# Non-dietary personal pesticide exposure using silicone wristbands across 10 European countries

Daniel Martins Figueiredo, Hongyu Mu, Rima Osman, Vera Silva, Martien Graumans, Maurice Van Dael, Souleiman El Balkhi, Hans Mol, Anke Huss, Roel Vermeulen, et al.

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Daniel Martins Figueiredo, Hongyu Mu, Rima Osman, Vera Silva, Martien Graumans, et al. Non-dietary personal pesticide exposure using silicone wristbands across 10 European countries. Environment International, 2025, 203, pp.109734. 10.1016/j.envint.2025.109734. hal-05482181

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Non-dietary personal pesticide exposure using silicone wristbands across 10
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Daniel Martins Figueiredo<sup>a, *, c</sup>, Hongyu Mu<sup>b</sup>, Rima Osman<sup>b</sup>, Vera Silva<sup>b, c</sup>,
Martien Graumans<sup>c</sup>, Maurice van Dael<sup>p, c</sup>, Souleiman El Balkhi<sup>d</sup>, Hans Mol<sup>e</sup>, Anke Huss<sup>a, c</sup>,
Roel Vermeulen<sup>a</sup>, Jakub Hofman<sup>f</sup>, Nelson Abrantes<sup>g</sup>, Isabel Campos<sup>g</sup>, Francisco Alcon<sup>h</sup>,<sup>i</sup>
Josefa Contreras<sup>h</sup>, Isabelle Baldi<sup>i</sup>, Mathilde Bureau<sup>i</sup>, Abdallah Alaoui<sup>j</sup>, Florian Christ<sup>j</sup>,<sup>k</sup>
Daniele Mandrioli<sup>k</sup>, Daria Sgargi<sup>k</sup>,<sup>l</sup> Igor Pasković<sup>l</sup>, Marija Polić Pasković<sup>l</sup>,
Matjaž Glavan<sup>m</sup>,<sup>o</sup> Paula Harkes<sup>b, o</sup>, Esperanza Huerta Lwanga<sup>b</sup>, Lingtong Gai<sup>b</sup>,
Trine Norgaard<sup>n</sup>,<sup>o</sup> Violette Geissen<sup>b</sup>, Vivi Schlünssen<sup>o</sup>,<sup>l</sup> Paul T.J. Scheepers<sup>c, n</sup>

ARTICLE INFO

Handling Editor: Olga Kalantzi

Keywords:

Plant protection product

Passive sampler

Environmental exposure

Resident

Farmer

A B S T R A C T

Individuals are exposed to pesticides via dietary and non-dietary exposure routes. There is a growing need for aggregated exposure estimates across occupationally and non-occupationally exposed populations. Exposure to current-use and legacy pesticides was studied using silicone wristbands worn by farmers, residents living close to treated fields (neighbors), and the general population (consumers), in total, 641 participants across 10 European countries. Wristband extracts were analyzed for 193 pesticides by targeted Liquid and Gas Chromatography-Mass Spectrometry. Information on exposure predictors, such as frequency of cleaning, was collected by questionnaire. Logistic regressions and multivariable generalized least squares models were used to study predictors of pesticide occurrence and concentrations, respectively. We detected 173 out of 193 pesticides (89 %) across all samples. Per wristband 9 – 74 (median of 36) pesticides were detected in conventional farmers (FC), 8 – 66 (median of 20) in organic farmers, 3 – 66 (median of 20) in neighbors and 2 – 54 (median of 17) in consumers. Insecticides, like permethrin and chlorpyrifos, were commonly detected across all groups (>85 %). Concentrations varied significantly across groups, with the highest levels often observed in FC. We identified pesticide use and having pets as important predictors of personal non-dietary exposure. Mixture analysis revealed one major combination (in 72 % of all samples) of three insecticides, namely permethrin, chlorpyrifos, DDE p,p', and the synergist piperonyl butoxide. This study highlights that farm workers using a conventional farming system are a highly exposed group. It is unclear what the identified prevalent mixtures mean for cumulative exposure and resulting health risks.

<footnote><sup>a</sup> Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands</footnote>

<footnote>b Soil Physics and Land Management Group, Wageningen University & Research, Wageningen, Netherlands</footnote>

<footnote>c Radboud Institute for Biological and Environmental Sciences, Radboud University, Nijmegen, the Netherlands</footnote>

<footnote>d Inserm, CHU Limoges, Pharmacology & Toxicology, U 1248, F-87000 Limoges, France</footnote>

<footnote>e Wageningen Food Safety Research - Part of Wageningen University & Research, Wageningen, Netherlands</footnote>

<footnote>f RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic</footnote>

<footnote>g CESAM and Department of Biology, University of Aveiro, Portugal</footnote>

<footnote>h Agricultural Engineering School, Universidad Politécnica de Cartagena, Spain</footnote>

<footnote>i Univ. Bordeaux, INSERM, BPH, U1219, F-33000 Bordeaux, France</footnote>

<footnote>j Institute of Geography, University of Bern, Hallerstrasse 12, 3012 Bern, Switzerland</footnote>

<footnote>k Cesare Maltoni Cancer Research Center, Ramazzini Institute, Italy</footnote>

<footnote>l Department of Agriculture and Nutrition, Institute of Agriculture and Tourism, K. Huguesa 8, 52440 Poreč, Croatia</footnote>

<footnote>m Agronomy Department, Biotechnical Faculty, University of Ljubljana, Jamnikarjeva 101, 1000 Ljubljana, Slovenia</footnote>

<footnote>n Department of Agroecology, Aarhus University, Denmark</footnote>

<footnote>o Department of Public Health, Research Unit for Environment, Occupation and Health, Danish Ramazzini Centre, Aarhus University, Aarhus, Denmark</footnote>

<footnote>p Radboud Institute for Health Sciences, Radboudumc, Nijmegen, the Netherlands</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: d.m.figueiredo@uu.nl (D.M. Figueiredo).</footnote>
# 1. Introduction

## 1.1. Current-use and legacy pesticides

Conventional farming relies on pesticides to guarantee sufficient crop yields and food security across the food value chain. In Europe, around 500 active substances are authorized for professional use in the EU (Silva et al., 2019). Environmental exposure to a mix of current-use pesticides (CUPs) (i.e. currently approved) and legacy (banned) pesticides occurs frequently (e.g. Silva et al., 2023). Banned pesticides, such as DDT and endosulfan, and their metabolites or environmental transformation products, have been shown to persist and lead to a continuous background exposure across EU populations (e.g. Pena et al., 2022, Costapoulou et al., 2024). Several CUPs, on the other hand, have shown to underlie seasonal environmental exposure periods, mostly varying with spraying application time windows (e.g. Figueiredo et al., 2021a, Wang et al., 2024). Even though CUPs are synthesized to be less volatile and to degrade more rapidly in the environment, many are frequently detected in several environmental matrixes, such as soil (Brühl et al., 2024), air (Mayer & Degrendele et al., 2024), water (Navarro et al., 2024), indoor dust (Figueiredo et al., 2022) and biological matrices, such as urine (Ottenbros et al., 2023), hair (Béranger et al., 2020) and blood (Çelik et al., 2021).

## 1.2. Health concerns for occupational and non-occupationally exposed populations

Given the co-occurrence in the environment of both legacy pesticides and several CUPs, concerns have been raised regarding possible health risks from these mixtures (Dixit et al., 2019, Yang et al., 2024). The possible detrimental impact of single pesticide exposure on health has been highlighted in studies linking specific pesticides or pesticide groups to cancer (Cavalier et al., 2023), Parkinson's disease (Li et al., 2023) and other health outcomes (e.g. Fucic et al., 2021). Studies have also shown that exposure does not only occur in occupational settings (e.g. farmers and farm workers), but also in the general population from dietary and also from non-dietary routes and sources of exposure.

## 1.3. Aggregated exposure to pesticides

Assessing aggregated (i.e. multiple routes) exposure of pesticide mixtures is complex. To address these, we need to understand what the prevalent mixtures are, at which concentrations mixtures occur in the environment and what the possible associated health risks are. Although the methodology to assess pesticide mixture exposure-response relationships are still under development, several in-vitro studies indicate that pesticides can behave differently when mixed, even at low concentrations (e.g. Ghasemnejad-Berenji et al., 2021, Alehashem et al., 2024). In addition, humans can be exposed via different routes and, although most studies indicate diet to be the main contributor for pesticide exposure in non-occupationally exposed populations (e.g. Berlivet et al., 2024, Udovicki et al., 2024), some recent studies suggest an additional contribution from other routes of exposure, such as inhalation and dermal exposure, as equally or possibly more important than dietary exposure for certain CUPs (e.g. Oerlemans et al., 2021, Nijssen et al., 2024). In order to understand both cumulative and aggregate exposure to pesticides there is a need for novel exposure assessment tools.

## 1.4. Silicone wristbands as a tool to study non-dietary exposure

In recent years, the exploration of innovative methods for assessing pesticide environmental exposures in humans has gained considerable momentum (Gamboa et al., 2020). While existing methods, such as urinary biomarkers and self-reported pesticide use data have provided valuable insights, they are not without limitations. These range from the

limited number of included pesticides, short biological half-lives in the case of urinary biomarkers and potential recall bias regarding self-report exposures (Goodman et al., 2020).

Passive samplers have emerged as a promising approach to overcome some of these limitations, as they simultaneously capture a wide array of chemicals, do not require power supply, have lower cost and maintenance, can be more easily deployed for longer sampling periods, are non-invasive (e.g. operate silently), require minimal human intervention and are usually lightweight and portable (Figueiredo et al., 2023). Silicone wristbands are a type of passive sampler and have demonstrated efficacy in capturing exposures to different pollutants (Samon et al., 2022, Levasseur et al., 2024), including pesticides (Mu et al., 2024). Recent studies have also shown how chemicals targeted in wristbands are associated with their respective metabolites in urine (e.g. Dixon et al., 2022, Waclawik et al., 2025). It has been shown to be a good proxy for inhalation and dermal exposure routes (Aerts et al., 2018), since it captures volatile and semi-volatile compounds that are in the air and also compounds that adhere to the skin or are dermally excreted (McLarnan et al., 2024), offering a valuable alternative to understand the contribution of environmental pollution to personal exposure levels.

## 1.5. Study aims

The objective of this study was to assess exposure to multiple pesticides and their environmental transformation products using silicone wristbands in humans across different European countries. We aim: (i) to assess if there are different exposure profiles between and within subgroups of farmers, neighbours, and the general population; (ii) what the predominant mixtures of pesticides are, and to what extent their occurrences correlate; and (iii) to assess if there are predictors (e.g. living close to an agricultural area or using pesticides at home) that help to understand pesticide occurrence and variability in measured concentrations.

# 2. Materials and methods

## 2.1. Study design

This study is part of the EU Horizon 2020 program funded SPRINT project, where measurements of a wide variety of environmental and biological samples took place during the spraying season of 2021. Participants were recruited in each country in a specific location, henceforth designated as case-study site (CSS). The CSS locations covered the different crops in most European climate zones. A field work protocol explaining in more detail the selected CSS, pesticides and overall project design can be found in Silva et al., 2021. Participants were either farmers, neighbours (i.e. people living in the vicinity of agricultural fields and without being involved professionally in farming) or the general population (i.e. people living at greater distances from the fields than neighbours and having no professional involvement in farming). Recruited participants were asked to wear passive sampling silicone wristbands for one week (7 days continuously) during the 2021 growing season. In total, silicone wristbands were worn by 641 participants across 10 European countries.

A total of 193 pesticides representing different physicochemical properties were analysed (58 herbicides, 66 insecticides, 68 fungicides and 1 insecticide synergist). From this, 168 were suitable to be analysed by liquid chromatography tandem mass spectrometry (LC-MS/MS) and the remaining 25 were analysed by gas chromatography-tandem mass spectrometry (GC-MS/MS). Glyphosate and AMPA were not included because of their high polarity. They are not expected to be well absorbed in wrist bands and their analysis requires a separate LC-MS/MS method.

## 2.2. Sampling using wristbands

Silicone wristbands (202 x 12 x 2 mm, weight of 5.00 ± 0.5 g) were
purchased in one batch from Promofit, Etten-Leur, The Netherlands. All wristbands were conditioned (pre-cleaned) in the laboratory using a standard cleaning procedure according to Aerts et al. (2018). Cleaned wristbands were dried and inserted separately in an airtight and non-transparent zip-lock bag to protect them from contamination during shipment and storage described in a standard operating protocol (SOP). The wristbands were shipped to the CSS together with instruction brochures for the participants regarding handling the wristbands. Before providing the wristband to the study participant, a code of predefined format was written on the label of the bag. The participants were instructed to take the wristband out from the zip-lock bag and close the bag again, and then instructed to wear the band around the wrist of the dominant hand. The instruction was to wear the wristband continuously, day and night and during all activities (also including taking baths or showers) over a period of seven days, in line with previous studies (e. g. Fuhrimann et al., 2022, Veludo et al., 2024). After that period, the wristband was inserted in the original zip-lock bag to protect the wristband during shipment and storage from contamination or photo-degradation. The date and time of the end of the sampling period was noted. Conditioned wristbands from the laboratory were added as blank reference and treated in the same way as any of the used wristbands. All participants reported wearing the wristband for no less and no more than 7 days (with a few hours of variation). This small variability was deemed negligible, eliminating the need to normalize the data to the deployment period. One participant, however, reported losing the wristband after 3 days; their data was therefore excluded from the analysis.

## 2.3. Questionnaires

Each participant provided information regarding lifestyle and socio-demographics, such as dietary habits, time spent indoors, age, height and sex. Most of the questions asked were adopted from previous exposure assessment studies questionnaires, namely: Onderzoek Bestrijdingsmiddelen en Omwonenden (OBO) project (see Appendix 4 from Figueiredo et al., 2021b) and the HBM4EU-SPECIMEn study (See Annex 4 from HBM4EU). For example, the participants were asked about bringing shoes inside the home, number and type of pets, professional and home use of pesticides, amongst others. The questionnaire data was entered by two researchers independently (double-entry).

## 2.4. Chemical analyses

### 2.4.1. Extraction method

Wristbands were taken out of the sealed bag and cut into four pieces which were put into a 50 mL Greiner tube. The silicone material was extracted with 20 mL of ethyl acetate, containing internal standards (diazinon-D10, bentazone-D6), for 1 h using a head-over-head shaker (Heidolph REAX 2). The entire extract was aliquot-wise transferred into a 12 mL glass tube containing 100 µL of DMSO as keeper and evaporated under a stream of nitrogen at 40 °C (Biotage, Turbovap). The residue was reconstituted in 2.0 mL of acetonitrile containing 1 % formic acid. For LC-MS/MS analysis, 250 µL of the acetonitrile extract was diluted with 250 µL of water containing 0.1 % formic acid and internal standards (2,4-D-D3 and diuron-D6). For the determination of pesticides not amenable to LC-MS/MS, a 1.0 mL aliquot of the acetonitrile extract was cleaned by dispersive SPE (dSPE), i.e. transferred into a tube containing 25 mg of octadecyl silica (C18), 25 mg of primary and secondary amine (PSA), and 150 mg of MgSO4 (Waters, Milford USA). The tubes were vortexed for one minute and subsequently centrifuged for five minutes. 500 µL supernatant was transferred to an injection vial and sent to Limoges University Hospital laboratory of Pharmacology for GC-MS analysis. Recovery and precision data are provided as supplementary material (see Supplementary material VII).

### 2.4.2. LC-MS/MS analysis, validation and batch quality control

For LC-MS/MS analysis, 5 µL of extract were analysed using an LC system equipped with a 2.1 x 100 mm UPLC column, HSS T3 1.8 µm (Waters) and a Triple Quad 6500 + tandem mass spectrometer (SCIEX), with an ESI interface. Two separate injections were made, one in positive mode and one in negative mode. For each pesticide, two multiple reaction monitoring (MRM) transitions (m/z) were acquired. Quantification of the pesticides in the extract was done using bracketing external calibration against a matrix-matched standard prepared by addition of the standard mix to extracts of blank wristbands (as provided to the participants). The latter was prepared in a blank wristband extract at 5 ng/mL. Internal standards were only used for quality control purposes, to monitor extraction and injection, i.e. responses were not normalised to the internal standards.

The method was validated in line with the EU guidance documents SANTE/2020/12830 and SANTE/11312/2021. For initial validation, linearity of response was determined by calibration standards in solvent (0, and 0.2 to 10 ng/mL, 6 points). Selectivity was assessed by analysis of two blank wristbands. Recovery and repeatability were established by spiking wristbands with a mix-solution containing all pesticides in acetone at two levels (1 and 5 ng/g wristband, 3 replicates). The acetone was allowed to evaporate before analysis.

For quality control during analysis of the samples, with each batch of samples, one blank and two positive controls (1 ng/g and 5 ng/g wristband for each of the pesticides) were included. In addition, the linearity was re-assessed to justify the one-point bracketing quantification approach.

The average recovery and (intermediate) precision obtained during initial and on-going validation (concurrently with sample analysis) are summarized in Supplementary material VII. The majority of the LC-MS/MS pesticides complied with the recovery (70-120 %) and precision (≤20 %) criteria. The LOQ was defined as the lowest concentration complying with these criteria. The limit of detection (LOD) was defined as the concentration corresponding with a signal-to-noise ratio of three, based on the quantifier ion.

### 2.4.3. GC-MS/MS analysis, validation and batch quality control

Quantitative GC-MS/MS screening was performed by using an Agilent 7890B Gas chromatograph (GC) coupled to an Agilent 7010B Triple Quad mass spectrometer (MS/MS) (Agilent, USA). Helium (He) as carrier gas was led through an Ultra Inert HP-5 ms (30 m x 0.25 mm, 0.25 µm) column for chromatographic separation (Agilent, USA). Pesticide characterisation and quantification was done by multiple reaction monitoring (MRM) using a quantification and qualifier ion transitions (m/z). Positive pesticide identification was accepted by using the ratio between the quantitative and qualifying transitions within ± 20 % of the calibration standards. For GC-MS/MS analytical method validation, the calibration curves for the measured analytes were constructed using matrix match calibration points (n = 6/analyte) with concentrations between 0.01 and 10 µg/L, depending on the detection capacities of the used system. While the conventional methods for determining (LOD) are recommended, we have found that, in practice, these approaches sometimes yield LOD values that are either unrealistically low (i.e., not reproducible under routine conditions) or overly conservative given the true analytical capabilities of the GC-MS/MS system. Hence, in order to determine the level of confidence for detecting each analyte at the specified LODs, six qualified mass spectrometry experts were asked to interpret the lowest concentration calibration point. Validation was only granted if all six experts considered the analyte to be detectable. Coefficient of variations (CV%) for the intra-day and inter-day assays are provided as supplementary material (see Supplementary material VII).

## 2.5. Results below the limit of detection (left-censored data)

Levels above the LOD but below the LOQ may be more accurate than imputed values (Succop et al., 2004). Therefore, we used the LOD as cut-
off for detection. All pesticides which were detected (i.e., above LOD) in at least 40% of the wristbands were imputed. This threshold (40%) was chosen based on previous studies that imputed pesticides in wristbands (Fuhrimann et al., 2022) and other matrices (e.g. Figueiredo et al., 2022). Given that at least 40% of the distribution was known, we imputed the remaining (left-censored) values based on the truncated distribution (values above LOD). Imputation was done using maximum-likelihood (Lubin et al., 2004) based on log10 transformed values and with country and other pesticide levels as predictors. This estimation is accounting for both correlation and distribution of all pesticides data applying the R package "survival" with upper bound of imputation set to LOD (R Core team, 2021).

## 2.6. Statistical analysis

All statistical analyses were performed using R version 4.4.0 (2024-04-24) (R core team, 2021). By analyzing 193 pesticides in wristbands we could study mixture profiles, correlations between pesticides, as well as how often they occur or not occur together. Descriptive statistics, such as mean, median, standard deviation, minimum and maximum values were only calculated for pesticides with imputed data. For pesticides with non-imputed data (i.e. more than 60 % of values were below LOD) we only calculated detection frequencies and maximum values, as other metrics (e.g. mean) would only reflect a truncated distribution. Spearman correlation plots, as well as Upset plots (i.e. co-occurrence plots), were used to visualize and interpret correlation across pesticides and prevalent mixtures, respectively. Correlations for imputed pesticides were calculated using spearman correlation coefficients. For non-imputed pesticides we used Cohen's kappa test for quantification of the level of agreement between detects and non-detects across pesticide combinations. Here, we set minimum of 10 observations above LOD and presented agreements equal or above a value of 0.40 (i.e. week agreement to almost perfect agreement, according to McHugh (2012). A matrix of combined spearman correlations and Cohen's kappa was produced. This matrix helps to understand the degree of association between the presence and concentration of various pesticides across our samples.

A two-proportion z-test was used to identify whether the proportion of detects was similar between the subgroups of organic and conventional farmers, neighbors and consumers. Mann-Whitney U Test was used to compare median concentrations between the same groups. Exposure variability was assessed based on the interquartile range (IQR) and considered to be small when the IQR is within the same order of magnitude.

Multivariable generalized least squares (GLS) regression models were used to study predictors of pesticide concentrations in wristbands, as assumptions for ordinary least squares were not met (Supplementary material I). We ran GLS regression models for each individual pesticide where sufficient data was available (i.e. 40 % data > LOD). These resulted in 15 individual models. In each model the country was set as a fixed variable. Logistic regression models were applied to all detected pesticides, including non-imputed ones. Here, the outcome was detect vs. non-detect. A stepwise (both-sides) selection was performed for both the linear and logistic regression models. GLS models were built separately per subgroup (of conventional farmers, organic farmers, neighbors and consumers) as predictors may differ between groups. For logistic regression, all farmers were taken as one group due to low number of detects for several pesticides when separated.

For model construction, predictors (independent variables added to the model) were a-priori chosen based on findings from previous studies (Harley et al., 2019; Chetty-Mhlanga et al., 2021; Molomo et al., 2021; Teysseire et al., 2021). This resulted in the following 12 predictors: age, sex, distance to agricultural fields (categorical variable, divided in intervals: 0–250 m, >250–1000 m and further away); time spent indoors (hours per day); percentage of organic vegetable and fruit consumption (proxy for handling possible contaminated vegetables and fruit); being

an agricultural worker (yes/no); frequency of use of natural ventilation (%, 100*(X hours a day/24 h)); type of flooring (carpet vs smooth); smoking habit of participant; cleaning frequency; use of pesticides at home in the last 12 months; having pets at home in the last 12 months. Country was added as a fixed predictor. Additionally, and only for fipronil and fipronil sulfone model, the question "Use of pet care products or external anti flea treatments?" was evaluated as predictor, given that fipronil is present in many of these products (Aerts et al., 2018). However, this variable was discarded due to multicollinearity with the question "Having pets at home in the last 12 months?".

As a sensitivity analysis, we repeated multivariate models for complete-case information regarding the independent variables (N participants drops to 529). With this approach any influence of assumptions made on missing data or answers provided as "don't know" were avoided and it allows direct comparison between results from each model.

## 3. Results

### 3.1. Occurrence of pesticides (and their transformation products) in wristbands

Overall, we detected 172 of the 193 pesticides/ transformation products (89%) targeted (See Supplementary material II for full list). All samples contained a mixture of substances (at least 2 substances above LOD). The median number of detected substances was 20 over all samples from the 10 EU countries. Conventional farmers (FC) had 9–74 (median of 36) substances detected per wristband. Organic farmers (FO) had 8–66 (median of 20) substances detected per wristband. Neighbors (N) had 3–66 (median of 20) substances detected per wristband and consumers (C) had 2–54 (median of 17) substances detected per wristband. Detection frequency (%), per group, of the analyzed pesticides with more than 5% detection across all wristbands are provided in Table 1. A table including all results per group for all analysed pesticides can be found in Supplementary material III. Twenty one pesticides were not detected (<LOD) in any sample, these were: chlorpyrifos transformation product – methyl-desmethyl, cyprodinil transformation product – CGA304075, DDT transformation product – DDD p,p', emamectin, fenpropidin, foramsulfuron, imazalil (aka enilconazole), imidacloprid transformation product – desnitro, penoxsulam, pirimiphos-methyl transformation product – desmethyl, propamocarb-hydrochloride, pyrimethanil transformation product – M605F002, S-metolachlor transformation product – ethane sulfonic acid, S-metolachlor transformation product – oxanilic acid, spinetoram, spinosyn A, spinosyn D, spirotetramat transformation product – enol-glucoside, spirotetramat transformation product – mono-hydroxy, spiroxamine, tricyclazole.

From the 60 analyzed fungicides, 57 showed significant differences in detection frequency between the wristbands of at least two different groups. Fungicides such as azoxystrobin, boscalid, and fludioxonil had relatively high overall detection frequency (over 50%) across all groups. Most fungicides had higher detection frequency in FC in comparison to other groups. Also, often, FO and N presented significantly higher detection frequencies in comparison with consumers. For all fungicides except meptyldinocap – phenol, FO and N, showed similar detection frequencies. For most comparisons consumers presented the lowest detection frequency, with exception for chlorothalonil- 4-hydroxy, that was more abundant in consumers compared to organic farmers.

From the 54 analyzed herbicides, 43 showed significant differences in detection frequency between the wristbands of at least two different groups. For most of the comparisons, herbicides showed similar trends as fungicides, with herbicides being more frequently detected in farmers from conventional fields than other groups. Here, significant differences in detection between FC and FO were only observed for eight pesticides. Again, pesticides were overall less frequently detected in consumers, with an exception for terbutryn, where detection was significantly
<caption>**Table 1**</caption>

Detection frequency (%) of the pesticide residues in the wristbands per group (FC = farmer from conventional field, FO = farmer from organic field, N = neighbors, C = consumers).

<table><thead><tr><th>CAS-RN</th><th>Pesticides</th><th>FC</th><th>FO</th><th>N</th><th>C</th><th>N>LOQ*</th><th>Group comparison</th></tr></thead><tbody><tr><td colspan="8" style="text-align: center; font-weight: bold;">Fungicides</td></tr><tr><td>131860-33-8</td><td>Azoxystrobin</td><td>89</td><td>57</td><td>73</td><td>75</td><td>472</td><td>FC > N > FO | FC > C > FO</td></tr><tr><td>188425-85-6</td><td>Boscalid</td><td>78</td><td>63</td><td>74</td><td>71</td><td>460</td><td>FC > FO</td></tr><tr><td>131341-86-1</td><td>Fludioxonil</td><td>77</td><td>60</td><td>60</td><td>60</td><td>403</td><td>FC > ALL</td></tr><tr><td>99-30-9</td><td>Dicloran</td><td>70</td><td>67</td><td>59</td><td>40</td><td>360</td><td>FC > C | FO > C | N > C</td></tr><tr><td>60207-90-1</td><td>Propiconazole</td><td>50</td><td>59</td><td>52</td><td>36</td><td>308</td><td>FC > C | FO > C | N > C</td></tr><tr><td>119446-68-3</td><td>Difenoconazole</td><td>70</td><td>49</td><td>39</td><td>14</td><td>243</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>148-79-8</td><td>Thiabendazole</td><td>31</td><td>30</td><td>36</td><td>45</td><td>236</td><td>C > FC | C > FO</td></tr><tr><td>10605-21-7</td><td>Dimethomorph</td><td>49</td><td>35</td><td>37</td><td>16</td><td>207</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>53112-28-0</td><td>Pyrimethanil</td><td>38</td><td>24</td><td>29</td><td>31</td><td>195</td><td>FC > FO</td></tr><tr><td>175013-18-0</td><td>Mandipropamid</td><td>50</td><td>30</td><td>34</td><td>11</td><td>185</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>252913-85-2</td><td>Metalaxyl-M [TF] - CGA62826</td><td>61</td><td>29</td><td>26</td><td>11</td><td>177</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>67564-91-4</td><td>Tolylfluanid [TF] - DMST</td><td>31</td><td>26</td><td>22</td><td>25</td><td>163</td><td>≈</td></tr><tr><td>141517-21-7</td><td>Trifloxystrobin</td><td>51</td><td>27</td><td>25</td><td>10</td><td>161</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>220899-03-6</td><td>Pyraclostrobin</td><td>59</td><td>22</td><td>21</td><td>10</td><td>154</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>907204-31-3</td><td>Metrafenone</td><td>44</td><td>21</td><td>25</td><td>13</td><td>151</td><td>FC > N > C | FC > FO</td></tr><tr><td>374726-62-2</td><td>Carbendazim</td><td>34</td><td>22</td><td>14</td><td>26</td><td>143</td><td>FC > FO | FC > N | C > N</td></tr><tr><td>107534-96-3</td><td>Fluopyram</td><td>48</td><td>21</td><td>18</td><td>11</td><td>136</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>57966-95-7</td><td>Tebuconazole</td><td>52</td><td>21</td><td>16</td><td>6</td><td>127</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>658066-35-4</td><td>Fluxapyroxad</td><td>45</td><td>20</td><td>13</td><td>9</td><td>119</td><td>FC > FO > C | FC > N</td></tr><tr><td>239110-15-7</td><td>Cymoxanil</td><td>41</td><td>24</td><td>19</td><td>3</td><td>118</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>133-07-3</td><td>Zoxamide</td><td>33</td><td>15</td><td>21</td><td>3</td><td>104</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>156052-68-5</td><td>Fluopicolide</td><td>35</td><td>16</td><td>19</td><td>3</td><td>103</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>67747-09-5</td><td>Folpet</td><td>23</td><td>17</td><td>16</td><td>1</td><td>82</td><td>FC > C | FO > C | N > C</td></tr><tr><td>135319-73-2</td><td>Sedaxane</td><td>35</td><td>12</td><td>8</td><td>2</td><td>74</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>140923-17-7</td><td>Ametoctrin</td><td>13</td><td>12</td><td>17</td><td>3</td><td>71</td><td>FC > C | FO > C | N > C</td></tr><tr><td>28343-61-5</td><td>Iprovalicarb</td><td>23</td><td>14</td><td>11</td><td>3</td><td>70</td><td>FC > N > C | FO > C</td></tr><tr><td>88671-89-0</td><td>Cyflufenamid</td><td>25</td><td>10</td><td>12</td><td>1</td><td>66</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>121552-61-2</td><td>Myclobutanil</td><td>19</td><td>8</td><td>5</td><td>10</td><td>61</td><td>FC > ALL</td></tr><tr><td>66246-88-6</td><td>Cyprodinil</td><td>25</td><td>12</td><td>7</td><td>2</td><td>61</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>120983-64-4</td><td>Prothioconazole [TF] - desthio</td><td>32</td><td>9</td><td>4</td><td>2</td><td>57</td><td>FC > ALL | FO > C</td></tr><tr><td>180409-60-3</td><td>Chlorothalonil [TF] - 4-hydroxy</td><td>7</td><td>4</td><td>7</td><td>11</td><td>50</td><td>C > FO</td></tr><tr><td>581809-46-3</td><td>Penconazole</td><td>25</td><td>7</td><td>6</td><td>1</td><td>50</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>66063-05-6</td><td>Tetraconazole</td><td>16</td><td>8</td><td>8</td><td>1</td><td>45</td><td>FC > N > C | FO > C</td></tr><tr><td>126833-17-8</td><td>Meptyldinocap [TF] - phenol</td><td>13</td><td>3</td><td>9</td><td>2</td><td>41</td><td>FC > FO | FC > C | N > FO | N > C</td></tr><tr><td>131-72-6</td><td>Pencycuron</td><td>12</td><td>11</td><td>5</td><td>1</td><td>40</td><td>FC > N > C | FO > C</td></tr><tr><td>139542-32-8</td><td>Prochloraz</td><td>11</td><td>2</td><td>6</td><td>5</td><td>39</td><td>FC > FO</td></tr><tr><td>3687-22-7</td><td>Meptyldinocap</td><td>13</td><td>3</td><td>8</td><td>2</td><td>39</td><td>FC > FO | FC > C | N > C</td></tr><tr><td>79622-59-6</td><td>Fenhexamid</td><td>15</td><td>4</td><td>5</td><td>3</td><td>37</td><td>FC > ALL</td></tr><tr><td>688046-61-9</td><td>Fluazinam</td><td>13</td><td>8</td><td>5</td><td>1</td><td>36</td><td>FC > N > C | FO > C</td></tr><tr><td>66332-96-5</td><td>Pyriofenone</td><td>13</td><td>5</td><td>5</td><td>2</td><td>36</td><td>FC > N > C | FC > FO</td></tr><tr><td colspan="8" style="text-align: center; font-weight: bold;">Herbicides</td></tr><tr><td>886-50-0</td><td>Terbutryn</td><td>17</td><td>25</td><td>31</td><td>39</td><td>195</td><td>N > FC | C > FC | C > FO</td></tr><tr><td>5915-41-3</td><td>Terbuthylazine</td><td>43</td><td>17</td><td>22</td><td>5</td><td>124</td><td>FC > FO > C | FC > N > C</td></tr></tbody></table>

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<table><caption>Table 1 (continued)</caption><tbody><tr><td>83164-33-4</td><td>Diflufenican</td><td>43</td><td>20</td><td>11</td><td>5</td><td>105</td><td>FC > FO > N > C</td></tr><tr><td>330-54-1</td><td>Diuron</td><td>17</td><td>10</td><td>14</td><td>14</td><td>89</td><td>≈</td></tr><tr><td>101-21-3</td><td>Chlorpropham</td><td>24</td><td>18</td><td>10</td><td>3</td><td>74</td><td>FC &amp; FO > N > C</td></tr><tr><td>163515-14-8</td><td>Dimethenamid-P</td><td>23</td><td>14</td><td>8</td><td>0</td><td>57</td><td>FC > N > C | FO > C</td></tr><tr><td>52888-80-9</td><td>Prosulfocarb</td><td>25</td><td>8</td><td>8</td><td>2</td><td>57</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>40487-42-1</td><td>Pendimethalin</td><td>36</td><td>4</td><td>4</td><td>1</td><td>55</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>42874-03-3</td><td>Oxyfluorfen</td><td>17</td><td>8</td><td>7</td><td>1</td><td>45</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>94-74-6</td><td>MCPA</td><td>18</td><td>6</td><td>6</td><td>2</td><td>44</td><td>FC > FO > C | FC > N > C</td></tr><tr><td>23950-58-5</td><td>Propyzamide</td><td>16</td><td>5</td><td>7</td><td>2</td><td>43</td><td>FC > N > C | FC > FO</td></tr><tr><td>30125-63-4</td><td>Terbuthylazine [TF] - desethyl</td><td>15</td><td>6</td><td>8</td><td>0</td><td>41</td><td>FC > FO > C | N > C</td></tr><tr><td>81777-89-1</td><td>Clomazone</td><td>22</td><td>3</td><td>3</td><td>1</td><td>35</td><td>FC > ALL</td></tr><tr><td>26225-79-6</td><td>Ethofumesate</td><td>15</td><td>9</td><td>0</td><td>1</td><td>29</td><td>FC > N | FC > C | FO > N | FO > C</td></tr><tr><td colspan="8" style="text-align: center; font-weight: bold;">Insecticides</td></tr><tr><td>52645-53-1</td><td>Permethrin</td><td>94</td><td>95</td><td>95</td><td>96</td><td>609</td><td>≈</td></tr><tr><td>2921-88-2</td><td>Chlorpyrifos</td><td>91</td><td>91</td><td>86</td><td>90</td><td>571</td><td>≈</td></tr><tr><td>51-03-6</td><td>Piperonyl butoxide [Syn]</td><td>89</td><td>91</td><td>87</td><td>84</td><td>558</td><td>≈</td></tr><tr><td>3424-82-6</td><td>DDT [TF] - DDE p,p'</td><td>97</td><td>97</td><td>85</td><td>79</td><td>558</td><td>FC > N | FC > C | FO > N | FO > C</td></tr><tr><td>120068-37-3</td><td>Fipronil</td><td>69</td><td>70</td><td>76</td><td>74</td><td>468</td><td>≈</td></tr><tr><td>50-29-3</td><td>DDT p,p'</td><td>75</td><td>71</td><td>66</td><td>70</td><td>446</td><td>≈</td></tr><tr><td>52315-07-8</td><td>Cypermethrin</td><td>66</td><td>51</td><td>60</td><td>66</td><td>392</td><td>FC > FO | C > FO</td></tr><tr><td>120068-36-2</td><td>Fipronil [M] - sulfone</td><td>49</td><td>56</td><td>59</td><td>46</td><td>337</td><td>N > C</td></tr><tr><td>789-02-6</td><td>DDT o,p'</td><td>56</td><td>62</td><td>40</td><td>53</td><td>324</td><td>FC > N | FO > N | C > N</td></tr><tr><td>114-26-1</td><td>Propoxur</td><td>48</td><td>32</td><td>39</td><td>26</td><td>227</td><td>FC > FO | FC > C | N > C</td></tr><tr><td>52918-63-5</td><td>Deltamethrin</td><td>43</td><td>25</td><td>30</td><td>35</td><td>211</td><td>FC > FO | FC > N</td></tr><tr><td>138261-41-3</td><td>Imidacloprid</td><td>33</td><td>22</td><td>34</td><td>30</td><td>195</td><td>N > FO</td></tr><tr><td>72-55-9</td><td>DDT [TF] - DDE o,p'</td><td>30</td><td>48</td><td>15</td><td>22</td><td>164</td><td>FO > FC > N | FO > C</td></tr><tr><td>58-89-9</td><td>Lindane</td><td>32</td><td>29</td><td>25</td><td>12</td><td>146</td><td>ALL > C</td></tr><tr><td>60-57-1</td><td>Dieldrin</td><td>8</td><td>13</td><td>22</td><td>34</td><td>139</td><td>C > N > FC | C > FO</td></tr><tr><td>82657-04-3</td><td>Bifenthrin</td><td>34</td><td>22</td><td>12</td><td>18</td><td>124</td><td>FC > ALL | FO > N</td></tr><tr><td>95737-68-1</td><td>Pyriproxyfen</td><td>16</td><td>15</td><td>21</td><td>21</td><td>123</td><td>≈</td></tr><tr><td>7696-12-0</td><td>Tetramethrin</td><td>29</td><td>25</td><td>16</td><td>9</td><td>113</td><td>FC > N > C | FO > N > C</td></tr><tr><td>135410-20-7</td><td>Acetamiprid</td><td>48</td><td>10</td><td>15</td><td>6</td><td>109</td><td>FC > N > C | FC > FO</td></tr><tr><td>121-29-9</td><td>Pyrethrin II</td><td>15</td><td>26</td><td>15</td><td>14</td><td>107</td><td>FO > ALL</td></tr><tr><td>121-21-1</td><td>Pyrethrin I</td><td>17</td><td>21</td><td>16</td><td>13</td><td>104</td><td>≈</td></tr><tr><td>91465-08-6</td><td>Lambda-Cyhalothrin</td><td>49</td><td>11</td><td>12</td><td>5</td><td>102</td><td>FC > N > C | FC > FO</td></tr><tr><td>53-19-0</td><td>DDT [TF] - DDD o,p'</td><td>19</td><td>20</td><td>15</td><td>10</td><td>97</td><td>FC > C | FO > C</td></tr><tr><td>6515-38-4</td><td>Chlorpyrifos [TF] - TCPy</td><td>36</td><td>10</td><td>10</td><td>11</td><td>93</td><td>FC > ALL</td></tr><tr><td>732-11-6</td><td>Phosmet</td><td>18</td><td>15</td><td>14</td><td>12</td><td>92</td><td>≈</td></tr><tr><td>203313-25-1</td><td>Spirotetramat</td><td>27</td><td>7</td><td>16</td><td>9</td><td>90</td><td>FC > N > C | FC > N > FO</td></tr><tr><td>500008-45-7</td><td>Chlorantraniliprole</td><td>19</td><td>7</td><td>11</td><td>9</td><td>70</td><td>FC > FO | FC > C</td></tr><tr><td>173584-44-6</td><td>Indoxacarb</td><td>30</td><td>8</td><td>9</td><td>3</td><td>68</td><td>FC > N > C | FC > FO</td></tr><tr><td>111988-49-9</td><td>Thiacloprid</td><td>28</td><td>7</td><td>5</td><td>2</td><td>53</td><td>FC > FO > C | FC > N</td></tr><tr><td>29232-93-7</td><td>Pirimiphos-methyl</td><td>19</td><td>5</td><td>8</td><td>3</td><td>49</td><td>FC > N > C | FC > FO</td></tr><tr><td>102851-06-9</td><td>Tau-Fluvalinate</td><td>19</td><td>6</td><td>4</td><td>2</td><td>39</td><td>FC > FO > C | FC > N</td></tr></tbody></table>

Coloring is based on percentage detects. From light color to darker color = (0 to < 25 %, 25 % to < 50 %, 50 to < 75 %, ≥ 75 %). [TF] = Transformation product; [Syn] = Synergist. Pesticide column includes those detected in more than 5 % of all wristbands. N > LOQ = Total number of samples above limit of quantification; ≈ (Percentage detection similar between groups); * Max possible value = 641 Group comparison displays only those where α < 0.05 in the two-proportion z-test.

higher in consumers when compared to both conventional and organic farmers' groups.

From the 59 analyzed insecticides, 44 showed significant differences in detection frequency between the wristbands of at least two different groups. For insecticides, comparisons between groups followed less

clear trends than for fungicides and herbicides. For 8 insecticides there was no significant difference in overall detection between groups. For some insecticides, such as dieldrin, consumers showed higher detection frequencies than both conventional and organic farmer groups. The previously observed trend that farmers from conventional fields
presented higher detection frequency for several pesticides was still observed, but less pronounced for insecticides.

Finally, certain pesticides had very low occurrence across all groups. For instance, transformation products or less commonly used pesticides like bixafen-desmethyl and metaflumizone showed detection frequency below 1 %. The presence of transformation products (e.g., azoxystrobin – O-demethyl) was generally lower compared to their parent compounds, although for some the transformation product was more frequently detected (e.g. tolylfluanid – DMST).

## 3.2. Predictors of occurrence

Logistic regression models of detects vs non-detects resulted in 178 unique models (each model corresponding to one pesticide (e.g. acetamiprid) and one group (e.g. farmers). Selected predictors are highlighted with information on the probability of detection for each pesticide (see Fig. 1). The heatmap highlights the varying impact of different predictors on pesticide detection. The full results of the selected predictors for each model, including 95 % confidence intervals can be consulted in Supplementary material IV.

When observing the general pattern, we see that many moderate to high-probability (>50 %, yellow to red) squares belong to the farmers group. This indicates a higher likelihood of occurrence of various pesticides in this group. Four predictors were shown to be related to increased probability of pesticide detection. Namely, being responsible for pesticide spraying application, use of pesticide(s) at home, working in the agricultural sector and having pets in the last 12 months. From these, the first two, were predominantly select in farmers' models. Some predictors show to influence both higher and lower detection (wide probability range), depending on the pesticide. This is the case for sex, cleaning frequency and distance to agricultural fields. Though, when looking at cleaning frequency, most associations with a decrease in pesticide detection are from neighbours' models. Interestingly, for sex, there is a clear different in influence on probability of detection of certain pesticides depending on the subgroup. In farmers, for 8 pesticides, there is an indication that if a farmer is male there is a higher probability for detecting those. In contrast, in the consumer group, there is an indication that if a consumer is male there is a lower probability for detection of any of seven pesticides.

There is a remarkable pattern of yellow (≈ 50 % probability) for Orgfreqcons across most pesticides in the farmer's group. This is likely explained by the pre-determined selection of organic farmers and conventional farmers, with organic consumption reported predominantly in the group of organic farmers.

Notably, 11 analysed pesticides were detected less frequently in wristbands of participants that reported smoking. Finally, the remaining predictors had a consistent association with pesticide detection (indicated by small range in x axis), but with no clear association with increase or decrease in detection.

## 3.3. Concentrations of pesticides in wristbands

For 15 pesticides, sufficient data above the limit of detection (LOD) (≥40 % of samples) allowed for imputation for quantitative analysis. The boxplots (Fig. 2) show log10-transformed concentrations (nanograms of pesticide per gram of wristband) for each pesticide, grouped by farmers, neighbours, and consumers, within country. Wilcoxon-test comparisons within each country revealed 237 significant differences (see Supplementary Material V for the complete list). Minimum, maximum and median values for each targeted pesticide can be found in Supplementary Material IX.

Farmers from conventional fields (FC) had significantly higher pesticide concentrations than organic-field farmers (FO) in 40 comparisons, neighbours (N) in 47 comparisons, and consumers (C) in 52 comparisons. Specifically, azoxystrobin, boscalid, and fludioxonil show high concentrations in the FC group across most countries. The FO group

showed higher concentrations than N in 22 comparisons and then C in 33 comparisons. Neighbours had higher concentrations than C in 43 comparisons, but C never showed significantly higher concentrations than any other group.

The above comparisons were not consistently significant across all pesticides. For instance, FC had significantly higher cypermethrin levels than C in seven countries, while this difference was seen in only two countries for permethrin and propoxur, both without plant protection product (PPP) approval. Overall trends show the hierarchy FC > FO > N > C in three countries for 9 of the 15 pesticides, and FC > N > C in two countries for 13 out of 15 pesticides.

With the exception for the pyrethroids cypermethrin and permethrin, which had maximum values measured of 10999 ng/g and 47217 ng/g, respectively, all remaining 13 pesticides presented concentrations in the range of 0,0001 ng/g to 5000 ng/g. Some pesticides, like propoxur, chlorpyrifos, and boscalid, display low variability with narrow IQRs across countries. In contrast, p,p'-DDT, fipronil, and cypermethrin show high variability with broader IQRs. For certain banned pesticides, such as DDT, and propoxur, concentration differences between groups are minimal, with only a maximum of three significant comparisons per group.

Overall average concentrations varied between 1.5 and 267 ng/g and several country-specific trends emerged. Cypermethrin concentrations were generally two orders of magnitude lower (p-value < 0.05) in Denmark (DK) and the Netherlands (NL) (both averages around 0.01 ng/g) compared to all other countries, except Switzerland (CH). Propiconazole concentrations were on average two orders of magnitude higher (p-value < 0.05) in Denmark (DK) (0.3 ng/g) than in Spain (SP) and Italy (IT) (both averages around 0.03 ng/g). Average chlorpyriphos concentrations in wristbands from Portugal (PT), driven by FC and FO groups, was close to 100 ng/g, four times or more higher than other countries. Similarly, but for propoxur, average concentrations in CZ were 40 ng/g, which is 40 times higher than almost all other countries.

## 3.4. Mixtures

The correlation matrix (Fig. 3) displays the relationships between different pesticides based on correlation strength (spearman rho), as well as percentage of co-occurrence (for Cohen's Kappa test). The higher co-occurrence between samples, the higher the Cohen's Kappa. Fig. 3 shows that DDT transformation products showed strong positive correlations among themselves, indicating that the presence of one was highly related to the presence of the others. This was expected due to their common origin and similar environmental behaviour. Fungicides, like azoxystrobin and boscalid, were moderately correlated between themselves and with another fungicide, fludioxonil. Correlations between fungicides and insecticides were overall low (Spearman rho < 0.3). Within the insecticides group, fipronil also correlated strongly with its transformation product (fipronil – sulfone). Some pesticides, like dicloran, propiconazole and chlorpyrifos exhibited near-zero correlations with many other pesticides. Interestingly, the insecticide synergist piperonyl butoxide displayed very low correlation with legacy pesticides, and low to moderate correlations with current-used pesticides, including some fungicides such as boscalid and fludioxonil and some pyrethroids, such as permethrin and cypermethrin.

Large co-occurrence, indicated by Cohen's Kappa, was observed between parent compounds and their transformation product (e.g. meptyldinocap) or between certain transformation products (e.g. spirotetramat transformation products). In addition, there were three pairs where Cohen's Kappa was high: ethofumesate and fluoxastrobin, between fenvalerate and esfenvalerate (both synthetic pyrethroid insecticides), and between pyraflufen-ethyl and metamitron.

To better identify predominant mixtures across the studied population we produced Upset Plots, with indication of number of times a given mixture occurs in a country (bar chart) and percentage it occurs in a given subgroup (farmer, neighbour, consumer) (see Fig. 4).
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Predictors selected in each logistic regression model. Each row represents a specific pesticide, indicating its probability of occurrence with different predictors. Each model is built per pesticide (y-axis) and per group (panel columns). The x-axis are the predictors included in the initial model. Colors represent selected predictors (via step-wise both-ways selection) and color range indicates the transformed model coefficient (from odds to percentage), indicating probability of detection. The grey cells indicate predictors that were not selected in the final model. White rows indicate the model (combination of pesticide and group) was not run due to insufficient samples > LOD. Definition of predictor variables: *AgrilabourAllYes* = Worked in the agricultural sector in the past 12 months; *ApppestYes* = Responsible for pesticide spraying application; *PetlastYes* = having pets at home in the last 12 months; *PesttreatHouseYes* = reported use of pesticide(s) at home; *SexMale* = reported being male; *Cleaninghome* > Once a week = reported cleaning home more than once a week; *DistanceAgriFields* = Reported to living within 0–250 m or within 250–1000 m from agricultural fields. *Organfreqcons* = % of reported food items that were from organic/biological source; *Summernatvent* = % of time during measuring campaign that the house had natural ventilation [i.e. windows open]; *SmokerYes* = Reported to being a smoker.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Measured concentrations for the 15 imputed pesticides in wristbands. Results grouped per pesticide and displayed by grouping (farmers, neighbors and consumers) and country. The y axis shows log10 concentrations in ng/g.</figcaption></figure>

In 441 wristbands out of 614 (72 % of samples), we observed a common mixture of 3 insecticides and 1 synergist, namely permethrin, chlorpyrifos, the DDT transformation product p,p'-DDE and piperonyl butoxide. In 198 out of the 441 wristbands (45 %) we found azoxystrobin, boscalid and fipronil were co-occurring. The above two mixtures of overall seven pesticides were observed in more than 30 % of the samples. These mixtures were among the most frequently detected across multiple countries, with some countries showing slightly higher prevalence of certain mixtures. For example, in Spain (ES), an additional 30 wristbands out of 173 appear with the same mixture of the seven aforementioned pesticides, but without fipronil. When accounting for more pesticides into the possible mixtures (Supplementary Material X), an increased mixture complexity was observed, with several additional pesticides, such as propiconazole and dicloran, appearing often together with the mixture of seven (seen in Fig. 4). Interestingly, some mixture profiles were country-specific, for example: wristbands collected in Denmark showed 14 wristbands with 12 to 13 pesticides, but in all

instances without cypermethrin in the mixture.

When looking at the prevalence of certain mixtures across groups, we saw that, overall, mixtures occurred in similar proportions across farmers, neighbours and consumers. There were some exceptions, which was the case for the mixture without the DDT transformation product – DDE o,p', found in 11 % of the consumers, versus 1 % in farmers and 6 % in neighbours. Finally, most of the pesticides that were present as predominant mixtures were insecticides (darker grey lines in front of pesticide names, in Fig. 4). Some fungicides were found in these predominant mixtures, such as azoxystrobin and boscalid. No herbicides were found among the most prevalent mixtures.

### 3.5. Multivariable model – predictors of variability in concentrations

For each regression model, the selected variables and their coefficient is presented in Table 2. The list including confidence intervals can be consulted in Supplementary Material VI. Here, we see for some
<figure><img src="image_4.png" /><figcaption>Fig. 3. Spearman correlation and Cohen's Kappa between pesticides measured in wristbands. Spearman Correlation: Represented by a gradient from green (negative correlation) to purple (positive correlation). Cohen's Kappa: Represented by a gradient from light blue (low agreement) to dark blue (high agreement). Percentage indicates mismatch between samples (i.e. number of times an x-axis pesticide was detected in one sample, but the y-axis pesticide was not detected). The blocks (grey lines) represent groups: The first group (names in green) are fungicides, the second group (names in purple are herbicides) and the third group (names in orange) are insecticides. Calculations were only performed for pesticides that co-occurred in at least 10 samples. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4. Top-7 of predominantly occurring pesticide mixtures with grouping indication by country and by subgroup of farmer, neighbor and consumer. Panel A shows the detection frequency in each of the subgroups (farmers, neighbors and consumers). Panel B shows the number of times each mixture occurred in each country. Panel C shows the top 7 predominant mixtures across all samples, with fungicides highlighted in light grey and insecticides highlighted in dark grey.</figcaption></figure>

dichotomous variables that the concentrations often increase when compared to the reference (e.g. fipronil: concentration increases if participants had pets in last 12 months when compared to participants without pets), for non-dichotomous (i.e. numerical) variables, it is in relation to the variable unit (e.g. boscalid): By an increase in one year of age there is an 6.8 % increase in concentration in FC group. Since the outcome variable is log10 transformed, the calculation is as follows:

$$((10^{0.029}) - 1) \times 100 = 6.8 \%$$

Reporting spraying pesticides showed to consistently lead to higher pesticide concentrations, with exception for propoxur. Some predictors, such as "Cleaning > once", "OrganF" and "SmokerYes", were often associated to a lower exposure to pesticides (i.e. lower concentration). We observed inconsistent associations in regard to distance to closest field, where for certain pesticide concentrations are higher for people
<caption>**Table 2**</caption>

Results of the multivariable models for pesticides with imputed left-censored data. Beta coefficients presented. [FC = Farmer from conventional farm, FO = Farmer from organic farm, N=Neighbor, C=Consumer].

<table><thead><tr><th rowspan="2">Pesticides</th><th rowspan="2">Gr.</th><th colspan="4">Numerical variables</th><th colspan="8">Dichotomous variables</th></tr><tr><th>OrganF</th><th>Sumnat</th><th>Age</th><th>Time Indoors</th><th>D, 0-250m</th><th>D &gt; 250-1000m</th><th>Pets Yes</th><th>AppPest Yes</th><th>Sex Male</th><th>Pest Yes</th><th>Cleaning &gt; Once</th><th>SmokerYes</th></tr></thead><tbody><tr><td rowspan="4">Azoxystrobin</td><td>FC</td><td>0.003</td><td></td><td></td><td></td><td>-0.882</td><td>-0.873</td><td></td><td></td><td></td><td></td><td></td><td>-0.457</td></tr><tr><td>FO</td><td>-0.005</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>N</td><td>-0.002</td><td></td><td></td><td>0.025</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.385</td></tr><tr><td>C</td><td>-0.003</td><td>-0.013</td><td></td><td></td><td></td><td></td><td>-0.173</td><td>-0.171</td><td></td><td>0.236</td><td>-0.268</td></tr><tr><td rowspan="4">Boscalid</td><td>FC</td><td></td><td></td><td>0.029</td><td></td><td></td><td></td><td></td><td></td><td>0.348</td><td></td><td></td><td></td></tr><tr><td>FO</td><td></td><td>0.014</td><td>-0.012</td><td></td><td>0.742</td><td>0.596</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>N</td><td>-0.003</td><td>0.014</td><td></td><td></td><td></td><td></td><td></td><td>0.334</td><td>-0.153</td><td></td><td>-0.228</td></tr><tr><td>C</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.223</td><td>-0.164</td><td></td><td></td></tr><tr><td rowspan="4">Chlorpyrifos</td><td>FC</td><td></td><td>0.015</td><td>0.012</td><td></td><td>-0.306</td><td>0.142</td><td>-0.275</td><td></td><td></td><td></td><td></td><td>-0.463</td></tr><tr><td>FO</td><td>-0.003</td><td></td><td></td><td></td><td></td><td></td><td>-0.267</td><td>0.667</td><td>-0.345</td><td></td><td></td><td></td></tr><tr><td>N</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.241</td><td></td><td>-0.479</td><td></td></tr><tr><td>C</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.215</td><td></td><td></td><td>0.175</td></tr><tr><td rowspan="4">Cypermethrin</td><td>FC</td><td></td><td></td><td>0.020</td><td></td><td></td><td></td><td></td><td>0.831</td><td></td><td></td><td></td><td></td></tr><tr><td>FO</td><td></td><td></td><td>0.021</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.630</td><td></td><td></td></tr><tr><td>N</td><td></td><td>-0.021</td><td>0.011</td><td>-0.038</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-1.035</td><td></td></tr><tr><td>C</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.523</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">DDE pp</td><td>FC</td><td></td><td></td><td></td><td></td><td>-0.081</td><td>0.191</td><td>0.151</td><td></td><td>0.153</td><td></td><td></td><td></td></tr><tr><td>FO</td><td></td><td></td><td></td><td></td><td>-0.144</td><td>0.398</td><td></td><td></td><td>0.145</td><td></td><td>-0.276</td><td></td></tr><tr><td>N</td><td></td><td></td><td></td><td>0.029</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.276</td></tr><tr><td>C</td><td>-0.009</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.216</td><td></td><td></td><td></td></tr><tr><td rowspan="4">DDT op</td><td>FC</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.519</td><td>0.584</td><td></td><td>0.587</td><td></td><td></td></tr><tr><td>FO</td><td></td><td></td><td></td><td></td><td>-0.567</td><td>0.017</td><td></td><td>0.665</td><td></td><td></td><td>-0.509</td><td></td></tr><tr><td>N</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.466</td><td></td></tr><tr><td>C</td><td>-0.022</td><td></td><td></td><td>0.030</td><td></td><td></td><td></td><td>0.192</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">DDT pp</td><td>FC</td><td></td><td></td><td>-0.013</td><td></td><td></td><td></td><td>0.346</td><td></td><td>0.469</td><td></td><td></td><td></td></tr><tr><td>FO</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.517</td><td></td><td></td></tr><tr><td>N</td><td>-0.001</td><td></td><td></td><td>0.022</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.407</td><td></td></tr><tr><td>C</td><td>-0.019</td><td></td><td></td><td>0.024</td><td></td><td></td><td>0.229</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Dicloran</td><td>FC</td><td></td><td>0.028</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FO</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.461</td><td></td><td></td><td></td><td>-0.632</td><td>0.504</td></tr><tr><td>N</td><td></td><td></td><td>-0.010</td><td></td><td></td><td></td><td>0.305</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>C</td><td></td><td></td><td>-0.019</td><td>0.044</td><td></td><td></td><td>-0.314</td><td></td><td></td><td>-0.812</td><td></td><td>-0.493</td></tr><tr><td rowspan="4">Fipronil</td><td>FC</td><td></td><td></td><td></td><td></td><td>0.158</td><td>-0.546</td><td>0.549</td><td></td><td>-0.782</td><td></td><td></td><td></td></tr><tr><td>FO</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.783</td><td></td><td></td><td>-0.574</td><td></td></tr><tr><td>N</td><td></td><td>-0.010</td><td></td><td></td><td></td><td></td><td></td><td>0.502</td><td></td><td></td><td></td><td></td></tr><tr><td>C</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.646</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Fipronil sulfone</td><td>FC</td><td></td><td>0.014</td><td></td><td></td><td></td><td></td><td>0.397</td><td></td><td>-0.854</td><td></td><td>-0.464</td><td></td></tr><tr><td>FO</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.696</td><td></td><td></td><td>-0.480</td><td></td></tr><tr><td>N</td><td></td><td></td><td></td><td>0.036</td><td></td><td></td><td></td><td>0.440</td><td></td><td></td><td></td><td></td></tr><tr><td>C</td><td></td><td>0.019</td><td></td><td></td><td></td><td></td><td></td><td>0.606</td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Fludioxonil</td><td>FC</td><td>0.006</td><td></td><td>-0.016</td><td>0.047</td><td></td><td></td><td>0.310</td><td>0.379</td><td></td><td>-0.934</td><td></td><td></td></tr><tr><td>FO</td><td>-0.006</td><td></td><td>-0.013</td><td>-0.036</td><td></td><td></td><td></td><td></td><td>-0.400</td><td></td><td></td><td>0.332</td></tr><tr><td>N</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.422</td><td></td></tr><tr><td>C</td><td>-0.002</td><td>-0.013</td><td>0.008</td><td></td><td>0.341</td><td>0.330</td><td>-0.265</td><td></td><td>-0.300</td><td>0.402</td><td></td><td></td></tr><tr><td rowspan="4">Permethrin</td><td>FC</td><td></td><td></td><td>-0.013</td><td></td><td></td><td></td><td>0.650</td><td>0.880</td><td>0.539</td><td></td><td>-0.674</td><td>-0.539</td><td>-0.536</td></tr><tr><td>FO</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.587</td><td></td><td></td></tr><tr><td>N</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.057</td><td></td><td></td><td></td><td></td><td>-0.502</td></tr><tr><td>C</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.042</td><td>-0.538</td><td>0.008</td><td>0.408</td><td></td><td></td></tr><tr><td rowspan="4">Piperonyl butoxide</td><td>FC</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.269</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FO</td><td>-0.002</td><td></td><td>-0.012</td><td></td><td>-0.582</td><td>-0.235</td><td>0.638</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>N</td><td></td><td>-0.017</td><td></td><td>-0.042</td><td></td><td></td><td></td><td>0.428</td><td></td><td>0.508</td><td></td><td>-0.302</td></tr><tr><td>C</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.215</td><td></td><td></td><td></td></tr><tr><td rowspan="4">Propiconazole</td><td>FC</td><td></td><td></td><td>-0.014</td><td></td><td></td><td></td><td>-0.739</td><td>-0.659</td><td>-0.357</td><td></td><td></td><td></td></tr><tr><td>FO</td><td>0.002</td><td>0.017</td><td></td><td>0.024</td><td></td><td></td><td></td><td></td><td></td><td>0.200</td><td>0.302</td><td></td></tr><tr><td>N</td><td></td><td>0.015</td><td></td><td></td><td></td><td></td><td></td><td>-0.389</td><td></td><td></td><td></td><td>-0.212</td></tr><tr><td>C</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.132</td><td>0.250</td><td>0.259</td><td>0.256</td><td></td><td></td></tr><tr><td rowspan="4">Propoxur</td><td>FC</td><td></td><td></td><td>0.009</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>FO</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.453</td><td>-0.002</td><td>0.243</td><td>-0.410</td><td>-0.326</td><td></td></tr><tr><td>N</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.396</td><td></td><td></td><td></td></tr><tr><td>C</td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.190</td><td>0.268</td><td>0.331</td><td>-0.257</td><td>-0.245</td><td></td></tr></tbody></table>

p<0.05 p<0.01 p<0.001

Pesticide concentrations used as outcome are log10 transformed (e.g, if beta coefficient = 0.029, that means an increase in one unit of the predictor variable will lead to an increase in ((10^0.029)-1)x100 in the outcome variable).

Dichotomous variables (YES or NO; D > 250–1000 m = Reported to living within 250–1000 m from agricultural fields; D, 0–250 m = Reported to be living within 0–250 m from agricultural fields; PetsYes = having pets at home in the last 12 months; AppPestYes = Responsible for pesticide spraying application; PestYes = reported use of pesticide(s) at home; SexMale = reported being male; Cleaning > Once = reported cleaning home more than once a week; SmokerYes = Reported to being a
smoker. Numerical variables: OrganF = % of reported food items that were from organic/biological sourceSumnat = % of time during measuring campaign that the house had natural ventilation [i.e. windows open]; Age = age (in years); Time indoors = reported time spent indoors in a 24 h period (in hours).

living in closer proximity to fields, while for other pesticides led to reduction (Table 2).

## 4. Discussion

In our study, 89 % of the 193 analyzed pesticides or their transformation products were detected in the silicone wristbands worn by farmers from conventional and organic fields, neighbors, and consumers, and all the samples contained a mixture of substances with 2 or more above LOD. Farmers in conventional fields had higher detection frequencies for several fungicides and herbicides compared to other groups. Insecticides, like permethrin and chlorpyrifos showed similar detection frequency across all subgroups. Logistic regression identified strong predictors of pesticide detection, such as working in agriculture and pesticide application. Concentrations of pesticides varied significantly by study location, and for certain pesticides also varied significantly between groups. Factors like having pets and reporting applying pesticides lead to an increase in concentration for certain pesticides. Predominant mixtures including five or more pesticides were found in many samples.

It is important to emphasize that the targeted substances have very diverse physicochemical properties, which influence uptake rate. As such, the observed relationships are not only a reflection of environmental presence, but also the result of specific sorption mechanisms on wristbands, which were not addressed in this study.

### 4.1. Detection frequency and predictors of occurrence

Our study revealed substantial variation in the occurrence of pesticides across different population groups, with a higher detection among farmers from conventional fields when compared to consumers, but not so much different than farmers from organic fields and neighbours. This result is not surprising, as neighbours live in the vicinity of agricultural fields and previous studies have shown higher likelihood for pesticide detection closer to fields than further away (Figueiredo et al., 2021a, Boonupara et al., 2023). Fungicides such as azoxystrobin, boscalid, and fludioxonil showed high detection frequency (over 60 %) across all groups, reflecting a possible combination of widespread use in agriculture and environmental persistence, given that these fungicides are moderately to very persistent in soil (Lewis et al., 2016). High detection frequency were also observed for some current-use and banned insecticides, such as cypermethrin and DDT (and its transformation products), respectively. These findings align with previous wristband studies, such as those by Harley et al. (2019) and Donald et al. (2016), which more frequently detected insecticides and fungicides than herbicides. This last group is applied in specific time windows and mainly via downward spraying to kill weeds with effective drift reduction, which may explain why herbicides were less frequently detected. The choice of collecting our data during the growth season can also have an influence on lower herbicides detection since these are much more likely applied at the beginning of the growth season to remove weed before seeding the crop. Moreover, wristbands are a proven method for capturing semi-volatile compounds, but most of the studied herbicides are likely particle bound, except for a few that were more often detected, such as pendimethalin and chlorpropham, which are volatile. Despite this, it is important to stress that some herbicides like diflufenican exhibited higher detection rates in farmers, underscoring a contribution from occupational exposure. These results are consistent with previous studies. For example, Schummer et al. (2012), found diflufenican as the most frequent detected herbicide in hair from farmers sampled in Luxembourg. Other pesticides such as the insecticides permethrin and chlorpyrifos were detected at similar rates across all groups, indicating a more uniform exposure distribution, likely due to the use as biocide for

permethrin. Several other studies, both performed inside and outside the EU, observed similar patterns in detection, where these insecticides were prevalent in both rural and urban areas across different years (Heudorf & Angerer, 2001, Hermant et al., 2018, Normann et al., 2024).

Certain pesticides and their transformation products, such as bixafen – desmethyl and metaflumizone, had very low detection rates across all groups, suggesting either limited use or rapid environmental degradation.

The logistic regression analysis identified several statistically significant predictors of pesticide occurrence in wristbands. Here, working in the agricultural sector and involvement in pesticide application were strong predictors, confirming the higher exposure likelihood associated with occupational activities. The presence of pets and home pesticide use also emerged as significant predictors. Pets, especially those spending time outside in the green space land carry pesticides in their fur into the home. These findings are in line with a study performed in the Netherlands by reporting similar predictors for residential pesticide exposure Figueiredo et al. (2022). In contrast, a study performed solely in farmworkers did not find pets to influence wristband concentrations (Harley et al., 2019).

### 4.2. Correlations and mixture assessment

The correlation analysis showed strong positive correlations among DDT transformation products, which is expected due to their common origin and similar environmental behavior. Fungicides like azoxystrobin and boscalid, recently studied as ternary mixture effects in non-target pollinators (Dong et al., 2023), also showed moderate correlations with each other and with other fungicides like fludioxonil. In contrast, some pesticides, such as dicloran and chlorpyrifos, exhibited near-zero correlations with others, indicating independent occurrence patterns. The diverse correlation landscape underscores the complexity of pesticide exposure and the need for comprehensive mixture assessments.

As expected there were strong agreements on detection between parent compounds and their transformation products. Some additional pairs were often detected together, which was the case between fenvalerate and esfenvalerate, which are both insecticides, although esfenvalerate has replaced fenvalerate (mix of isomers) as active ingredient in currently approved PPPs. The case of pyraflufen-ethyl and metamitron, both herbicides, that can be used in combination (Grüninger & Kiyokawa, 2016). In line with this, we also observed combined detection of ethofumesate and fluoxastrobin. Interestingly, for this last pair we did not find reasons behind the common detection, as the first is an herbicide and the second is a fungicide and this finding requires further study regarding the mixture aspect.

Our study revealed frequent co-occurrence of multiple pesticides, with common mixtures including permethrin, chlorpyrifos, DDT transformation products, and piperonyl butoxide (used as co-formulant and synergist in pyrethroid-based products such as permethrin). These mixtures were prevalent across all countries and population groups, indicating widespread environmental contamination. Since these are three insecticides and one insecticide synergist, it could be of interest for future studies to assess possible additive and synergistic effects on health or ecosystems. Also, some study locations had more unique exposure mixtures which are likely a reflection of i) local policy and not EU policy, ii) local differences in crops and agricultural practices and iii) time frames the wristbands were used.

The mixtures that we found contain components such as pyrethroids and piperonyl butoxide that are in addition to use for plant protection also used as biocide or veterinary drug which can explain higher detections in wrist bands. When a persistent active ingredient has no longer authorization for PPP use (e.g. propoxur) it is not surprising that non-dietary exposure instead of food becomes more prominent as
exposure source. For many other components, such as fungicides, our
results show that there is a clear contribution to overall exposure from
non-dietary routes.

4.3. *Concentrations in wristbands and predictors of non-dietary exposure*

Non-approved (banned) pesticides, exhibited lower (but still pro-
nounced) variability across countries in contrast to current-use pesti-
cides. This contrast is likely due to the influence of national policies,
different agricultural practices and how local climates influence
persistence of current-use pesticides. Also, banned pesticides are very
persistent and their widespread occurrence have been reported in other
studies linked to possible illegal use (McGinley et al., 2023) and/or long-
range atmospheric transport pesticides in the gas-phase (Mayer et al.,
2024).

Concentrations measured in our study are in the same orders of magnitude as in previous published studies, such as Howe et al. (2025) and Essandoh et al. (2025). Although these were conducted in Costa Rica and Uganda, respectively, they reported similar median concentrations of propiconazole, fipronil, p,p'-DDT and p,p'-DDE in silicone wristbands. Though, the Uganda study had overall higher concentration in the worker population in comparison to the farmers from our study. In a pilot study performed in the Netherlands in 2017 by Ottenbros et al. (2025), the fungicides boscalid and azoxystrobin, were also found in most of the wristbands and in the same concentration ranges has we measured in our study. There is a difference of four years, which may indicate routine use of these fungicides and resulting long-term (continuous) exposure.

Farmers from conventional fields had the highest pesticide exposure, with farmers from organic fields showing lower concentrations, and neighbours and consumers experiencing progressively lower concentrations for several pesticides. This pattern reinforces the notion that conventional farming involves higher exposure, as anticipated. For certain pesticides, such as cypermethrin, we also observed pronounced differences between study locations, with often one or two countries presenting lower concentrations. This might reflect stricter regulatory controls and/or different crops and agricultural practices compared to other EU countries during the time of sampling, as recently noted by Gensch et al., 2024 when estimating pesticide loads across the EU.

We identified several predictors of non-dietary pesticide exposure, with occupational factors being the most predominant. Direct involvement in pesticide application were the strongest predictors of pesticide concentrations in wristbands. This is consistent with numerous studies, as highlighted in recent reviews by Cancino et al. (2023), Frengidou et al. (2023) and others, which discussed the high exposure levels among agricultural workers.

The presence of pets was a significant predictor for an increase in certain pesticide concentrations in wristbands, such as propoxur, fipronil and its transformation product sulfone. This finding provides robustness to the model results, as both are not authorized for agricultural uses, but commonly used in a home-setting with an authorization as biocide application, e.g. in anti-flea treatment of pets (e.g. Testa et al., 2019). Wise et al. (2021) also reported high levels of fipronil in silicone samplers from participants who reported using flea and tick products containing fipronil on their dog. This predictor was also significant in some of the logistic regression models. Contrarily to other studies, like Figueiredo et al. (2021a), living closer to agricultural fields (0–1000 m) did not appear often as predictor for an increase in pesticide concentrations. Although here, distance to field was a self-reported variable.

Our results confirm the importance of certain predictors to non-
dietary exposure. Additionally, predictors such as smoking, use of nat-
eral ventilation and proximity to agricultural fields showed variable
associations with pesticide concentrations in wristbands, reflecting the
multifaceted nature of exposure pathways. Although smoking influence
is likely related to more time spent outdoors, this was not observed in
our study. Frequent cleaning was often associated with reduced

pesticide concentrations, likely due to the removal of pesticide residues
from indoor surfaces, as also noted by McCauley et al., 2006. Interest-
ingly, we also observed a negative association between being male and
pesticide concentration in 3 models, suggesting that men had lower
exposure levels as compared to women. It is unclear which underlying
differences in behaviors could explain the observed differences in
exposure levels.

4.4. Strengths & limitations

This study has several strengths, including the use of wristbands as
passive samplers to provide an integrated measure of personal pesticide
exposure over time. The large sample size, the central supply and
preparation of wristbands, use of harmonized protocol for sample
collection and central laboratory analysis enhances comparability of the
findings across the sampling sites. Additionally, the comprehensive
analysis of 193 pesticides and their transformation products, along with
the assessment of mixture profiles, provides valuable insights into the
complexity of pesticide exposure, which can be used in decision making
for future toxicological and epidemiological studies.

However, there are also limitations to consider. The most significant
limitation relates to the analytical GC-MS/MS method used, as it was
not fully validated because matrix effect verification was not feasible.
Matrix effects (ion suppression/enhancement) were only accounted for
by the clean wristbands, not taking into account a possible contribution
from co-extractants of the worn wristbands, which may affect the ac-
curacy of the exposure assessment. Also, this may be influenced by the
detection of pesticides below the limit of quantification (LOQ) and
detection limits dependent on pesticide.. In this context, a limitation of
the study is the relatively low detection frequency threshold (40 %) used
for including pesticides in the imputation process. We acknowledge that
imputing a substantial proportion of data, particularly when detection
frequencies are low, can introduce bias if not handled carefully.
Therefore, fixed imputation methods were explicitly avoided for this
reason, as they can skew summary statistics towards the imputed value.
Instead, imputation was conducted using a distribution-based approach
involving repeated single imputation, where values below LOD are
drawn from a defined distribution and iterated until the best fit or
convergence is reached. This method has been previously applied in
pesticide exposure studies (e.g., Fuhrimann et al., 2022; Figueiredo
et al., 2021a) and offers a robust solution for handling left-censored
data, which is common in environmental datasets where a large pro-
portion of the measurements falls below detection capability. Never-
theless, we recognize that this approach still carries assumptions and
potential limitations, particularly at lower detection frequencies, and
recommend cautious interpretation of imputed values.

The study's reliance on self-reported data for certain predictors, such as pesticide use and distance to field, might introduce some non-differential misclassification in model estimates. The cross-sectional design limits our ability to reflect on long-term exposure.

It is important to stress that there may be potential selectivity to-
wards pesticides that are well-absorbed in silicone compared to others
that are not well-absorbed due to high polarity or lost due to high vapor
pressure. For example, glyphosate and AMPA were a-priori not included
in the list of targeted pesticides to do high polarity. Substances with an
octanol-water partition coefficient lower than three (logP < 3) are likely
to accumulate poorly in wristbands (Samon et al., 2022) and might be
easily washed off, e.g., during bathing or taking a shower. Hence, ob-
servations pertaining to pesticides with logP < 3 should be interpreted
with caution. A full list of logP for each targeted pesticide is provided in
*Supplementary Material III*.

Related to the above, a subset of 21 pesticides were not detected (<LOD) in any wristband sample, which likely reflects a combination of chemical properties, environmental factors, and the passive sampling approach rather than a complete absence of exposure. Although both GC-MS and LC-MS/MS were employed to broaden analytical coverage,
some compounds—particularly transformation products and highly polar metabolites—may still fall outside the effective detection range due to poor retention in the silicone matrix of the wristbands. For example, the sulfonic and oxanilic acid metabolites of S-metolachlor are very polar and water-soluble, characteristics that make them less likely to partition into the hydrophobic silicone material. Similarly, some transformation products, such as desmethyl-imidacloprid or the enol-glucoside form of spirotetramat, may appear only briefly under specific environmental conditions and not persist long enough to be sequestered by wristbands. Additionally, large or highly hydrophilic compounds, like spinetoram, may have low affinity for silicone and therefore low uptake despite environmental presence. These limitations highlight that the absence of detection should not be interpreted as evidence of zero exposure, but rather as a reflection of the physico-chemical selectivity of the passive sampling system used. It is important to stress that several compounds with logP < 3 were frequently found in wristbands (>30 % detection), such as boscalid, azoxystrobin, cymoxanil, propoxur and many others.

For future research it is suggested to include longitudinal designs to capture temporal variations and explore the long-term health effects of chronic pesticide exposure. Additionally, harmonized methods for pesticide detection and quantification across studies would improve comparability and reliability of results. Pesticide concentrations are expressed by weight of the wristband. These concentrations cannot be compared to regulatory standards. Currently, there is no agreed methodology to accurately calculate these values to air concentration equivalents.

## 5. Conclusions

Our study offers a comprehensive analysis of non-dietary pesticide exposure patterns among various populations across the EU, under-scoring its widespread prevalence and identifying significant occupational and residential predictors. We detected several common pesticide mixtures, although implications for combined exposure and associated health risks remain unclear. Future research should prioritize further studies of the most prevalent mixtures identified here, along with their potential interactions, as a substantial portion of the population may be exposed to these compounds through environmental pathways in addition to dietary intake. These findings could inform the development of enhanced regulatory measures, which are crucial for mitigating the risks linked to pesticide exposure and safeguarding both human health and ecosystem impact. Additionally, the identification of the most prevalent current-used pesticides could support ongoing initiatives aimed at the transition toward more sustainable agricultural practices.

### CRediT authorship contribution statement

**Daniel Martins Figueiredo:** Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization.
**Hongyu Mu:** Writing – review & editing, Validation, Methodology, Data curation.
**Rima Osman:** Writing – review & editing, Data curation.
**Vera Silva:** Writing – review & editing, Project administration.
**Martien Graumans:** Writing – review & editing.
**Maurice van Dael:** Methodology.
**Souleiman El Balkhi:** Data curation.
**Hans Mol:** Writing – review & editing, Validation.
**Anke Huss:** Writing – review & editing, Supervision.
**Roel Vermeulen:** Writing – review & editing, Supervision.
**Jakub Hofman:** Writing – review & editing, Resources, Investigation, Conceptualization.
**Nelson Abrantes:** Writing – review & editing, Resources, Conceptualization.
**Isabel Campos:** Writing – review & editing, Resources.
**Francisco Alcon:** Writing – review & editing, Resources.
**Josefa Contreras:** Writing – review & editing, Resources.
**Isabelle Baldi:** Writing – review & editing, Resources.
**Mathilde Bureau:** Writing – review & editing, Resources.
**Abdallah Alaoui:** Writing – review & editing, Resources.
**Florian Christ:** Writing – review & editing, Resources.
**Daniele Mandrioli:** Writing – review & editing, Resources.

**Resources. Daria Sgargi:** Writing – review & editing, Resources.
**Igor Pasković:** Writing – review & editing, Resources.
**Marija Polić Pasković:** Writing – review & editing, Resources.
**Matjaž Glavan:** Writing – review & editing, Resources.
**Paula Harkes:** Writing – review & editing, Resources, Project administration.
**Esperanza Huerta Lwanga:** Writing – review & editing, Resources.
**Lingtong Gai:** Writing – review & editing, Data curation.
**Trine Norgaard:** Writing – review & editing, Resources.
**Violette Geissen:** Writing – review & editing, Project administration, Methodology, Funding acquisition, Conceptualization.
**Vivi Schlünssen:** Writing – review & editing, Resources, Data curation.
**Paul T.J. Scheepers:** Writing – review & editing, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

The SPRINT project (on Sustainable Plant Protection Transition, https://sprint-h2020.eu/), leading to this publication, has received funding from the European Union's Horizon 2020 Programme for research & innovation under grant agreement n° 862568. The European Commission is not responsible for any use that may be made of the information it contains. The authors would like to thank all Case Study Sites participants for their support to this publication and SPRINT activities.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.envint.2025.109734.

### Data availability

Data will be made available on request.

### References

Aerts, R., Joly, L., Szternfeld, P., Tsilikas, K., de Cremer, K., Castelain, P., Aerts, J.M., van Orshoven, J., Somers, B., Hendrickx, M., Andjelkovic, M., van Nieuwenhuyse, A., 2018. Silicone wristband passive samplers yield highly individualized pesticide residue exposure profiles. Environ. Sci. Tech. 52 (1), 298–307. https://doi.org/10.1021/acs.est.7b05039.

Alehashem, M., Peters, R., Fajana, H.O., Eslamizad, S., Hogan, N., Hecker, M., Siciliano, S.D., 2024. Herbicides and pesticides synergistically interact at low concentrations in complex mixtures. Chemosphere 353, 141431. https://doi.org/10.1016/j.chemosphere.2024.141431.

Béranger, R., Hardy, E.M., Binter, A.-C., Charles, M.-A., Zaros, C., Appenzeller, B.M.R., Chevrier, C., 2020. Multiple pesticides in mothers' hair samples and children's measurements at birth: results from the French national birth cohort (ELFE). Int. J. Hyg. Environ. Health 223 (1), 22–33. https://doi.org/10.1016/j.ijheh.2019.10.010.

Berlivet, J., Payrastre, L., Rebouillat, P., Fougerat, A., Touvier, M., Hercberg, S., Lairon, D., Pointereau, P., Guillou, H., Vidal, R., Baudry, J., Kesse-Guyot, E., 2024. Association between dietary pesticide exposure profiles and body weight change in French adults: results from the NutriNet-Santé cohort. Environ. Int. 184, 108485. https://doi.org/10.1016/j.envint.2024.108485.

Boonupara, T., Udomkun, P., Khan, E., Kajitvichyanukul, P., 2023. Airborne pesticides from agricultural practices: a critical review of pathways, influencing factors, and human health implications. Toxics 11 (10), 858. https://doi.org/10.3390/toxics11100858.

Brühl, C.A., Engelhard, N., Bakanov, N., et al., 2024. Widespread contamination of soils and vegetation with current use pesticide residues along altitudinal gradients in a European Alpine valley. Commun. Earth Environ. 5, 72. https://doi.org/10.1038/s43247-024-01220-1.

Cancino, J., Soto, K., Tapia, J., Muñoz-Quezada, M.T., Lucero, B., Contreras, C., Moreno, J., 2023. Occupational exposure to pesticides and symptoms of depression in agricultural workers a systematic review. Environm. Res. 231, 116190. https://doi.org/10.1016/j.envres.2023.116190.
Cavalier, H., Trasande, L., Porta, M., 2023. Exposures to pesticides and risk of cancer: evaluation of recent epidemiological evidence in humans and paths forward. Int. J. Cancer 152 (5), 879-912. https://doi.org/10.1002/ijc.34300.

Çelik, S., Akbaba, M., Nazlican, E., et al., 2021. Association between occupational and environmental pesticide exposure in Cukurova region by hair and blood biomonitoring. *Environ. Sci. Pollut. Res.* 28, 63191–63201. https://doi.org/10.1007/s11356-021-15227-0.

Chetty-Mhlanga, S., Fuhrimann, S., Basera, W., Eeftens, M., Röösli, M., Dalvie, M.A., 2021. Association of activities related to pesticide exposure on headache severity and neurodevelopment of school-children in the rural agricultural farmlands of the Western Cape of South Africa. *Environ. Int.* 146, 106237.

Costopoulou, D., Kedikoglou, K., Yafeiadi, K., Roumeliotaki, T., Margetaki, K., Stephanou, E.G., Myridakis, A., Leondiadis, L., 2024. Systematic investigation of organochlorine pesticides and polychlorinated biphenyls blood levels in Greek children from the Rhea birth cohort suggests historical exposure to DDT and through diet to DDE. *Environ. Int.* 187, 108686. https://doi.org/10.1016/j.envint.2024.108686.

Dixit, S., Srivastava, M. P., & Sharma, Y. K. (2019). Pesticide and Human Health: A Rising Concern of the 21st Century. In K. Wani & Mamta (Eds.), *Handbook of Research on the Adverse Effects of Pesticide Pollution in Aquatic Ecosystems* (pp. 85-104). IGI Global Scientific Publishing. doi: 10.4018/978-1-5225-6111-8_c005.

Dixon, H.M., Bramer, L.M., Scott, R.P., Calero, L., Holmes, D., Gibson, E.A., Cavalier, H. M., Rohlman, D., Miller, R.L., Calafat, A.M., Kincl, L., Waters, K.M., Herbstman, J.B., Anderson, K.A., 2022. Evaluating predictive relationships between wristbands and urine for assessment of personal PAH exposure. *Environ. Int.* 163, 107226. https://doi.org/10.1016/j.envint.2022.107226.

Donald, C.E., Scott, R.P., Blaustein, K.L., Halbleib, M.L., Sarr, M., Jepson, P.C., Anderson, K.A., 2016. Silicone wristbands detect individuals' pesticide exposures in West Africa. *R. Soc. Open Sci.* 3 (8), 160433. https://doi.org/10.1098/rsos.160433.

Dong, J., Huang, M., Guo, H., Zhang, J., Tan, X., Wang, D., 2023. Ternary mixture of azoxystrobin, boscalid and pyraclostrobin disrupts the gut microbiota and metabolic balance of honeybees (apis cerana cerana). *Int. J. Mol. Sci.* 24 (6), 5354. https://doi.org/10.3390/ijms24065354.

Essandoh, Y.E., Steiniche, T., Xia, C., Romanak, K., Ogwang, J., Mutegeki, R., Wasserman, M., Venier, M., 2025. Tracking toxic chemical exposure in Uganda: insights from silicone wristbands. *Environ. Res.* 277, 121522. https://doi.org/10.1016/j.envres.2025.121522.

Figueiredo, D.M., Duyzer, J., Huss, A., Krop, E.J.M., Gooijer, Y., Vermeulen, R.C.H., 2021a. Spatio-temporal variation of outdoor and indoor pesticide air concentrations in homes near agricultural fields. *Atmos. Environ.* 262 (June), 118612. https://doi.org/10.1016/j.atmosenv.2021.118612.

Figueiredo, D.M., Krop, E.J.M., Duyzer, J., Gerritsen-Ebben, R.M., Gooijer, Y.M., Holterman, H.J., Huss, A., Jacobs, C.M.J., Kivits, C.M., Kruijne, R., Mol, H.J.G.J., Oerlemans, A., Sauer, P.J.J., Scheepers, P.T.J., van de Zande, J.C., van den Berg, E., Wenneker, M., Vermeulen, R.C.H., 2021b. Pesticide exposure of residents living close to agricultural fields in the Netherlands: protocol for an observational study. *JMIR Res Protoc* 10 (4), e27883. https://doi.org/10.2196/27883.

Figueiredo, D.M., Lö, S., Krop, E., Meijer, J., Beeltje, H., Lamoree, M.H., Vermeulen, R., 2023. Do cats mirror their owner? Paired exposure assessment using silicone bands to measure residential PAH exposure. *Environ. Res.* 222, 115412. https://doi.org/10.1016/j.envres.2023.115412.

Figueiredo, D.M., Nijssen, R., Krop, E.J., Buijtenhuijs, D., Gooijer, Y., Lageschaar, L., Vermeulen, R.C., 2022. Pesticides in doormat and floor dust from homes close to treated fields: spatio-temporal variance and determinants of occurrence and concentrations. *Environ. Pollut.* 301, 119024.

Frengidou, E., Galanis, P., Malesios, C., 2023. Pesticide exposure or pesticide poisoning and the risk of depression in agricultural populations: a systematic review and meta-analysis. *J. Agromedicine* 29 (1), 91–105. https://doi.org/10.1080/1059924X.2023.2278801.

Fucic, A., Duca, R.C., Galea, K.S., Maric, T., Garcia, K., Bloom, M.S., Andersen, H.R., Vena, J.E., 2021. Reproductive health risks Associated with Occupational and Environmental Exposure to Pesticides. *Int. J. Environ. Res. Public Health* 18 (12), 6576. https://doi.org/10.3390/ijerph18126576.

Fuhrimann, S., Mol, H.G.J., Dias, J., Dalvie, M.A., Röösli, M., Degrendele, C., Figueiredo, D.M., Huss, A., Portengen, L., Vermeulen, R., 2022. Quantitative assessment of multiple pesticides in silicone wristbands of children/guardian pairs living in agricultural areas in South Africa. *Sci. Total Environ.* 812, 152330. https://doi.org/10.1016/j.scitotenv.2021.152330.

Gamboa, C.L., Solano Diaz, K., Ruepert, C., van Wendel de Jode, B., 2020. Passive monitoring techniques to evaluate environmental pesticide exposure: results from the Infant's Environmental Health study (ISA). *Environ. Res.* 184, 109243. https://doi.org/10.1016/j.envres.2020.109243.

Gensch, L., Jantke, K., Rasche, L., Schneider, U.A., 2024. Pesticide risk assessment in European agriculture: distribution patterns, ban-substitution effects and regulatory implications. *Environ. Pollut.* 348, 123836. https://doi.org/10.1016/j.envpol.2024.123836.

Ghasemnejad-Berenji, M., Nemati, M., Pourheydar, B., Gholizadeh, S., Karimipour, M., Mohebbi, J., Jafari, A., 2021. Neurological effects of long-term exposure to low doses of pesticides mixtures in male rats: biochemical, histological, and neurobehavioral evaluations. *Chemosphere.* 264, 128464. https://doi.org/10.1016/j.chemosphere.2020.128464.

Goodman, J.E., Prueitt, R.L., Boffetta, P., Halsall, C., Sweetman, A., 2020. "Good epidemiology practice" guidelines for pesticide exposure assessment. *Int. J. Environ. Res. Public Health* 17 (14), 5114. https://doi.org/10.3390/ijerph1714514.

Grüninger, K., & Kiyokawa, T. (2016). Herbicidal composition and method of use. WO2016133188A1. World Intellectual Property Organization.

Harley, K.G., Parra, K.L., Camacho, J., Bradman, A., Nolan, J.E.S., Lessard, C., Anderson, K.A., Poutasse, C.M., Scott, R.P., Lazaro, E., Cardoso, E., Gallardo, D., Gunier, R.B., 2019. Determinants of pesticide concentrations in silicone wristbands worn by Latina adolescent girls in a California farmworker community: the CMSECHA youth participatory action study. *Sci. Total Environ.* 652, 1022–1029. https://doi.org/10.1016/j.scitotenv.2018.10.276.

HBM4EU. https://www.hbm4eu.eu/work-packages/additional-deliverable-15-7-joint-survey-of-pesticides-details-of-approach-and-contributions/.

Heudorf, U., Angerer, J., 2001. Metabolites of pyrethroid insecticides in urine specimens: current exposure in an urban population in Germany. *Environ. Health Perspect.* • 109 (3). http://ehpnet1.niehs.nih.gov/docs/2001/109p213-217heudorf/abstract.htm.

Hermant, M., Blanchard, O., Perouel, G., Boulanger, G., Merlo, M., Desvignes, V., 2018. Environmental exposure of the adult french population to permethrin. *Risk Anal.* 38 (4), 853–865. https://doi.org/10.1111/risa.12866.

Howe, M.E., Essandoh, Y.E., Romanak, K., Xia, C., Alvarado, G.B., Bravo, G.S., Wasserman, M., Wiley, A.S., Venier, M., 2025. Determinants of pesticide exposure among girls in rural Costa Rica: a silicone wristband study. *Environ. Pollut.* 380, 126577. https://doi.org/10.1016/j.envpol.2025.126577.

Levasseur, J.L., Hoffman, K., Zhang, S., et al., 2024. Monitoring human exposure to four parabens and triclosan: comparing silicone wristbands with spot urine samples as predictors of internal dose. *J. Exposure Sci. Environ. Epidemiol.* 34, 670–678. https://doi.org/10.1038/s41370-024-00663-0.

Lewis, K.A., Tzilivakis, J., Warner, D., Green, A., 2016. An international database for pesticide risk assessments and management. *Hum. Ecol. Risk Assess.* *Int. J.* 22 (4), 1050–1064. https://doi.org/10.1080/10807039.2015.1133242.

Li, S., Ritz, B., Gong, Y., Cockburn, M., Folle, A.D., del Rosario, I., Yu, Y., Zhang, K., Castro, E., Keener, A.M., Bronstein, J., Paul, K.C., 2023. Proximity to residential and workplace pesticides application and the risk of progression of parkinson's diseases in central California. *Sci. Total Environ.* 864, 160851. https://doi.org/10.1016/j.scitotenv.2022.160851.

Lubin, J.H., Colt, J.S., Camann, D., Davis, S., Cerhan, J.R., Severson, R.K., Hartge, P., 2004. Epidemiologic evaluation of measurement data in the presence of detection limits. *Environ. Health Perspect.* 112 (17), 1691–1696.

Mayer, L., Degrendele, C., Senk, P., Kohoutek, J., Pribylová, P., Kukučka, P., Melymuk, L., Durand, K., Ravier, S., Alastuey, A., Baker, A.R., Baltensperger, U., Baumann-Stanzer, K., Biermann, T., Bohlin-Nizzetto, P., Cebrunis, D., Conil, S., Couret, C., Degór ska, A., Lammel, G., 2024. Widespread Pesticide distribution in the european atmosphere questions their degradability in air. *Environ. Sci. Technol.* 58 (7), 3342–3352. https://doi.org/10.1021/acs.est.3c08488.

McCauley, L.A., Travers, R., Lasarev, M., Muniz, J., Nailon, R., 2006. Effectiveness of cleaning practices in removing pesticides from home environments. *J. Agromed.* 11, 81–88. https://doi.org/10.1300/J096v11n02_11.

McGinley, J., Healy, M.G., Ryan, P.C., Harmon O'Driscoll, J., Mellander, P.-E., Morrison, L., Siggins, A., 2023. Impact of historical legacy pesticides on achieving legislative goals in Europe. *Sci. Total Environ.* 873, 162312. https://doi.org/10.1016/j.scitotenv.2023.162312.

McHugh, M.L., 2012. Interrater reliability: the kappa statistic. *Biochemia Medica* 22 (3), 276–282.

McLlarnan, S.M., Bramer, L.M., Dixon, H.M., et al., 2024. Predicting personal PAH exposure using high dimensional questionnaire and wristband data. *J. Exposure Sci. Environ. Epidemiol.* 34, 679–687. https://doi.org/10.1038/s41370-023-00617-y.

Molomo, R.N., Basera, W., Chetty-Mhlanga, S., Fuhrimann, S., Mugari, M., Wiesner, L., Dalvie, M.A., 2021. Relation between organophosphate pesticide metabolite concentrations with pesticide exposures, socio-economic factors and lifestyles: A cross-sectional study among school boys in the rural Western Cape, South Africa. *Environ. Pollut.* 275, 116660.

Mu, H., Yang, X., Wang, K., Osman, R., Xu, W., Liu, X., Ritsema, C.J., Geissen, V., 2024. Exposure risk to rural residents: insights into particulate and gas phase pesticides in the indoor-outdoor nexus. *Environ. Int.* 184, 108457.

Navarro, I., de la Torre, A., Sanz, P., Abrantes, N., Campos, I., Alaoui, A., Christ, F., Alcon, F., Contreras, J., Glavan, M., Pasković, I., Pasković, M.P., Nørgaard, T., Mandrioli, D., Sarggi, D., Hofman, J., Aparicio, V., Baldi, I., Bureau, M., Martínez, M.A., 2024. Assessing pesticide residues occurrence and risks in water systems: a Pan-European and Argentina perspective. *Water Res.* 254, 121419. https://doi.org/10.1016/j.watres.2024.121419.

Nijssen, R., Lommen, A., van den Top, H., et al., 2024. Assessment of exposure to pesticides: residues in 24 h duplicate diets versus their metabolites in 24 h urine using suspect screening and target analysis. *Anal. Bioanal. Chem.* 416, 635–650. https://doi.org/10.1007/s00216-023-04918-x.

Normarro, S.S., Beck, I.H., Nielsen, F., Andersen, M.S., Bilenberg, N., Jensen, T.K., Andersen, H.R., 2024. Prenatal exposure to pyrethroids and chlorpyrifos and IQ in 7-year-old children from the odense child cohort. *Neurotoxicol. Teratol.* 103, 107352. https://doi.org/10.1016/j.ntt.2024.107352.

Oerlemans, A., Figueiredo, D.M., Mol, J.G.J., Nijssen, R., Anzion, R.B.M., van Dael, M.F. P., Scheepers, P.T.J., 2021. Personal exposure assessment of pesticides in residents: the association between hand wipes and urinary biomarkers. *Environ. Res.* 199, 111282. https://doi.org/10.1016/j.envres.2021.111282.

Ottenbros, I., Lebreit, E., Huber, C., Lommen, A., Antignac, J.-P., Ķupr, P., Šulc, L., Míkes, O., Szigeti, T., Középesy, S., Martinsone, I., Martinsone, Z., Akulova, L., Pardo, O., Fernández, S.F., Coscollá, C., Pedraza-Diaz, S., Krauss, M., Debrauwer, L., Vlaanderen, J., 2023. Assessment of exposure to pesticide mixtures in five European countries by a harmonized urinary suspect screening approach. *Int. J. Hyg. Environ. Health* 248, 114105. https://doi.org/10.1016/j.ijheh.2022.114105.

Ottenbros, I.B., Vermeulen, R.C.H., Krop, E.J.M., Beeltje, H., Fuhrimann, S., Figueiredo, D.M., 2025. Characterization of aggregated exposure to multiple
pesticides near agricultural fields: an application of silicone wristbands. Environ. Res. Commun. 7 (4), 041002.

Pena, A., Duarte, S., Pereira, A.M.P.T., Silva, L.J.G., Laranjeiro, C.S.M., Oliveira, M., Lino, C., Morais, S., 2022. Human biomonitoring of selected hazardous compounds in portugal: part i—lessons learned on polycyclic aromatic hydrocarbons, metals, metalloids, and pesticides. Molecules 27, 242. https://doi.org/10.3390/molecules27010242.

R Core Team (2021). R: A language and environment for statistical ## computing. R Foundation for Statistical Computing, Vienna, Austria. ## URL https://www.R-project.org/.

Samon, S.M., Hammel, S.C., Stapleton, H.M., Anderson, K.A., 2022. Silicone wristbands as personal passive sampling devices: current knowledge, recommendations for use, and future directions. Environ. Int. 169, 107339. https://doi.org/10.1016/j.envint.2022.107339.

Schummer, C., Salquèbre, G., Briand, O., Millet, M., Appenzeller, B.M.R., 2012. Determination of farm workers' exposure to pesticides by hair analysis. Toxicol. Lett. 210 (2), 203-210. https://doi.org/10.1016/j.toxlet.2011.11.019.

Silva, V., Alaoui, A., Schlünssen, V., Vested, A., Graumans, M., van Dael, M., et al., 2021. Collection of human and environmental data on pesticide use in Europe and Argentina: field study protocol for the SPRINT project. PLoS One 16 (11), e0259748. https://doi.org/10.1371/journal.pone.0259748.

Silva, V., Gai, L., Harkes, P., Tan, G., Ritsema, C.J., Alcon, F., Contreras, J., Abrantes, N., Campos, I., Baldi, I., Bureau, M., Christ, F., Mandrioli, D., Sgargi, D., Pasković, I., Polić Pasković, M., Glavan, M., Hofman, J., Huerta Lwanga, E., Geissen, V., 2023. Pesticide residues with hazard classifications relevant to non-target species including humans are omnipresent in the environment and farmer residences. Environ. Int. 181, 108280. https://doi.org/10.1016/j.envint.2023.108280.

Silva, V., Mol, H.G.J., Zomer, P., Tienstra, M., Ritsema, C.J., Geissen, V., 2019. Pesticide residues in European agricultural soils – a hidden reality unfolded. Sci. Total Environ. 653, 1532–1545. https://doi.org/10.1016/j.scitotenv.2018.10.441.

Succop, P.A., Clark, S., Chen, M., Galke, W., 2004. Imputation of data values that are less than a detection limit. J. Occup. Environ. Hyg. 1 (7), 436–441.

Testa, C., Salis, S., Rubattu, N., Roncada, P., Miniero, R., Brambilla, G., 2019. Occurrence of Fipronil in residential house dust in the presence and absence of pets: a hint for a

comprehensive toxicological assessment. J. Environ. Sci. Health B 54 (6), 441–448. https://doi.org/10.1080/03601234.2019.1607133.

Teysseire, R., Manangama, G., Baldi, I., Carles, C., Brochard, P., Bedos, C., Delva, F., 2021. Determinants of non-dietary exposure to agricultural pesticides in populations living close to fields: a systematic review. Sci. Total Environ. 761, 143294. https://doi.org/10.1016/j.scitotenv.2020.143294.

Udovicki, B., Tomic, N., Brkić, D., Sredojevic, A., Kaludjerovic, M., Spirovic Trifunovic, B., Smigic, N., Djekic, I., 2024. Cumulative risk assessment of dietary exposure of the adult population in Serbia to pesticides that have chronic effects on the thyroid gland through fresh fruits and vegetables. Food Chem. Toxicol. 186, 114541. https://doi.org/10.1016/j.fct.2024.114541.

Veludo, A.F., Röösli, M., Dalvie, M.A., Stuchlík Fšerová, P., Prokeš, R., Pribylová, P., Šenk, P., Kohoutek, J., Mugari, M., Klánová, J., Huss, A., Figueiredo, D.M., Mol, H., Dias, J., Degrendele, C., Fuhrimann, S., 2024. Child exposure to organophosphate and pyrethroid insecticides measured in urine, wristbands, and household dust and its implications for child health in South Africa: a panel study. Environ. Epidemiol. 8 (1). https://doi.org/10.1097/EE9.0000000000000282.

Waclawik, M., Skwarlo, D., Jurewicz, J., et al., 2025. Assessment of exposure to synthetic pyrethroids with the use of silicone wristbands and biomonitoring of urinary metabolites: a pilot study. Expo Health 17, 221–232. https://doi.org/10.1007/s12403-024-00656-2.

Wang, F., Xie, Y., Lü, Y., Wang, Y., Zhang, Z., Xi, F., Yu, P., 2024. Unveiling the development trends of environmental and human health concerns for pesticides: Integrating an intelligent approach and data mining across diverse databases. Sci. Total Env. 954, 176477.

Wise, C.F., Hammel, S.C., Herkert, N.J., Ospina, M., Calafat, A.M., Breen, M., Stapleton, H.M., 2021. Comparative assessment of pesticide exposures in domestic dogs and their owners using silicone passive samplers and biomonitoring. Environ. Sci. Technol. 56 (2), 1149–1161.

Yang, M., Wang, Y., Yang, G., Wang, Y., Liu, F., Chen, C., 2024. A review of cumulative risk assessment of multiple pesticide residues in food: current status, approaches and future perspectives. Trends Food Sci. Technol. 144, 104340. https://doi.org/10.1016/j.tifs.2024.104340.