Characteristics of Translocation, Distribution, and Transformation of the Nematicide Fluopyram in Cucumber and Tomato Seedlings and Risk Assessment Based on QSAR Model Prediction

Abstract

Fluopyram is a widely used nematicide with a growing number of varieties registered both domestically and overseas. However, its absorption, transportation, and metabolism behaviors in plants have not been fully elucidated, thus hindering comprehensive assessment of the risks associated with its use. This study investigated the plant uptake, distribution, and metabolic behavior of fluopyram through 168 h hydroponic experiments. Fluopyram was easily absorbed by the roots of the tested crops, and almost 90.5% and 70.9% of fluopyram was transformed in cucumber and tomato, respectively, leading to the tentative identification of 16 metabolites using Quadrupole Time-of-Flight mass spectrometry. The metabolic reactions involved were hydroxylation, hydroxylation–dechlorination, dehydrogenation, dechlorination, and glucuronidation conjugation. Most metabolites were detected in leaves, suggesting that they have considerable potential to accumulate in the upper parts, even the edible parts. Model prediction indicated that fluopyram and high-toxicity metabolites (M430A, M412C) pose significant risks to aquatic ecosystems across trophic levels, while M574A and M574B showed reduced toxicity due to glucuronidation conjugation. These findings deepen our understanding of the behavioral characteristics of fluopyram within plants, and serve as an important reference for comprehensively assessing its risks.

1. Introduction

Plant-parasitic nematodes rank among the most devastating plant pathogens globally, posing severe challenges to food security. Chen et al. estimated that these nematodes cause global annual agricultural losses of around $157 billion across key crop production systems, highlighting the severe threat they pose to food security [1]. Fluopyram (N-{2-[3-Chloro-5-(trifluoromethyl)pyridin-2-yl]ethyl}-2-(trifluoromethyl)benzamide) is a highly efficient succinate dehydrogenase inhibitor nematicide developed by Bayer CropScience [2]. Characterized by outstanding nematicidal activity, higher prevention rates, and lower resistance compared to other nematicides, fluopyram is frequently used today, with an increasing number of varieties registered both domestically and overseas; in addition, it has increasingly been employed in China to control intractable and devastating root-knot nematodes occurring in greenhouse-grown cucumber and tomato seedlings, among other crops. After being applied to the soil via drenching, fluopyram forms a protective zone around the root system, exerting excellent nematicidal activity [3]. However, this application method can result in high residual fluopyram being absorbed by the roots and transmitted to the upper parts of the plant, even the edible parts, due to its high systemicity, as well as the formation of toxic/non-toxic metabolites within the plants, thus raising concerns about its environmental fate and potential risks.

Currently, analytical research on fluopyrams primarily focuses on its residues with or without metabolites in edible matrices, as well as environmental samples. For example, Feng et al. simultaneously determined the residues of fluopyram and metabolite M25 in melons using QuEChERS combined with LC-MS/MS [4], and Cui et al. reported that higher fluopyram residues occurred in cowpeas compared with cucumbers; moreover, they found that fluopyram dissipated faster in cucumbers, indicating a need for continuous assessment of fluopyram residues and the potential effects of its accumulation [5]. In addition, Yang et al. reported a higher fluopyram risk quotient of 23–40% for root irrigation compared to foliar spray [6]. Regarding the environmental behavior of fluopyram, Zhou et al. evaluated the transport capacity of fluopyram in three soils, and found that it exhibited high adsorption and low leaching potential in all of them [7]. Yun and Choi reported an 87–231-day half-life of fluopyram in soil, and found that its uptake by scallion roots increased over time [8], meanwhile, Wei et al. identified a maximum half-life of 5.7 d and three metabolites were reported for fluopyram in fruit vegetables using GC-MS/MS [9]. Understanding how pollutants, including pesticides and their transformation products, behave in plants is key to conducting comprehensive assessments of environmental fate and risk. However, the adsorption, translocation, and accumulation of fluopyram in plants are relatively rarely reported.

In recent decades, the widespread utilization of high-resolution mass spectrometry (HR-MS) has significantly advanced metabolite identification in pesticide research [10,11,12]. Specifically, HR-MS enables precise acquisition of data on parent pesticides and metabolites, ensuring the accuracy of key analytical processes, including mass calculation, elemental composition confirmation, structural feature inference, and transformation pathway deduction; this is consistent with the technical advantages of HR-MS highlighted in our prior work [13]. Lu et al. successfully identified nine isoproturon metabolites in wheat via UPLC-TOF-MS [14], while Zhang et al. uncovered 10 atrazine degradation products in alfalfa using the same technique [15]. In addition, using HRLC-Q-TOF-MS, Zhang et al. identified ten I-phase metabolites and eight II-phase conjugates as the main products of acetochlor degradation by soil microorganisms [16].

In this study, a hydroponic experiment was conducted to investigate the uptake, translocation, and transformation of fluopyram in cucumber (genus Cucumis, family Cucurbitaceae) and tomato (genus Solanum, family Solanaceae) seedlings. They were chosen as model horticultural crops not only because they are widely cultivated greenhouse crops that are prone to severe root-knot nematode infestations and frequently subjected to fluopyram use, but also because they possess contrasting physiological characteristics relevant to pesticide behavior. Differences in root morphology, lipid content, and transpiration intensity between these two species are known to influence the absorption and internal transport of systemic agrochemicals. Consequently, this study provides a practical and comparative framework for examining species-dependent patterns of fluopyram behavior under controlled conditions with the use of HR-MS technology enabling the screening and identification of transformation products. This research offers valuable perspectives on the comparable fate characteristics of fluopyram and its metabolites in target crops, and improves our understanding of the potential risks of fluopyram exposure through the food chain.

2. Materials and Methods

2.1. Reagents and Materials

Fluopyram (CAS: 658066-35-4, purity: 99.6%) was purchased from Beijing Qinchengyixin Technology Development Co., Ltd. (Beijing, China); acetonitrile, methanol (≥99.9% purity), formic acid (≥98.0% purity) (Thermo Fisher, Waltham, MA USA) (HPLC-grade), and sodium chloride (NaCl) (analytical grade) were obtained from Guangdahengyi Technology Co. (Beijing, China); and a nylon syringe filter equipped with a 0.22 µm membrane was obtained from Agela Technologies (Tianjin, China).

2.2. Plant Cultivation and Exposure Experiments

Cucumber (Jingyanxiamei F₁) and tomato (Yingfen No. 8) seeds were acquired from JingyanYinong (Beijing, China) Seed Sci-Tech Co., Ltd. (Beijing, China). Seed germination was carried out in Petri dishes filled with sterile damp filter paper, maintained at a constant temperature of 25 ± 1 °C. After 3 days of germination, the seedlings were transferred to 24-hole hydroponic containers holding 10 L of modified Hoagland nutrient solution (pH = 7.0, EC: 800–1000 μS/cm), with the formula adapted from our previous study [17]. Seedlings were subjected to secondary transplantation when the third true leaf on the main stem was fully expanded to ensure uniform growth status.

For subsequent hydroponic cultivation, the seedlings were transferred to containers holding 0.6 L of the same nutrient solution, with supplementary solution added periodically to maintain stable growth conditions. All plants were cultured in an environmentally controlled greenhouse (16 h light/8 h dark cycle, relative humidity 60–70%), and growth parameters were recorded throughout the experimental period.

Robust cucumber (n = 24) and tomato seedlings (n = 24) exhibiting uniform growth were separately transplanted into hydroponic containers filled with 0.6 L of the nutrient solution spiked with fluopyram at a theoretical concentration of 0.5 mg/L. A fluopyram control group without seedlings was established to detect the depletion of fluopyram in the nutrient solution. A solvent control group was established to verify whether the solvent acetonitrile had an adverse influence on the plants, and a blank control group was established to detect potential contamination during the experiment. The volume of the nutrient solution in each treatment was regularly monitored, and the initial volume was replenished over time.

In the exposure experiment, both cucumber and tomato seedlings were sampled at 0, 1, 3, 7, 10, and 14 days. Three individual plants were taken out from each bucket, and their roots were rinsed with purified water and then dried using absorbent paper. The rinse water was mixed with the nutrient solution for each group, and the nutrient solutions (volume: 2.0 mL) were then sampled from each group. The plant tissue samples were packaged, weighed, and stored at a temperature of −20 °C. Once they had frozen, they were ground with liquid nitrogen to a solid powder.

2.3. Sample Extraction and Analysis

Each 2.0 g plant sample was mixed with 2.0 mL acetonitrile and subjected to 10 min of oscillation for extraction. Subsequently, 0.4 g of NaCl was added to the mixture, which was then agitated for 5 min to promote phase separation before centrifugation at 4000 rpm for 5 min. The resulting supernatant samples were filtered through a 0.22 μm membrane and then transferred to sample vials. For 2.0 mL solution samples, 2.0 mL of acetonitrile was added to each sample, and the processes of mixing, salting out, centrifugation, and filtration were conducted according to the steps described above. All sample extracts were utilized in duplicate for instrumental analyses of fluopyram and its potential products.

The parent fluopyram was determined using a Waters ACQUITY UPLC system coupled with a triple-quadrupole tandem mass spectrometer (MS) (Waters Corp., Milford, MA, USA). Chromatographic separation was achieved using an analytical column (Waters ACQUITY BEH C₁₈; 2.1 × 100.0 mm, 1.7 μm), and acetonitrile (A) and aqueous solution spiked with 0.1% formic acid (B) were employed as the mobile phase. The gradient elution program, which was the same as our prior study [17], was as follows: 10% A from 0 to 1.1 min; 90% A from 1.1 to 4.1 min; and 10% A from 4.1 to 5.0 min. The sample extracts with 1 mL were injected, and the temperatures of the column and samples were set to 40 °C and 20 °C, respectively. MS analysis was conducted in positive electrospray ionization (ESI+) mode. The multiple reaction monitoring parameters for fluopyram were optimized as follows: a precursor ion of m/z 396.9 with a cone voltage of 40 V; a qualitative ion of m/z 172.9 with a collision energy of 46 V; and a qualitative ion of m/z 144.8 with a collision energy of 80 V (Table S1). The MS parameters were configured as follows: source temperature of 150 °C, desolvation temperature of 350 °C, capillary voltage of 3.5 kV, nitrogen cone gas flow rate of 50 L/h, and nitrogen desolvation gas flow rate of 700 L/h. Data analysis was conducted using MassLynx v4.2 software.

Fluopyram metabolites were identified via HPLC-Q-TOF-MS (Agilent Corp., Santa Clara, CA, USA), and an analytical column (Waters ACQUITY HSS T3; 2.1 mm × 100 mm, 1.8 μm) was adopted for the chromatographic separation of potential analytes with the injection volume set to 5 μL. The gradient elution program was as follows (A: 0.1% formic acid dissolved in pure water; B: acetonitrile): 90% A from 0.0 to 1.0 min; 90–10% A from 1.0 to 7.0 min; 10% A from 7.0 to 10.5 min; 10–90% A from 10.5 to 11.0 min at a 0.3 mL/min flow rate for 11 min. For metabolite identification, the samples were initially analyzed in full-scan mode. Subsequently, the data were analyzed using Metabolite ID 4.0 software (Agilent), and a list of potential metabolites was obtained through molecular feature analysis, binary comparison, isotope distribution, mass defect filtration, and other algorithms. Target MS/MS analysis was carried out using HPLC-Q-TOF, and the structure of metabolites was further deduced via secondary mass spectrometry. The MS scanning conditions were as follows: an electrospray ion source (Dual AJS ESI, Agilent Corp., Santa Clara, CA, USA) with an internal table reference ion (m/z = 121.05, 922.00) for accurate mass number correction, a gas temperature of 280 °C, a drying gas flow rate of 7.0 L/min, a capillary voltage of 3.0 kV, an atomizer pressure of 30.0 psi, a sheath gas temperature of 370 °C, a sheath gas flow rate of 12.0 L/min, and a fragmentor voltage of 120 V. The MS scanning acquisition mode conditions were as follows: a scanning range of 100–1000 m/z and a full-scanning speed of 1 spectrogram/s. In the targeted MS/MS acquisition mode, the targeted MS was identified with Metabolite ID software; the spectrograms/MS scanning range was 60–1000 m/z, the full-scanning speed was 5 spectrogram/s, and the secondary mass spectrogram scanning speed was 2 spectrogram/s.

2.4. Quality Assurance and Quality Control

A recovery experiment was carried out to validate the accuracy of fluopyram determination in hydroponic solution, as well as in the roots, stems, and leaves of cucumber and tomato plants. Each 2.0 g blank plant sample or 2.0 mL blank solution sample was spiked with an appropriate volume of standard fluopyram, vortexed for 30 s, and then equilibrated at room temperature for 2 h. The sample extraction and analysis were conducted in the same manner as the procedure described in Section 2.3. Fortification levels of 10, 100, and 500 μg/L (for hydroponic solution) or μg/kg (for plant tissues) were set, with spiked samples analyzed in quintuplicate. As listed in Table S2, the average fluopyram recovery rates were in the range of 95.6–116.4% for all matrices, with relative standard deviations all below 8.8%, demonstrating high accuracy and precision. The coefficients of determination (all exceeding 0.99), as well as the results of the residual analysis and lack-of-fit test, demonstrated excellent linearity for all calibration curves, with concentration ranges of 5–500 μg/L. The limits of quantification for fluopyram were 10 μg/L in solutions, and that was 10 μg/kg in plant tissues; these values were defined as the lowest spiked fluopyram concentrations for the test matrices. To ensure proper quality control, the blank solvent and procedural blank, as well as the matrix-spiked sample and matrix-dependent standards, were simultaneously analyzed for all 12 samples. The fluopyram concentrations in the samples were calibrated using the matrix-dependent standard curves. In ESI+ mode, a reference solution that produced reference ions (m/z 121.05 and m/z 922.00) was periodically injected to ensure accurate measurements of molecular mass during analysis. Routine instrumental calibration was performed to detect potential mass errors.

2.5. Data Calculations and Analysis

The root concentration factor (RCF) serves as an indicator of a plant’s capacity to take up organic substances from its surrounding environment, while the translocation factor (TF) quantifies its ability to transport organic compounds between different tissues (e.g., root to stem, stem to leaf). The RCFs and TFs were calculated using the following formulas:

RCF (mL/g) = C_root (μg/g)/C_solution (mg/L)

(1)

TF_stem/root = C_stem (μg/g)/C_root (μg/g)

(2)

TF_leaf/stem = C_leaf (μg/g)/C_stem (μg/g)

(3)

where C_solution is the fluopyram concentrations in nutrient solution (mg/L), and C_root, C_stem, and C_leaf are the fluopyram concentrations in cucumber and tomato roots, stems, and leaves (μg/g). The statistical analysis was performed using the IBM SPSS Statistics 23.0, and the p values for continuous variables were analyzed using the non-parametric Mann–Whitney U test (n = 2) or the Kruskal–Wallis one-way ANOVA test (n > 2). Two-tailed p values < 0.05 were considered statistically significant.

3. Results and Discussion

3.1. Fluopyram Elimination Kinetics in Nutrient Solution

As shown in Figure 1A, the fluopyram concentration remained stable in the unplanted fluopyram control group throughout the experiment period, indicating the relative stability of fluopyram in the nutrient solution. In the planted cucumber solution, the concentration of fluopyram progressively decreased (Figure 1A), reaching 70.0% of the initial level after 1 day and 3.8% by the end of the experiment. The decrease in fluopyram in the planted cucumber solution followed first-order kinetics with a good fit (R² = 0.96), and the calculated degradation half-life (t_1/2) of fluopyram was 1.2 days.

As illustrated in Figure 1B, the concentration of fluopyram in tomato plant nutrient solutions decreased over time (Figure 1B). On the first day of the experiment, an average of 94.6% of the initial fluopyram concentration was detected, which dropped to 21.4% by the end. The dissipation dynamics of fluopyram in tomato plant nutrient solutions also followed a first-order pattern and were well-fitted (R² = 0.97), and the calculated t_1/2 for fluopyram was 6.6 d. These findings indicate continuous uptake of fluopyram by cucumber and tomato roots.

3.2. Root Uptake of Fluopyram

The fluopyram concentrations in the roots of cucumber and tomato seedlings are illustrated in Figure 2. The RCF, which reflects a plant’s potential to absorb organic compounds from the environment, was calculated as the root-to-solution concentration ratio. The RCFs for fluopyram at various experimental time points are shown in Figure 3A.

After 1 day, fluopyram concentrations in cucumber roots peaked at 1221.9 ± 21.4 μg/kg (Figure 2A), and then gradually declined, eventually reaching a minimum of 186.5 ± 13.6 μg/kg by the end of the experiment. This trend could be attributed to the initial uptake of fluopyram by cucumber roots, followed by a reduction as the fluopyram levels in the nutrient solution diminished over time; alternatively, it could be attributed to translocation, the dilution effects of plant growth, or transformation. In cucumber plants (Figure 3), the average RCF values for fluopyram progressively increased, starting at 3.4 mL/g on day 1 and reaching 9.8 mL/g by day 16.

As shown in Figure 2B, the fluopyram concentration in tomato roots was 753.9 ± 10.4 μg/kg after 1 day, and reached a maximum of 1739.6 ± 14.1 μg/kg at 7 days. After this peak, the fluopyram concentration in the roots steadily decreased, dropping to a minimum of 559.9 ± 15.4 μg/kg by the end of the exposure experiment. For tomato plants (Figure 3A), the average RCF values for fluopyram exhibited much lower fluctuations compared to those of cucumber plants, remaining within the range of 1.6–7.8 mL/g throughout the experimental period. These results show that fluopyram can be taken up by cucumber and tomato roots, aligning with the results of Yun and Choi, who reported that fluopyram uptake from the soil by scallion roots increased over time [8].

3.3. Translocation of Fluopyram Within Plants

The concentrations of fluopyram in plant stems and leaves are illustrated in Figure 2. As shown in Figure 2A, fluopyram was detected in cucumber stems and leaves, indicating that the absorbed fluopyram was translocated from the roots to upper parts of the plants, possibly via transpiration. The fluopyram concentration in the stem samples reached a maximum of 1062.1 ± 13.1 μg/kg at 1 d and then decreased over time. At 14 d, the fluopyram concentration in the cucumber stems was 195.3 ± 10.7 μg/kg. The fluopyram concentration in the leaves gradually increased, reaching a maximum of 655.2 ± 14.6 μg/kg at 3 d, followed by a slight decrease.

Meanwhile, it was observed that fluopyram was translocated within tomato plant tissues, with its concentration in tomato stems increasing to 499.2 ± 18.0 μg/kg at 3 d (Figure 2B), then decreasing to 350.2 μg/kg by the end of the experiment. The fluopyram concentration in tomato leaves first increased and then stabilized, with a resulting concentration of 409.1 ± 16.3 μg/kg (Figure 2B).

The TFs of fluopyram at various experimental time points are summarized in Figure 3B,C. The TF values for fluopyram in cucumber plants varied from 0.6 to 1.2, showing a gradually declining trend (Figure 3B). However, fluopyram exhibited limited root-to-stem translocation in tomato plants, with none of the TF values exceeding 0.6 (Figure 3C).

As defined by Bhalsod et al., a TF ≥ 1 indicates efficient transport of compounds from roots to shoots in plants, while a TF < 1 suggests limited upward translocation [18]—this criterion is well-suited for evaluating the translocation characteristics of fluopyram in cucumber and tomato seedlings. Since fluopyram has low water solubility with a log_KOW value of 3.3, it is enriched in tomato roots and not easily transferred upward [19,20]. Similar results were observed for the absorption mode of azoxystrobin, with a log _KOW value of 2.5 in wheat [21], and that of difenoconazole, with a log _KOW value of 3.2 in Chinese cabbage [22] and rice [23].

Conversely, following a 7-day period, the TFs for cucumber seedlings exceeded 1, suggesting that fluopyram can be readily translocated from their roots to their stems and then gradually accumulate in the upper parts of the plant. The disparity in lipid content between tomato and cucumber roots may explain the divergent transport rates of fluopyram in these two plant species. Chen et al. found that benzene kresoxim-methyl moves more easily in vegetables with lower root lipid content [24], while Chiou et al. noted that lipid content influences uptake process, for organic substances with log_KOW > 3, while water and carbohydrate content have little effect [25]. It is hypothesized that the differences in fluopyram metabolism between the two plants may be related to the inconsistent translocation of fluopyram, which necessitates further elucidation in future research.

3.4. Distribution of Fluopyram in the Plant–Solution Systems

The contents of fluopyram in various plant parts were calculated separately by integrating the content measured in the plant with accurate measurements of plant biomass. Figure 4 presents the percentage distributions of fluopyram in the roots, stems, leaves, and nutrient solution, as well as the loss of fluopyram in the system at different experimental time points.

Figure 4A indicates that prior to 3 days, fluopyram was mostly distributed in the cucumber nutrient solution; however, its proportion in the solution subsequently decreased from 68.0% to 3.0%, and that in the roots reached a maximum of 2.8% at 1 day and a minimum of 0.8% at 14 days. In the cucumber stems, the fluopyram proportion remained stable, accounting for 1.8–2.2% of the total fluopyram mass in the system, while it increased over time in the leaves. A mass loss of 24.0% occurred in the hydroponic cucumber–solution system after 1 day of exposure, and the fluopyram proportion gradually increased over time, peaking at 72.0% by the end of the exposure experiment.

As shown in Figure 4B, the fluopyram proportion in the tomato nutrient solution decreased from 94.6% at 1 day to 21.3% at 14 days. The proportion of fluopyram in tomato roots increased from 0.9% at 1 day to a maximum value of 2.7% at 10 days, and then declined to 1.8% at 14 days, and its distribution in the stems increased from 1.5% at 1 day to a maximum value of 3.7% at 14 days. The proportion of fluopyram in the tomato system increased over time and reached approximately 70.9% loss at 14 days.

Significant mass loss of fluopyram suggests the occurrence of extensive fluopyram biotransformation in both the cucumber and tomato plant systems, which provides a basis for subsequent metabolite identification.

3.5. Identification of Metabolites

The plant and nutrient solution samples were analyzed using HPLC-Q-TOF-MS. The obtained data underwent non-targeted screening to identify unknown fluopyram metabolites. In total, sixteen metabolites (M430A, M412A, M394A, M378A, M412B, M430B, M412C, M362, M412D, M394B, M378B, M378C, M412E, M426, M574A, M574B) were tentatively identified in hydroponic vegetable systems (

Table 1. Identified transformation products for fluopyram within plants.

Compound	Retention Time (min)	Calculated [M + H] m/z	Estimated [M + H] m/z (Mass Error, ppm)	Formula	Characteristic Fragments m/z (Formula, Mass Error, ppm)	Metabolic Reaction	Sources	Structure (Tentative)	Confidence Level a
Parent	7.62	397.0537	397.0535 (−0.5)	C16H11ClF6N2O	173.0211 (C8H4F3O, 1.3) 208.0137 (C8H6F3NCl, 0.8) 145.0260 (C7H4F3, 0.3) 190.0475 (C8H7F3NO, 0.4)	-	-		-
M430A	5.60	431.0592	431.0591 (−0.2)	C16H13ClF6N2O3	190.0468 (C8H7F3NO, 0.4) 173.0214 (C8H4F3O, 3.1) 242.0196 (C8H7ClF3NO2, 2.5) 224.0079 (C8H5ClF3NO, −2.7) 212.0082 (C7H6ClF3NO, −1.2)	Hydroxylation	Cucumber leaf Tomato leaf		2b
M430B	6.69	431.0592	431.0591 (−0.2)	C16H13ClF6N2O3	190.0484 (C8H7F3NO, 0.4) 173.0214 (C7H6ClF3NO, 0.7) 242.0196 (C8H7ClF3NO2, 2.5) 224.0087 (C8H5ClF3NO, 0.9) 212.0086 (C7H6ClF3NO, 0.7)	Hydroxylation	Cucumber leaf Tomato leaf		2b
M412A	5.65	413.0486	413.0483 (−0.7)	C16H11ClF6N2O2	190.0484 (C8H7F3NO, 0.4) 173.0214 (C8H4F3O, 3.1) 395.0386 (C16H11ClF6N2O, 1.5) 224.0090 (C8H5ClF3NO, 2.2)	Hydroxylation	Cucumber leaf Tomato leaf		2b
M412B	6.59	413.0486	413.0483 (−0.7)	C16H11ClF6N2O2	173.0214 (C8H4F3O, 3.1) 224.0087 (C8H5ClF3NO, 0.9)	Hydroxylation	Cucumber leaf Tomato leaf		3
M412C	6.89	413.0486	413.0483 (−0.7)	C16H11ClF6N2O2	224.0085 (C8H5ClF3NO, 0) 208.0141 (C8H6F3NCl, 2.9) 190.0474 (C8H7F3NO, −0.1) 173.0214 (C8H4F3O, 3.1) 395.0386 (C16H11ClF6N2O, 1.5)	Hydroxylation	Cucumber leaf Cucumber root Tomato leaf		3
M412D	7.01	413.0486	413.0483 (−0.7)	C16H11ClF6N2O2	208.0141 (C8H6F3NCl, 2.9) 196.0141 (C7H6F3NCl, 3.1) 189.0163 (C8H4F3O2, 2.7) 161.0214 (C7H5F3O, 3.1) 225.0406 (C8H9ClF3N2, 2.3)	Hydroxylation	Cucumber leaf Cucumber root Tomato leaf		3
M412E	7.12	413.0486	413.0483 (−0.7)	C16H11ClF6N2O2	189.0163 (C8H4F3O2, 2.7) 208.0141 (C8H6F3NCl, 2.9)	Hydroxylation	Cucumber leaf Cucumber stem		3
M378A	5.93	379.0876	379.0875 (−0.3)	C16H12F6N2O2	190.0473 (C8H7F3NO, −0.7) 173.0214 (C8H4F3O, 3.1)	Hydroxylation dechlorination	Cucumber leaf Tomato leaf		3
M378B	7.06	379.0631	379.0631 (0)	C16H12ClF5N2O	173.0214 (C8H4F3O, 3.1) 190.0235 (C8H7F2ClN, 2.9)	Defluorination	Cucumber leaf		2b
M378C	6.56	379.0876	379.0875 (−0.3)	C16H12F6N2O2	190.0475 (C8H7F3NO, 0.4) 173.0214 (C8H4F3O, 3.1)	Hydroxylation dechlorination	Cucumber leaf Tomato leaf		3
M394A	5.87	395.038	395.0376 (−1.0)	C16H9ClF6N2O	173.0214 (C8H4F3O, 3.1) 145.0263 (C7H4F3, 2.4)	Dehydrogenation	Cucumber leaf Tomato leaf		2b
M394B	7.02	395.038	395.0376 (−1.0)	C16H9ClF6N2O	173.0214 (C8H4F3O, 3.1) 145.0263 (C7H4F3, 2.4)	Dehydrogenation	Cucumber leaf Tomato leaf		2b
M426	7.60	427.0643	427.0640 (−0.7)	C17H13ClF6N2O2	203.0314 (C9H6F3O2, 0) 208.0141 (C8H6F3NCl, 2.9) 220.0585 (C9H6F3O2N, 2.3)	Hydroxymethylation	Tomato solution Cucumber root		2b
M362	6.94	363.0927	363.0924 (−0.8)	C16H12F6N2O	174.0531 (C8H7F3N, 3.4) 173.0214 (C8H4F3O, 3.1)	Dechlorination	Cucumber leaf Tomato leaf		2b
M574A	5.60	575.1014	575.1012 (−0.3)	C22H21ClF6N2O7	413.0489 (C16H11ClF6N2O2, 0.7) 395.0392 (C16H11ClF6N2O, 3.0) 173.0220 (C8H4F3O, 3.5)	Phase II metabolites	Cucumber leaf Cucumber root Tomato leaf		2b
M574B	5.88	575.1014	575.1012 (−0.3)	C22H21ClF6N2O7	395.0379 (C16H11ClF6N2O, −0.3) 173.0221 (C8H4F3O, 4.0)	Phase II metabolites	Cucumber leaf Cucumber root Tomato leaf Tomato root		2b

^a The confidence levels of compounds were evaluated by Schymanski et al. [28].

). All 16 metabolites were detected in ESI+ mode and had shorter retention times than fluopyram, indicating higher polarity than the parent compound [26,27]. Of the 16 tentatively identified metabolites, all were phase I products apart from M574A and M574B, which were phase II metabolites.

As summarized in Table 1, M430A and M430B were tentatively identified as hydroxylation products, with identical fragment ion data (m/z 190.0468, 173.0214, 242.0196, 224.0090, 212.0090) but distinct retention times (5.60 and 6.69 min), indicating that they are isomers of each other. The fragment at m/z 242 suggested that hydroxylation occurred on the pyridine ring. Complementary to fragment m/z 190, the formation of m/z 224 was due to the neutral loss of water (H₂O) and the subsequent cleavage between the C=C bond, producing m/z 212. Products M430A and M430B had the same spectral features but different elution capacities; thus, they were deduced to be isomers with level 2b confidence by referring to the study by Schymanski et al. [28].

The metabolic behavior differed among plant species. M430A, M430B, M412A, M412B, M412C, M412D, M378A, M378C, M394A, M394B, M362, M574A, and M574B were all detected in the leaves of the two vegetables; however, M378B (level 2b confidence) was only present in cucumber leaves, and M225 and M387 were only found in tomato leaves. In addition, M412C, M412D (level 3 confidence), M426, M574A, and M574B (level 2b confidence) were found in cucumber roots, and M574B was found in tomato roots, while the hydroxylate product M412E (level 3 confidence) was only identified in cucumber leaves and stems. The representative chromatograms and mass spectra of these metabolites are shown in Figure S1.

In the hydroponic systems for growing cucumber and tomato seedlings, fluopyram underwent multiple metabolic reactions, including hydroxylation, hydroxylation–dechlorination, dehydrogenation, dechlorination, and glucuronidation conjugation, which is consistent with our previous observations on pesticide metabolism in vegetables [12]. Based on comparison with previously reported fluopyram metabolites in plants and environmental matrices, several phase I transformation products identified in this study, particularly those formed via hydroxylation, dechlorination, and dehydrogenation, are consistent with metabolic patterns reported for fluopyram or structurally related succinate dehydrogenase inhibitors. Alarmingly, a subset of the detected products (M378B, M426, M574A, and M574B) was identified for the first time in hydroponically cultivated vegetable systems, with no previous reports in crop metabolism studies of fluopyram. Notably, the majority of these metabolites were detected in leaves, implying a significant risk of translocation to the edible parts of the two crops. Figure 5 illustrates the proposed metabolic pathway of fluopyram in the hydroponic systems.

Phase I metabolism is primarily mediated by plant enzymes, among which the metabolic processes were found to be dominated by the cytochrome P450 enzyme [29]. Usually, phase II metabolites in plants are formed through the conjugation reaction between phase I metabolites and endogenous substances, such as glucose, amino acids, and glutathione [13,30]. Glycosyl transferases (GTs) are often involved in phase II metabolic reactions and catalyze the transfer of sugar groups from the donor to acceptor molecules [31,32]. Lu et al. showed that isoproturon metabolites in wheat were primarily glycosylated metabolites [14], while Zhang et al. found that GTs were involved in the detoxification of atrazine in medicago sativa, and detected two O-glycosylated metabolites and homoglutathione (hGSH) conjugates [15]. Our research showed that M574A and M574B were the metabolites of M394A and M394B conjugated with glucose, respectively.

In this study, cucumber and tomato serve as representative greenhouse vegetables; thus, the applicability of the present findings to other horticultural species should be interpreted cautiously. The identified uptake patterns and major metabolic reactions of fluopyram, such as hydroxylation, dechlorination, and conjugation, are likely to occur in a broad range of dicotyledonous crops. However, the magnitude of fluopyram accumulation, the translocation efficiency, and the relative abundance of specific metabolites may vary among plant species due to differences in anatomical structure, lipid composition, enzymatic activity, and growth dynamics. Therefore, while cucumber and tomato provide useful model systems for understanding the behavior of fluopyram in vegetables, further investigations involving additional crop species are required to support broader generalization.

Moreover, this study systematically assesses the fate characteristics of fluopyram in vegetable seedlings under controlled hydroponic conditions. The use of a simplified exposure system reduces environmental interference and facilitates clear interpretation of plant-specific processes. Focusing on the seedling stage enables detailed observation of early uptake of and metabolic responses to root-applied fluopyram during crop establishment. However, experimental exposure systems cannot fully reflect the complexity of agricultural soils. Moreover, metabolic behavior and accumulation patterns may change during later growth stages or prolonged exposure. Future research should extend the present work to field-based or soil-cultivation systems, cover longer exposure periods, and examine the behavior of fluopyram across different developmental stages.

In addition, in this study, Q-TOF-MS was utilized for the detailed identification of fluopyram metabolites due to its high resolution and sensitivity, which are essential for detecting metabolites at low concentrations. However, despite these significant advantages, Q-TOF-MS is highly dependent on sample preparation and is susceptible to matrix effects, which can hinder the detection of certain metabolites. Additionally, non-targeted analyses may lead to false positives or missed identifications, especially when metabolites have similar mass-to-charge ratios. To mitigate these challenges, rigorous quality control measures and matrix-spiked samples were applied in this study. In future studies, combining Q-TOF-MS with high-sensitivity multi-dimensional chromatography or mass spectrometry instruments could further enhance the accuracy and comprehensiveness of metabolite identification.

3.6. Toxicity of Fluopyram and Its Metabolites

Experimental risk assessments are generally time-consuming, costly, and equipment-dependent; therefore, there is an urgent need for a convenient, cost-effective, and convenient computational approach to toxicity screening and evaluation [32]. The developed ECOSAR program has been proven to be a reliable method for theoretically predicting the toxicities of transformation products [33,34]; it has attracted increasing attention from researchers and has become widely applied for predicting the toxicity of a variety of compounds [35]. The experimental residue data generated in this study provide an essential empirical basis for interpreting the QSAR-based risk predictions. The measured uptake and translocation patterns demonstrate that fluopyram and several transformation products can be effectively transported from roots to aboveground tissues, indicating their potential availability for subsequent release into the environment or entry into the food chain. In particular, the substantial formation of multiple metabolites and their preferential accumulation in leaves highlight the most relevant compounds for toxicity prediction, rather than relying solely on the parent compound.

Based on the fluopyram metabolites identified above, further risk assessment was carried out using ECOSAR v1.11 (this software was employed due to the lack of standardization for metabolites), primarily focusing on predicting their acute and chronic toxicity to three representative aquatic organisms (fish, daphnids, and green algae) located at different trophic levels. The key toxicity endpoints were the 96 h LC₅₀ for fish, 48 h LC₅₀ for daphnids, and 96 h EC₅₀ for green algae. The predicted acute and chronic toxicity results for fluopyram and its metabolites are summarized in

Table 2. ECOSAR predictions of toxicities of fluopyram and its metabolites.

	Predicted Acute Toxicity (mg/L)			Predicted Chronic Toxicity (mg/L)
Compound	Fish LC50 (96 h)	Daphnid LC50 (48 h)	Green Algae EC50 (96 h)	Fish ChV	Daphnid ChV	Green Algae ChV
fluopyram	1.209	0.369	0.069	0.011	0.13	0.211
M430A	0.339	0.328	1.005	0.024	0.057	0.723
M412A	9.969	8.73	6.16	1.155	1.993	4.341
M412B	1.27	1.067	3.922	0.18	0.202	1.79
M412C	0.342	0.401	1.37	0.054	0.076	0.619
M412D	1.27	1.067	3.922	0.18	0.202	1.79
M378A	0.967	0.852	3.101	0.139	0.161	1.413
M378B	3.139	1.268	0.158	0.02	0.299	0.372
M378C	0.967	0.852	3.101	0.139	0.161	1.413
M394A	1.713	0.577	0.094	0.014	0.177	0.261
M426	0.504	0.54	1.883	0.077	0.102	0.853
M362	3.19	1.31	0.159	0.02	0.302	0.369
M574A	480.622	705.774	13.208	0.568	26.205	9.531
M574B	194.503	221.746	6.018	0.319	11.833	5.475

EC₅₀: median effective concentration. LC₅₀: median lethal concentration. ChV: chronic toxicity value.

.

The predicted acute toxicity values for fluopyram were 1.209 (fish LC₅₀), 0.369 (daphnid LC₅₀), and 0.069 mg/L (green algae EC₅₀), classifying it as toxic to fish (1 < LC₅₀ < 10 mg/L) and very toxic to daphnids and green algae according to the acute toxicity classification criteria specified in Annex VI of the European Union Directive 67/548/EEC. In terms of chronic toxicity, based on the Chinese hazard evaluation guidelines for new chemical substances (HJ/T 154-2004 [36]), fluopyram (fish ChV = 0.011 mg/L) was very chronically toxic to fish, chronically toxic to green algae (ChV = 0.211 mg/L), and not very chronically toxic to daphnids (ChV = 0.13 mg/L).

Fluopyram has been reported to exhibit low toxicity to adult zebrafish (96 h LC₅₀ = 17.82 mg/L) [37], moderate toxicity to zebrafish embryos (with 96 h LC₅₀ = 4.375 mg/L [38], and moderate toxicity to daphnids and green algae [39], and the ECOSAR prediction results generally aligned well with these experimental data. This offers a strong foundation for preliminary ecological risk assessment in the context of limited empirical data.

Metabolites exhibited divergent toxicity profiles relative to the parent. M430A (fish acute toxicity: 3.57 × higher, LC₅₀ = 0.339 mg/L) and M412C (fish acute toxicity: 3.53 × higher, LC₅₀ = 0.342 mg/L) showed significantly elevated acute toxicity and were chronically toxic to fish (ChV < 0.1 mg/L) and daphnids (ChV < 0.1 mg/L). M574A (fish acute toxicity: 1/397.5 of parent, LC₅₀ = 480.622 mg/L) and M574B (fish acute toxicity: 1/160.9 of parent, LC₅₀ = 194.503 mg/L) exhibited extremely low acute/chronic toxicity to all organisms, indicating potential detoxification, and M412A also showed reduced toxicity (fish chronic toxicity: 1/105 of parent, ChV = 1.155 mg/L). Other key classifications included six metabolites (e.g., M378B, M394A) that were very chronically toxic to fish, six (e.g., M394A, M362) chronically toxic to green algae, and seven non-chronically toxic to green algae.

Fluopyram and its high-toxicity metabolites (M430A, M412C) pose substantial risks to aquatic ecosystems spanning multiple trophic levels, whereas M574A and M574B exhibit significantly reduced acute and chronic toxicity, which is attributed to the glucuronidation conjugation process during metabolism. Given the disparate toxicity profiles of the metabolites, their environmental risks require urgent attention. From a toxicological and ecological perspective, the identification of multiple transformation products is of particular relevance. Several metabolites retained structural features associated with biological activity, and some exhibited predicted toxicities comparable to or higher than the parent fluopyram, indicating that metabolic transformation does not necessarily imply detoxification. In contrast, conjugated metabolites showed markedly reduced predicted toxicity, suggesting a potential detoxification role of phase II metabolism. These findings underscore the necessity of including transformation products, especially those with enhanced or persistent toxicity, in environmental risk assessment for fluopyram. Even metabolites detected at relatively low level may contribute to ecological risk due to their biological potency or prolonged environmental presence.

The experimental residue data of fluopyram in cucumber and tomato seedlings refined the QSAR-based risk assessment. Firstly, cucumber showed a TF > 1 after 7 days, meaning fluopyram and its metabolites were easily translocated to aboveground parts; thus, the QSAR-predicted high toxicity of M430A and M412C mainly manifested in aboveground exposure risks (dietary risk of edible parts, aquatic risk of surface runoff). Tomato had a TF < 0.6, with the parent compound and metabolites mainly retained in roots, leading to concentrated risks in the root–soil system. This crop-specific spatial risk pattern was not reflected in the universal QSAR prediction, and the experimental TF data effectively compensated for this deficiency. Moreover, empirical identification confirmed that M574A and M574B are glucuronidation conjugation products of M394A and M394B, and this phase II conjugation metabolism is the key cause of their significant reduction in toxicity. It provided molecular biological mechanism verification for the QSAR-predicted low toxicity of M574A and M574B, making the model predictions more credible beyond simple structure–-activity relationship inferences. In addition, fluopyram in cucumber nutrient solution had a short dissipation half-life (1.2 d) and a high metabolic transformation rate (90.5%), resulting in the cucumber planting system being dominated by the short-term acute risk of high-toxicity metabolites (M430A, M412C). In contrast, tomato had a longer half-life (6.6 d) and a lower transformation rate (70.9%), so its planting system not only faced metabolite risks but also required attention to the long-term chronic risk of the parent compound due to the prolonged retention of the parent in nutrient solution and roots. The residue kinetic data effectively added a temporal dimension to QSAR risk prediction, making the risk assessment more hierarchical.

However, the ECOSAR prediction results only provide a basis for preliminary judgment of their toxicities. Further experimental validation and long-term monitoring are recommended to comprehensively evaluate the potential hazards posed by fluopyram and its metabolites.

4. Conclusions

This study systematically investigated the uptake, translocation, distribution, and metabolism of fluopyram in cucumber and tomato seedlings through hydroponic experiments. Fluopyram was readily absorbed by the roots of both crops, but showed species-specific translocation and distribution patterns: cucumber exhibited strong root-to-shoot translocation (TF > 1 after 7 days), while tomato showed limited translocation (TF < 0.6). Extensive biotransformation of fluopyram occurred in both plant systems, with 90.5% and 70.9% of the parent compound transformed in cucumber and tomato, respectively. A total of 16 metabolites were tentatively identified, and the metabolic pathways included hydroxylation, hydroxylation–dechlorination, dehydrogenation, dechlorination, and glucuronidation conjugation. Most metabolites accumulated in the leaves, indicating a high potential for translocation to the edible parts.

Toxicity prediction using ECOSAR v1.11 showed that metabolites such as M430A and M412C exhibited enhanced toxicity to aquatic organisms, while M574A and M574B showed reduced toxicity due to glucuronidation conjugation. The combination of experimental residue analysis and QSAR-based toxicity prediction demonstrates how empirical data can support and refine modeled risk assessments, providing a more comprehensive evaluation of the potential ecological risks posed by fluopyram and its transformation products. These findings enhance our comprehension of the environmental behavior of fluopyram in agricultural crops and emphasize the importance of incorporating metabolite toxicity into the comprehensive risk assessment framework for this nematicide. Future research should focus on validating the toxicity of key metabolites and evaluating the risks of long-term exposure through the food chain.