# 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

Adriana Fernandes Veludo, Martin Röösli, Mohamed Aqiel Dalvie, Petra Stuchlík Fišerová, Roman Prokeš, Petra Přibylová, Petr Šenk, Jiří Kohoutek, Mufaro Mugari, Jana Klánová, et al.

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## ► To cite this version:

Adriana Fernandes Veludo, Martin Röösli, Mohamed Aqiel Dalvie, Petra Stuchlík Fišerová, Roman Prokeš, et al. 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. Environmental Epidemiology, 2023, 8, 10.1097/ee9.0000000000000282. hal-04920577

HAL Id: hal-04920577

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Submitted on 30 Jan 2025

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# 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

Adriana Fernandes Veludo<sup>a,b</sup>, Martin Rööslja<sup>a,b</sup>, Mohamed Aqiel Dalvie<sup>c</sup>, Petra Stuchlík Fišerová<sup>d</sup>, Roman Prokeš<sup>d,e</sup>, Petra Přibylová<sup>d</sup>, Petr Šenk<sup>g</sup>, Jiří Kohoutek<sup>d</sup>, Mufaro Mugari<sup>c,d</sup>, Jana Klánová<sup>d</sup>, Anke Huss<sup>f</sup>, Daniel Martins Figueiredo<sup>f</sup>, Hans Mol<sup>g</sup>, Jonatan Dias<sup>g</sup>, Céline Degrendele<sup>d,h</sup>, Samuel Fuhrimann<sup>a,b,h</sup>

**Background:** Children in agricultural areas are exposed to organophosphate (OP) and pyrethroid (PYR) insecticides. This explorative study investigated child exposure to OPs and PYRs, comparing temporal and spatial exposure variability within and among urine, wristbands, and dust samples.

**Methods:** During spraying season 2018, 38 South African children in two agricultural areas (Grabouw/Hex River Valley) and settings (farm/village) participated in a seven-day study. Child urine and household dust samples were collected on days 1 and 7. Children and their guardians were wearing silicone wristbands for seven days. Intraclass correlation coefficients (ICCs) evaluated temporal agreements between repeated urine and dust samples, Spearman rank correlations (Rs) evaluated the correlations among matrices, and linear mixed-effect models investigated spatial exposure predictors. A risk assessment was performed using reverse dosimetry.

**Results:** Eighteen OPs/PYRs were targeted in urine, wristbands, and dust. Levels of chlorpyrifos in dust (ICC = 0.92) and diethylphosphate biomarker in urine (ICC = 0.42) showed strong and moderate temporal agreement between day 1 and day 7, respectively. Weak agreements were observed for all others. There was mostly a weak correlation among the three matrices (Rs = -0.12 to 0.35), except for chlorpyrifos in dust and its biomarker 3,5,6-trichloro-2-pyridinol in urine (Rs = 0.44). No differences in exposure levels between living locations were observed. However, 21% of the urine biomarker levels exceeded the health-risk threshold for OP exposure.

**Conclusions:** Observed high short-term variability in exposure levels during spraying season highlights the need for repeated sampling. The weak correlation between the exposure matrices points to different environmental and behavioral exposure pathways. Exceeding risk thresholds for OP should be further investigated.

**Keywords:** Child exposure; Biomonitoring; Pesticide; Vulnerable populations; Endocrine disruptors

## Introduction

Pesticides in the chemical groups of organophosphates (OPs) and pyrethroids (PYRs) are among the most used insecticides in agriculture,¹ for disease vector control and household pest control.² Due to their modes of action and toxicity, OPs act as acetylcholinesterase (AChE) inhibitors and PYRs as sodium channel modulators, and human exposure to these groups of chemicals has been linked to several adverse health effects.³⁻⁹ Children and young adolescents

are particularly vulnerable to pesticide exposure due to increased body burden from dermal absorption of chemicals (higher surface body area/weight ratios as compared with adults), increased respiratory rates, and fragile immune system.¹⁰ Studies have shown associations between early-life exposure to OPs and PYRs and impaired cognitive development,⁶,¹¹ attention deficit hyperactivity disorder,¹²,¹³ or autism-related traits¹⁴ in children and adolescents.

Over time, cumulative exposure to OPs and PYRs occurs via many occupational and environmental exposure pathways.¹⁵⁻¹⁷

This project is imbedded within the South African-Swiss Bilateral SARChI Chair in Global Environmental Health and supported by the South Africa National Research Foundation (NRF; grant number: 94883), the Swiss State Secretariat for Education, Research and Innovation (SERI), the University of Basel and the Swiss TPH. S.F.'s effort was supported by a fellowship of the Swiss National Science Foundation (SNSF; grant number: P4P4PM_199228,P400PM_180757 and TMSGI3_211325). The authors thank the Research Infrastructure RECETOX (No LM2023069) and

## What this study adds

Insecticides are widely applied for agriculture and residential use, and cumulative exposure over time may pose a possible health concern for local communities. We studied children's organophosphate and pyrethroid insecticides exposure who are living in agricultural areas of South Africa. Therefore, we integrated multiple exposure assessment methods to understand different exposure routes. We found a high short-term exposure variability and highlighted the need for repeated sampling for accurate exposure characterization, which is particularly important for epidemiological studies. Also, our findings indicated that 21% of the children in our study exceeded the health-risk threshold for cumulative exposure to organophosphates, which should be followed up in larger, in-depth studies to understand exposure pathways and health impacts.

<footnote><sup>a</sup>Swiss Tropical and Public Health Institute (Swiss TPH), Allschwil, Switzerland;</footnote>

<footnote><sup>b</sup>University of Basel, Basel, Switzerland; <sup>c</sup>Centre for Environmental and Occupational Health Research, School of Public Health, University of Cape Town, Cape Town, South Africa; <sup>d</sup>Masaryk University, Faculty of Science, RECETOX, Brno, Czech Republic; <sup>e</sup>Global Change Research Institute of the Czech Academy of Sciences, Brno, Czech Republic; <sup>f</sup>Institute for Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands; <sup>g</sup>Wageningen Food Safety Research, part of Wageningen University & Research, Wageningen, The Netherlands; and</footnote>

<footnote><sup>h</sup>Aix-Marseille University, CNRS, LCE, Marseille, France</footnote>
Children, particularly those living in agricultural areas, can be exposed during their daily-life activities (e.g., playing in previously sprayed fields or when partaking in agricultural activities) or ingesting contaminated food or water.<sup>18-21</sup> Children can additionally be exposed at home via dust contaminated with pesticides that accumulate in carpets or other surfaces due to indoor pesticide use,<sup>22</sup> spray drift from nearby farms,<sup>23,24</sup> or take-home pesticides from household members' shoes or clothes.<sup>23</sup>

Although multiple exposure studies rely on self-reported data to understand determinants of exposure,<sup>2,19,21,26</sup> there are growing efforts to use different methods as tools to investigate potential exposure sources (e.g., combining biomonitoring with point-of-contact or scenario-based assessments).<sup>27-32</sup> Biomonitoring is used to measure biological indicators (i.e., biomarkers) after exposure has occurred.<sup>33,34</sup> Urinary biomarkers can be indicators of exposure to specific active ingredients (e.g., 3,5,6-trichloro-2-pyridinol [TCPy], the biomarker of chlorpyrifos and chlorpyrifos-methyl) or to chemical groups (e.g., dialkyl phosphate metabolites [DAPs] reflect exposure to multiple OPs).<sup>15,34,35</sup> Nevertheless, these are rapidly metabolized and excreted from the body after exposure (half-lives ranging from 2 to 41 hours, depending on the pesticide and the exposure route). Moreover, their levels can vary greatly between and within individuals, and there is still a lack of understanding of how urinary levels correlate with external exposure matrices.<sup>36</sup> Point-of-contact exposure assessments use personal monitoring equipment that records cumulative individual exposure levels over time. Silicone wristbands have been increasingly used as personal passive samplers as they can capture multiple organic pollutants such as pesticides. They are low-cost and noninvasive tools that reflect dermal and inhalation exposure routes in different microenvironments (i.e., indoor and outdoor).<sup>37-41</sup> Finally, scenario-based assessments are used to determine pesticide exposure in specific environments.<sup>42,43</sup> For example, home exposure to OPs and PYRs via ingestion of dust particles has been assessed using measured pesticide levels in household dust<sup>44</sup> and information on daily dust ingestion rates (age-dependent modeled values<sup>45,46</sup>), body weight, and time spent at home.<sup>47,48</sup>

Such studies that integrate multiple methods to assess exposure are largely lacking in low- and middle-income countries,

where pesticides are heavily applied in agriculture and for household pest control. South Africa is the heaviest pesticide user in sub-Saharan Africa, with more than 20000 tonnes of pesticides used in agriculture yearly.<sup>49</sup> This results from an intensification of the cropping systems and expansion of agricultural areas.<sup>50</sup> More than 50 OPs and PYRs active ingredients are registered for agriculture or household insect control in South Africa. Further, there are reports that some are decanted and illegally sold on street markets for household pest control.<sup>22,51</sup>

Hence, in this article, we aimed to tackle the gaps in the literature by measuring children's exposure to OPs and PYRs using different exposure assessment methods (urinary biomarkers, silicone wristbands, and household dust) and to determine their spatial and temporal variability during the 1 week in the spraying season in two agricultural areas of South Africa. The four specific objectives were (1) to study the temporal agreement between repeated urine and dust measurements, (2) to investigate the correlation among urinary biomarkers, silicone wristbands, and household dust levels, (3) to understand if individual exposure mixtures cluster according to their living locations, and (4) to estimate the health risk of exposure using repeated urinary biomarker levels.

## Methods

### Study area

The present study was conducted in the Western Cape, South Africa region, which covers approximately 13 million hectares, of which two million are dedicated to agricultural use.<sup>52</sup> Within the Western Cape, two rural study areas were selected based on their different crop profiles: Hex River Valley (33°28′34.7″S19°39′51.9″E), with table grapes representing 98% of the agricultural land use, and Grabouw (34°09′16.8″S18°59′56.7″E), where pome fruits represent 81% of the agricultural land use.

### Study design

This panel study is part of the ongoing "Child Health Agricultural Pesticide Cohort Study in South Africa" (CapSA) project, which aims to determine the association between agricultural pesticide exposure and its potential health effects on 1000 children.<sup>19,53</sup>

Out of the CapSA cohort, 40 households were purposely selected alongside the main urine sampling round of all 1000 children to guarantee equal participant numbers living in two agricultural areas (Hex River Valley and Grabouw, Figure 1A). Within each area, half of the households were located on farms (within 50 m of agricultural land use) and half in nearby villages (at least 0.5 km from the closest agricultural land use).<sup>37</sup> These two household settings were a priori selected to account for possible spatial differences in pesticide concentrations within each area.

## Data collection

The sampling campaign was conducted in 2018 for over 7 days, during the main pesticide spraying season in Hex River Valley (between the 23 and 29 October) and in Grabouw (between the 31 October and the 6 November). Out of the 40 selected households, two did not complete the assessments (i.e., collection of urine and wristbands) and/or wished not to participate, resulting in 38 households included in the study (Figure 1B). In total, 76 wristbands (38 children and their respective guardians), 76 urine samples (38 on days 1 and 7), and 50 dust samples (12 on day 1 and 38 on day 7) were analyzed. Unfortunately, due to logistical constraints, only a subsample of the household dust samples could be analyzed on day 1.

A trained interviewer gave children and guardians separate structured questionnaires developed for the CapSA study<sup>53</sup> (Table 1). The children's questionnaire was administered after

project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632) financed by the Czech Ministry of Education, Youth and Sports for supportive background. This project was supported by the European Union's Horizon 2020 research and innovation program under grant agreement No 857560. This publication reflects only the author's view, and the European Commission is not responsible for any use that may be made of the information it contains.

S.F., M.A.D., M.R., C.D., R.P., and J.K.: conception and planning; C.D., R.P., and S.F.: data collection; C.D., P.S.F., J.K., P.S., P.P., M.M., H.M., and J.D.: laboratory analysis and QA-QC; A.F.V., S.F., A.H., and D.M.F.: analysis and interpretation of data. A.F.V. and S.F.: drafting of the manuscript. A.F.V., M.R., M.A.D., P.S.F., R.P., P.P., P.S., J.K., M.M., J.K., A.H., D.M.F., H.M., J.D., C.D., and S.F. participated in editing the final version of the manuscript.

The code and the data protected by the institutional review board can be requested from the corresponding author.

<figure><img src="image_3.png" /></figure>

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.environepidem.com).

*Corresponding Author. Address: Swiss Tropical and Public Health Institute (Swiss TPH), Kreuzstrasse 2, 4123 Allschwil, Switzerland. E-mail: samuel.fuhrimann@swisstph.ch (S. Fuhrimann).

Copyright © 2023 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of The Environmental Epidemiology. All rights reserved.

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Environmental Epidemiology (2024) 8:e282

Received 28 June, 2023; Accepted 2 November, 2023

Published online 29 December 2023

DOI: 10.1097/EE9.0000000000000282
## Organophosphate and pyrethroid exposure

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Study design (A) and consort flow chart showing the participant and sample selection (B).</figcaption></figure>

<figure><img src="image_5.png" /></figure>

each urine sampling at their respective schools. Children's questionnaires included questions on sociodemographic characteristics (e.g., sex, age, and education), activities on the farm (i.e., picking fruits or helping with other tasks), and leisure activities (i.e., swimming in ponds or spending time in agricultural fields). The guardians were interviewed during home visits when the dust samples were collected. The guardian's questionnaire included sociodemographic characteristics, occupation, and household pesticide use. These questionnaires were only administered on day 1 as it was anticipated that there would be little variations between day 1 and day 7. Both child and guardian interviews were conducted in the participant's mother tongue (i.e., English, Afrikaans, or Xhosa), and the questionnaires were back translated to English.

### Urine sample collection

Within the measurement week, spot urine samples were collected from each child twice. The first urine samples were collected at the beginning of the study (day 1), whereas the last samples were gathered on the last day of the study (day 7). The urine samples were collected during a morning school break at the participant's respective school (these do not represent first-morning voids). Urine plastic containers were given to each child and collected after they were filled with urine, according to the sampling protocol. These were subsequently collected into 8-mL plastic vials and kept in a freezer until they were shipped to the accredited trace analytical laboratories, as described by Fišerová et al.⁵⁴ Urine samples were sent to RECETOX, Masaryk University, Czech Republic, for further analysis.

### Wristband collection

The selected children and their guardians were further asked to wear a wristband for the whole study duration (also during showering and sleeping). Every morning, children were examined at school to confirm if they were wearing their wristbands. For good compliance, the children were reminded daily about compliance at school. On day 7, the wristbands were collected,

stored, and shipped in a cool box at 0 °C to Wageningen Food Safety Research (WFSR), Wageningen University, the Netherlands, for analysis.³⁷

### Household dust collection

Repeated dust samples were collected from each household, once on day 1 and once on day 7. The dust samples were taken from the children's bedroom using a stainless-steel inlet equipped with a preseparation mesh connected to a vacuum cleaner, as previously reported by Degrendele et al.⁴⁴ Following collection, the dust samples were packed in two layers of aluminum foil and sealed in a plastic bag. The samples in a cooler box were shipped to RECETOX laboratory, Masaryk University, the Czech Republic and stored at -18 °C until processing.

### Sample preparation and analysis

The detailed sample preparation, extraction, and analysis (including quality assurance and quality control) have been previously described for the urine,⁵⁴ wristbands,³⁷ and household dust⁴⁴ samples used in this study. A brief description of the respective methods is provided below. The selection of pesticides to be analyzed was motivated by four factors: (1) their current use in South Africa for agricultural purposes;⁵⁵ (2) their past use in agriculture;⁵⁶,⁵⁷ (3) their potential use at household level;⁵⁸ and (4) the selection was limited to the analytical capacity of the selected laboratory method.

### Urine sample preparation and analysis

Overall, 10 OPs and three PYRs urinary biomarkers were targeted. OPs biomarkers included five unspecific DAP metabolites (diethylphosphate [DEP], diethylthiophosphate [DETP], diethyldithiophosphate [DEDTP], dimethylphosphate [DMP], and dimethylthiophosphate [DMTP]) and five more specific metabolites (malathion dicarboxylic acid [MDA], *p*-nitrophenol [PNP], 3-chloro-4-methylumbelliferone [CMHC], 3,5,6-trichloro-2-pyridinol, and 2-isopropyl-4-methyl-6-hydroxypyrimidine
<caption>Table 1.</caption>

Sociodemographic and occupational characteristics of participating children and their guardians stratified by study area

<table><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2">Total, n (%)</th><th colspan="2">Grabouw, n (%)</th><th rowspan="2">Hex River Valley, n (%)</th></tr><tr><th>38 (100)</th><th>19 (100)</th></tr></thead><tbody><tr><td rowspan="2">Place of living</td><td>Farm</td><td>20 (52.6)</td><td>10 (52.6)</td><td></td><td>10 (52.6)</td></tr><tr><td>Village</td><td>18 (47.4)</td><td>9 (47.4)</td><td></td><td>9 (47.4)</td></tr><tr><td rowspan="2">Child</td><td>Female</td><td>16 (42.1)</td><td>7 (36.8)</td><td></td><td>9 (47.4)</td></tr><tr><td>Male</td><td>22 (57.9)</td><td>12 (63.2)</td><td></td><td>10 (52.6)</td></tr><tr><td rowspan="2">Sex</td><td>Female</td><td>(12, 11–13)</td><td>(12, 11–12)</td><td></td><td>(12, 11–13)</td></tr><tr><td>Male</td><td>(12, 11–12)</td><td>(12, 10–13)</td><td></td><td>(12, 11–12)</td></tr><tr><td rowspan="2">Age in years (median; IQR)</td><td>Female</td><td>(17.2, 15.3–18.6)</td><td>(17.1, 15.5–20.1)</td><td></td><td>(17.6, 14.5–18.1)</td></tr><tr><td>Male</td><td>(17.2, 15.4–20.6)</td><td>(18.8, 16.5–21.5)</td><td></td><td>(15.2, 14.6–16.3)</td></tr><tr><td rowspan="2">BMI in kg/m² (median; IQR)<sup>a</sup></td><td>Observed spraying activities<sup>b</sup></td><td></td><td></td><td></td><td></td></tr><tr><td>7 days prior the measurement week</td><td>Yes</td><td>32 (84.2)</td><td>16 (84.2)</td><td>16 (84.2)</td></tr><tr><td></td><td>During the measurement week</td><td>Yes</td><td>30 (78.9)</td><td>16 (84.2)</td><td>14 (73.7)</td></tr><tr><td rowspan="2">Swum in a pond or river<sup>c</sup></td><td>7 days prior the measurement week</td><td>Yes</td><td>18 (47.4)</td><td>8 (42.1)</td><td>10 (52.6)</td></tr><tr><td>During the measurement week</td><td>Yes</td><td>18 (47.4)</td><td>9 (47.4)</td><td>9 (47.4)</td></tr><tr><td rowspan="2">Planted in agriculture fields treated with pesticides<sup>d</sup></td><td>7 days prior the measurement week</td><td>Yes</td><td>13 (34.2)</td><td>6 (31.6)</td><td>7 (36.8)</td></tr><tr><td>During the measurement week</td><td>Yes</td><td>10 (26.3)</td><td>5 (26.3)</td><td>5 (26.3)</td></tr><tr><td rowspan="2">Helped to pick fruits from fields<sup>e</sup></td><td>7 days prior the measurement week</td><td>Yes</td><td>17 (44.7)</td><td>4 (21.1)</td><td>13 (68.4)</td></tr><tr><td>During the measurement week</td><td>Yes</td><td>14 (36.8)</td><td>7 (36.8)</td><td>7 (36.8)</td></tr><tr><td rowspan="2">Engaged in pesticide-handling activities<sup>f</sup></td><td>7 days prior the measurement week</td><td>Yes</td><td>15 (39.5)</td><td>10 (52.6)</td><td>5 (26.3)</td></tr><tr><td>During the measurement week</td><td>Yes</td><td>21 (55.3)</td><td>10 (52.6)</td><td>11 (57.9)</td></tr><tr><td rowspan="2">Guardian<sup>g</sup></td><td>Sex</td><td>Female</td><td>35 (92.1)</td><td>18 (94.7)</td><td>17 (89.5)</td></tr><tr><td>Interviewed guardian works on farm<sup>h</sup></td><td>Male</td><td>3 (7.9)</td><td>1 (5.3)</td><td>2 (10.5)</td></tr><tr><td>Any household member working on a farm<sup>i</sup></td><td>Yes</td><td>14 (36.8)</td><td>4 (21.1)</td><td>10 (52.6)</td></tr><tr><td>Any household member washes working clothes at home<sup>j</sup></td><td>Yes</td><td>25 (65.8)</td><td>10 (52.6)</td><td>15 (78.9)</td></tr><tr><td rowspan="2">Any pesticide sprayed at home</td><td>Yes</td><td>21 (55.3)</td><td>6 (31.6)</td><td>15 (78.9)</td></tr><tr><td>No</td><td>7 (18.4)</td><td>6 (78.9)</td><td>1 (5.3)</td></tr><tr><td></td><td>Last year</td><td>10 (26.3)</td><td>6 (31.6)</td><td>4 (21.1)</td></tr><tr><td></td><td>Last week<sup>k</sup></td><td>21 (55.3)</td><td>7 (36.8)</td><td>14 (73.7)</td></tr></tbody></table>

<footnote><sup>a</sup>Two children did not provide information on their height and weight; therefore, BMI was calculated based on 36 observations (19 in Grabouw and 17 Hex River Valley).</footnote>

<footnote><sup>b</sup>Observed spraying: 63% and 66% of those that answered "yes" prior and during measurement week, respectively, live in farms; swum in pond/river: 72% and 56% of those that answered "yes" prior and during measurement week, respectively, live in farms; play in sprayed fields: 69% and 80% of those that answered "yes" prior and during measurement week, respectively, live in farms; helped picking fruits from fields: 71% and 64% of those that answered "yes" prior and during measurement week, respectively, live in farms; and engaged in pesticide-handling activities: 80% and 81% of those that answered "yes" prior and during measurement week, respectively, live in farms.</footnote>

<footnote><sup>c</sup>Questionnaire data collected on day 1.</footnote>

<footnote><sup>d</sup>Guardian works in farm: 79% of those that answered "yes" live in farms; household member works in farm: 76% of those that answered "yes" live in farms; wash work clothes at home: 71% of those that answered "yes" live in farms; and sprayed pesticides at home the week prior the measurement week: 33% of those that answered "yes" live in farms.</footnote>
[IMPy]). PYRs biomarkers included two unspecific metabolites (3-phenoxybenzoic acid [3-PBA] and cis-ltrans-3-(2,2-dichlorovinyl)-2,2-dimethyl cyclopropane-carboxylic acid [DCCA]) and one specific metabolite (4-fluoro-3-phenoxybenzoic acid [4F3-PBA]). The extraction of four DAP metabolites (i.e., DEP, DETP, DMP, and DMTP) was done by applying the QuEChERS-based method. The remaining pesticide metabolites were extracted via solid-phase extraction. Pesticide metabolites and creatinine analysis were performed using high-performance liquid chromatography coupled to mass spectrometry (HPLC-MS).⁵³

The urinary biomarkers and creatinine concentrations are in Table S1; http://links.lww.com/EE/A252. To adjust for urinary dilution, the following equation was used:

### Creatinine-adjusted concentrations

$$ (\mu \text{ g/g creatinine}) = \frac{\text{biomarker concentration } (\mu \text{ g/mL})}{\text{creatinine concentration (g/mL)}} \quad (1) $$

### Wristband precleaning, extraction, and analysis

The wristbands were precleaned and shipped to South Africa, where they were placed in individual zip-lock mylar bags (DS M&T Inc., Fontana, California). After the sampling campaign, the wristbands were extracted and analyzed using a gas chromatography-tandem mass spectrometry system.³⁷ Overall, five OPs (chlorpyrifos, diazinon, malathion, prothiofos, and dimethoate) and three PYRs (cypermethrin, deltamethrin, and λ-cyhalothrin) were targeted in wristbands.

### Household dust sample preparation and analysis

Three OPs (chlorpyrifos, diazinon, and malathion) were targeted in the dust. The samples were extracted with methanol using an ultrasonic bath for about 1 hour and further analyzed using HPLC-MS.⁴⁴ Unfortunately, no PYRs were analyzed in dust samples due to the limited analytical capacity of the selected laboratory.

### Data analysis

Descriptive statistics provided information on the quantification frequency, median (interquartile range [IQR]), and maximum levels of OPs and PYRs above the quantification limit in each of the three matrices (creatinine-corrected biomarkers, wristbands, and dust) (Table 2). The raw data for each matrix can be found in Tables S1–S3; http://links.lww.com/EE/A252. All analyses were performed using R software (Foundation for Statistical Computing, version 3.5.3, RStudio Version 1.1.4).

### Temporal agreement between repeated urine and dust measurements

Spearman's rank correlations (Rs) and intraclass correlation coefficient (ICC) were calculated for urinary biomarkers and dust pesticides to understand the temporal correlation and agreement between the repeated samples (i.e., on day 1 and day 7). Rs values between ±0 and ±0.3 indicate weak correlations, values between ±0.4 and ±0.6 indicate moderate correlations, values between ±0.7 and ±0.9 indicate strong correlations, and any value equal to ±1 indicates a perfect correlation.⁵⁹ ICC values below 0.5 indicate poor temporal agreement, values between 0.5 and 0.75 indicate moderate agreement, values between 0.75 and 0.9 indicate good agreement, and any value above 0.9 indicates excellent agreement.⁶⁰ The urinary biomarkers 4F3-PBA and malathion in dust were never quantified on day 7 and day 1, respectively, and were dropped from the analysis.

### Correlation among urinary biomarkers, wristbands, and household dust levels

A correlation matrix using pairwise Spearman's rank correlations was created to understand the levels of correlation between compounds measured on day 7 in the different matrices (i.e., urinary biomarkers, wristbands, and household dust). Measurements collected on day 1 were not included in the matrix due to incomplete data for dust samples. However, this information can be found in Figure S1; http://links.lww.com/EE/A252 and Table S4; http://links.lww.com/EE/A252. Only the compounds with representation in at least two matrices were included (i.e., PNP and 4F3-PBA biomarkers were excluded due to no representation in wristbands or dust; Table 2). To deal with the different unit levels inherent to each matrix, the concentrations were first log transformed and standardized to a mean of zero and a standard deviation of one.

### Spatial clustering of exposure mixtures

To visualize the clustering of individuals exposed to different insecticide mixtures, heatmaps were created showing the log-standardized levels of child urinary biomarkers (day 1 and day 7) and child and guardian wristbands and household dust (day 1 and day 7). The allocated household IDs were hierarchically clustered using the Spearman's rank correlation as a distance measure to understand how the exposure levels correlated between participants living in different areas and household settings.

Multivariable linear mixed-effect (LME) models, using household IDs as a random effect, were performed to study potential exposure predictors (study area [Grabouw and Hex River Valley] and household setting [farm and village]) while correcting for the sex of the participant (with exception to the dust models). Even though age is an important confounder, the models were not corrected for this, given the little variability in age between participants (Table 1) and the small sample size of the study population. A first model (hereafter, the overall model) treated all pesticide values (regardless of the matrix) as repeated measurements. Similarly to what was performed in Correlation among urinary biomarkers, wristbands, and household dust levels, the data was log transformed and standardized to account for the different unit levels inherent to each matrix. Subsequent models were first stratified per matrix (i.e., urine, dust, and wristbands) and then stratified per pesticide within the matrices. This allows us to understand whether overall exposure to pesticides is strongly dictated by the living location of the participant or whether this is highly variable according to the exposure route or the specific pesticide analyzed. A P value <0.05 was considered statistically significant.

### Health-risk assessment using the urinary biomonitoring levels

A reverse dosimetry approach was used to evaluate the exposure risk to the specific OPs (chlorpyrifos, diazinon, and parathion) and PYRs (cypermethrin and deltamethrin) insecticides.

The repeated urinary biomarker measurements were used to perform a rough estimate of the daily intake (EDI, µg/kg/d) of pesticides as follows:⁶¹⁻⁶³

$$ EDI = \frac{(C_U \times V_{24} \times MW_P)}{(F_{UE} \times BW \times MW_M)} \quad (2) $$

where C_U represents the urinary concentration of the metabolite (µg/L) (values not corrected for creatinine, Table S1; http://links.lww.com/EE/A252), V_24 the child's daily urinary excretion volume (L/d), MW_P the molecular weight of the active ingredient (g/mol), F_UE the urinary excretion factor of each active ingredient (unitless) (Table S5; http://links.lww.com/EE/A252), BW the
<caption>Table 2.</caption>

<caption>Quantification frequency and levels of the targeted OP and PYR insecticides in urine, wristbands, and household dust samples</caption>

<table><caption>Urine samples (µg/g creatinine)ª</caption><thead><tr><th rowspan="2">Biomarker</th><th rowspan="2">Group</th><th rowspan="2">Active ingredient</th><th colspan="2">Total (n = 76)</th><th colspan="4">Day 1 (n = 38)</th><th colspan="4">Day 7 (n = 38)</th></tr><tr><th>Detects, n (%)</th><th>Median (IQR)</th><th>Max</th><th>Detects, n (%)</th><th>Median (IQR)</th><th>Max</th><th>Detects, n (%)</th><th>Median (IQR)</th><th>Max</th></tr></thead><tbody><tr><td>3-PBA</td><td>PVR</td><td>Unspecific<sup>c</sup></td><td>75 (98.7)</td><td>1.0 (0.68–1.5)</td><td>9.6</td><td>37 (97.4)</td><td>1.0 (0.7–1.6)</td><td>9.6</td><td>38 (100)</td><td>1 (0.7–1.3)</td><td>5.1</td></tr><tr><td>4F3-PBA</td><td>PVR</td><td>Cyfluthrin<sup>c</sup></td><td>18 (23.7)</td><td>1.1 (1.0–1.4)</td><td>4</td><td>18 (47.4)</td><td>1.1 (1.0–1.4)</td><td>4</td><td>Not detected</td><td></td><td></td></tr><tr><td>CMHC</td><td>OP</td><td>Coumaphos</td><td>2 (2.6)</td><td>0.04 (0.04–0.04)</td><td>0.04</td><td>Not detected</td><td></td><td>2 (5.3)</td><td>Not detected</td><td>0.04 (0.04–0.04)</td><td>0.04</td></tr><tr><td>DCCA</td><td>PVR</td><td>Unspecific<sup>d</sup></td><td>76 (100)</td><td>0.8 (0.6–1.1)</td><td>12.9</td><td>38 (100)</td><td>0.8 (0.6–1.2)</td><td>12.9</td><td>38 (100)</td><td>0.6 (0.6–0.9)</td><td>3</td></tr><tr><td>DEP</td><td>OP</td><td>Unspecific<sup>d</sup></td><td>70 (92.1)</td><td>2.5 (1.8–4.9)</td><td>22.8</td><td>35 (92.1)</td><td>2.6 (2.1–5.4)</td><td>22.8</td><td>35 (92.1)</td><td>2.3 (1.7–4.4)</td><td>22.6</td></tr><tr><td>DETP</td><td>OP</td><td>Unspecific<sup>d</sup></td><td>76 (100)</td><td>0.8 (0.5–1.1)</td><td>15.1</td><td>38 (100)</td><td>0.8 (0.6–1.1)</td><td>15</td><td>38 (100)</td><td>0.8 (0.4–1.3)</td><td>15.1</td></tr><tr><td>DMP</td><td>OP</td><td>Unspecific<sup>d</sup></td><td>74 (97.4)</td><td>13.5 (8.7–22.1)</td><td>112.4</td><td>36 (94.7)</td><td>11.1 (8.6–16.5)</td><td>63.2</td><td>38 (100)</td><td>14.7 (9.3–24.4)</td><td>112.4</td></tr><tr><td>DMTP</td><td>OP</td><td>Unspecific<sup>d</sup></td><td>76 (100)</td><td>2.0 (1.5–3.4)</td><td>19.2</td><td>38 (100)</td><td>2.3 (1.5–3.2)</td><td>19.2</td><td>38 (100)</td><td>2.4 (1.5–3.6)</td><td>17.1</td></tr><tr><td>IMPy</td><td>OP</td><td>Diazinon</td><td>62 (81.6)</td><td>0.41 (0.2–1.2)</td><td>21.5</td><td>36 (94.7)</td><td>0.6 (0.2–2.3)</td><td>21.5</td><td>26 (68.4)</td><td>0.3 (0.1–0.6)</td><td>15.8</td></tr><tr><td>TCPy</td><td>OP</td><td>Chlorpyrifos<sup>f</sup></td><td>74 (94.7)</td><td>1.0 (0.4–2.6)</td><td>113</td><td>38 (100)</td><td>1.9 (0.8–3.9)</td><td>113</td><td>34 (89.5)</td><td>0.4 (0.1–1.0)</td><td>6.8</td></tr><tr><td>DEDTP</td><td>OP</td><td>Parathion<sup>g</sup></td><td>54 (71.1)</td><td>0.3 (0.1–0.6)</td><td>3.5</td><td>30 (78.9)</td><td>0.2 (0.1–0.5)</td><td>3.47</td><td>24 (63.2)</td><td>0.5 (0.2–0.7)</td><td>1.1</td></tr><tr><td>MDA</td><td>OP</td><td>Unspecific<sup>g</sup></td><td></td><td></td><td></td><td></td><td>Not detected</td><td></td><td></td><td></td><td></td></tr><tr><td colspan="12" style="text-align: left; font-weight: bold;">Wristband samples (ng/g wristband)</td></tr><tr><th rowspan="2">Active ingredient</th><th rowspan="2">Group</th><th rowspan="2">Group</th><th colspan="2">Total (n = 76)</th><th colspan="4">Children (n = 38)</th><th colspan="4">Guardians (n = 38)</th></tr><tr><th>Detects, n (%)</th><th>Median (IQR)</th><th>Max</th><th>Detects, n (%)</th><th>Median (IQR)</th><th>Max</th><th>Detects, n (%)</th><th>Median (IQR)</th><th>Max</th></tr><tr><td>Deltamethrin</td><td>PVR</td><td>PVR</td><td>69 (90.8)</td><td>3.8 (2.5–5.6)</td><td>352.3</td><td>36 (94.7)</td><td>4.0 (3.2–6.0)</td><td>107</td><td>33 (86.8)</td><td>3.1 (2.0–4.9)</td><td>352.3</td></tr><tr><td>Chlorpyrifos</td><td>OP</td><td>OP</td><td>61 (80.3)</td><td>62.7 (25.7–158.0)</td><td>658.5</td><td>29 (76.3)</td><td>89.8 (43.2–273.6)</td><td>658.5</td><td>32 (84.2)</td><td>29.5 (14.0–88.3)</td><td>386.1</td></tr><tr><td>Cypermethrin</td><td>PVR</td><td>PVR</td><td>43 (56.6)</td><td>19.2 (9.9–32.0)</td><td>88.6</td><td>24 (63.2)</td><td>20.2 (12.5–38.4)</td><td>88.6</td><td>19 (50)</td><td>15.9 (6.2–26.8)</td><td>45.6</td></tr><tr><td>Diazinon</td><td>OP</td><td>OP</td><td>35 (46.1)</td><td>3.0 (1.9–8.1)</td><td>98.4</td><td>22 (57.9)</td><td>2.9 (1.8–6.3)</td><td>98.4</td><td>23 (34.2)</td><td>3.0 (1.9–11.1)</td><td>80.5</td></tr><tr><td>Prothiofos</td><td>OP</td><td>OP</td><td>35 (46.1)</td><td>26.4 (10.0–55.8)</td><td>582.6</td><td>20 (52.6)</td><td>36.6 (15.1–71.2)</td><td>582.6</td><td>25 (39.5)</td><td>14.5 (5.0–41.5)</td><td>96.5</td></tr><tr><td>Malathion</td><td>OP</td><td>OP</td><td>17 (22.4)</td><td>2.1 (1.4–2.8)</td><td>14.3</td><td>10 (26.3)</td><td>2.5 (1.5–2.7)</td><td>6.3</td><td>7 (18.4)</td><td>1.9 (1.3–5.3)</td><td>14.3</td></tr><tr><td>Dimethoate</td><td>OP</td><td>OP</td><td></td><td></td><td></td><td></td><td>Not detected</td><td></td><td></td><td></td><td></td></tr><tr><td>λ-cyhalothrin</td><td>PVR</td><td>PVR</td><td></td><td></td><td></td><td></td><td>Not detected</td><td></td><td></td><td></td><td></td></tr><tr><td colspan="12" style="text-align: left; font-weight: bold;">Household dust samples (ng/g dust)</td></tr><tr><th rowspan="2">Active ingredient</th><th rowspan="2">Group</th><th rowspan="2">Group</th><th colspan="2">Total (n = 50)</th><th colspan="4">Day 1 (n = 12)</th><th colspan="4">Day 7 (n = 38)</th></tr><tr><th>Detects, n (%)</th><th>Median (IQR)</th><th>Max</th><th>Detects, n (%)</th><th>Median (IQR)</th><th>Max</th><th>Detects, n (%)</th><th>Median (IQR)</th><th>Max</th></tr><tr><td>Chlorpyrifos</td><td>OP</td><td>OP</td><td>48 (96.0)</td><td>367.8 (152.0–1009.5)</td><td>19528.3</td><td>12 (100)</td><td>381.1 (215.7–616.9)</td><td>3800</td><td>36 (94.7)</td><td>367.8 (131.6–1075.0)</td><td>19528.3</td></tr><tr><td>Diazinon</td><td>OP</td><td>OP</td><td>36 (72.0)</td><td>15.4 (7.2–39.3)</td><td>2209.5</td><td>11 (91.7)</td><td>10.9 (5.8–19.8)</td><td>1396</td><td>25 (65.8)</td><td>17.8 (10.9–54.5)</td><td>2209.5</td></tr><tr><td>Malathion</td><td>OP</td><td>OP</td><td>2 (4.0)</td><td>96.7 (70.3–123.1)</td><td>149.5</td><td></td><td>Not detected</td><td></td><td>2 (5.3)</td><td>96.7 (70.3–123.1)</td><td>149.5</td></tr></tbody></table>

<footnote>ªUnadjusted urine levels are available in Table S1; http://links.lww.com/EEA252.</footnote>

<footnote>ᵇBiomarker of cypermethrin, deltamethrin, etofenprox, tempropathin, fenvalerate, estenvalerate, λ-cyhalothrin, permethrin, and τ-fluvalinate.</footnote>

<footnote>ᶜAlso β-cyfluthrin.</footnote>

<footnote>ᵇBiomarker of cyfluthrin, cypermethrin, and permethrin.</footnote>

<footnote>ᵃAn extended list of parent compounds can be found in the literature. ³⁰</footnote>

<footnote>ᵇAlso chlorpyrifos-methyl.</footnote>

<footnote>ᶜAlso methyl-parathion.</footnote>
child's body weight (kg), and MW<sub>M</sub> is the molecular weight of the biomarker (g/mol). The mean values of V<sub>24</sub> were taken from the literature according to each child's age group.⁶⁴ For the children that did not provide their BW (two children), we took the mean BW value from children of the same age and sex. Where available, the EDI of each pesticide was calculated using the specific urinary biomarker for that pesticide. For chlorpyrifos, the EDI was calculated using both the specific biomarker (TCPy) and the ΣDEPs (i.e., DEP + DETP [DMP and DMTP only reflect exposure to chlorpyrifos-methyl⁶⁵]) for comparison between approaches. The urinary levels of DCCA and 3-PBA were considered for cypermethrin and deltamethrin, respectively (no specific biomarkers are available). The parameters used to calculate the EDI are shown in Table S5; http://links.lww.com/EE/A252. Although biomarkers can be detected in their form in environmental samples,²⁹,⁶⁶ here we assumed that the presence of the biomarkers in urine was exclusively derived from exposure to the active ingredient and not to the metabolite itself. After calculating the EDI values, we estimated the hazard quotient (HQ) by dividing the EDI by the acceptable daily intake (ADI), a toxicological reference value:

$$ HQ = EDI/ADI \quad (3) $$

A value below one indicates a low risk of exposure to that specific pesticide. The ADIs used in this study were those proposed by the European Food Safety Authority (EFSA).⁶⁷,⁶⁸ Finally, for each group of pesticides with the same mode of action (MoA), the hazard index (HI) was calculated to account for the cumulative risk of exposure to a specific group of pesticides:⁶⁹

$$ HI_{MoA} = \sum HQ_i \quad (4) $$

The HI was calculated separately for OPs (AChE inhibitors) and PYRs (sodium channel modulators) due to their different modes of action. For a higher specificity in the HI for OPs, the HQ for chlorpyrifos using TCPy was preferred over the ΣDEPs. A value above one indicates a high risk associated with exposure to OPs or PYRs. The median (IQR), 95th percentile, and maximum EDI, HQ, and HI were calculated for each pesticide or pesticide group on day 1 and day 7 (Table S6; http://links.lww.com/EE/A252).

## Handling data below the limit of detection

For pesticides and biomarkers showing at least 40% of data above limit of detection (LOD) (Table 2), measurements below LOD were imputed using a maximum likelihood estimation approach, taking the area and household setting as predictors. Imputations were achieved using bootstrap randomly selected values from a log-normal estimated parameter distribution.⁷⁰ The imputed data was used for ICC calculations, LME models, and health-risk assessment. Pesticides and biomarkers with less than 40% of data above LOD were dropped from these analyses.

## Ethical approval

Written informed consent was obtained from each guardian and children assented to participate in the study. The study received ethical clearance from the University of Cape Town's Research Ethics Committee (HREC 637/2018).

## Results

### Demographics of the study population

The sociodemographic and occupational characteristics of the participating children and their guardians are presented

in Table 1. The guardian questionnaires on day 7 were not answered appropriately; therefore, only data from day 1 is shown.

The median age of the 38 children participating in the study was 12 years (IQR, 11–13 years old). Slightly more than half of the participants were boys (58%) and lived on a farm (53%). The median body mass index (BMI) for both boys and girls was 17 kg/m² (IQR, 15.3–18.6 and 16.5–21.5 kg/m², respectively). Most children reported having observed spraying events in surrounding farms prior and during the measurement week (84% and 79%, respectively). Almost half of the children (47%) reported having swum in a pond/river or having helped picking fruits from agriculture fields 7 days before the measurement week. More than half of the children (55%) reported engaging in pesticide-handling activities during the measurement week (including assisting in pesticide storage or helping with spraying, mixing, or loading). It was also observed that the majority of the children that have reported engaging in activities linked with pesticide exposure before and during the measurement week (i.e., swimming in ponds/rivers, playing in previously sprayed fields, picking fruits from agriculture fields, and engaging in pesticide-handling activities) live in farms.

Of the 38 participating guardians, the majority were females (92%) and reported that at least one of the household members has a farm-related job (66%). More than half (55%) reported washing their work clothes at home and having sprayed pesticides in the household the week before the measurement week. It was observed that the majority of the guardians having reported working on a farm (79%), having any other household member working on a farm (76%), and washing work clothes at home (71%) also live on farms. On the other hand, most of those reporting to have sprayed pesticides at home before the measurement week live in villages (67%).

### OPs and PYRs levels in urine, wristbands, and dust and temporal agreement between repeated measurements

Across the analyzed matrices, a total of 18 different OPs and PYRs were targeted (Table 2), of which 15 were detected at least once.

In urine, DETP, DMTP, and DCCA were detected in all 76 urine samples (100%), followed by 3-PBA, DMP, TCPy, and DEP, which were detected in more than 90% of the samples (Table 2). The DAP metabolites were generally present in higher concentrations than the remaining OPs and PYRs metabolites, with DMP detected the highest (median, 13.4 µg/g creatinine; IQR, 8.7–22.1 µg/g creatinine).

In the 76 wristband samples, deltamethrin was detected most often (90.8%), followed by chlorpyrifos (80.3%) and cypermethrin (56.6%), with chlorpyrifos being the insecticide detected in higher concentrations (median, 61.1 ng/g wristband; IQR, 25.7–158.0 ng/g wristband).

Chlorpyrifos was also the insecticide detected in higher concentrations in the 50 dust samples analyzed (median, 367.8 ng/g dust; IQR, 152.0–1009.5 ng/g dust) and the most frequently detected (96%). Diazinon was detected in 72% of the dust samples (median, 15.4 ng/g dust; IQR, 7.2–39.3 ng/g dust), whereas malathion was only detected in 4% of the samples (median, 96.7 ng/g dust; IQR, 70.3–123.1 ng/g dust). No PYR insecticides were targeted in household dust.

The Spearman's rank correlations and the ICCs calculated to understand the temporal agreement between repeated measurements are presented in Figure 2. For the majority of urinary biomarkers analyzed, weak correlations and agreements were observed between levels measured on day 1 and day 7 (Rs, -0.12 to 0.22; ICC, 0.00–0.40). Nonetheless, moderate positive correlations were observed for TCPy (Rs, 0.41), DETP (Rs, 0.42), and 3-PBA (0.45), with DETP showing also a moderate agreement between repeated samples (ICC, 0.62). As to the dust
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Scatter plots showing log concentrations of urinary biomarkers (µg/g creatinine) and household dust (ng/g dust) for day 1 and day 7. Spearman's rank correlations (Rs) and ICCs between repeated measurements are also presented.</figcaption></figure>

samples, a strong positive correlation (Rs, 0.69) and excellent agreement (ICC, 0.93) were observed between chlorpyrifos levels measured on both days. The opposite was observed for diazinon levels in dust (Rs, 0.34; ICC, 0.15).

## Correlation within and between insecticide levels measured in urine, wristbands, and dust

The correlation coefficients between compounds measured on day 7 in the different matrices (i.e., urinary biomarkers, wristbands, and household dust) are presented in Figure 3, whereas the respective *P* values can be found in Table S4; http://links.lww.com/EE/A252. A moderate correlation was observed for urinary biomarkers between TCPy and DEP (Rs, 0.60) and between the PYRs metabolites, that is, 3-PBA and DCCA (Rs, 0.56). A weak-to-moderate correlation was observed between pesticides measured in children's (Rs, 0.28–0.61) and guardians' (Rs, 0.02–0.57) wristbands. The children's wristbands were weak to moderately correlated to their guardian's wristbands, with the strongest pair correlation observed for diazinon (Rs, 0.58). In dust, a moderate negative correlation was observed between chlorpyrifos and diazinon (Rs, −0.52) (Figure 3, Table S4; http://links.lww.com/

EE/A252). In all analyzed matrices, mostly weak correlations were observed between OPs and PYRs insecticides, with the exception of prothiofos in children's and guardian's wristbands that showed moderate correlations with deltamethrin in guardian's (Rs, 0.46) and cypermethrin in children's wristbands (Rs, −0.43), respectively.

When examining the correlations between urine biomarker levels and the active ingredient levels measured in wristbands, weak correlations were observed for both OPs insecticides (Rs, −0.19 to 0.35) and PYRs insecticides (Rs, −0.12 to 0.08). Chlorpyrifos and diazinon were the only two compounds targeted in urine, wristbands, and dust. Dust levels of chlorpyrifos were moderately correlated with their specific urinary biomarkers TCPy and DETP (Rs, 0.44 and 0.42, respectively) but weakly correlated with the levels measured in wristbands (Rs, 0.16–0.17). In contrast, diazinon dust levels showed no correlation with its specific urinary biomarker IMPy (Rs, −0.02) and were weakly correlated to the wristband levels (Rs, −0.11 to −0.18).

## Spatial clustering of exposure mixtures

Using the hierarchical cluster analysis, we observed that within each study area (i.e., Grabouw or Hex River Valley), the
<figure><img src="image_7.png" /><figcaption>**Figure 3.** Correlation matrix showing the Spearman's rank correlations (Rs) between the levels of organophosphates (OPs) and pyrethroids (PYRs) in urine, wristbands, and dust on day 7.</figcaption></figure>

individuals living there tend to cluster based on their similar insecticide mixture profiles across measured matrices (Figure 4). Nevertheless, there are no clear exposure patterns distinguishing individuals living in the two different agricultural areas. A similar result was observed for residents living in either farm or village households. Even though particular clusters of individuals were found within each setting, these differences are not always clear.

The results from the LME models further emphasize this. The overall model (i.e., combining all pesticide levels across matrices) showed no statistical significant difference in exposure levels among residents living in different areas and settings. When looking at individual matrices, statistical differences were only observed for all joint biomarkers' levels with children living in Hex River Valley, having significantly higher biomarker (log transformed) concentrations than children living in Grabouw (β estimate, 0.36 µg/g creatinine, Table S7; http://links.lww.com/EE/A252). Further spatial differences were observed at the individual pesticide/biomarker level for four urinary biomarkers,

one OP in wristbands, and the two OPs measured in dust (Table 3). The log concentrations of PNP and 3-PBA biomarkers were significantly higher among children in Hex River Valley (β estimate, 2.44 and 0.45 µg/g creatinine, respectively). On the other hand, chlorpyrifos log concentrations in children's wristbands were significantly lower in Hex River Valley than in Grabouw (β estimate, -1.67 ng/g wristband). DMP in urine and diazinon in dust were measured in significantly higher concentrations in villages (β estimate, 0.45 µg/g creatinine and 1.69 ng/g dust, respectively). Still, the opposite was found for DMTP in urine and chlorpyrifos in dust, with village residents showing lower exposure levels than farm residents (β estimate, -0.32 µg/g creatinine and -1.28 ng/g dust, respectively).

## Health-risk assessment based on urinary biomonitoring levels

The median (IQR), 95th percentile, and max EDI (µg/kg/d), HQ, and HI (unitless) calculated on days 1 and 7 for each
<figure><img src="image_8.png" /><figcaption>**Figure 4.** Heatmap showing standardized levels for all insecticides across each urine and dust sample from child and guardian wristbands on day 1 and day 7. Households are ordered based on hierarchical clustering using Spearman's rank correlation. Blank spaces: households where dust samples were not measured.</figcaption></figure>

pesticide and pesticide group are presented in Table S5; http://links.lww.com/EE/A252. We observed a high variability in the estimated health risk from single and cumulative pesticide exposure depending on the urine samples used (i.e., samples collected on day 1 vs. day 7). Based on the urine measurements collected on day 1, five participants were estimated to be above the health-risk thresholds (HQ > 1) due to diazinon exposure and two participants due to chlorpyrifos and parathion exposure (one participant each, Figure S2; http://links.lww.com/EE/A252). Also, eight children were above health-risk thresholds due to cumulative exposure to OPs (HI > 1). On the other hand, using the urine measurements collected on day 7, only three participants were above the threshold due to single OPs exposure (two due to diazinon exposure and one due to chlorpyrifos exposure). Similarly, the number of children above the health-risk threshold due to cumulative OPs exposure on day 7 decreased to four participants. Single and cumulative exposure to PYRs was not above risk thresholds in any measured days (Figure S2; http://links.lww.com/EE/A252).

## Discussion

Over a week, we conducted an in-depth exposure assessment using two personal exposure matrices (i.e., biomonitoring [urine samples] and point-of-contact exposure assessment [wristbands]) and one indirect method (household dust) to understand children's exposure to OPs and PYRs in agricultural areas of South Africa.

Of all the OPs and PYRs analyzed across matrices, 83% were detected at least once, with chlorpyrifos showing the highest concentrations in both wristbands and household dust samples. This is in line with recent research showing a high detection frequency of multiple OPs and PYRs measured in silicone wristbands of children and adolescents living in Latina farm-worker communities in California³⁸,⁷¹ and North Carolina⁶⁷,⁷²,⁷³ but also in several Peruvian agricultural communities,⁷⁴ suggesting the broad use of these insecticides in agriculture. For both OPs and PYRs, the urinary levels observed in this study were lower than those previously reported for farm workers in the Western Cape region,⁹,³⁵ but the same order of magnitude as those reported for other children living in the same study areas 10 years previous to our study,²¹ likely a result of the similar exposure pathways throughout the years.

A strong temporal agreement between repeated samples was observed for chlorpyrifos in the dust (ICC, 0.92), and moderate temporal correlations were additionally observed for TCPy and two nonspecific OPs and PYRs urinary biomarkers (DETP and 3-PBA, respectively) (Rs, 0.41–0.45). Recent studies conducted in the Western Cape have shown the presence of chlorpyrifos in different environmental matrices (e.g., soil¹⁶,⁵⁵,⁷⁵), emphasizing its ubiquity in the region. Moreover, not only is this pesticide known to be applied on crops,⁵⁵ but it is also known to be used for household pest control.²²,⁵¹ This could lead to an accumulation of household dust particles and continuous exposure of the study population to this insecticide.

Yet, no temporal correlation was found for 69% of the urinary biomarkers analyzed, and only DETP showed a moderate
<table><caption>**Table 3.**

Linear mixed-effect model to study the effect of area and location on the overall log-standardized concentrations and models stratified per matrix and per pesticide within each matrix</caption><thead><tr><th></th><th>Predictor</th><th>β estimate</th><th>95% CI</th><th>P value</th></tr></thead><tbody><tr><td colspan="5">Urinary biomarkers (log µg/g creatinine)</td></tr><tr><td rowspan="3">TCPy</td><td>Setting<sup>a</sup>: Village</td><td>-0.47</td><td>-1.40, 0.45</td><td>0.33</td></tr><tr><td>Area<sup>b</sup>: Hex River Valley</td><td>-0.15</td><td>-1.07, 0.78</td><td>0.76</td></tr><tr><td>Sex<sup>c</sup>: Male</td><td>-0.05</td><td>-0.99, 0.89</td><td>0.92</td></tr><tr><td rowspan="3">IMPy</td><td>Setting: Village</td><td>-0.42</td><td>-1.25, 0.40</td><td>0.32</td></tr><tr><td>Area: Hex River Valley</td><td>0.36</td><td>-0.47, 1.19</td><td>0.40</td></tr><tr><td>Sex: Male</td><td>0.57</td><td>-0.27, 1.41</td><td>0.20</td></tr><tr><td rowspan="3">PNP</td><td>Setting: Village</td><td>0.01</td><td>-0.74, 0.76</td><td>0.98</td></tr><tr><td>Area: Hex River Valley</td><td>2.44</td><td>1.69, 3.19</td><td>&lt;0.001</td></tr><tr><td>Sex: Male</td><td>0.31</td><td>-0.44, 1.07</td><td>0.43</td></tr><tr><td rowspan="3">DEP</td><td>Setting: Village</td><td>-0.11</td><td>-0.54, 0.33</td><td>0.64</td></tr><tr><td>Area: Hex River Valley</td><td>0.09</td><td>-0.35, 0.52</td><td>0.70</td></tr><tr><td>Sex: Male</td><td>-0.18</td><td>-0.62, 0.26</td><td>0.43</td></tr><tr><td rowspan="3">DETP</td><td>Setting: Village</td><td>-0.20</td><td>-0.69, 0.28</td><td>0.42</td></tr><tr><td>Area: Hex River Valley</td><td>-0.08</td><td>-0.57, 0.41</td><td>0.75</td></tr><tr><td>Sex: Male</td><td>-0.21</td><td>-0.71, 0.3</td><td>0.41</td></tr><tr><td rowspan="3">DMP</td><td>Setting: Village</td><td>0.45</td><td>0.11, 0.79</td><td>0.01</td></tr><tr><td>Area: Hex River Valley</td><td>-0.24</td><td>-0.58, 0.11</td><td>0.19</td></tr><tr><td>Sex: Male</td><td>0.06</td><td>-0.29, 0.40</td><td>0.76</td></tr><tr><td rowspan="3">DMTP</td><td>Setting: Village</td><td>-0.32</td><td>-0.64, -0.01</td><td>0.05</td></tr><tr><td>Area: Hex River Valley</td><td>0.06</td><td>-0.25, 0.38</td><td>0.71</td></tr><tr><td>Sex: Male</td><td>-0.13</td><td>-0.45, 0.19</td><td>0.44</td></tr><tr><td rowspan="3">3-PBA</td><td>Setting: Village</td><td>-0.17</td><td>-0.55, 0.21</td><td>0.39</td></tr><tr><td>Area: Hex River Valley</td><td>0.45</td><td>0.07, 0.83</td><td>0.03</td></tr><tr><td>Sex: Male</td><td>-0.14</td><td>-0.52, 0.24</td><td>0.48</td></tr><tr><td rowspan="3">DCCA</td><td>Setting: Village</td><td>-0.33</td><td>-0.67, 0.01</td><td>0.07</td></tr><tr><td>Area: Hex River Valley</td><td>0.33</td><td>-0.01, 0.67</td><td>0.06</td></tr><tr><td>Sex: Male</td><td>0.09</td><td>-0.26, 0.42</td><td>0.63</td></tr><tr><td colspan="5">Wristband pesticides (log ng/g wristband)</td></tr><tr><td rowspan="3">Chlorpyrifos</td><td>Setting: Village</td><td>0.00</td><td>-1.37, 1.37</td><td>0.99</td></tr><tr><td>Area: Hex River Valley</td><td>-1.67</td><td>-3.05, -0.29</td><td>0.02</td></tr><tr><td>Sex: Male</td><td>0.51</td><td>-0.89, 1.91</td><td>0.48</td></tr><tr><td rowspan="3">Diazinon</td><td>Setting: Village</td><td>0.13</td><td>-1.68, 1.94</td><td>0.89</td></tr><tr><td>Area: Hex River Valley</td><td>-1.57</td><td>-3.39, 0.24</td><td>0.10</td></tr><tr><td>Sex: Male</td><td>0.08</td><td>-1.76, 1.93</td><td>0.93</td></tr><tr><td rowspan="3">Prothiofos</td><td>Setting: Village</td><td>0.56</td><td>-1.41, 2.54</td><td>0.58</td></tr><tr><td>Area: Hex River Valley</td><td>-1.29</td><td>-3.28, 0.69</td><td>0.21</td></tr><tr><td>Sex: Male</td><td>0.76</td><td>-1.25, 2.77</td><td>0.46</td></tr><tr><td rowspan="3">Cypermethrin</td><td>Setting: Village</td><td>0.99</td><td>-0.49, 2.48</td><td>0.20</td></tr><tr><td>Area: Hex River Valley</td><td>0.85</td><td>-0.65, 2.34</td><td>0.27</td></tr><tr><td>Sex: Male</td><td>-0.73</td><td>-2.23, 0.78</td><td>0.35</td></tr><tr><td rowspan="3">Deltamethrin</td><td>Setting: Village</td><td>0.22</td><td>-0.39, 0.82</td><td>0.49</td></tr><tr><td>Area: Hex River Valley</td><td>-0.59</td><td>-1.20, 0.01</td><td>0.06</td></tr><tr><td>Sex: Male</td><td>-0.26</td><td>-0.88, 0.35</td><td>0.41</td></tr><tr><td colspan="5">Household dust pesticides (log ng/g dust)</td></tr><tr><td rowspan="3">Chlorpyrifos</td><td>Setting: Village</td><td>-1.28</td><td>-2.30, -0.26</td><td>0.02</td></tr><tr><td>Area: Hex River Valley</td><td>0.28</td><td>-0.74, 1.29</td><td>0.59</td></tr><tr><td>Setting: Village</td><td>1.69</td><td>0.74, 2.65</td><td>0.001</td></tr><tr><td rowspan="3">Diazinon</td><td>Area: Hex River Valley</td><td>-0.43</td><td>-1.39, 0.52</td><td>0.38</td></tr></tbody></table>

The ID was taken as a random effect.

aFarm was taken as the reference value.

bGrabouw was taken as the reference value.

cFemale was taken as the reference value.

CI indicates confidence interval.

temporal agreement between levels measured 7 days apart. Most of the studied insecticides are very quickly metabolized and excreted from the body after exposure (half-life in the order of hours),³⁴,⁷⁶ which could result in low agreement between repeated measurements. Also, high temporal variability in pesticide exposure is likely to occur even within short time windows (e.g., due to pesticide drift and food exposure), further contributing to these results. Therefore, caution should be taken when using and interpreting single urine measurements as a proxy for long-term exposure.⁷⁷

Only a few studies have compared measured levels of pesticides in different matrices.²⁸,²⁹,⁶⁶,⁷⁸⁻⁸³ To the best of our

knowledge, this is the first study comparing pesticide levels in urine, wristbands, and household dust. Comparing different exposure matrices enhances the understanding of exposure pathways and how these correlate. Weak correlations were observed between OP and PYR insecticides measured in urine and wristband, suggesting no (linear) relationship between OPs and PYRs exposure. This could be related to intrinsic pesticide characteristics (i.e., different half-lives in soil or molecular weights) that could further influence exposure (e.g., via spray drift) but it could also be related to the application of different pesticide mixtures for different purposes or crops. Additionally, this study is limited in the number of PYRs targeted, which
could further lead to underestimating this relationship. For both
OPs and PYRs, weak correlations were found between personal
exposure matrices (i.e., urine and wristbands), a finding that has
also been observed in other studies comparing wristband lev-
els with spot urine samples.⁴⁰,⁸⁴ This lack of agreement can be
partly explained by the different exposure routes (e.g., dietary
exposure is not measured using wristbands⁴¹) or the different
exposure-time windows captured by each matrix (i.e., urinary
biomarkers are indicators of short-term exposures,³⁴,⁷⁶ whereas
wristbands represent cumulative exposures over time). Also, this
study was limited by the collection of nonfirst-morning-void
spot urine samples, which (although still providing important
information on pesticide exposure occurrence) can underes-
timate significantly accurate estimations on the excreted con-
centration of pesticides that vary along the day.⁸⁵ Hence, future
studies would benefit from multiple urine samples or 24-hour
urine collection to bridge the exposure window gap between
urine biomarkers and wristbands. Finally, although wristbands
have been widely used to measure exposure to several types of
chemicals, there is remaining uncertainty regarding the extent to
which some external factors (e.g., temperature, humidity, com-
pound properties, sampling rates, or the partition coefficient
between silicone and air) may influence the uptake (or release
after previous uptake) of pesticides in wristbands.³⁸,⁸⁶,⁸⁷

Only chlorpyrifos and diazinon were targeted in urine, wrist-
bands, and dust. TCPy showed moderate positive correlations
with chlorpyrifos measured in dust, suggesting that home expo-
sure via contaminated dust particles can be a relevant expo-
sure route to chlorpyrifos. In-depth studies would be valuable
to understand the actual contribution of dust ingestion to uri-
nary biomarker levels⁴⁴,⁸⁸ as this agreement might be due to
co-occurrence and not necessarily a cause-effect relationship.
The dust sample collection was limited to the child's bedroom
floor, which may not represent the entire household.⁸⁹ It is also
worth noting that TCPy⁶⁶ and DAP metabolites²⁹ have been
found in environmental matrices (e.g., dust) as a degradation
product of their parent compounds. Therefore, the urinary lev-
els might not only be a result of exposure to chlorpyrifos or
other OPs but also to the metabolites themselves. IMPy, on the
other hand, did not correlate with diazinon measured in dust
and wristbands, suggesting that different exposure routes (e.g.,
ingestion of contaminated food or water) are likely to play a
more prominent role in children's exposure to diazinon.

We observed that individuals living in the same areas and
household settings tend to cluster based on their exposure levels
measured across all matrices. Considering the Western Cape's
divided agricultural landscape, with Grabouw being dominated
by pome fruits and Hex River Valley producing mainly table
grapes,⁵³ such clusters were a priori expected.¹⁶,³⁷,⁵⁵,⁷⁵ Different
exposure clusters for farm and village residents were also
expected⁹,²¹,²⁴ due to their proximity to agricultural fields treated
with pesticides²³,²⁴ and the fact that most of the farm children in
this study reported participating in agricultural activities, poten-
tially enhancing exposure levels.²¹,⁸⁰,⁹⁰ However, most of these
spatial variances were statistically insignificant, suggesting that
the residential location (area and setting) might not be the pri-
mary exposure predictor to the insecticides in question and that
children residing in different locations share similar exposure
patterns. It is worth noting that this study was limited by the
narrow sample size, hence the significance of the results should
be interpreted with caution. Future studies with larger sample
sizes would be beneficial to identify crucial exposure routes and
factors.

Finally, we estimated that several children could be at high
risk of adverse health effects due to exposure to diazinon,
chlorpyrifos, and parathion (HQ > 1) and cumulative expo-
sure to OPs. Although our measured levels vary from day 1 to
day 7 considerably, for some individuals, their levels were, at
both visits, elevated above the risk thresholds for chlorpyrifos,

diazinon, and cumulative exposure to OPs. Moreover, cap-
turing nonfirst-morning-void samples (used in this study) has
been shown to underestimate the percentage of children with
estimated doses exceeding the recommended guidelines (com-
pared with 24-hour samples).⁸⁵ Hence, the presented risks of
OPs are likely to be underestimated in our study. As previously
stated, early-life exposure to this chemical group of insecticides
has been linked to impaired neurocognitive function and other
adverse health effects. It is important to improve policies and
create awareness to reduce exposure levels in such risk groups.
In contrast, single and cumulative exposure to PYRs were
not above risk thresholds. This is aligned with what has been
reported in Europe, where human biomonitoring data revealed
low health concerns related to PYR exposure.⁹¹ Nevertheless,
it should be noted that in this study, the calculated exposure is
likely to be poorly estimated as specific PYR biomarkers (e.g.,
cis-DBCA,⁹¹ the specific biomarker for deltamethrin) were not
available for use. Therefore, further studies tackling this limita-
tion will allow us to understand better the health risks of single
and cumulative exposure to PYRs for our population.

In conclusion, this study shows how exposure estimates from
different matrices are compared in low- and middle-income
settings. We observed that children and their guardians are
exposed to OPs and PYRs insecticides, which were measured in
urine, wristbands, and household dust samples. Little temporal
agreement was observed for most urinary biomarkers, reflect-
ing a high within-individual variability over a 1-week sampling
during pesticide spraying season. Therefore, interpreting single
urine measurements as exposure proxies to assess the associ-
ation with chronic health effects has inherent challenges. Low
agreements were found between personal exposure matrices
(i.e., urinary biomarkers and wristbands), likely due to the dif-
ferent exposure routes targeted by each matrix and the differ-
ent exposure-time frames captured by these methods. Finally,
we showed that during the main spraying season, 21% of our
study population was estimated to be above the risk threshold
of adverse health effects due to cumulative exposure to OPs.
Therefore, efforts are needed to reduce children's exposure to
this group of pesticides. Additional studies are necessary to
understand the main risk factors associated with higher expo-
sure levels.

**Conflicts of interest statement**

The authors declare that they have no conflicts of interest with
regard to the content of this report.

**Acknowledgments**

We gratefully acknowledge the study participants and the
whole CapSA team, in particular Barblin Michelson, Keith Van
Aarde, Neville Peterson, Kharan Vanmali, Wisdom Basera, and
Phillancia Januarie, for their active involvement in the data col-
lection. We would also like to thank Andrea Gomez Chamorro
and Min Yang for their help with the data analysis and Professor
Nicole Probst-Hensch and Professor Roel Vermeulen for their
help with the funding acquisition.

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