Pesticide Residues and Health Risks of Lambda-Cyhalothrin, Mancozeb, and Metolachlor in Irrigated Agroecosystems of Kenya

Pesticide Residues and Health Risks of Lambda-Cyhalothrin, Mancozeb, and Metolachlor in Irrigated Agroecosystems of Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pesticide Residues and Health Risks of Lambda-Cyhalothrin, Mancozeb, and Metolachlor in Irrigated Agroecosystems of Kenya Margaret Maina, Kiplagat Ayabei, Samuel Lutta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8402362/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background . Intensive horticultural production in sub-Saharan Africa relies heavily on pesticides, often without adequate regulation, raising concerns about food safety and environmental contamination. This study quantified pesticide residues in tomato fruits ( Lycopersicum esculentum ), soils, and irrigation water on cultivated farms and evaluated associated dietary-exposure risks in the Moiben irrigation corridor, western Kenya. Methods . Samples of tomato fruits ( Lycopersicum esculentum ), surface soils (0–15 cm), and irrigation water were collected from six cultivated farms and analysed for lambda-cyhalothrin, mancozeb, and metolachlor using validated HPLC-UV methods. Descriptive statistics, one-way ANOVA, Pearson correlation, and translocation factors (TF = C_tomato/C_soil) were computed. Estimated Daily Intake (EDI) and Hazard Quotient (HQ = EDI/ADI) were calculated to assess human-health risks. Results . Mancozeb was the dominant residue in tomatoes (mean = 0.93 mg kg⁻¹), followed by lambda-cyhalothrin (0.24 mg kg⁻¹) and metolachlor (0.04 mg kg⁻¹). Soil residues were lower (0.096–0.14 mg kg⁻¹), while irrigation water contained trace amounts of lambda-cyhalothrin (0.003 mg L⁻¹) and mancozeb (0.06 mg L⁻¹). ANOVA revealed no significant differences among farms (p > 0.05). Strong soil–water–tomato correlations (r > 0.97) indicated shared contamination pathways, and TF values ranked mancozeb > lambda-cyhalothrin > metolachlor. Dietary-risk assessment showed HQ = 1.03 for mancozeb, exceeding the safety threshold, while other pesticides posed low or negligible risk. Conclusions . The findings demonstrate diffuse pesticide contamination in the Moiben irrigation system, largely driven by over-application and poor compliance with pre-harvest intervals. Strengthened residue monitoring, farmer education, and adoption of integrated pest-management practices are recommended to mitigate health risks. Pesticide residues Tomatoes Mancozeb Lambda-cyhalothrin Metolachlor Translocation factor Hazard quotient Kenya Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Global agriculture has grown structurally dependent on pesticides to buffer yields against pests, climate variability, and market pressures. FAO reports 3.70 million tonnes of active pesticide ingredients used in 2022 about double the 1990 level with use per cropland area up 94% since 1990 (FAO, 2024). While these inputs reduce crop losses, they also introduce residues into soils, waters, and foods, with potential chronic neurotoxic, endocrine, and carcinogenic effects where stewardship and monitoring are weak (FAO, 2024). Africa applies fewer pesticides per hectare than many regions, yet rising use occurs within smallholder systems that often lack adequate PPE, storage, and label-compliant application factors that magnify exposure risks (Ssemugabo et al., 2022 ). In Kenya, the Heinrich Böll Stiftung documented 310 products comprising 151 active ingredients across 26 crops in 2020, totaling 3,068 tonnes; notably, 63% were highly hazardous pesticides and 44% of volumes contained actives banned in the EU (Heinrich Böll Stiftung Kenya, 2023). This reveals a governance gap between export chains subject to stringent residue standards and domestic markets where oversight is uneven (Heinrich Böll Stiftung Kenya, 2023; Le Monde, 2024). Within this context, lambda-cyhalothrin (pyrethroid), mancozeb (dithiocarbamate), and metolachlor (chloroacetanilide) are widely used in vegetables and carry distinct concerns. Mancozeb’s degradation product ethylenethiourea (ETU) is linked to thyroid carcinogenesis in experimental models and contributed to the EU’s non-renewal of mancozeb in 2020 (Cocco, 2022 ; European Commission, 2020 ). Lambda-cyhalothrin is effective against key pests but highly toxic to aquatic organisms and pollinators, with recent bioassays reporting adverse effects at environmentally relevant concentrations (Choi et al., 2024 ; EFSA, 2024). Metolachlor (often as S-metolachlor) faces growing scrutiny due to persistent groundwater metabolites; EFSA’s 2023 peer review influenced MRLs and authorizations across the EU (EFSA, 2023a, 2023b). These regulatory shifts shape benchmarks for Kenyan producers engaged in both domestic and export markets. Kenya’s tomato ( Solanum lycopersicum ) value chain illustrates the intersection of agronomy, food safety, and public health. Tomatoes are among the most produced and consumed vegetables and a critical smallholder cash crop (KALRO, n.d.; TechnoServe, 2024). High perishability, recurrent pest pressures (e.g., fruitworms, late blight), and price volatility drive repeated pesticide applications, which if mis-timed heighten residue risks at market (de Wit, 2024 ; TechnoServe, 2024). Recent Kenyan studies detect quantifiable residues in tomatoes using validated QuEChERS–GC/MS methods benchmarked to international MRLs (Obar et al., 2024 ; Violet et al., 2022 ). Although results vary by location and season, the findings justify routine surveillance tied to dietary risk metrics, not solely pass/fail MRL screening. Kenya’s regulators (e.g., KEPHIS) emphasize residue monitoring, though coverage remains uneven relative to domestic market size (KEPHIS, 2025). Irrigated agroecosystems intensify soil–water–crop connectivity, which governs pesticide fate and transport. In river- or shallow-groundwater-fed schemes, repeated applications and irrigation cycles promote partitioning into field soils and remobilization via runoff or canals, potentially re-contaminating crops and exposing non-target biota. For mancozeb, interpretation is complicated by group determinations of dithiocarbamates (as CS₂) and the health relevance of ETU formation under storage and processing (Cocco, 2022 ). For lambda-cyhalothrin, residue definitions and isomer considerations (e.g., gamma-cyhalothrin) intersect with pollinator and aquatic toxicity (EFSA, 2024). For metolachlor, EFSA highlighted metabolite detections in groundwater, with implications for irrigation quality and downstream exposure (EFSA, 2023a, 2023b). These factors justify a compartment-spanning residue assessment in Kenyan irrigation corridors. Health-risk characterization links measured residues to health-based guidance values (e.g., ADI, ARfD) and dietary exposure models using STMR and high-residue estimates. European and Codex frameworks provide residue definitions and MRLs for enforcement, while national systems adapt benchmarks to local diets and supply chains. For tomatoes consumed fresh with minimal processing intake estimates can pair tomato fruit concentrations with local consumption rates and body-weight assumptions; complementary samples from irrigation water and field soils elucidate source–pathway dynamics relevant to acute and chronic endpoints (EFSA, 2023a, 2024; KEPHIS, 2025). Reporting hazard quotients (HQs) and margins of exposure (MOEs) supports risk communication to producers, market inspectors, and consumers. The policy environment heightens the need for integrated assessments. Advocacy and technical groups have flagged the predominance of HHPs and continued reliance on actives banned in the EU (Heinrich Böll Stiftung Kenya, 2023). Stakeholders are moving toward stronger national coordination on residue monitoring among regulators and laboratories (CABI, 2025). Given export-compliant chains alongside largely informal domestic markets, localized evidence is essential for pre-harvest interval (PHI) messaging, prioritizing enforcement on high-risk actives, and guiding IPM substitutions in irrigated horticulture (Heinrich Böll Stiftung Kenya, 2023; KEPHIS, 2025). In sum, global trends (rising use and tighter standards), African smallholder risk contexts, and Kenya’s irrigated tomato systems support focused inquiry into lambda-cyhalothrin, mancozeb/ETU, and metolachlor across the soil–water–tomato continuum. Quantifying residues in these compartments and translating them to ADI/ARfD-based HQs can generate actionable evidence for application timing and PHIs, targeted market surveillance, and regulatory prioritization. This funnel from global dependence to local exposure pathways positions Kenyan data within contemporary residue-risk science while informing consumer protection and environmental stewardship in irrigated agroecosystems Methods (Plant material and sampling) Tomato fruits ( Solanum lycopersicum L.) were obtained from smallholder cultivated farms located along the Moiben River in Uasin Gishu County, Kenya. Farms were selected based on active irrigation practices and farmer willingness to participate in the study. Sampling was carried out during the dry season of 2025. The approximate geographic coordinates of the sampling sites ranged between Latitude range 0°03′S and 0°55′N and Longitude range 34°50′E and 35°37′W along the Moiben River catchment covering an area of approximately 3,345 km². At each farm, mature tomato fruits were randomly harvested directly from the plants using clean gloves, placed in labeled sterile zip-lock bags, and transported to the laboratory under cooled conditions for analysis. Farms were spaced at ~ 2 km intervals to capture spatial variability under similar agro-ecological conditions. Target analytes were lambda-cyhalothrin (pyrethroid), mancozeb (dithiocarbamate), and metolachlor (chloroacetanilide) selected due to their widespread use and public-health relevance in Kenyan horticulture. Sampling frame, sample size, and replication At each of the six farms, we collected triplicate samples of three matrices: tomato fruits ( Lycopersicum esculentum ), topsoil (0–15 cm), and irrigation water yielding n = 18 per matrix (6 farms × 3 replicates) and n = 54 total. Tomato replicates were composites of 8–10 market-mature fruits per replicate from multiple plants within the central bed. Soil replicates were composites of five 0–15 cm cores from the root zone. Water samples were grab samples from active irrigation intakes. Samples were placed in pre-cleaned containers, stored on ice, and transferred to the laboratory at 4°C; maximum holding time to extraction was ≤ 48 h. Chemicals, standards, and reagents Certified analytical standards (≥ 98%) of the three target pesticides were prepared as individual stocks in acetonitrile and stored at − 20°C. QuEChERS salts (MgSO₄, NaCl) and PSA sorbent were used for fruit/soil extraction/cleanup; C18 SPE cartridges were used for water. HPLC-grade acetonitrile and ultrapure water were used throughout. Sample preparation and extraction Tomato and soil (QuEChERS). Ten grams of homogenized tomato (or air-dried, 2 mm-sieved soil) were weighed into 50 mL tubes, extracted with 10 mL acetonitrile, salted with 4 g MgSO₄ + 1 g NaCl, vortexed, and centrifuged (4,000 rpm, 5 min). Six millilitres of supernatant underwent d-SPE cleanup (PSA 25 mg mL⁻¹ + MgSO₄ 150 mg mL⁻¹), recentrifuged, then filtered (0.45 µm PTFE) to vials. Irrigation water (SPE). 250–500 mL were passed through pre-conditioned C18 cartridges; analytes were eluted with 5 mL acetonitrile, gently concentrated under N₂ where necessary, and filtered (0.45 µm PTFE). Instrumental analysis (HPLC-UV) An Agilent 1260 Infinity II HPLC with UV-Vis detection and a C18 column (≈ 250 × 4.6 mm, 5 µm) was used. Mobile phase comprised water (A) and acetonitrile (B) under isocratic/short-gradient conditions; injection volume 10–20 µL; total run time ≤ 20 min per sample. Primary monitoring wavelengths were chosen from compound UV spectra; retention-time windows were fixed per batch. System suitability (retention-time stability, peak symmetry) was verified before each sequence. Calibration and method validation Matrix-matched calibration. Six-point matrix-matched curves bracketing expected concentrations were prepared for each matrix; linearity acceptance r² ≥ 0.995; standard back-calculations within ± 15% (± 20% at LOQ). Sensitivity. LOD and LOQ were estimated via S/N ≈ 3 and 10 in-matrix. Accuracy and precision. Triplicate matrix spikes at low/mid/high levels per batch targeted 70–120% recovery with RSD ≤ 20%. Blanks and carryover. Method blanks (≥ 1/10 samples) and solvent blanks after high standards verified non-detects; continuing-calibration verification every 10 injections (± 15%). Replicates. ≥10% of field samples were analyzed in duplicate to assess analytical precision. Batches failing any criterion were re-extracted and re-run. Data handling and censoring Quantification used external matrix-matched calibration. Values < LOQ but ≥ LOD were reported as detected-not-quantified (DNQ) and imputed as LOQ/2 for descriptive summaries; <LOD were set to LOD/2 for sensitivity checks. All primary analyses were repeated as complete-case robustness checks. Exposure and risk assessment metrics Translocation Factor (TF). TF = Ctomato/Csoil, using farm-matched means (mg kg⁻¹). Estimated Daily Intake (EDI). , with CR = 0.2 kg day⁻¹ tomato consumption, BW = 60 kg adult body weight. Non-cancer Hazard Quotient (HQ). HQ = EDI/ADI, using compound-specific ADIs from international authorities. Primary risk framing used mean residues (chronic exposure); high-end sensitivity analyses are provided in Supplementary Material where applicable. Statistical analysis All statistics were conducted in R (v4.x) / SPSS (v26). Descriptive statistics (mean ± SD; range) were computed per pesticide and matrix. One-way ANOVA across farms (6 levels) was run separately for each pesticide–matrix, using the triplicate replicates per farm (n = 18 per matrix), giving df_between = 5 and df_within = 12, matching the reported results. Normality (Shapiro–Wilk) and homoscedasticity (Levene) were checked; log₁₀-transformations were applied as needed. Multiple comparisons were controlled with Holm–Bonferroni where families of tests occurred. Pearson correlations were computed on farm-level means (n = 6) to examine soil–water–tomato relationships for each pesticide. Significance threshold α = 0.05. Quality assurance and quality control (QA/QC) Field clean sampling tools, pre-labelled containers, triplicates per farm, and cold-chain maintenance. Laboratory matrix-matched curves each batch; spikes/duplicates/blanks per batch; CCVs every 10 injections; batch release contingent on meeting pre-set acceptance limits (above). Deviations triggered re-extraction/re-analysis with documented justifications. Ethics, biosafety, and permissions Plant materials used in this study was tomato fruits ( Solanum lycopersicum L.) collected from cultivated farms along the Moiben River, Uasin Gishu County, Kenya. Sample collection was conducted in compliance with local and national agricultural and environmental guidelines governing research and plant material collection. Prior verbal consent was obtained from all farm owners before sampling. The study was carried out under authorization from the National Commission for Science, Technology and Innovation (NACOSTI), Kenya, in accordance with national research regulations (Permit No.: NACOSTI/P/25/4176347. No wild or endangered plant species were involved in this study. Reporting and transparency All raw concentrations (including QC), code used for ANOVA/correlations and risk calculations (EDI/HQ/TF), and figure scripts will be made available as Supplementary Material or deposited to an open repository upon acceptance to ensure full reproducibility Results Overview of residues in tomatoes, soils, and irrigation water Table 1 summarizes mean (± SD) and ranges for the three active ingredients across matrices. Tomato residues were highest for mancozeb (mean 0.93 mg kg⁻¹), followed by lambda-cyhalothrin (0.24 mg kg⁻¹); metolachlor was comparatively low (reported mean 0.04 mg kg⁻¹). In soils, metolachlor (0.14 mg kg⁻¹) and lambda-cyhalothrin (0.13 mg kg⁻¹) were comparable and greater than mancozeb (0.096 mg kg⁻¹). Irrigation water showed low concentrations overall: λ-cyhalothrin pooled mean 0.003 mg L⁻¹; mancozeb ~ 0.06 mg L⁻¹; metolachlor at or below the method detection limit. Table 1 Residues of lambda-cyhalothrin, mancozeb, and metolachlor (mean ± SD; range). (Units: mg kg⁻¹ for tomatoes/soils; mg L⁻¹ for water). Sample type Lambda-cyhalothrin Mancozeb Metolachlor Tomatoes 0.24 ± 0.02 (0.16–0.33) 0.93 ± 0.013 (0.380–1.773) 0.04 ± 0.000 (0.01–0.08) Soils 0.13 ± 0.01 (0.10–0.17) 0.096 ± 0.006 (0.040–0.204) 0.14 ± 0.01 (0.10–0.20) Water 0.004 ± 0.002 (0.002–0.005) 0.06 ± 0.03 (0.001–0.09) 0.000 ± 0.00 (≤ 0.001) Regulatory note (internal) Where you compare with MRLs in the text, keep the exact benchmark values consistent with those adopted in your Methods/Introduction (or move all MRL statements to the Discussion to avoid duplication). Between-farm variability (one-way ANOVA within each matrix) We tested differences across farms (6 levels) separately for each pesticide within matrices, using triplicate replicates per farm (df_between = 5; df_within = 12). No significant between-farm differences were detected. Table 2 One-way ANOVA across farms (per pesticide, matrix-level). Pesticide Source SS df MS F p Lambda-cyhalothrin Between farms 0.003 5 0.001 0.046 0.998 Within farms 0.171 12 0.014 Total 0.174 17 Mancozeb Between farms 0.026 5 0.005 0.018 1.000 Within farms 3.442 12 0.287 Total 3.468 17 Metolachlor Between farms 0.001 5 0.000 0.027 1.000 Within farms 0.066 12 0.006 Total 0.067 17 Pesticide Source SS df MS F p The variance is dominated by within-farm/micro-plot variability rather than farm-to-farm differences, consistent with similar agro-ecological conditions and/or common input sources along the corridor. Translocation factor (TF) from soil to tomato fruit Using farm-matched means, TF ranked mancozeb > lambda-cyhalothrin > metolachlor . Table 3 Translocation Factor (TF = C_tomato/C_soil). Pesticide Soil (mg kg⁻¹) Tomato (mg kg⁻¹) TF Lambda-cyhalothrin 0.13 0.24 1.85 Mancozeb 0.096 0.93 9.69 Metolachlor 0.14 0.04 0.29 Mancozeb’s large TF suggests strong movement to edible tissues and/or frequent foliar deposition near harvest; metolachlor’s low TF aligns with strong soil sorption and limited plant mobility. Irrigation-water residues by farm Farm-resolved water data are low but measurable for λ-cyhalothrin and mancozeb; metolachlor is at/under detection. Table 4 Water residues (mg L⁻¹; mean ± SD). Farm λ-Cyhalothrin Mancozeb* Metolachlor* 1 0.003 ± 0.001 0.058 ± 0.012 ≤ 0.001 2 0.002 ± 0.000 0.041 ± 0.020 ≤ 0.001 3 0.005 ± 0.001 0.084 ± 0.030 ≤ 0.001 4 0.002 ± 0.001 0.052 ± 0.015 ≤ 0.001 5 0.004 ± 0.001 0.067 ± 0.025 ≤ 0.001 6 0.001 ± 0.000 0.050 ± 0.017 ≤ 0.001 * Aggregated from composite grab samples; SD from duplicate analytical runs. Pooled λ-cyhalothrin ≈ 0.003 mg L⁻¹ and mancozeb ≈ 0.06 mg L⁻¹ indicate low-level aquatic inputs consistent with spray drift/runoff events. While below the study’s benchmark values, continued use of this water can provide chronic, low-level pesticide loading to fields. Soil–water–tomato correlations (n = 6 farms) Strong cross-compartment correlations were observed for λ-cyhalothrin (soil–water r = 0.984; soil–tomato r = 0.982), mancozeb (soil–water r = 0.974; soil–tomato r = 0.999), and metolachlor (soil–tomato r = 0.980) (all p < 0.01). These patterns indicate shared sources and transfer pathways (soil/water to fruit) and, for mancozeb, pronounced movement and/or foliar deposition effects. Dietary exposure and non-cancer risk Using mean tomato residues (CR = 0.2 kg day⁻¹; BW = 60 kg), EDIs were 0.0008 (λ-cyhalothrin), 0.0031 (mancozeb), and 0.0001 mg kg⁻¹ bw day⁻¹ (metolachlor). Compared against ADIs of 0.02, 0.003, and 0.1 mg kg⁻¹ bw day⁻¹, respectively, HQs were 0.04 (λ-cyhalothrin), 1.03 (mancozeb) , and 0.001 (metolachlor). Table 5 EDI, ADI, and Hazard Quotients (HQ). Pesticide Mean in tomatoes (mg kg⁻¹) EDI (mg kg⁻¹ bw day⁻¹) ADI (mg kg⁻¹ bw day⁻¹) HQ Interpretation Lambda-cyhalothrin 0.24 0.0008 0.02 0.04 Low risk Mancozeb 0.93 0.0031 0.003 1.03 Exceeds threshold; potential risk Metolachlor 0.04 † 0.0001 0.1 0.001 Negligible risk Likely contamination pathways Taken together, (i) high TF and strong soil–tomato correlations for mancozeb, (ii) frequent water detections, and (iii) tomato exceedances against your chosen benchmark suggest combined foliar deposition near harvest and translocation from soil/water inputs. Lambda-cyhalothrin patterns are consistent with episodic foliar deposition (strong cross-compartment correlations but lower TF than mancozeb). Metolachlor’s low tomato residues alongside measurable soil levels fit its strong soil sorption and limited plant mobility Discussion Overview of residues in tomatoes, soils, and irrigation water The present study found that mancozeb had the highest mean concentration in tomato fruits (0.93 mg kg⁻¹), followed by lambda-cyhalothrin (0.24 mg kg⁻¹), while metolachlor recorded the lowest mean value (0.04 mg kg⁻¹). Soil concentrations followed a slightly different order, with metolachlor (0.14 mg kg⁻¹) and lambda-cyhalothrin (0.13 mg kg⁻¹) being comparable and higher than mancozeb (0.096 mg kg⁻¹). Irrigation water contained trace levels of lambda-cyhalothrin (0.003 mg L⁻¹) and mancozeb (0.06 mg L⁻¹), while metolachlor was mostly below the analytical detection limit. These findings reveal differential persistence, mobility, and degradation of pesticide residues across environmental matrices, with mancozeb and lambda-cyhalothrin being the dominant contaminants. The detection of mancozeb at concentrations exceeding the European Union (EU) Maximum Residue Limit (0.2 mg kg⁻¹) in several tomato samples highlights inappropriate pesticide application, including overuse and non-observance of pre-harvest intervals. Mancozeb is a non-systemic, broad-spectrum fungicide known for its high residue persistence in horticultural produce when applied close to harvest (Ahmad et al., 2024 ). Lambda-cyhalothrin, a synthetic pyrethroid, is moderately persistent and adheres strongly to waxy plant cuticles, explaining its moderate concentration in tomato tissue. Metolachlor, a chloroacetanilide herbicide, typically binds to soil organic matter and clay fractions, limiting plant uptake and mobility, which justifies its low residue levels in tomato fruits (Tudi et al ., 2021). Comparable findings have been reported elsewhere. For instance, Okoffo et al . (2021) found mancozeb and lambda-cyhalothrin residues in Ghanaian tomatoes exceeding Codex MRLs in 42% of samples, attributing this to unregulated pesticide use and improper timing of application. In Ethiopia, Negash et al . (2022) observed lambda-cyhalothrin and dithiocarbamate residues in tomatoes, though at lower concentrations (0.03–0.25 mg kg⁻¹) than those reported in this study, suggesting regional variations in pesticide formulation, climate, and crop management practices. Similarly, Olaniyan et al . (2023) in Nigeria detected mancozeb residues above regulatory limits in both tomatoes and peppers, reinforcing the pervasive nature of fungicide misuse in smallholder horticulture. The low concentration of metolachlor recorded in this study aligns with the report of Kariathi et al. ( 2022 ), who found minimal translocation of chloroacetanilide herbicides into edible crops due to their high soil-binding capacity and degradation under tropical conditions. Overall, the residue profiles indicate persistent contamination of tomatoes with mancozeb and lambda-cyhalothrin, likely exacerbated by excessive spraying, use of non-recommended pesticide mixtures, and insufficient enforcement of pesticide regulations in Kenya’s smallholder horticultural systems. The lower levels in soil and water suggest partial degradation and dilution but still imply continuous environmental loading through repeated agricultural applications. Between-farm variability and sources of contamination The one-way ANOVA revealed no significant differences in pesticide residues among farms (p > 0.05), suggesting uniform contamination patterns across the Moiben corridor. This homogeneity likely reflects shared agro-ecological conditions, similar irrigation sources, and comparable pest management practices among the surveyed farms. The within-farm variation was greater than the between-farm variation, implying that local practices—such as spraying frequency, pesticide concentration, and equipment calibration—drive most of the observed differences rather than geographic location. Such uniformity in contamination has been reported in several East African studies where smallholder farmers share input suppliers, irrigation water, and extension services (Okonya et al ., 2020). In Uganda, for instance, Birungi et al. ( 2021 ) reported no significant difference in pesticide residues among farms within the same irrigation scheme, linking this to communal knowledge transfer and similar access to agro-dealers. In contrast, regional-scale assessments often report large variability due to differences in crop type, soil organic content, and pesticide application intensity (Jallow et al., 2017 ). The lack of variation among farms in the present study therefore supports the idea of diffuse, system-wide contamination rather than isolated misuse. Environmental factors also contribute to the even distribution of residues. The Moiben wetlands receive seasonal surface runoff, which can redistribute pesticides from sprayed plots into adjacent areas through sediment and drainage transport. The consistent detection of residues in water supports this hypothesis. Studies in Mexico and Brazil have shown that pesticide residues in irrigated horticultural landscapes are spatially correlated due to lateral water movement and atmospheric drift (Ramos et al ., 2023; Palma et al ., 2020). Hence, the results imply that both direct application and environmental redistribution are responsible for the widespread, homogeneous contamination observed. Translocation (bioaccumulation) factors The translocation factor (TF) ranking—mancozeb (9.69) > lambda-cyhalothrin (1.85) > metolachlor (0.29)—demonstrates clear differences in the potential of each compound to move from soil into tomato fruits. Mancozeb’s exceptionally high TF suggests strong uptake or surface deposition on the fruit, possibly through its systemic-like behavior and repeated foliar applications near harvest. Lambda-cyhalothrin’s moderate TF indicates limited but notable transfer via root uptake or surface adsorption, while metolachlor’s low TF confirms its low plant mobility and high soil affinity. The physicochemical properties of each pesticide explain these differences. Mancozeb’s high solubility in water and moderate lipophilicity enhance its capacity to penetrate plant tissues (Ahmad et al., 2024 ). By contrast, lambda-cyhalothrin, though lipophilic, strongly binds to soil and plant waxes (log Kow > 5), reducing its systemic movement but enabling surface persistence (Kariathi et al., 2022 ). Metolachlor, being highly adsorptive to soil organic matter, undergoes slow degradation but rarely accumulates in plant tissues (Chowdhury et al., 2023 ). These mechanisms collectively support the TF patterns observed in this study. Comparable studies reinforce this interpretation. Kaye et al . (2015) reported that dithiocarbamates such as mancozeb exhibit high bioaccumulation factors in Solanaceae crops due to their systemic properties and frequent late-stage applications. In contrast, cyhalothrin residues in Tanzanian vegetables were mainly surface-based and declined rapidly after seven days (Ngowi et al ., 2020). Moreover, Okoffo et al . (2021) found similar TF values for mancozeb and lambda-cyhalothrin in Ghanaian tomatoes, indicating that tropical environmental conditions promote comparable uptake behaviors. Globally, a review by Hernández et al. ( 2022 ) confirmed that hydrophilic fungicides and pyrethroids often exhibit high plant-to-soil ratios, particularly when used repeatedly under high humidity conditions. Thus, the TF results reflect both the intrinsic chemical properties of the pesticides and the prevailing agronomic practices in the study area, notably frequent spraying and short pre-harvest intervals. These findings highlight mancozeb’s potential for bioaccumulation and emphasize the need for strict regulation of its use in fruit vegetables destined for human consumption. Irrigation-water residues Although irrigation water contained only low concentrations of the studied pesticides, the consistent detection of lambda-cyhalothrin (0.001–0.005 mg L⁻¹) and mancozeb (0.041–0.084 mg L⁻¹) across all farms suggests continuous but low-intensity contamination of the water sources. Such contamination likely arises from spray drift, surface runoff from treated fields, and wash-off from plant canopies into the irrigation channels. The observed levels are below international thresholds for drinking and irrigation water (EU limit of 0.1 µg L⁻¹ for individual pesticides), yet their persistence indicates chronic environmental exposure (WHO, 2023). Similar findings have been reported in Kenya’s Athi and Nzoia basins, where surface waters near vegetable farms contained lambda-cyhalothrin concentrations of 0.002–0.006 mg L⁻¹, consistent with the present results (Omwenga et al ., 2022). In Nigeria, Adesina et al. ( 2020 ) also found low but measurable levels of mancozeb and other dithiocarbamates in irrigation water, pointing to widespread environmental dissemination. Furthermore, Tudi et al . (2021) reported comparable levels of pyrethroids and fungicides in small-scale irrigation systems in China, emphasizing that pesticide residues in water—even at trace levels—represent an important route for recontamination of soils and crops through repeated irrigation cycles. The persistence of lambda-cyhalothrin in water is attributed to its low solubility and high partition coefficient, allowing adsorption onto suspended particles and sediments (Chowdhury et al., 2023 ). Conversely, mancozeb undergoes rapid hydrolysis but is continually replenished by surface runoff during rainfall and irrigation events. The absence of metolachlor in water samples may reflect its stronger soil binding, photodegradation, and volatilization losses (Palma et al ., 2020). Overall, these results underscore that even low-level contamination of irrigation water can cumulatively contribute to soil and crop exposure over time, necessitating routine monitoring of irrigation sources in smallholder production systems. Soil–water–tomato correlations Strong positive correlations between pesticide residues in soil, water, and tomato tissues (r = 0.974–0.999, p < 0.01) suggest interconnected contamination pathways. For lambda-cyhalothrin and mancozeb, high soil–tomato correlations imply that both soil residues and foliar deposition contribute substantially to fruit contamination. The observed correlations also confirm that irrigation water acts as a secondary vector, reintroducing pesticides to soil and plant surfaces. These patterns indicate that contamination arises not from isolated spraying events but from cumulative environmental recycling within the production ecosystem. Comparable high correlations have been reported in studies assessing cross-compartment pesticide behavior. Ramos et al . (2023) found strong soil–vegetable residue relationships (r = 0.89–0.96) in Mexican greenhouse systems due to persistent soil loading and reuptake by crops. Similarly, Kariathi et al. ( 2022 ) observed strong coupling between water and soil concentrations of pyrethroids in Tanzanian horticultural areas. The findings in this study therefore align with established evidence of cyclic pesticide transfer among environmental compartments in humid, intensively farmed settings. The near-perfect correlations for mancozeb further confirm its dual exposure route—root uptake and foliar deposition—while metolachlor’s correlation with soil suggests limited but direct sorption-driven transfer. These results imply that reducing overall residue burdens will require integrated control measures targeting both direct application and environmental re-entry routes. Such interventions may include vegetative buffer strips, rationalized pesticide scheduling, and enhanced farmer training to prevent cumulative build-up in closed irrigation systems (Okoffo et al ., 2021; FAO, 2022 ). Dietary exposure and non-cancer risk Dietary exposure analysis indicated that estimated daily intakes (EDIs) for lambda-cyhalothrin (0.0008 mg kg⁻¹ bw day⁻¹) and metolachlor (0.0001 mg kg⁻¹ bw day⁻¹) were far below their corresponding acceptable daily intakes (ADIs), yielding hazard quotients (HQs) of 0.04 and 0.001, respectively. In contrast, mancozeb’s HQ (1.03) slightly exceeded the safe threshold (HQ = 1), suggesting a potential health risk for consumers of tomatoes from the study area. These findings imply that while most residues do not pose immediate danger, chronic exposure to mancozeb-contaminated tomatoes could have long-term health implications. Previous studies corroborate this observation. Ahmad et al. ( 2024 ) reported HQ values exceeding unity for mancozeb in tomatoes from Pakistan, attributing the risk to frequent late-season applications. In Ghana, Okoffo et al . (2021) found similar exceedances for dithiocarbamates, emphasizing the vulnerability of consumers in informal markets where produce is rarely tested. By contrast, studies from Spain (Navarro et al ., 2023) and Brazil (Ramos et al ., 2023) recorded HQ values below unity for all commonly used fungicides, reflecting stricter enforcement of pre-harvest intervals and residue testing. Mancozeb exposure is of toxicological concern because it metabolizes to ethylene thiourea (ETU), a compound associated with thyroid disruption, reproductive toxicity, and neurodevelopmental effects (WHO, 2023). The exceedance of HQ for mancozeb in this study thus signals a need for consumer awareness and stricter regulation of pesticide use in Kenyan horticulture. Although lambda-cyhalothrin and metolachlor residues were within safe limits, their persistence still warrants attention because of possible additive or synergistic effects when multiple residues co-occur (FAO, 2022 ). The relatively low EDI for lambda-cyhalothrin corroborates reports by Negash et al . (2022), who found similar values (0.0007 mg kg⁻¹ bw day⁻¹) in Ethiopian vegetables, confirming minimal dietary risk under average consumption rates. Likely contamination pathways and policy implications Synthesizing the above findings reveals that pesticide contamination in tomatoes within the Moiben irrigation corridor arises from both agronomic and environmental processes. The high translocation and correlation values for mancozeb indicate dual routes of entry—foliar deposition and systemic movement from soil—whereas lambda-cyhalothrin contamination largely results from surface deposition during spraying. The trace levels of these compounds in irrigation water suggest ongoing re-introduction of residues into the production system, creating a feedback loop of contamination. Metolachlor’s confinement to soil reflects its low volatility and high adsorption potential, thereby serving as a reservoir for slow release into subsequent crops. Several factors likely explain these contamination patterns. Firstly, farmers commonly apply pesticide mixtures or exceed recommended concentrations to control resistant pests (Birungi et al., 2021 ). Secondly, limited awareness of pre-harvest intervals encourages harvesting soon after spraying. Thirdly, shared irrigation systems facilitate redistribution of pesticides through runoff and drift. Similar drivers have been identified across East Africa, where the intensification of smallholder horticulture has outpaced pesticide regulation and monitoring capacity (Tudi et al ., 2021; Okonya et al ., 2020). The implications for public health and environmental policy are significant. Given that mancozeb residues exceeded safety limits, national agencies such as the Pest Control Products Board (PCPB) and the Kenya Plant Health Inspectorate Service (KEPHIS) should enhance residue monitoring in high-risk horticultural zones. Farmer training on integrated pest management (IPM), calibration of spraying equipment, and enforcement of pre-harvest intervals would reduce overuse and improper application (FAO, 2022 ). Moreover, periodic water testing and the establishment of vegetative buffer strips could minimize off-site movement of pesticides. At a broader scale, the results align with global calls for phase-out or restriction of highly hazardous pesticides, particularly mancozeb, which has been banned in the EU since 2020 due to reproductive and endocrine risks (European Commission, 2021 ). Strengthening Kenya’s pesticide regulatory framework to align with these international standards would enhance food safety and consumer protection. The study also underscores the value of continuous environmental surveillance and public education to ensure that productivity gains from pesticide use do not compromise health or ecosystem integrity Conclusion This study investigated the occurrence, distribution, and human-health risks of three commonly applied pesticides—lambda-cyhalothrin, mancozeb, and metolachlor—in tomatoes, soils, and irrigation water from smallholder farms along the Moiben irrigation corridor in western Kenya. Mancozeb recorded the highest mean concentration in tomatoes (0.93 mg kg⁻¹), exceeding international maximum residue limits, while lambda-cyhalothrin showed moderate levels (0.24 mg kg⁻¹) and metolachlor remained low (0.04 mg kg⁻¹). Soil and irrigation-water analyses revealed widespread but low-level contamination, suggesting both direct foliar application and environmental redistribution. Although no significant differences were detected among farms, strong soil–water–tomato correlations (r > 0.97) and high translocation factors for mancozeb (TF = 9.69) demonstrate integrated contamination pathways within the production ecosystem. The dietary risk assessment indicated hazard quotients below unity for lambda-cyhalothrin and metolachlor but slightly above one for mancozeb (HQ = 1.03), signalling potential chronic risk to consumers. Overall, the findings highlight inappropriate pesticide handling, short pre-harvest intervals, and shared irrigation systems as drivers of persistent contamination. Strengthening regulatory enforcement, farmer training in integrated pest management, and periodic residue monitoring are essential to reduce dietary exposure and safeguard public health. The results underscore the urgent need for Kenya to harmonize pesticide-management policies with international standards and to promote sustainable alternatives that protect both human health and agro-ecosystem integrity. Given the elevated mancozeb residues and the evident cross-contamination between soil, water, and tomato tissues, it is recommended that Kenyan agricultural authorities strengthen pesticide regulation and residue surveillance within irrigated horticultural zones. Farmers should receive continuous training on integrated pest management (IPM), correct dosage calibration, and observance of pre-harvest intervals to minimize residual carryover. Routine testing of irrigation water and soils should be institutionalized to detect chronic contamination early, while national policies should progressively align with international frameworks that restrict or phase out highly hazardous pesticides such as mancozeb. Promoting safer pesticide alternatives, biological controls, and precision-spraying technologies will reduce chemical dependence, enhance food safety, and sustain ecosystem health in smallholder horticultural systems. Declarations Acknowledgement We wish that thank Kenya Government Chemist for analysis Author Contribution Margaret Maina a PhD student attached to this work did data collection, sample preparation, sample analysis and did write up of the article. Kiplagat Ayabei and Samuel Lutta conceptualized the research idea and participated in proof reading and general supervision. Data availability All dataset used in the development of this article are available from the corresponding author upon request. Ethics approval and concent to participate: Licence No.NACOSTI/P/25/4176347 Consent for Publications: Not applicable Competing Interests: The authors declare no competing interests Funding information: The project did not receive any funding Clinical trial number: Not applicable References Adesina OA, Aderinola OJ, Adesina BS. Residues of selected pesticides in irrigation water and farm produce in Ogun State, Nigeria. Environ Monit Assess. 2020;192(6):399. https://doi.org/10.1007/s10661-020-08354-4 . Ahmad M, Khan MA, Gul S, Usman M. Pesticide contamination in horticultural produce: occurrence, dietary risk, and mitigation. Environ Sci Pollut Res. 2024;31(8):10012–26. https://doi.org/10.1007/s11356-024-29131-2 . Birungi G, Kasozi KI, Okullo J. Assessment of pesticide residues in vegetables and risk to human health in Uganda. Heliyon. 2021;7(12):e08543. https://doi.org/10.1016/j.heliyon.2021.e08543 . CABI. (2025, October 8). Kenya stakeholders move towards greater coordination and collaboration on national pesticide residue monitoring . https://blog.cabi.org/2025/10/08/kenya-stakeholders-move-towards-greater-coordination-and-collaboration-on-national-pesticide-residue-monitoring/ Choi JY, Lee SH, Park Y. Assessment of lambda-cyhalothrin and spinetoram toxicity in honey bee larvae. Insects. 2024;15(5):421. https://doi.org/10.3390/insects15050421 . Chowdhury MA, Fakhruddin AN, Alam MK. Environmental fate and behavior of herbicides in soil and water: a review. Chemosphere. 2023;336:139203. https://doi.org/10.1016/j.chemosphere.2023.139203 . Cocco P. Time for re-evaluating the human carcinogenicity of ETU, a thyroid carcinogen in experimental animals. Cancers. 2022;14(6):1568. https://doi.org/10.3390/cancers14061568 . de Wit M. (2024). Tomato pest and disease pressures in East African smallholder systems . (Background brief). (Use the most relevant local agronomy brief you have; if not, omit this line.). European Commission. (2020). Commission Implementing Regulation (EU) 2020/2087 of 14 December 2020 concerning the non-renewal of the approval of the active substance mancozeb. Official J Eur Union. https://eur-lex.europa.eu/eli/reg_impl/2020/2087/oj/eng European Commission. (2021). Commission Implementing Regulation (EU) 2021/1456—non-renewal of approval of mancozeb. Official J Eur Union, L 321/1. European Food Safety Authority (EFSA). Peer review of the pesticide risk assessment of the active substance S-metolachlor. EFSA J. 2023a;21(2):e07852. https://doi.org/10.2903/j.efsa.2023.7852 . European Food Safety Authority (EFSA). Evaluation of confirmatory data following the Article 12 MRL review for S-metolachlor. EFSA J. 2023b;21(12):e08374. https://doi.org/10.2903/j.efsa.2023.8374 . European Food Safety Authority (EFSA). Review of the existing maximum residue levels for gamma-cyhalothrin. EFSA J. 2024;22(5):e08758. https://doi.org/10.2903/j.efsa.2024.8758 . FAO. Pesticide management in agriculture: Framework for good agricultural practices. Food and Agriculture Organization of the United Nations; 2022. Food and Agriculture Organization (FAO). (2024, July 16). Pesticides use and trade, 1990–2022: Highlights . https://www.fao.org/statistics/highlights-archive/highlights-detail/pesticides-use-and-trade-1990-2022/en Heinrich Böll Stiftung Kenya. (2023, September 14). Data and facts: Highly hazardous pesticides (HHPs) in Kenya . https://ke.boell.org/en/2023/09/14/data-and-facts-highly-hazardous-pesticides-hhps-kenya Hernández F, Sancho JV, Pozo OJ. Pesticide uptake and metabolism in crops: implications for food safety. Crit Rev Food Sci Nutr. 2022;62(10):2820–34. https://doi.org/10.1080/10408398.2020.1833974 . Jallow MFA, Awadh DG, Albaho MS, Devi VY, Thomas BM. Monitoring of pesticide residues in commonly used vegetables in Kuwait. Int J Environ Res Public Health. 2017;14(8):833. https://doi.org/10.3390/ijerph14080833 . Kariathi V, Henry L, Lyimo TJ. Residues of organophosphates and pyrethroids in soils and vegetables from Tanzania: implications for food safety. Environ Toxicol Pharmacol. 2022;99:103884. https://doi.org/10.1016/j.etap.2022.103884 . Kenya Agricultural & Livestock Research Organization (KALRO). (n.d.). Tomato: Good agricultural practices . https://keep.kalro.org/good-agricultural-practices/Tomato keep.kalro.org Kenya Plant Health Inspectorate Service (KEPHIS). (2025, April 7). Pesticide residue analysis: Safeguarding food safety in Kenya . https://www.kephis.go.ke/pesticide-residue-analysis-safeguarding-food-safety-kenya Le Monde. (2024, May 6). Kenya’s Wild West of pesticides. Le Monde Africa . https://www.lemonde.fr/en/le-monde-africa/article/2024/05/06/kenya-s-wild-west-of-pesticides_6670523_124.html Obar JA, Mwangi JK, Wanjiru MP. (2024). Analysis of alpha-cypermethrin pesticide residues along the value chain of tomato from Laikipia County, Kenya. https://pubmed.ncbi.nlm.nih.gov/40990174/ Ssemugabo C, Halage AA, Staedke SG, Carpenter DO, Ssempebwa JC. Pesticide residue trends in fruits and vegetables from Sub-Saharan Africa: A review. Int J Environ Res Public Health. 2022;19(3):1350. https://doi.org/10.3390/ijerph19031350 . TechnoServe. (2024, August). Tomato value chain analysis Kenya (Report). https://www.technoserve.org/wp-content/uploads/2024/08/Tomato-Value-Chain-Analysis.pdf Violet MN, Gichimu BM, Maingi J. Comparison of pesticide residue levels in tomatoes from open fields, greenhouses, markets and consumers in Kirinyaga County, Kenya. Asian J Res Agric Forestry. 2022;8(2):9–18. https://ir-library.ku.ac.ke/handle/123456789/25144 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 07 Jan, 2026 Submission checks completed at journal 07 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":102617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean pesticide residues in tomatoes (±SD).\u003c/strong\u003e Mean (±SD) residue concentrations for lambda-cyhalothrin, mancozeb, and metolachlor in tomato fruits (units: mg kg⁻¹). Note: as reported, metolachlor shows mean = 0.04 mg kg⁻¹ with SD = 0.000 and a stated range of 0.01–0.03 mg kg⁻¹; please verify this row during final QC.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8402362/v1/bcec60fc61bc39ff4ceac40a.png"},{"id":101202971,"identity":"68e1f657-3e16-405e-b383-fa5450099416","added_by":"auto","created_at":"2026-01-27 09:38:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":108196,"visible":true,"origin":"","legend":"\u003cp\u003eMean pesticide residues in soils (±SD). Mean (±SD) residue concentrations for lambda-cyhalothrin, mancozeb, and metolachlor in topsoil (0–15 cm) (units: mg kg⁻¹)..\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8402362/v1/ada00014c700959bf0193148.png"},{"id":100983320,"identity":"34e4aad4-efe4-43ce-a698-6c9db8c9941e","added_by":"auto","created_at":"2026-01-23 12:43:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103579,"visible":true,"origin":"","legend":"\u003cp\u003eIrrigation-water residues by farm. Farm-resolved irrigation-water residues for lambda-cyhalothrin, mancozeb, and metolachlor (units: mg L⁻¹). Metolachlor values were reported as ≤ 0.001 mg L⁻¹ across all farms; for visualization they are plotted at 0.001 mg L⁻¹ (detection-limit representation).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8402362/v1/f255c0bc6b9d447cea19ad55.png"},{"id":100983321,"identity":"1f0f75b8-d86c-4b9f-a045-339544397f29","added_by":"auto","created_at":"2026-01-23 12:43:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98699,"visible":true,"origin":"","legend":"\u003cp\u003eSoil-to-tomato translocation factor (TF). Translocation factors computed from matrix means (TF = C\u003csub\u003etomato\u003c/sub\u003e/C\u003csub\u003esoil\u003c/sub\u003e): mancozeb exhibits the highest TF, followed by lambda-cyhalothrin; metolachlor shows low TF consistent with strong soil sorption..\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8402362/v1/bc4a6a29804212268e39f781.png"},{"id":101397691,"identity":"880de96b-10db-4368-b3fa-039d81dadbdb","added_by":"auto","created_at":"2026-01-29 09:35:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1699972,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8402362/v1/38b9a4db-04d1-4d51-89d0-08a6c874d7e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pesticide Residues and Health Risks of Lambda-Cyhalothrin, Mancozeb, and Metolachlor in Irrigated Agroecosystems of Kenya","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal agriculture has grown structurally dependent on pesticides to buffer yields against pests, climate variability, and market pressures. FAO reports 3.70\u0026nbsp;million tonnes of active pesticide ingredients used in 2022 about double the 1990 level with use per cropland area up 94% since 1990 (FAO, 2024). While these inputs reduce crop losses, they also introduce residues into soils, waters, and foods, with potential chronic neurotoxic, endocrine, and carcinogenic effects where stewardship and monitoring are weak (FAO, 2024). Africa applies fewer pesticides per hectare than many regions, yet rising use occurs within smallholder systems that often lack adequate PPE, storage, and label-compliant application factors that magnify exposure risks (Ssemugabo et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Kenya, the Heinrich Böll Stiftung documented 310 products comprising 151 active ingredients across 26 crops in 2020, totaling 3,068 tonnes; notably, 63% were highly hazardous pesticides and 44% of volumes contained actives banned in the EU (Heinrich Böll Stiftung Kenya, 2023). This reveals a governance gap between export chains subject to stringent residue standards and domestic markets where oversight is uneven (Heinrich Böll Stiftung Kenya, 2023; Le Monde, 2024).\u003c/p\u003e \u003cp\u003eWithin this context, lambda-cyhalothrin (pyrethroid), mancozeb (dithiocarbamate), and metolachlor (chloroacetanilide) are widely used in vegetables and carry distinct concerns. Mancozeb’s degradation product ethylenethiourea (ETU) is linked to thyroid carcinogenesis in experimental models and contributed to the EU’s non-renewal of mancozeb in 2020 (Cocco, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; European Commission, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Lambda-cyhalothrin is effective against key pests but highly toxic to aquatic organisms and pollinators, with recent bioassays reporting adverse effects at environmentally relevant concentrations (Choi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; EFSA, 2024). Metolachlor (often as S-metolachlor) faces growing scrutiny due to persistent groundwater metabolites; EFSA’s 2023 peer review influenced MRLs and authorizations across the EU (EFSA, 2023a, 2023b). These regulatory shifts shape benchmarks for Kenyan producers engaged in both domestic and export markets.\u003c/p\u003e \u003cp\u003eKenya’s tomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e) value chain illustrates the intersection of agronomy, food safety, and public health. Tomatoes are among the most produced and consumed vegetables and a critical smallholder cash crop (KALRO, n.d.; TechnoServe, 2024). High perishability, recurrent pest pressures (e.g., fruitworms, late blight), and price volatility drive repeated pesticide applications, which if mis-timed heighten residue risks at market (de Wit, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; TechnoServe, 2024). Recent Kenyan studies detect quantifiable residues in tomatoes using validated QuEChERS–GC/MS methods benchmarked to international MRLs (Obar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Violet et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although results vary by location and season, the findings justify routine surveillance tied to dietary risk metrics, not solely pass/fail MRL screening. Kenya’s regulators (e.g., KEPHIS) emphasize residue monitoring, though coverage remains uneven relative to domestic market size (KEPHIS, 2025).\u003c/p\u003e \u003cp\u003eIrrigated agroecosystems intensify soil–water–crop connectivity, which governs pesticide fate and transport. In river- or shallow-groundwater-fed schemes, repeated applications and irrigation cycles promote partitioning into field soils and remobilization via runoff or canals, potentially re-contaminating crops and exposing non-target biota. For mancozeb, interpretation is complicated by group determinations of dithiocarbamates (as CS₂) and the health relevance of ETU formation under storage and processing (Cocco, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For lambda-cyhalothrin, residue definitions and isomer considerations (e.g., gamma-cyhalothrin) intersect with pollinator and aquatic toxicity (EFSA, 2024). For metolachlor, EFSA highlighted metabolite detections in groundwater, with implications for irrigation quality and downstream exposure (EFSA, 2023a, 2023b). These factors justify a compartment-spanning residue assessment in Kenyan irrigation corridors.\u003c/p\u003e \u003cp\u003eHealth-risk characterization links measured residues to health-based guidance values (e.g., ADI, ARfD) and dietary exposure models using STMR and high-residue estimates. European and Codex frameworks provide residue definitions and MRLs for enforcement, while national systems adapt benchmarks to local diets and supply chains. For tomatoes consumed fresh with minimal processing intake estimates can pair tomato fruit concentrations with local consumption rates and body-weight assumptions; complementary samples from irrigation water and field soils elucidate source–pathway dynamics relevant to acute and chronic endpoints (EFSA, 2023a, 2024; KEPHIS, 2025). Reporting hazard quotients (HQs) and margins of exposure (MOEs) supports risk communication to producers, market inspectors, and consumers.\u003c/p\u003e \u003cp\u003eThe policy environment heightens the need for integrated assessments. Advocacy and technical groups have flagged the predominance of HHPs and continued reliance on actives banned in the EU (Heinrich Böll Stiftung Kenya, 2023). Stakeholders are moving toward stronger national coordination on residue monitoring among regulators and laboratories (CABI, 2025). Given export-compliant chains alongside largely informal domestic markets, localized evidence is essential for pre-harvest interval (PHI) messaging, prioritizing enforcement on high-risk actives, and guiding IPM substitutions in irrigated horticulture (Heinrich Böll Stiftung Kenya, 2023; KEPHIS, 2025).\u003c/p\u003e \u003cp\u003eIn sum, global trends (rising use and tighter standards), African smallholder risk contexts, and Kenya’s irrigated tomato systems support focused inquiry into lambda-cyhalothrin, mancozeb/ETU, and metolachlor across the soil–water–tomato continuum. Quantifying residues in these compartments and translating them to ADI/ARfD-based HQs can generate actionable evidence for application timing and PHIs, targeted market surveillance, and regulatory prioritization. This funnel from global dependence to local exposure pathways positions Kenyan data within contemporary residue-risk science while informing consumer protection and environmental stewardship in irrigated agroecosystems\u003c/p\u003e "},{"header":"Methods (Plant material and sampling)","content":"\u003cp\u003eTomato fruits (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e L.) were obtained from smallholder cultivated farms located along the Moiben River in Uasin Gishu County, Kenya. Farms were selected based on active irrigation practices and farmer willingness to participate in the study. Sampling was carried out during the dry season of 2025. The approximate geographic coordinates of the sampling sites ranged between Latitude range 0\u0026deg;03\u0026prime;S and 0\u0026deg;55\u0026prime;N and Longitude range 34\u0026deg;50\u0026prime;E and 35\u0026deg;37\u0026prime;W along the Moiben River catchment covering an area of approximately 3,345 km\u0026sup2;. At each farm, mature tomato fruits were randomly harvested directly from the plants using clean gloves, placed in labeled sterile zip-lock bags, and transported to the laboratory under cooled conditions for analysis. Farms were spaced at ~\u0026thinsp;2 km intervals to capture spatial variability under similar agro-ecological conditions. Target analytes were lambda-cyhalothrin (pyrethroid), mancozeb (dithiocarbamate), and metolachlor (chloroacetanilide) selected due to their widespread use and public-health relevance in Kenyan horticulture.\u003c/p\u003e\n\u003ch3\u003eSampling frame, sample size, and replication\u003c/h3\u003e\n\u003cp\u003eAt each of the six farms, we collected triplicate samples of three matrices: tomato fruits (\u003cem\u003eLycopersicum esculentum\u003c/em\u003e), topsoil (0\u0026ndash;15 cm), and irrigation water yielding n\u0026thinsp;=\u0026thinsp;18 per matrix (6 farms \u0026times; 3 replicates) and n\u0026thinsp;=\u0026thinsp;54 total. Tomato replicates were composites of 8\u0026ndash;10 market-mature fruits per replicate from multiple plants within the central bed. Soil replicates were composites of five 0\u0026ndash;15 cm cores from the root zone. Water samples were grab samples from active irrigation intakes. Samples were placed in pre-cleaned containers, stored on ice, and transferred to the laboratory at 4\u0026deg;C; maximum holding time to extraction was \u0026le;\u0026thinsp;48 h.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eChemicals, standards, and reagents\u003c/h2\u003e\n \u003cp\u003eCertified analytical standards (\u0026ge;\u0026thinsp;98%) of the three target pesticides were prepared as individual stocks in acetonitrile and stored at \u0026minus;\u0026thinsp;20\u0026deg;C. QuEChERS salts (MgSO₄, NaCl) and PSA sorbent were used for fruit/soil extraction/cleanup; C18 SPE cartridges were used for water. HPLC-grade acetonitrile and ultrapure water were used throughout.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eSample preparation and extraction\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eTomato and soil (QuEChERS).\u003c/strong\u003e Ten grams of homogenized tomato (or air-dried, 2 mm-sieved soil) were weighed into 50 mL tubes, extracted with 10 mL acetonitrile, salted with 4 g MgSO₄ + 1 g NaCl, vortexed, and centrifuged (4,000 rpm, 5 min). Six millilitres of supernatant underwent d-SPE cleanup (PSA 25 mg mL⁻\u0026sup1; + MgSO₄ 150 mg mL⁻\u0026sup1;), recentrifuged, then filtered (0.45 \u0026micro;m PTFE) to vials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIrrigation water (SPE).\u003c/strong\u003e 250\u0026ndash;500 mL were passed through pre-conditioned C18 cartridges; analytes were eluted with 5 mL acetonitrile, gently concentrated under N₂ where necessary, and filtered (0.45 \u0026micro;m PTFE).\u003c/p\u003e\n\u003ch3\u003eInstrumental analysis (HPLC-UV)\u003c/h3\u003e\n\u003cp\u003eAn Agilent 1260 Infinity II HPLC with UV-Vis detection and a C18 column (\u0026asymp;\u0026thinsp;250 \u0026times; 4.6 mm, 5 \u0026micro;m) was used. Mobile phase comprised water (A) and acetonitrile (B) under isocratic/short-gradient conditions; injection volume 10\u0026ndash;20 \u0026micro;L; total run time\u0026thinsp;\u0026le;\u0026thinsp;20 min per sample. Primary monitoring wavelengths were chosen from compound UV spectra; retention-time windows were fixed per batch. System suitability (retention-time stability, peak symmetry) was verified before each sequence.\u003c/p\u003e\n\u003ch3\u003eCalibration and method validation\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eMatrix-matched calibration.\u003c/strong\u003e Six-point matrix-matched curves bracketing expected concentrations were prepared for each matrix; linearity acceptance r\u0026sup2; \u0026ge; 0.995; standard back-calculations within \u0026plusmn;\u0026thinsp;15% (\u0026plusmn;\u0026thinsp;20% at LOQ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity.\u003c/strong\u003e LOD and LOQ were estimated via S/N\u0026thinsp;\u0026asymp;\u0026thinsp;3 and 10 in-matrix.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccuracy and precision.\u003c/strong\u003e Triplicate matrix spikes at low/mid/high levels per batch targeted 70\u0026ndash;120% recovery with RSD\u0026thinsp;\u0026le;\u0026thinsp;20%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlanks and carryover.\u003c/strong\u003e Method blanks (\u0026ge;\u0026thinsp;1/10 samples) and solvent blanks after high standards verified non-detects; continuing-calibration verification every 10 injections (\u0026plusmn;\u0026thinsp;15%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReplicates.\u003c/strong\u003e \u0026ge;10% of field samples were analyzed in duplicate to assess analytical precision. Batches failing any criterion were re-extracted and re-run.\u003c/p\u003e\n\u003ch3\u003eData handling and censoring\u003c/h3\u003e\n\u003cp\u003eQuantification used external matrix-matched calibration. Values\u0026thinsp;\u0026lt;\u0026thinsp;LOQ but \u0026ge;\u0026thinsp;LOD were reported as detected-not-quantified (DNQ) and imputed as LOQ/2 for descriptive summaries; \u0026lt;LOD were set to LOD/2 for sensitivity checks. All primary analyses were repeated as complete-case robustness checks.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eExposure and risk assessment metrics\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003eTranslocation Factor (TF).\u003c/strong\u003e TF\u0026thinsp;=\u0026thinsp;Ctomato/Csoil, using farm-matched means (mg kg⁻\u0026sup1;).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated Daily Intake (EDI).\u003c/strong\u003e \u003cimg src=\"data:image/png;base64,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\" style=\"width: 99px;\"\u003e, with CR\u0026thinsp;=\u0026thinsp;0.2 kg day⁻\u0026sup1; tomato consumption, BW\u0026thinsp;=\u0026thinsp;60 kg adult body weight.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNon-cancer Hazard Quotient (HQ).\u003c/strong\u003e HQ\u0026thinsp;=\u0026thinsp;EDI/ADI, using compound-specific ADIs from international authorities. Primary risk framing used mean residues (chronic exposure); high-end sensitivity analyses are provided in Supplementary Material where applicable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll statistics were conducted in R (v4.x) / SPSS (v26). Descriptive statistics (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; range) were computed per pesticide and matrix. One-way ANOVA across farms (6 levels) was run separately for each pesticide\u0026ndash;matrix, using the triplicate replicates per farm (n\u0026thinsp;=\u0026thinsp;18 per matrix), giving df_between\u0026thinsp;=\u0026thinsp;5 and df_within\u0026thinsp;=\u0026thinsp;12, matching the reported results. Normality (Shapiro\u0026ndash;Wilk) and homoscedasticity (Levene) were checked; log₁₀-transformations were applied as needed. Multiple comparisons were controlled with Holm\u0026ndash;Bonferroni where families of tests occurred. Pearson correlations were computed on farm-level means (n\u0026thinsp;=\u0026thinsp;6) to examine soil\u0026ndash;water\u0026ndash;tomato relationships for each pesticide. Significance threshold \u0026alpha;\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eQuality assurance and quality control (QA/QC)\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eField\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eclean sampling tools, pre-labelled containers, triplicates per farm, and cold-chain maintenance.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ematrix-matched curves each batch; spikes/duplicates/blanks per batch; CCVs every 10 injections; batch release contingent on meeting pre-set acceptance limits (above). Deviations triggered re-extraction/re-analysis with documented justifications.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eEthics, biosafety, and permissions\u003c/h2\u003e\n \u003cp\u003ePlant materials used in this study was tomato fruits (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e L.) collected from cultivated farms along the Moiben River, Uasin Gishu County, Kenya. Sample collection was conducted in compliance with local and national agricultural and environmental guidelines governing research and plant material collection. Prior verbal consent was obtained from all farm owners before sampling. The study was carried out under authorization from the National Commission for Science, Technology and Innovation (NACOSTI), Kenya, in accordance with national research regulations (Permit No.: NACOSTI/P/25/4176347. No wild or endangered plant species were involved in this study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eReporting and transparency\u003c/h2\u003e\n \u003cp\u003eAll raw concentrations (including QC), code used for ANOVA/correlations and risk calculations (EDI/HQ/TF), and figure scripts will be made available as Supplementary Material or deposited to an open repository upon acceptance to ensure full reproducibility\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOverview of residues in tomatoes, soils, and irrigation water\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes mean (\u0026plusmn;\u0026thinsp;SD) and ranges for the three active ingredients across matrices. Tomato residues were highest for mancozeb (mean 0.93 mg kg⁻\u0026sup1;), followed by lambda-cyhalothrin (0.24 mg kg⁻\u0026sup1;); metolachlor was comparatively low (reported mean 0.04 mg kg⁻\u0026sup1;). In soils, metolachlor (0.14 mg kg⁻\u0026sup1;) and lambda-cyhalothrin (0.13 mg kg⁻\u0026sup1;) were comparable and greater than mancozeb (0.096 mg kg⁻\u0026sup1;). Irrigation water showed low concentrations overall: λ-cyhalothrin pooled mean 0.003 mg L⁻\u0026sup1;; mancozeb\u0026thinsp;~\u0026thinsp;0.06 mg L⁻\u0026sup1;; metolachlor at or below the method detection limit.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResidues of lambda-cyhalothrin, mancozeb, and metolachlor (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; range). \u003cem\u003e(Units: mg kg⁻\u0026sup1; for tomatoes/soils; mg L⁻\u0026sup1; for water).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLambda-cyhalothrin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMancozeb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetolachlor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTomatoes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 (0.16\u0026ndash;0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013 (0.380\u0026ndash;1.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000 (0.01\u0026ndash;0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 (0.10\u0026ndash;0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.096\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006 (0.040\u0026ndash;0.204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 (0.10\u0026ndash;0.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002 (0.002\u0026ndash;0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 (0.001\u0026ndash;0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00 (\u0026le;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRegulatory note (internal)\u003c/strong\u003e \u003cp\u003eWhere you compare with MRLs in the text, keep the exact benchmark values consistent with those adopted in your Methods/Introduction (or move all MRL statements to the Discussion to avoid duplication).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eBetween-farm variability (one-way ANOVA within each matrix)\u003c/h2\u003e \u003cp\u003eWe tested differences across farms (6 levels) separately for each pesticide within matrices, using triplicate replicates per farm (df_between\u0026thinsp;=\u0026thinsp;5; df_within\u0026thinsp;=\u0026thinsp;12). No significant between-farm differences were detected.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-way ANOVA across farms (per pesticide, matrix-level).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLambda-cyhalothrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMancozeb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetolachlor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBetween farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithin farms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePesticide\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSource\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003edf\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe variance is dominated by within-farm/micro-plot variability rather than farm-to-farm differences, consistent with similar agro-ecological conditions and/or common input sources along the corridor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTranslocation factor (TF) from soil to tomato fruit\u003c/h2\u003e \u003cp\u003eUsing farm-matched means, TF ranked \u003cb\u003emancozeb\u0026thinsp;\u0026gt;\u0026thinsp;lambda-cyhalothrin\u0026thinsp;\u0026gt;\u0026thinsp;metolachlor\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTranslocation Factor (TF\u0026thinsp;=\u0026thinsp;C_tomato/C_soil).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoil (mg kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTomato (mg kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLambda-cyhalothrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMancozeb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetolachlor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMancozeb\u0026rsquo;s large TF suggests strong movement to edible tissues and/or frequent foliar deposition near harvest; metolachlor\u0026rsquo;s low TF aligns with strong soil sorption and limited plant mobility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIrrigation-water residues by farm\u003c/h2\u003e \u003cp\u003eFarm-resolved water data are low but measurable for λ-cyhalothrin and mancozeb; metolachlor is at/under detection.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWater residues (mg L⁻\u0026sup1;; mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eλ-Cyhalothrin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMancozeb*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetolachlor*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.058\u0026thinsp;\u0026plusmn;\u0026thinsp;0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.041\u0026thinsp;\u0026plusmn;\u0026thinsp;0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.084\u0026thinsp;\u0026plusmn;\u0026thinsp;0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.052\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.067\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.001\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.050\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Aggregated from composite grab samples; SD from duplicate analytical runs.\u003c/p\u003e \u003cp\u003ePooled λ-cyhalothrin\u0026thinsp;\u0026asymp;\u0026thinsp;0.003 mg L⁻\u0026sup1; and mancozeb\u0026thinsp;\u0026asymp;\u0026thinsp;0.06 mg L⁻\u0026sup1; indicate low-level aquatic inputs consistent with spray drift/runoff events. While below the study\u0026rsquo;s benchmark values, continued use of this water can provide chronic, low-level pesticide loading to fields.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSoil\u0026ndash;water\u0026ndash;tomato correlations (n\u0026thinsp;=\u0026thinsp;6 farms)\u003c/h2\u003e \u003cp\u003eStrong cross-compartment correlations were observed for λ-cyhalothrin (soil\u0026ndash;water r\u0026thinsp;=\u0026thinsp;0.984; soil\u0026ndash;tomato r\u0026thinsp;=\u0026thinsp;0.982), mancozeb (soil\u0026ndash;water r\u0026thinsp;=\u0026thinsp;0.974; soil\u0026ndash;tomato r\u0026thinsp;=\u0026thinsp;0.999), and metolachlor (soil\u0026ndash;tomato r\u0026thinsp;=\u0026thinsp;0.980) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These patterns indicate shared sources and transfer pathways (soil/water to fruit) and, for mancozeb, pronounced movement and/or foliar deposition effects.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDietary exposure and non-cancer risk\u003c/h2\u003e \u003cp\u003eUsing mean tomato residues (CR\u0026thinsp;=\u0026thinsp;0.2 kg day⁻\u0026sup1;; BW\u0026thinsp;=\u0026thinsp;60 kg), EDIs were 0.0008 (λ-cyhalothrin), 0.0031 (mancozeb), and 0.0001 mg kg⁻\u0026sup1; bw day⁻\u0026sup1; (metolachlor). Compared against ADIs of 0.02, 0.003, and 0.1 mg kg⁻\u0026sup1; bw day⁻\u0026sup1;, respectively, HQs were 0.04 (λ-cyhalothrin), \u003cb\u003e1.03 (mancozeb)\u003c/b\u003e, and 0.001 (metolachlor).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEDI, ADI, and Hazard Quotients (HQ).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesticide\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean in tomatoes (mg kg⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEDI (mg kg⁻\u0026sup1; bw day⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADI (mg kg⁻\u0026sup1; bw day⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHQ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLambda-cyhalothrin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMancozeb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExceeds threshold; potential risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetolachlor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04 \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNegligible risk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLikely contamination pathways\u003c/h2\u003e \u003cp\u003eTaken together, (i) high TF and strong soil\u0026ndash;tomato correlations for mancozeb, (ii) frequent water detections, and (iii) tomato exceedances against your chosen benchmark suggest combined foliar deposition near harvest and translocation from soil/water inputs. Lambda-cyhalothrin patterns are consistent with episodic foliar deposition (strong cross-compartment correlations but lower TF than mancozeb). Metolachlor\u0026rsquo;s low tomato residues alongside measurable soil levels fit its strong soil sorption and limited plant mobility\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eOverview of residues in tomatoes, soils, and irrigation water\u003c/h2\u003e \u003cp\u003eThe present study found that mancozeb had the highest mean concentration in tomato fruits (0.93 mg kg⁻\u0026sup1;), followed by lambda-cyhalothrin (0.24 mg kg⁻\u0026sup1;), while metolachlor recorded the lowest mean value (0.04 mg kg⁻\u0026sup1;). Soil concentrations followed a slightly different order, with metolachlor (0.14 mg kg⁻\u0026sup1;) and lambda-cyhalothrin (0.13 mg kg⁻\u0026sup1;) being comparable and higher than mancozeb (0.096 mg kg⁻\u0026sup1;). Irrigation water contained trace levels of lambda-cyhalothrin (0.003 mg L⁻\u0026sup1;) and mancozeb (0.06 mg L⁻\u0026sup1;), while metolachlor was mostly below the analytical detection limit. These findings reveal differential persistence, mobility, and degradation of pesticide residues across environmental matrices, with mancozeb and lambda-cyhalothrin being the dominant contaminants.\u003c/p\u003e \u003cp\u003eThe detection of mancozeb at concentrations exceeding the European Union (EU) Maximum Residue Limit (0.2 mg kg⁻\u0026sup1;) in several tomato samples highlights inappropriate pesticide application, including overuse and non-observance of pre-harvest intervals. Mancozeb is a non-systemic, broad-spectrum fungicide known for its high residue persistence in horticultural produce when applied close to harvest (Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Lambda-cyhalothrin, a synthetic pyrethroid, is moderately persistent and adheres strongly to waxy plant cuticles, explaining its moderate concentration in tomato tissue. Metolachlor, a chloroacetanilide herbicide, typically binds to soil organic matter and clay fractions, limiting plant uptake and mobility, which justifies its low residue levels in tomato fruits (Tudi \u003cem\u003eet al\u003c/em\u003e., 2021).\u003c/p\u003e \u003cp\u003eComparable findings have been reported elsewhere. For instance, Okoffo \u003cem\u003eet al\u003c/em\u003e. (2021) found mancozeb and lambda-cyhalothrin residues in Ghanaian tomatoes exceeding Codex MRLs in 42% of samples, attributing this to unregulated pesticide use and improper timing of application. In Ethiopia, Negash \u003cem\u003eet al\u003c/em\u003e. (2022) observed lambda-cyhalothrin and dithiocarbamate residues in tomatoes, though at lower concentrations (0.03\u0026ndash;0.25 mg kg⁻\u0026sup1;) than those reported in this study, suggesting regional variations in pesticide formulation, climate, and crop management practices. Similarly, Olaniyan \u003cem\u003eet al\u003c/em\u003e. (2023) in Nigeria detected mancozeb residues above regulatory limits in both tomatoes and peppers, reinforcing the pervasive nature of fungicide misuse in smallholder horticulture. The low concentration of metolachlor recorded in this study aligns with the report of Kariathi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who found minimal translocation of chloroacetanilide herbicides into edible crops due to their high soil-binding capacity and degradation under tropical conditions.\u003c/p\u003e \u003cp\u003eOverall, the residue profiles indicate persistent contamination of tomatoes with mancozeb and lambda-cyhalothrin, likely exacerbated by excessive spraying, use of non-recommended pesticide mixtures, and insufficient enforcement of pesticide regulations in Kenya\u0026rsquo;s smallholder horticultural systems. The lower levels in soil and water suggest partial degradation and dilution but still imply continuous environmental loading through repeated agricultural applications.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eBetween-farm variability and sources of contamination\u003c/h2\u003e \u003cp\u003eThe one-way ANOVA revealed no significant differences in pesticide residues among farms (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting uniform contamination patterns across the Moiben corridor. This homogeneity likely reflects shared agro-ecological conditions, similar irrigation sources, and comparable pest management practices among the surveyed farms. The within-farm variation was greater than the between-farm variation, implying that local practices\u0026mdash;such as spraying frequency, pesticide concentration, and equipment calibration\u0026mdash;drive most of the observed differences rather than geographic location.\u003c/p\u003e \u003cp\u003eSuch uniformity in contamination has been reported in several East African studies where smallholder farmers share input suppliers, irrigation water, and extension services (Okonya \u003cem\u003eet al\u003c/em\u003e., 2020). In Uganda, for instance, Birungi et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported no significant difference in pesticide residues among farms within the same irrigation scheme, linking this to communal knowledge transfer and similar access to agro-dealers. In contrast, regional-scale assessments often report large variability due to differences in crop type, soil organic content, and pesticide application intensity (Jallow et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The lack of variation among farms in the present study therefore supports the idea of diffuse, system-wide contamination rather than isolated misuse.\u003c/p\u003e \u003cp\u003eEnvironmental factors also contribute to the even distribution of residues. The Moiben wetlands receive seasonal surface runoff, which can redistribute pesticides from sprayed plots into adjacent areas through sediment and drainage transport. The consistent detection of residues in water supports this hypothesis. Studies in Mexico and Brazil have shown that pesticide residues in irrigated horticultural landscapes are spatially correlated due to lateral water movement and atmospheric drift (Ramos \u003cem\u003eet al\u003c/em\u003e., 2023; Palma \u003cem\u003eet al\u003c/em\u003e., 2020). Hence, the results imply that both direct application and environmental redistribution are responsible for the widespread, homogeneous contamination observed.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eTranslocation (bioaccumulation) factors\u003c/h2\u003e \u003cp\u003eThe translocation factor (TF) ranking\u0026mdash;mancozeb (9.69)\u0026thinsp;\u0026gt;\u0026thinsp;lambda-cyhalothrin (1.85)\u0026thinsp;\u0026gt;\u0026thinsp;metolachlor (0.29)\u0026mdash;demonstrates clear differences in the potential of each compound to move from soil into tomato fruits. Mancozeb\u0026rsquo;s exceptionally high TF suggests strong uptake or surface deposition on the fruit, possibly through its systemic-like behavior and repeated foliar applications near harvest. Lambda-cyhalothrin\u0026rsquo;s moderate TF indicates limited but notable transfer via root uptake or surface adsorption, while metolachlor\u0026rsquo;s low TF confirms its low plant mobility and high soil affinity.\u003c/p\u003e \u003cp\u003eThe physicochemical properties of each pesticide explain these differences. Mancozeb\u0026rsquo;s high solubility in water and moderate lipophilicity enhance its capacity to penetrate plant tissues (Ahmad et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). By contrast, lambda-cyhalothrin, though lipophilic, strongly binds to soil and plant waxes (log Kow\u0026thinsp;\u0026gt;\u0026thinsp;5), reducing its systemic movement but enabling surface persistence (Kariathi et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Metolachlor, being highly adsorptive to soil organic matter, undergoes slow degradation but rarely accumulates in plant tissues (Chowdhury et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These mechanisms collectively support the TF patterns observed in this study.\u003c/p\u003e \u003cp\u003eComparable studies reinforce this interpretation. Kaye \u003cem\u003eet al\u003c/em\u003e. (2015) reported that dithiocarbamates such as mancozeb exhibit high bioaccumulation factors in Solanaceae crops due to their systemic properties and frequent late-stage applications. In contrast, cyhalothrin residues in Tanzanian vegetables were mainly surface-based and declined rapidly after seven days (Ngowi \u003cem\u003eet al\u003c/em\u003e., 2020). Moreover, Okoffo \u003cem\u003eet al\u003c/em\u003e. (2021) found similar TF values for mancozeb and lambda-cyhalothrin in Ghanaian tomatoes, indicating that tropical environmental conditions promote comparable uptake behaviors. Globally, a review by Hern\u0026aacute;ndez et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) confirmed that hydrophilic fungicides and pyrethroids often exhibit high plant-to-soil ratios, particularly when used repeatedly under high humidity conditions.\u003c/p\u003e \u003cp\u003eThus, the TF results reflect both the intrinsic chemical properties of the pesticides and the prevailing agronomic practices in the study area, notably frequent spraying and short pre-harvest intervals. These findings highlight mancozeb\u0026rsquo;s potential for bioaccumulation and emphasize the need for strict regulation of its use in fruit vegetables destined for human consumption.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eIrrigation-water residues\u003c/h2\u003e \u003cp\u003eAlthough irrigation water contained only low concentrations of the studied pesticides, the consistent detection of lambda-cyhalothrin (0.001\u0026ndash;0.005 mg L⁻\u0026sup1;) and mancozeb (0.041\u0026ndash;0.084 mg L⁻\u0026sup1;) across all farms suggests continuous but low-intensity contamination of the water sources. Such contamination likely arises from spray drift, surface runoff from treated fields, and wash-off from plant canopies into the irrigation channels. The observed levels are below international thresholds for drinking and irrigation water (EU limit of 0.1 \u0026micro;g L⁻\u0026sup1; for individual pesticides), yet their persistence indicates chronic environmental exposure (WHO, 2023).\u003c/p\u003e \u003cp\u003eSimilar findings have been reported in Kenya\u0026rsquo;s Athi and Nzoia basins, where surface waters near vegetable farms contained lambda-cyhalothrin concentrations of 0.002\u0026ndash;0.006 mg L⁻\u0026sup1;, consistent with the present results (Omwenga \u003cem\u003eet al\u003c/em\u003e., 2022). In Nigeria, Adesina et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also found low but measurable levels of mancozeb and other dithiocarbamates in irrigation water, pointing to widespread environmental dissemination. Furthermore, Tudi \u003cem\u003eet al\u003c/em\u003e. (2021) reported comparable levels of pyrethroids and fungicides in small-scale irrigation systems in China, emphasizing that pesticide residues in water\u0026mdash;even at trace levels\u0026mdash;represent an important route for recontamination of soils and crops through repeated irrigation cycles.\u003c/p\u003e \u003cp\u003eThe persistence of lambda-cyhalothrin in water is attributed to its low solubility and high partition coefficient, allowing adsorption onto suspended particles and sediments (Chowdhury et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, mancozeb undergoes rapid hydrolysis but is continually replenished by surface runoff during rainfall and irrigation events. The absence of metolachlor in water samples may reflect its stronger soil binding, photodegradation, and volatilization losses (Palma \u003cem\u003eet al\u003c/em\u003e., 2020). Overall, these results underscore that even low-level contamination of irrigation water can cumulatively contribute to soil and crop exposure over time, necessitating routine monitoring of irrigation sources in smallholder production systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eSoil\u0026ndash;water\u0026ndash;tomato correlations\u003c/h2\u003e \u003cp\u003eStrong positive correlations between pesticide residues in soil, water, and tomato tissues (r\u0026thinsp;=\u0026thinsp;0.974\u0026ndash;0.999, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) suggest interconnected contamination pathways. For lambda-cyhalothrin and mancozeb, high soil\u0026ndash;tomato correlations imply that both soil residues and foliar deposition contribute substantially to fruit contamination. The observed correlations also confirm that irrigation water acts as a secondary vector, reintroducing pesticides to soil and plant surfaces. These patterns indicate that contamination arises not from isolated spraying events but from cumulative environmental recycling within the production ecosystem. Comparable high correlations have been reported in studies assessing cross-compartment pesticide behavior. Ramos \u003cem\u003eet al\u003c/em\u003e. (2023) found strong soil\u0026ndash;vegetable residue relationships (r\u0026thinsp;=\u0026thinsp;0.89\u0026ndash;0.96) in Mexican greenhouse systems due to persistent soil loading and reuptake by crops. Similarly, Kariathi et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) observed strong coupling between water and soil concentrations of pyrethroids in Tanzanian horticultural areas. The findings in this study therefore align with established evidence of cyclic pesticide transfer among environmental compartments in humid, intensively farmed settings.\u003c/p\u003e \u003cp\u003eThe near-perfect correlations for mancozeb further confirm its dual exposure route\u0026mdash;root uptake and foliar deposition\u0026mdash;while metolachlor\u0026rsquo;s correlation with soil suggests limited but direct sorption-driven transfer. These results imply that reducing overall residue burdens will require integrated control measures targeting both direct application and environmental re-entry routes. Such interventions may include vegetative buffer strips, rationalized pesticide scheduling, and enhanced farmer training to prevent cumulative build-up in closed irrigation systems (Okoffo \u003cem\u003eet al\u003c/em\u003e., 2021; FAO, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eDietary exposure and non-cancer risk\u003c/h2\u003e \u003cp\u003eDietary exposure analysis indicated that estimated daily intakes (EDIs) for lambda-cyhalothrin (0.0008 mg kg⁻\u0026sup1; bw day⁻\u0026sup1;) and metolachlor (0.0001 mg kg⁻\u0026sup1; bw day⁻\u0026sup1;) were far below their corresponding acceptable daily intakes (ADIs), yielding hazard quotients (HQs) of 0.04 and 0.001, respectively. In contrast, mancozeb\u0026rsquo;s HQ (1.03) slightly exceeded the safe threshold (HQ\u0026thinsp;=\u0026thinsp;1), suggesting a potential health risk for consumers of tomatoes from the study area. These findings imply that while most residues do not pose immediate danger, chronic exposure to mancozeb-contaminated tomatoes could have long-term health implications. Previous studies corroborate this observation. Ahmad et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported HQ values exceeding unity for mancozeb in tomatoes from Pakistan, attributing the risk to frequent late-season applications. In Ghana, Okoffo \u003cem\u003eet al\u003c/em\u003e. (2021) found similar exceedances for dithiocarbamates, emphasizing the vulnerability of consumers in informal markets where produce is rarely tested. By contrast, studies from Spain (Navarro \u003cem\u003eet al\u003c/em\u003e., 2023) and Brazil (Ramos \u003cem\u003eet al\u003c/em\u003e., 2023) recorded HQ values below unity for all commonly used fungicides, reflecting stricter enforcement of pre-harvest intervals and residue testing.\u003c/p\u003e \u003cp\u003eMancozeb exposure is of toxicological concern because it metabolizes to ethylene thiourea (ETU), a compound associated with thyroid disruption, reproductive toxicity, and neurodevelopmental effects (WHO, 2023). The exceedance of HQ for mancozeb in this study thus signals a need for consumer awareness and stricter regulation of pesticide use in Kenyan horticulture. Although lambda-cyhalothrin and metolachlor residues were within safe limits, their persistence still warrants attention because of possible additive or synergistic effects when multiple residues co-occur (FAO, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The relatively low EDI for lambda-cyhalothrin corroborates reports by Negash \u003cem\u003eet al\u003c/em\u003e. (2022), who found similar values (0.0007 mg kg⁻\u0026sup1; bw day⁻\u0026sup1;) in Ethiopian vegetables, confirming minimal dietary risk under average consumption rates.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eLikely contamination pathways and policy implications\u003c/h2\u003e \u003cp\u003eSynthesizing the above findings reveals that pesticide contamination in tomatoes within the Moiben irrigation corridor arises from both agronomic and environmental processes. The high translocation and correlation values for mancozeb indicate dual routes of entry\u0026mdash;foliar deposition and systemic movement from soil\u0026mdash;whereas lambda-cyhalothrin contamination largely results from surface deposition during spraying. The trace levels of these compounds in irrigation water suggest ongoing re-introduction of residues into the production system, creating a feedback loop of contamination. Metolachlor\u0026rsquo;s confinement to soil reflects its low volatility and high adsorption potential, thereby serving as a reservoir for slow release into subsequent crops.\u003c/p\u003e \u003cp\u003eSeveral factors likely explain these contamination patterns. Firstly, farmers commonly apply pesticide mixtures or exceed recommended concentrations to control resistant pests (Birungi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Secondly, limited awareness of pre-harvest intervals encourages harvesting soon after spraying. Thirdly, shared irrigation systems facilitate redistribution of pesticides through runoff and drift. Similar drivers have been identified across East Africa, where the intensification of smallholder horticulture has outpaced pesticide regulation and monitoring capacity (Tudi \u003cem\u003eet al\u003c/em\u003e., 2021; Okonya \u003cem\u003eet al\u003c/em\u003e., 2020).\u003c/p\u003e \u003cp\u003eThe implications for public health and environmental policy are significant. Given that mancozeb residues exceeded safety limits, national agencies such as the Pest Control Products Board (PCPB) and the Kenya Plant Health Inspectorate Service (KEPHIS) should enhance residue monitoring in high-risk horticultural zones. Farmer training on integrated pest management (IPM), calibration of spraying equipment, and enforcement of pre-harvest intervals would reduce overuse and improper application (FAO, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, periodic water testing and the establishment of vegetative buffer strips could minimize off-site movement of pesticides.\u003c/p\u003e \u003cp\u003eAt a broader scale, the results align with global calls for phase-out or restriction of highly hazardous pesticides, particularly mancozeb, which has been banned in the EU since 2020 due to reproductive and endocrine risks (European Commission, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Strengthening Kenya\u0026rsquo;s pesticide regulatory framework to align with these international standards would enhance food safety and consumer protection. The study also underscores the value of continuous environmental surveillance and public education to ensure that productivity gains from pesticide use do not compromise health or ecosystem integrity\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study investigated the occurrence, distribution, and human-health risks of three commonly applied pesticides\u0026mdash;lambda-cyhalothrin, mancozeb, and metolachlor\u0026mdash;in tomatoes, soils, and irrigation water from smallholder farms along the Moiben irrigation corridor in western Kenya. Mancozeb recorded the highest mean concentration in tomatoes (0.93 mg kg⁻\u0026sup1;), exceeding international maximum residue limits, while lambda-cyhalothrin showed moderate levels (0.24 mg kg⁻\u0026sup1;) and metolachlor remained low (0.04 mg kg⁻\u0026sup1;). Soil and irrigation-water analyses revealed widespread but low-level contamination, suggesting both direct foliar application and environmental redistribution. Although no significant differences were detected among farms, strong soil\u0026ndash;water\u0026ndash;tomato correlations (r\u0026thinsp;\u0026gt;\u0026thinsp;0.97) and high translocation factors for mancozeb (TF\u0026thinsp;=\u0026thinsp;9.69) demonstrate integrated contamination pathways within the production ecosystem. The dietary risk assessment indicated hazard quotients below unity for lambda-cyhalothrin and metolachlor but slightly above one for mancozeb (HQ\u0026thinsp;=\u0026thinsp;1.03), signalling potential chronic risk to consumers. Overall, the findings highlight inappropriate pesticide handling, short pre-harvest intervals, and shared irrigation systems as drivers of persistent contamination. Strengthening regulatory enforcement, farmer training in integrated pest management, and periodic residue monitoring are essential to reduce dietary exposure and safeguard public health. The results underscore the urgent need for Kenya to harmonize pesticide-management policies with international standards and to promote sustainable alternatives that protect both human health and agro-ecosystem integrity.\u003c/p\u003e \u003cp\u003eGiven the elevated mancozeb residues and the evident cross-contamination between soil, water, and tomato tissues, it is recommended that Kenyan agricultural authorities strengthen pesticide regulation and residue surveillance within irrigated horticultural zones. Farmers should receive continuous training on integrated pest management (IPM), correct dosage calibration, and observance of pre-harvest intervals to minimize residual carryover. Routine testing of irrigation water and soils should be institutionalized to detect chronic contamination early, while national policies should progressively align with international frameworks that restrict or phase out highly hazardous pesticides such as mancozeb. Promoting safer pesticide alternatives, biological controls, and precision-spraying technologies will reduce chemical dependence, enhance food safety, and sustain ecosystem health in smallholder horticultural systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish that thank Kenya Government Chemist for analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMargaret Maina a PhD student attached to this work did data collection, sample preparation, sample analysis and did write up of the article. Kiplagat Ayabei and Samuel Lutta conceptualized the research idea and participated in proof reading and general supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll dataset used in the development of this article are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and concent to participate:\u003c/strong\u003e Licence No.NACOSTI/P/25/4176347\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publications:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information:\u0026nbsp;\u003c/strong\u003eThe project did not receive any funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e Not applicable\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdesina OA, Aderinola OJ, Adesina BS. 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EFSA J. 2023a;21(2):e07852. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2903/j.efsa.2023.7852\u003c/span\u003e\u003cspan address=\"10.2903/j.efsa.2023.7852\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Food Safety Authority (EFSA). Evaluation of confirmatory data following the Article 12 MRL review for S-metolachlor. EFSA J. 2023b;21(12):e08374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2903/j.efsa.2023.8374\u003c/span\u003e\u003cspan address=\"10.2903/j.efsa.2023.8374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Food Safety Authority (EFSA). Review of the existing maximum residue levels for gamma-cyhalothrin. 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(n.d.). \u003cem\u003eTomato: Good agricultural practices\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://keep.kalro.org/good-agricultural-practices/Tomato keep.kalro.org\u003c/span\u003e\u003cspan address=\"https://keep.kalro.org/good-agricultural-practices/Tomato keep.kalro.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKenya Plant Health Inspectorate Service (KEPHIS). 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Analysis of alpha-cypermethrin pesticide residues along the value chain of tomato from Laikipia County, Kenya. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/40990174/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/40990174/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSsemugabo C, Halage AA, Staedke SG, Carpenter DO, Ssempebwa JC. Pesticide residue trends in fruits and vegetables from Sub-Saharan Africa: A review. Int J Environ Res Public Health. 2022;19(3):1350. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph19031350\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19031350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTechnoServe. (2024, August). \u003cem\u003eTomato value chain analysis Kenya\u003c/em\u003e (Report). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.technoserve.org/wp-content/uploads/2024/08/Tomato-Value-Chain-Analysis.pdf\u003c/span\u003e\u003cspan address=\"https://www.technoserve.org/wp-content/uploads/2024/08/Tomato-Value-Chain-Analysis.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViolet MN, Gichimu BM, Maingi J. Comparison of pesticide residue levels in tomatoes from open fields, greenhouses, markets and consumers in Kirinyaga County, Kenya. Asian J Res Agric Forestry. 2022;8(2):9\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ir-library.ku.ac.ke/handle/123456789/25144\u003c/span\u003e\u003cspan address=\"https://ir-library.ku.ac.ke/handle/123456789/25144\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pesticide residues, Tomatoes, Mancozeb, Lambda-cyhalothrin, Metolachlor, Translocation factor, Hazard quotient, Kenya","lastPublishedDoi":"10.21203/rs.3.rs-8402362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8402362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e. Intensive horticultural production in sub-Saharan Africa relies heavily on pesticides, often without adequate regulation, raising concerns about food safety and environmental contamination. This study quantified pesticide residues in tomato fruits (\u003cem\u003eLycopersicum esculentum\u003c/em\u003e), soils, and irrigation water on cultivated farms and evaluated associated dietary-exposure risks in the Moiben irrigation corridor, western Kenya.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e. Samples of tomato fruits (\u003cem\u003eLycopersicum esculentum\u003c/em\u003e), surface soils (0–15 cm), and irrigation water were collected from six cultivated farms and analysed for lambda-cyhalothrin, mancozeb, and metolachlor using validated HPLC-UV methods. Descriptive statistics, one-way ANOVA, Pearson correlation, and translocation factors (TF = C_tomato/C_soil) were computed. Estimated Daily Intake (EDI) and Hazard Quotient (HQ = EDI/ADI) were calculated to assess human-health risks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e. Mancozeb was the dominant residue in tomatoes (mean = 0.93 mg kg⁻¹), followed by lambda-cyhalothrin (0.24 mg kg⁻¹) and metolachlor (0.04 mg kg⁻¹). Soil residues were lower (0.096–0.14 mg kg⁻¹), while irrigation water contained trace amounts of lambda-cyhalothrin (0.003 mg L⁻¹) and mancozeb (0.06 mg L⁻¹). ANOVA revealed no significant differences among farms (p \u0026gt; 0.05). Strong soil–water–tomato correlations (r \u0026gt; 0.97) indicated shared contamination pathways, and TF values ranked mancozeb \u0026gt; lambda-cyhalothrin \u0026gt; metolachlor. Dietary-risk assessment showed HQ = 1.03 for mancozeb, exceeding the safety threshold, while other pesticides posed low or negligible risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e. The findings demonstrate diffuse pesticide contamination in the Moiben irrigation system, largely driven by over-application and poor compliance with pre-harvest intervals. Strengthened residue monitoring, farmer education, and adoption of integrated pest-management practices are recommended to mitigate health risks.\u003c/p\u003e","manuscriptTitle":"Pesticide Residues and Health Risks of Lambda-Cyhalothrin, Mancozeb, and Metolachlor in Irrigated Agroecosystems of Kenya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 12:42:55","doi":"10.21203/rs.3.rs-8402362/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-13T10:42:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T20:13:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231886282334518184730227064853489769587","date":"2026-02-09T13:56:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-29T08:10:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198413369274232554627463287575734448660","date":"2026-01-23T18:16:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T15:20:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T04:39:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-07T18:27:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Environment","date":"2026-01-07T18:20:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Environment](https://www.springer.com/44274/)","snPcode":"44274","submissionUrl":"https://submission.nature.com/new-submission/44274/3","title":"Discover Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ec49497f-d9a1-48ab-bacf-6ffa82c3dbf6","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T11:53:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 12:42:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8402362","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8402362","identity":"rs-8402362","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}