the June and July campaigns. Samples on the right side of the plot had higher concentrations of OA-metolachlor and ESA-metolachlor, predominantly in the October campaign and, to a lesser extent, in July. Samples closer to the origin displayed intermediate concentrations of the remaining analytes.

The sharp increase in metolachlor metabolite concentrations observed in October can be attributed to multiple factors. Seasonal dynamics, including autumn groundwater level rise, may enhance the mobilization of residues accumulated in the soil during the growing season. Additionally, cumulative application of metolachlor over time and microbial transformation processes could contribute to a higher metabolite formation during this period [1]. These potential mechanisms are consistent with the data presented.

3.3. Ecotoxicological Risk Assessment

Ecotoxicological risk assessment for aquatic environments was conducted using Toxic Units (TUs) and Risk Quotients (RQs) to evaluate potential acute and chronic effects at the different sampling sites. The cumulative toxic unit (∑TUi) at each site was calculated, results are presented in

Table 3. Sum of Toxic Units (∑TUi) for all detected pesticides in the different groundwater sampling points for different aquatic organisms in each sampling campaign (April, June, July and October 2018).

Sample	April			June			July			October
	Algae	Daphnia magna	Fish	Algae	Daphnia magna	Fish	Algae	Daphnia magna	Fish	Algae	Daphnia magna	Fish
GW-1	0.0036	-	-	0.0022	-	-	-	-	-	0.0009	-	-
GW-2	0.0098	0.0000	0.0000	0.0031	0.0004	0.0002	0.0003	0.1301	0.0006	0.0002	-	-
GW-3	-	-	-	0.0083	-	-	-	-	-	0.0015	-	-
GW-4	0.0026	0.1300	0.0006	0.0229	0.0021	0.0015	0.0024	0.0004	0.0003	0.0077	0.0002	0.0001
GW-5	0.0949	0.0001	0.0001	0.0053	0.0001	0.0001	0.0038	0.0005	0.0003	-	-	-
GW-6	0.0102	-	-	0.0037	-	-	0.0003	-	0.0001	0.0003	-	0.0001
GW-7	0.0051	-	-	0.0021	-	-	0.0002	0.0001	0.0001	0.0020	-	-
GW-8	0.0025	0.0001	0.0001	0.0083	0.0017	0.0012	0.2540	0.0011	0.0008	0.0004	-	-
GW-9	-	-	-	-	-	-	0.0013	-	-	-	-	-
GW-10	0.0035	-	-	0.0018	0.0001	-	0.0001	-	-	0.0011	-	-
GW-11	0.0041	-	-	0.0005	-	-	-	-	-	0.0053	-	0.0001
GW-12	0.0044	-	-	0.0005	0.2501	0.0011	0.0004	-	-	-	-	-
GW-13	0.0040	-	-	0.0023	0.0003	0.0002	0.0157	0.0038	0.0028	0.0008	0.0001	0.0002
GW-14	0.0137	0.0030	0.0021	-	-	-	-	-	-	0.0008	-	-
GW-15	0.0008	-	-	0.0050	0.0001	0.0001	0.0012	-	-	0.0001	-	-
GW-16	-	-	-	0.0226	-	-	-	-	-	-	-	-
GW-17	0.0007	-	-	-	0.1700	0.0007	0.0001	0.3200	0.0013	-	-	-
GW-18	-	-	-	0.0123	0.010	0.0007	0.0025	0.0001	0.0001	0.0012	-	0.0001
GW-19	-	-	-	-	-	-	0.0003	-	-	-	-	-
GW-20	-	-	-	-	-	-	-	-	-	0.0033	0.0001	0.0001

. None of the sampling sites exhibited TU_site values exceeding 1, which would indicate high ecological risk, for any of the aquatic organisms across the four sampling campaigns. Only six sites recorded values > 0.1 (five for Daphnia magna and one for algae), primarily during the June and July campaigns. These results highlight the sensitivity of Daphnia magna, representing aquatic insects and other zooplanktonic invertebrates, to the combined pesticide mixtures compared to algae or fish. Despite relatively high concentrations of certain individual pesticides, acute toxicity thresholds were not exceeded.

For a more accurate assessment of chronic ecological risk, passive samplers or other continuous, real-time monitoring techniques are recommended, as they provide more representative exposure data. Chronic exposure can also be approximated using the risk quotient (RQ), calculated based on the maximum detected concentrations (RQ_max) as a conservative, worst-case scenario.

Table 4. Predicted No-Effect Concentration (PNEC) values in mgL⁻¹ and Risk Quotients (RQ_max) for the three aquatic organisms calculated with the maximum concentrations for each detected pesticide in the study area for each sampling campaign (April, June, July and October 2018).

Analyte	Algae					Daphnia magna					Fish
	PNEC a	RQmax				PNEC b	RQmax				PNEC c	RQmax
		April	June	July	October		April	June	July	October		April	June	July	October
Fungicides
Metalaxyl	200	0.0002	0.0003	0.0006	0.0003	10	0.0041	0.0051	0.0120	0.0056	30	0.0014	0.0017	0.0040	0.0019
CGA-92370
Dimethomorph	27.3	-	-	0.0002	-	2.2	-	-	0.0236	-	0.56	-	-	0.0929	-
Tebuconazole	1.0	-	0.0110	-	-	0.1	-	0.1100	-	-	0.1	-	0.1100	-	-
Insecticides
Thiamethoxam	100	-	0.0002	0.0008	0.0005	1000	-	-	0.0001	-	200	-	0.0001	0.0004	0.0002
Imidacloprid	1000	-	0.0001	0.0001	0.0001	18	0.0013	0.0029	0.0043	0.0029	90.2	0.0003	0.0006	0.0009	0.0006
Chlorpyrifos	0.43	0.0302	0.0581	0.0744	-	0.046	0.2826	0.5435	0.6957	-	0.0014	9.286	17.86	22.86	-
Herbicides
Pyrimidinol	100	-	-	-	0.0003	100	-	-	-	0.0003	100	-	-	-	0.0003
Atrazine	1.0	0.0470	0.0130	0.0120	-	2.5	0.0188	0.0052	0.0048	-	20	0.0024	0.0007	0.0006	-
Chlorotoluron	0.0082	-	-	-	1.476	112	-	-	-	0.0011	4	-	-	-	0.0303
Terbuthylazine	3.18	0.0097	0.0044	0.0038	0.0211	0.19	0.1632	0.0737	0.0632	0.3526	0.9	0.0344	0.0156	0.0133	0.0744
DETbz	0.14	5.214	-	-	0.7071	42	0.0174	-	-	0.0024	18	0.0406	-	-	0.0055
HTbz	3.8	0.0055	0.0053	0.0053	-	2.8	0.0075	0.0071	0.0071	-	2.5	0.0084	0.0057	0.0044	-
Metolachlor	30	0.037	0.0232	0.0695	0.0185	7.07	0.1567	0.0986	0.2950	0.0784	10	0.1108	0.0697	0.2086	0.0554
ESA-metolachlor	100	-	-	-	0.0342	100	-	-	-	0.0342	43	-	-	-	0.0743
OA-metolachlor	0.015	-	-	-	179.6	16.6	-	-	-	0.1623	100	-	-	-	0.2690
Prosulfocarb	0.12	12.62	8.875	15.66	-	0.45	3.364	2.367	4.176	-	3.1	0.4884	0.3435	0.6061	-
Fluazifop-butyl	-	-	-	-	-	1.0	-	-	0.0170	-	2.38	-	-	0.0071	-
Pendimethalin	0.03	-	0.9667	2.900	-	0.145	-	0.2000	0.6000	-	0.06	-	0.4833	1.450	-
Metribuzin	1.9	13.18	3.137	0.3053	-	3.05	0.8210	0.1954	0.0190	-	56	0.0447	0.0106	0.0010	-
Bentazone	257	-	-	-	0.0009	1010	-	-	-	0.0002	480	-	-	-	0.0005

^a Calculated with the chronic 96/72h NOEC in algae; ^b Calculated with the chronic 96/72h NOEC in Daphnia magna; ^c Calculated with the chronic 21 days NOEC in fish. Values obtained from PPDB database [28].

summarizes the predicted no-effect concentrations (PNECs) and corresponding RQ_max values for algae, Daphnia magna, and fish during each sampling campaign. Wherever available, NOEC (No Observed Effect Concentration) values were prioritized over EC (Effect Concentration) values in deriving PNECs.

Overall, fungicides exhibited lower RQ values compared to insecticides, while herbicides showed the highest RQs. None of the detected fungicides posed an unacceptable ecological risk (RQ > 1) for the three aquatic organisms, which is likely attributable to their relatively low environmental concentrations. Only tebuconazole showed medium risk (0.1 < RQs < 1) for Daphnia magna and fish and low risk (0.01 < RQs < 0.1) for algae in June. Among insecticides, two compounds presented minimal risk (RQs < 0.01) for all organisms, whereas chlorpyrifos presented high risk for fish, medium for Daphnia magna and low for algae during April, June and July.

Herbicides recorded the highest RQ risk value, with OA-metolachlor reaching 179.6 and the highest number of unacceptable RQ values for Daphnia magna and algae in April, June and July. Metribuzin exceeded acceptable levels for algae in June and July. While pendimethalin recorded RQs higher than 1 in July for algae and fish. Chlorotoluron and the degradation product of terbuthylazine, DETbz, also exceeded RQ= 1 in one sample for algae. Herbicides with minimal or low risk (RQs < 0.01 or 0.01 < RQs < 0.1) included pyrimidinol and bentazone (minimal) and atrazine and the degradation products HTbz and OA-metolachlor (low or minimal).

Overall, the chronic risk values in this study were comparable to those reported in Greece [38], where the groundwater wells were considered safe. These values were lower than those observed in groundwater of Romania [52], where several pesticides exhibited RQ > 10, and Morocco [53], where most pesticides exceeded RQ = 1. The RQ values were also lower than those reported for surface waters in France [47] and in England [51], where high risks were observed.

Although no acute risk was identified, some herbicides may pose chronic risks under repeated or long-term exposure. This highlights the need for environmental management strategies beyond acute toxicity criteria. Our results suggest that continuous monitoring of sublethal concentrations and consideration of cumulative or subchronic effects should be incorporated into risk assessment frameworks. Current pesticide regulations, largely based on acute toxicity thresholds, may not adequately address chronic exposure risks, particularly in areas of intensive use or persistent compounds. Therefore, these findings support incorporating chronic risk criteria in regulatory assessments and promoting the use of less persistent, lower-toxicity compounds within integrated pest management programs.

4. Conclusions

This study provides an integrated evaluation of pesticide occurrence, distribution, and ecological risks in groundwater of the Duero River basin, an area strongly influenced by intensive agricultural practices, mainly focused on potato and maize cultivation. The results show that groundwater is impacted by a diverse mixture of pesticides and their degradation products, reflecting current agrochemical use patterns in the region. Metolachlor and its transformation products were consistently the predominant contaminants, underscoring the connection between local application practices and groundwater pollution in agricultural areas.

Although no acute ecological risks were identified, several herbicides—particularly transformation products—showed indications of chronic risk for sensitive aquatic organisms. These findings highlight the relevance of including both parent compounds and metabolites in groundwater monitoring and risk assessment frameworks.

Overall, this study underscores the persistence and environmental significance of certain herbicides and their degradation products in groundwater from intensively agricultural regions. The findings highlight the importance of integrating groundwater monitoring into broader water management and pesticide regulation frameworks, particularly in areas with intensive agricultural activity.

Future research should focus on long-term monitoring of pesticide residues and their metabolites in groundwater, considering seasonal variability and the influence of changing agricultural practices. In addition, modeling studies assessing the transport and persistence of these compounds in the subsurface environments, as well as investigations into alternative lower impact pest management strategies are recommended. Such approaches will contribute to improved risk assessment and more sustainable pesticide use in vulnerable aquifer systems across Europe.