# Quantitative assessment of multiple pesticides in silicone wristbands of children/guardian pairs living in agricultural areas in South Africa

Samuel Fuhrimann, Hans G.J. Mol, Jonatan Dias, Mohamed Aqiel Dalvie, Martin Röösli, Céline Degrendele, Daniel M Figueiredo, Anke Huss, Lutzen Portengen, Roel Vermeulen

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Samuel Fuhrimann, Hans G.J. Mol, Jonatan Dias, Mohamed Aqiel Dalvie, Martin Röösli, et al. Quantitative assessment of multiple pesticides in silicone wristbands of children/guardian pairs living in agricultural areas in South Africa. Science of the Total Environment, 2021, 812, pp.152330. 10.1016/j.scitotenv.2021.152330. hal-03537851

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# Quantitative assessment of multiple pesticides in silicone wristbands of children/guardian pairs living in agricultural areas in South Africa

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

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Samuel Fuhrimann<sup>a,b,c,*</sup>, Hans G.J. Mol<sup>d</sup>, Jonatan Dias<sup>d</sup>, Mohamed Aqiel Dalvie<sup>e</sup>, Martin Röösli<sup>b,c</sup>, Céline Degrendele<sup>f,g</sup>, Daniel M. Figueiredo<sup>a</sup>, Anke Huss<sup>a</sup>, Lutzen Portengen<sup>a</sup>, Roel Vermeulen<sup>a</sup>

## HIGHLIGHTS

* Assessed exposure to multiple pesticides in child/guardian pairs in the same household

* Targeted 21 and detected 16 current and past used pesticides on silicon wristbands

* The most frequently detected pesticides (>50%) were: Deltamethrin, chlorpyrifos, boscalid, cypermethrin and *p,p'*-DDT.

* Overall sharing the same household leads to similar exposures to pesticides.

* However, for several pesticides children had higher exposure levels than their guardians.

## GRAPHICAL ABSTRACT

Multiple pesticides in silicone wristbands of children/guardian pairs living in agricultural areas in South Africa

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

### Article history:

Received 7 October 2021

Received in revised form 6 December 2021

Accepted 7 December 2021

Available online 11 December 2021

### Keywords:

Boscalid

Exposure

Organophosphates

Organochlorines

Pesticide

Pyrethroids

Household

Wristbands

## ABSTRACT

Little is known about personal and time-integrated exposure to past and current used pesticides in agricultural areas and within-family exposure similarities. We aimed to assess exposure to pesticides using silicone wristbands in child/guardian pairs living on farms and in villages within two agricultural areas in South Africa.

Using silicone wristbands, we quantified 21 pesticides in child/guardian pairs in 38 households over six days in 2018. Levels (in ng/g wristband) of pesticides and their transformation products (12 current-use pesticides and nine organochlorine pesticides) were measured using GC-MS/MS. We assessed the correlation between pesticide levels and between household members using Spearman correlation coefficients ($r_2$). Multivariable generalized least squares (GLS) models, using household id as intercept, were used to determine level of agreement between household members, exposure differences between children and guardians and exposure predictors (study area, household location [farm vs. village] and household pesticide use).

We detected 16 pesticides with highest detection frequencies for deltamethrin (89%), chlorpyrifos (78%), boscalid (56%), cypermethrin (55%), and *p,p'*-DDT (48%). Most wristbands (92%) contained two or more pesticides (median seven (range one to 12)). Children had higher concentrations than guardians for four pesticides. Correlation between the pesticide levels were in most cases moderate (*rs* 0.30–0.68) and stronger in children than in guardians. Five pesticides showed moderate to strong correlation between household members, with the strongest correlation for boscalid

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

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

<footnote><sup>c</sup> University of Basel, Switzerland</footnote>

<footnote><sup>d</sup> Wageningen Food Safety Research (WFSR), part of Wageningen University & Research, Wageningen, the Netherlands</footnote>

<footnote><sup>e</sup> Centre for Environmental and Occupational Health Research, School of Public Health and Family Medicine, University of Cape Town, South Africa</footnote>

<footnote><sup>f</sup> Masaryk University, Faculty of Sciences, RECETOX Centre, Brno, Czech Republic</footnote>

<footnote><sup>g</sup> Now at Laboratory of Chemistry and Environment, Aix Marseille University, Marseille, France</footnote>

<footnote>* Corresponding author at: Swiss Tropical and Public Health Institute (Swiss TPH), Switzerland.
E-mail address: samuel.fuhrimann@swisstph.ch (S. Fuhrimann).</footnote>
(r<sub>s</sub> 0.84). Exposure differences between the two agricultural areas were observed for chlorpyrifos, diazinon, prothiofos, cypermethrin, boscalid, *p,p'*-DDT and *p,p'*-DDE and within areas for cypermethrin.
We showed that for several pesticides children had higher exposure levels than guardians. The positive correlations observed for child/guardian pairs living in the same household suggest non-occupational shared exposure pathways in these communities.

# 1. Introduction

Living in areas with intensive agriculture production has been shown to result in increased exposure to multiple pesticides (Brouwer et al., 2017) and is linked to adverse health outcomes in farmers, resident populations and their children (English et al., 2012; Gunier et al., 2017). In addition, living in the same household has been suggested to result in similar exposure levels for selected pesticides (Bravo et al., 2020). Most studies on residential pesticide exposure are either based on measurements of short-term urinary biomarkers (Bravo et al., 2020), self-reported pesticide-related activities and behaviors (Chetty-Mhlanga et al., 2020), or land-use-based pesticide exposure estimates (Brouwer et al., 2017). These pesticide exposure assessment methods are limited by several factors: (i) the number of pesticides investigated; (ii) the assessment of short-term exposure windows; (iii) possible reporting bias in the case of self-reported pesticide use from farmers, or misclassification of pesticide use when reported by experts; and (iv) ignoring individual behavior in environmental exposure modeling studies. These limitations are often compounded by practical problems when performing studies in vulnerable populations in remote areas. To overcome some of these limitations, tools such as silicone wristbands have been proposed that enable measurement of time-integrated exposure to multiple chemicals (Doherty et al., 2021; Hammel et al., 2020; Travis et al., 2020; Wise et al., 2020).

In South Africa, more than 3000 pesticide products are registered for agriculture use to control pests (CropLife, 2017; Dabrowski et al., 2014). Previous studies reported the presence of multiple current used pesticides but also legacy pesticides (e.g., organochlorine pesticides (OCPs), which are nowadays banned for agriculture use) in environmental matrices such as water (Curchod et al., 2019; Dalvie et al., 2003; Horak et al., 2021) and air (Degrendele et al., 2021; Fuhrimann et al., 2020; Klánová et al., 2009; Veludo et al., 2021). Other studies investigating human urinary biomarkers indicated that farmers and residents living on farms in the Western Cape in South Africa had higher levels of pyrethroid and organophosphate biomarkers than in other settings (Dalvie et al., 2011; Motsoeneng and Dalvie, 2015; Mwanga et al., 2016). However, there is limited data on cumulative personalized exposure overtime or on pesticide mixtures within and between exposure groups (e.g., at household level and agriculture areas).

We aimed to quantitatively assess exposure to multiple current used and legacy pesticides and their transformation products using silicone wristbands in children and their guardians living in the same household in two areas with intensive agriculture in South Africa. There were three main research questions investigated: (i) are there different exposure profiles in children and guardians?; (ii) is living at the same household resulting in similar pesticide exposure levels?; and (iii) does living in the same agricultural area, sharing the same household location (farm vs. village) or using household pesticide predict pesticides exposure levels?

# 2. Methods

## 2.1. Study population

We contacted 40 child/guardian pairs from the Child Agriculture Pesticide Cohort Study in South Africa (CapSA) (Fig. 1a). Households were purposely selected alongside the main urine sampling round of all 1000 children participating in CapSA. This to ensure to have equal numbers of the child/guardian pairs (n = 20) in the two agricultural areas (Grabouw, dominantly pomme fruits (34°09'16.8"S 18°59'56.7"E); and Hex River

Valley, dominantly table grape (33°28'34.7"S 19°39'51.9"E)) and to have half of the households on farms (within 50 m from agricultural land use) and half in nearby villages (at least 0.5 km away from the closest agricultural land use). Sampling was conducted during the main pesticide spraying season in Grabouw between 23.10 and 29.10 and in Hex River Valley between 31.10 and 6.11 in 2018.

## 2.2. Data collection

We administered children and guardians separate structured questionnaires that were developed for the CapSA study (Chetty-Mhlanga et al., 2018). For children, the questionnaire included questions on socio-demographic characteristics (e.g., sex, age, 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). For guardians, the questionnaire included questions on socio-demographic characteristics, occupation and household pesticide use.

In each study site, a child and their guardian each received a silicon wristband to put on their right wrist. Two sizes of uncolored silicone wristbands were used (for children size 1.9 × 11 × 177 mm; guardians' size 1.9 × 11 × 210 mm; MyExposome Inc., Corvallis; US). Participants were asked to wear it for six days constantly (i.e. also during showering and sleeping times). For each study area, one pre-packed wristband was taken along as a field-blank to observe if there was cross-contamination during packing and transportation of the samples. Children were checked every morning at school if they still were wearing their wristbands. After six days, wristbands were collected and stored in the original airtight mylar bag (DS M&T Inc., Fontana, US). The bags were then put in a dark box at room temperature and transported to the laboratory facility at the University of Cape Town within one day, where they were stored at -18 °C. Later they were shipped in a cool box at 0 °C to Wageningen Food Safety Research (WFSR), Wageningen University, the Netherlands, for analysis.

## 2.3. Selection of target pesticides

A total of 21 pesticides (18 insecticides and three fungicides) were selected for analysis (Table 1). Current used pesticides consisted of eight insecticides (five organophosphates, two pyrethroids, and one insect growth regulator) and three fungicides (two triazoles and one carboxamide). Legacy pesticides, previously used in large amounts in the study areas included two OCP insecticides and their degradation products: the *o,p'* and *p,p'* isomers of dichloro-diphenyl-trichloroethane (DDT) and its degradation products, dichlorodiphenyldichloroethylene (DDE) and dichlorodiphenyldichloroethane (DDD) and alpha and beta isomers of endosulfan and its degradation endosulfan sulfate. Selection of pesticides was motivated by four factors (i) their current use in South Africa in agriculture indicated by a survey with local farmers (Curchod et al., 2019) and a list with currently registered pesticides in South Africa (CropLife, 2017; Curchod et al., 2019); (ii) their past use in agriculture (Dalvie et al., 2009a, 2009b); (iii) their potential use at household level (Tolosana et al., 2009); and (iv) the selection was limited to the analytical capacity of the selected laboratory method.

## 2.4. Silicone wristband pre-cleaning

Prior sampling, wristbands were conditioned at the MyExposome Inc. (Anderson et al., 2017). In an oven (VacuTherm Vacuum Oven, Thermo Fischer, Type: VT 6060 P) racks at 300 °C for 24 h with Nitrogen purges at
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** a) Map showing the two study areas, the enrolled 38 households and the agriculture land use; b) box plots showing levels (ng/g) on log10 scale of all 16 pesticides detected above the limit of quantification. Level 1 = below limit of quantification (LOQ).</figcaption></figure>

15, 30, 45, 60, 120, and 180 min, and every hour after that for 3 h. Once the 24 hour period had elapsed, the chamber was purged with nitrogen, and the oven was turned off to cool (approximately 3 h). Once cooled, wristbands were placed in a pre-cleaned jar and stored at 4 °C. After shipment to South Africa, wristbands were placed in individual ziplock mylar bags (DS M&T Inc., Fontana, US), pre-labeled with a serial number, sizing details, and blank instructions on when to remove the wristband during deployment.

## 2.5. Silicon wristband extraction

The extraction protocol was based on the methods described by Aerts et al. (2018). Samples were cut into 0.5 cm pieces with a scissor previously cleaned with ethyl alcohol and purified water. After this, each sample was placed into a 50 mL centrifuge tube and 20 mL ethyl acetate was added. The tubes were shaken head-over-head for 1 h. Then extracts were transferred to an evaporation flask and the solvent was evaporated to dryness in a water bath at 40 °C under nitrogen flow. Because ethyl acetate was used

for extraction, lipids, squalene and fatty acids from the skin (Pappas, 2009), and thus present on the wristband, are co-extracted. Analysis of lipid-containing extracts may easily deteriorate GC performance (Grob and Kälin, 1991). Therefore, reconstitution was done in acetonitrile (good solubility for pesticides, low solubility for lipids) rather than a non-polar solvent, and a dispersive-solid phase extraction (dSPE) cleanup was performed as known from QuEChERS cleanup approaches (Anastassiades et al., 2003; Perestrelo et al., 2019). Here, C18 was used to remove lipids and squalene, and primary/secondary amine (PSA) to remove free fatty acids. For reconstitution/cleanup, the residue was reconstituted in 1 mL of acetonitrile and transferred to a tube already containing the two sorbents (25 mg of octadecyl silica (C18), 25 mg of PSA) and 150 mg of MgSO₄. These tubes were vortexed for 1 min and centrifuged for 5 min. Finally, 495 µL of extract were transferred to autosampler vials followed by the addition of 5 µL of PCB 198 solution (internal instrument standard, 1 µg/mL in acetonitrile) prior to injection into the gas chromatography coupled with tandem mass spectrometry system (GC-MS/MS).
<caption>**Table 1**</caption>

Characteristics and summary statistics of the 21 targeted pesticides levels (ng/g wristband) over six-sampling days, stratified by children and their guardians in the Western Cape in South Africa.

<table><thead><tr><th rowspan="2">Active ingredient<sup>a</sup></th><th rowspan="2">Group<sup>b</sup></th><th rowspan="2">Type<sup>c</sup></th><th rowspan="2">Spray<sup>d</sup></th><th colspan="3">Total (n = 76)</th><th colspan="3">Children (n = 38)</th><th colspan="3">Guardians (n = 38)</th></tr><tr><th>Detects<sup>e</sup> n (%)</th><th>Median (IQR)<sup>f</sup></th><th>Max<sup>f</sup></th><th>Detects<sup>e</sup> n (%)</th><th>Median (IQR)<sup>f</sup></th><th>Max<sup>f</sup></th><th>Detects<sup>e</sup> n (%)</th><th>Median (IQR)<sup>f</sup></th><th>Max<sup>f</sup></th></tr></thead><tbody><tr><td>Deltamethrin</td><td>PYR</td><td>I</td><td>n/h</td><td>69 (90.8)</td><td>3.8 (2.9)</td><td>352.3</td><td>36 (94.7)</td><td>4.0 (2.8)</td><td>107</td><td>33 (86.8)</td><td>3.2 (2.9)</td><td>352.3</td></tr><tr><td>Chlorpyrifos</td><td>OP</td><td>I</td><td>y/h</td><td>61 (80.3)</td><td>61.1 (131.5)</td><td>658.5</td><td>29 (76.3)</td><td>84.1 (210.2)</td><td>658.5</td><td>32 (84.2)</td><td>29.5 (74.3)</td><td>386.1</td></tr><tr><td>Boscalid</td><td>CAR</td><td>F</td><td>w</td><td>44 (57.9)</td><td>18.5 (21.5)</td><td>704.6</td><td>26 (68.4)</td><td>14.0 (17.9)</td><td>56.9</td><td>18 (47.4)</td><td>27.5 (46)</td><td>704.6</td></tr><tr><td>Cypermethrin</td><td>PYR</td><td>I</td><td>y/h</td><td>43 (56.6)</td><td>20.2 (21.4)</td><td>88.6</td><td>24 (63.2)</td><td>20.5 (25.4)</td><td>88.6</td><td>19 (50)</td><td>15.9 (20.6)</td><td>45.6</td></tr><tr><td>p,p'-DDT</td><td>OCP</td><td>I</td><td>b/m</td><td>38 (50.0)</td><td>4.3 (9.0)</td><td>31.0</td><td>18 (47.4)</td><td>6.7 (9.5)</td><td>21.9</td><td>20 (52.6)</td><td>2.06 (3)</td><td>31.0</td></tr><tr><td>Diazinon</td><td>OP</td><td>I</td><td>n</td><td>35 (46.1)</td><td>3.0 (6.2)</td><td>98.4</td><td>22 (57.9)</td><td>2.9 (4.5)</td><td>98.4</td><td>13 (34.2)</td><td>3.0 (9.2)</td><td>80.5</td></tr><tr><td>Prothiofos</td><td>OP</td><td>I</td><td>y</td><td>35 (46.1)</td><td>28.9 (43.7)</td><td>582.6</td><td>20 (52.6)</td><td>37.1 (55.1)</td><td>582.6</td><td>15 (39.5)</td><td>14.5 (36.5)</td><td>96.5</td></tr><tr><td>p,p'-DDE</td><td>OCP</td><td>I</td><td>b/m</td><td>34 (44.7)</td><td>2.3 (2.9)</td><td>9.3</td><td>22 (57.9)</td><td>2.7 (2.9)</td><td>9.3</td><td>12 (31.6)</td><td>1.8 (2.1)</td><td>4.6</td></tr><tr><td>Endosulfan alfa</td><td>OCP</td><td>I</td><td>b</td><td>18 (23.7)</td><td>1.7 (1.4)</td><td>16</td><td>12 (31.6)</td><td>1.8 (1.7)</td><td>7.1</td><td>6 (15.8)</td><td>1.6 (1)</td><td>16.0</td></tr><tr><td>Malathion</td><td>OP</td><td>I</td><td>n</td><td>17 (22.4)</td><td>1.9 (1.2)</td><td>14.3</td><td>10 (26.3)</td><td>2.2 (1.1)</td><td>6.3</td><td>7 (18.4)</td><td>1.9 (4)</td><td>14.3</td></tr><tr><td>o,p'-DDT</td><td>OCP</td><td>I</td><td>b</td><td>16 (21.1)</td><td>3.0 (3.1)</td><td>8</td><td>10 (26.3)</td><td>4.3 (2.5)</td><td>8</td><td>6 (15.8)</td><td>1.5 (1.4)</td><td>3.1</td></tr><tr><td>Endosulfan beta</td><td>OCP</td><td>I</td><td>b</td><td>9 (11.8)</td><td>1.4 (1.4)</td><td>11.2</td><td>5 (13.2)</td><td>1.8 (1.1)</td><td>11.2</td><td>4 (10.5)</td><td>1.1 (2.1)</td><td>9.5</td></tr><tr><td>p,p'-DDD</td><td>OCP</td><td>I</td><td>b/m</td><td>5 (6.6)</td><td>1.6 (0.7)</td><td>2.5</td><td>5 (13.2)</td><td>1.6 (0.7)</td><td>2.5</td><td>Not detected</td><td></td><td></td></tr><tr><td>Tebuconazole</td><td>TRI</td><td>F</td><td>y</td><td>4 (5.3)</td><td>4.8 (14.7)</td><td>46.9</td><td>Not detected</td><td></td><td></td><td>4 (10.5)</td><td>4.8 (14.7)</td><td>46.9</td></tr><tr><td>Penconazole</td><td>TRI</td><td>F</td><td>y</td><td>2 (2.6)</td><td>1.4 (0.1)</td><td>1.6</td><td>Not detected</td><td></td><td></td><td>2 (5.3)</td><td>1.4 (0.1)</td><td>1.6</td></tr><tr><td>o,p'-DDE</td><td>OCP</td><td>I</td><td>b/m</td><td>1 (1.3)</td><td>1.3 (0)</td><td>1.3</td><td>Not detected</td><td></td><td></td><td>1 (2.6)</td><td>1.3 (na)</td><td>11.3</td></tr><tr><td>Buprofezin</td><td>UNC</td><td>I</td><td>n</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td></tr><tr><td>o,p'-DDD</td><td>OCP</td><td>I</td><td>b/m</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td></tr><tr><td>Dimethoate</td><td>OP</td><td>I</td><td>n</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td></tr><tr><td>Endosulfan sulfate</td><td>OCP</td><td>I</td><td>b</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td></tr><tr><td>Lambda-cyhalothrin</td><td>PYR</td><td>I</td><td>n</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td><td colspan="3">Not detected</td></tr></tbody></table>

IQR = interquartile ran.

a DDT = dichloro-diphenyl-trichloroethane; DDE = dichlorodiphenyldichloroethylene; DDD = dichlorodiphenyldichloroethane.

b Chemical group: PYR = pyrethroids; OP = organophosphate; CAR = carboxamide; OCP = organochlorine; TRI = triazole; UNC = unclassified.

c Type of use: I = insecticide; F = fungicide.

d Spray evidence: w = reported on spray record of interviewed farms in the week during the assessment; y = reported on spray record of interviewed farms in the year before the assessment; n = not reported but registered for agriculture use in South Africa; b = banned for agriculture use in South Africa; h = registered as household insecticide; m = registered for malaria control.

e n = detects above limit of detection (ng/g wristband).

f Levels in ng/g wristband.

## 2.6. GC-MS/MS analysis of the silicone wristband extracts

Instrumental analysis of the extracts was performed using a Bruker 300-MS GC-MS/MS system. Chromatographic separation was carried out on a Restek RTX CL Pesticides column (30 m × 0.25 mm, Df 0.25 µm). Sample injection (5 µL) was performed in a programmed temperature vaporizing (PTV) injector. The carrier gas used was Helium at a constant flow rate of 1.2 mL min⁻¹. The total run time was 26 min. For each pesticide, two transitions were measured. Identification was based on matching retention time and ion ratios (criteria see EC, 2019), and quantification against multi-level matrix-matched standards, after normalisation of the response to that of the internal standard.

## 2.7. Validation

Before analysis of the wristbands, the method was validated in line with procedures described in the EU guidance document for pesticide residue analysis (EC, 2019). For this, blank wristbands and spiked wristbands were analysed together with calibration standards. Parameters evaluated included selectivity, linearity of the response, average recovery and repeatability. For validation, single wristbands were cut into pieces as described above, and put into separate 50 mL extraction tubes (pieces of one wristband in one tube). The validation set consisted of 20 tubes/wristbands. Eight were not spiked, of which two were used a blank control, and the others for preparation of matrix-matched calibration standards (see below). The remaining twelve were used for recovery experiments and divided in sets of three, which were spiked at four different levels: 1, 5, 10 and 100 ng/g wristband material (corresponding to 3.8, 19, 38 and 380 ng of each pesticide per wristband). The spiking was done by pipetting a mix-solvent standard onto the wristband pieces in the tubes. The solvent was allowed to evaporate (approx. 30 min). All wristbands/tubes were

extracted as described in Section 2.4. For preparation of matrix-matched standards, 485 µL of cleaned extract from the non-spiked wristbands were transferred into six autosampler vials, and 5 µL of internal standard (PCB198) was added. Then 10 µL of different concentrations of mix-calibration standard solutions was added to the autosampler vials to obtain matrix-matched standards of 0.5, 1, 5, 10, 50, and 100 ng/mL.

The non-spiked wristbands were found to be free of pesticides and interferences. Good linearity was observed (R² > 0.994, back-calculated concentrations mostly within 20% of the actual concentration) for the responses obtained for the matrix-matched calibration standards. The average recoveries at the highest level were in the range of 72%–98% (median 87%). At the lower levels, lower recoveries were obtained, typically in the 60%–70% range. The repeatability (RSD<sub>r</sub>) was better than 10% in most cases. For details, see Supplementary information (SI) Table S1. The LOQ was set at the lowest level included in the validation for which good repeatability was observed (i.e. for all pesticides, 1 ng/g wristband (3.8 ng/wristband)).

## 2.8. Statistical analyses

Descriptive statistics were applied to present the percentage of individuals and combinations of pesticides above LOQ (i.e., quantified) and levels in ng/g on a wristband. The correlation between individual pesticide levels was assessed for all participants and separately in children and guardians using Spearman correlations coefficients (r<sub>s</sub>) and was visualized using heatmaps and ciros plots. The correlation between pesticide levels in children and their guardians living in the same household was determined using Spearman correlations coefficients (r<sub>s</sub>) and was visualized using scatter plots. Further, all pesticides which were detected (i.e., above LOQ) in 40% of the wristbands were imput. Imputation was done using maximum-likelihood based on log10 transformed pesticide values and area as a predictor, this estimation is accounting for both correlation and
distribution of all pesticide data applying the R package "survival" (Lubin et al., 2004). Univariable and multivariable generalized least squares (GLS) models, using household id as intercept, were used to determine the level of agreement between household members (showing unadjusted (un-adj.) and adjusted (adj.) intraclass correlation coefficients (ICC)), exposure differences on the log 10 scale between children and guardians and exposure predictors (study area (Grabouw and Hex River Valley)), household location (farm and village) and household pesticide use (never, last year, last week). With an interaction term, study area and household location were separately looked at for children and guardians. Predictors were a-priori chosen based on findings from previous studies (Chetty-Mhlanga et al., 2020; Molomo et al., 2021). Across all analyses, *p*-values below 0.05 were considered statistically significant. Statistical analyses were done in R (Foundation for Statistical Computing, version 3.5.3, RStudio Version 1.1.4).

## 2.9. Ethical statement

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

# 3. Results

## 3.1. Demographics of the study population

Out of 40 contacted households, 38 households (children/guardian pairs) with 76 participants completed the assessments (Table 2). The households paired in: 21 female guardian/male children, 14 female guardian/female children pairs, two male guardians/female child pairs and one male guardian/one male child pair. The households were equally distributed between the two areas (each 19 households), and slightly more households were located on farms (n = 20) than in villages (n = 18).

The children's median age was 12 (IQR two), 22 were males, and 16 were females. Potential pesticide exposure related activities during the week of the assessment were reported by the children as follows: observed any spraying activity (79%), engaging with any pesticide handling activities (55%), swimming in a pond or river (47%), helped to pick fruits (37%) and playing in agriculture field (26%).

Most guardians were women (92%) and the child's mother (82%). About a third of the guardians worked on a farm (37%), while most households had at least one member working on a farm (66%). In about half of the households, working clothes were brought home and washed there (55%), while more than half of the households (55%) used pesticides to

control household pests during the sampling period. In total 12 different household pesticide products were obtained at home during the observation, including different pyrethroids (d-phenothrin, prallethrin, imiprothrin, cyphenothrin, d-trans-allethrin, alpha-cypermethrin), a organophospate (dichlorvos) and a carbamate (propoxur). Only alpha-cypermethrin was on our analysis target list and the product was only used by one household.

## 3.2. Pesticide detection

Out of the 21 targeted pesticides, 16 pesticides were detected above LOQ on the wristbands (Fig. 1b, Table 1). The median [range] number of pesticides detected in children's wristbands was seven [two to 11] and in their guardians five pesticides [one to 12]. Eight pesticides were present in more than 40% of the wristbands (deltamethrin (89%), chlorpyrifos (78%), boscalid (56%), cypermethrin (55%), *p*,*p*'-DDT (48%), prothiofos (45%), diazinon (44%), and *p*,*p*'-DDE (44%)). The highest median ng/g wristband [IQR] levels were measured for chlorpyrifos (61.1 [131.5]), followed by prothiofos (28.9 [43.7]) and cypermethrin (20.2 [21.4]) (Table 1, Fig. 1b). In 92% of the wristbands, two or more pesticides were found (Tables 1). Among the pesticides which were detected in more than 40% of wristbands, a mix of the four pesticides boscalid, cypermethrin, chlorpyrifos and deltamethrin was detected in one-quarter of the participants (26%).

## 3.3. Correlation between detected pesticide levels

Levels of *p*,*p*'-DDT and *p*,*p*'-DDE were moderately strong correlated (r<sub>s</sub> 0.72) (Fig. 2a), while the correlation between levels of chlorpyrifos, diazinon, malathion and prothiofos were weak to moderate (r<sub>s</sub> between 0.27 and 0.58). Levels of *p*,*p*'-DDT, *p*,*p*'-DDE and chlorpyrifos were also moderately correlated (r<sub>s</sub> between 0.48 and 0.42). Several correlations were different when separately looking at the children and guardian sub-samples (SI Fig. S2b and c). For example, the correlation between *p*,*p*' and *o*,*p'* DDT and DDE was stronger in children than in guardians (r<sub>s</sub> of 0.81 versus 0.68). On the other hand, guardians had stronger positive correlations for malathion with boscalid (0.56 versus 0.13), chlorpyrifos with deltamethrin (0.35 versus 0.03) and boscalid and chlorpyrifos (r<sub>s</sub> 0.16 versus -0.33).

## 3.4. Correlation of pesticide levels between child and guardian pairs

Correlation between child and guardian pairs were strong for boscalid (r<sub>s</sub> 0.84), while the other seven pesticides showed moderate correlations (0.28–0.58) (Fig. 3). Similarly, the ICCs showed a strong agreement for boscalid within household pairs. However, when adjusted for the relevant

<caption>**Table 2**</caption>

Socio-demographic and occupational characteristics of participating children and their guardians stratified by study area the Western Cape in South Africa.

<table><thead><tr><th colspan="2">Demographic and exposure variables number per household n (%)</th><th>Total</th><th>Area Grabouw</th><th>Area Hex River Valley</th></tr><tr><th></th><th></th><th>38 (100)</th><th>19 (100)</th><th>19 (100)</th></tr></thead><tbody><tr><td>Place of living</td><td>Farm</td><td>20 (52.6)</td><td>10 (52.6)</td><td>10 (52.6)</td></tr><tr><td></td><td>Village</td><td>18 (47.4)</td><td>9 (47.4)</td><td>9 (47.4)</td></tr><tr><td>Child</td><td>Female</td><td>16 (42.1)</td><td>7 (36.8)</td><td>9 (47.4)</td></tr><tr><td></td><td>Male</td><td>22 (57.9)</td><td>12 (63.2)</td><td>10 (52.6)</td></tr><tr><td></td><td>years</td><td>(12; 2)</td><td>(12; 1)</td><td>(12; 0.8)</td></tr><tr><td></td><td>Yes</td><td>30 (78.9)</td><td>16 (84.2)</td><td>14 (73.7)</td></tr><tr><td></td><td>Swum in a pond or river</td><td>18 (47.4)</td><td>9 (47.4)</td><td>9 (47.4)</td></tr><tr><td></td><td>Played in agriculture field during measurement week</td><td>10 (26.3)</td><td>5 (26.3)</td><td>5 (26.3)</td></tr><tr><td></td><td>Helped to pick fruits during measurement week</td><td>14 (36.8)</td><td>7 (36.8)</td><td>7 (36.8)</td></tr><tr><td></td><td>Engaged with pesticide handling activities during measurement week</td><td>21 (55.3)</td><td>10 (52.6)</td><td>11 (57.9)</td></tr><tr><td>Guardian</td><td>Female</td><td>35 (92.1)</td><td>18 (94.7)</td><td>17 (89.5)</td></tr><tr><td></td><td>Male</td><td>3 (7.9)</td><td>1 (5.3)</td><td>2 (10.5)</td></tr><tr><td></td><td>Yes</td><td>14 (36.8)</td><td>4 (21.1)</td><td>10 (52.6)</td></tr><tr><td></td><td>Yes</td><td>25 (65.8)</td><td>10 (52.6)</td><td>15 (78.9)</td></tr><tr><td></td><td>Any household member washes working clothes at home</td><td>21 (55.3)</td><td>6 (31.6)</td><td>15 (78.9)</td></tr><tr><td></td><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</td><td>21 (55.3)</td><td>7 (36.8)</td><td>14 (73.7)</td></tr></tbody></table>

IQR = interquartile range.
Heatmap showing correlation coefficients

a) Total population (n=78)

<figure><img src="image_6.png" /><figcaption>Fig. 2. Heatmap showing Spearman correlation coefficients and circular correlation plot of pesticide levels (ng/g wristband) of the 16 pesticides across all 76 participants.</figcaption></figure>

Circular correlation wristband

<figure><img src="image_7.png" /></figure>

predictors, the ICCs decreased considerably (unadj. 0.65 and adj. 0.17) (Table S3). Interestingly, the unadjusted and the adjusted ICCs for chlorpyrifos, *p,p'*-DDT, deltamethrin, diazinon were both showing a moderate agreement and only slightly changed due to the adjustment.

3.5. Determinants for the detected pesticide concentrations

GLS identified that children had higher concentrations than guardians for deltamethrin, *p,p'*-DDE, cypermethrin and *p,p'*-DDT (39, 32–29, and three-times higher, respectively) (Fig. 4 and Table S3). For several pesticides, differences between the two areas were found: children and guardians in Hex River Valley had higher levels of boscalid than those in Grabouw (geometric mean ratio (GMR) [95% confidence interval (CI)] of 301 [46; 1,966] and 1,646 [255; 10,632], respectively). Also, for guardians, higher cypermethrin levels (18 [2.6; 120]) were observed in Hex River Valley than in Grabouw (Fig. 4). In contrast, children in Hex River Valley had lower prothiofos (0.1 [0.01; 0.7]), chlorpyriphos (0.2 [0.04; 0.7]), *p,p'*-DDT (0.1 [0.03; 0.2]) and *p,p'*-DDE (0.2 [0.1; 0.3]) than in Grabouw. A difference between farm and village within an area could only be seen for cypermethrin, where guardians living on farms had higher levels (8.8 [1.3; 60.5]) than guardians living in villages. For pesticides used at home last week and last year, no significant different contrast could be observed to participants who never used pesticides at home.

4. Discussion

Using silicone wristbands, we detected 16 out of 21 targeted pesticides in children and their guardians (ten current-use pesticides in agriculture or on household level and six legacy OCPs). We quantified mixtures of current-used pesticides and legacy OCP on wristbands, with higher levels in children than their guardians. We showed that living in the same household can lead to similar exposure levels of individual pesticides, with greater within household similarities for pesticides which were recently applied. Further, for nine pesticides (chlorpyrifos, diazinon, prothiofos, cypermethrin, boscalid, *p,p'*-DDT and *p,p'*-DDE), important exposure differences could be observed for several pesticides between agriculture areas but only for cypermethrin within the areas (farm versus village).

Pesticides belonging to the chemical groups of the organophosphates and pyrethroids were detected in the majority of the participants. This is in line with the observed occurrence of the two chemical groups in resident populations in the study areas (Dalvie et al., 2011; Molomo et al., 2021; Motsoeneng and Dalvie, 2015; Mwanga et al., 2016) and in air (Degrendele et al., 2021; Fuhrimann et al., 2020; Veludo et al., 2021) and water (Curchod et al., 2019; Dalvie et al., 2003). Specifically, the organophosphates chlorpyrifos and prothiofos were detected in high concentrations on the wristbands, likely linked to ongoing use in the areas (recorded use in spray records in September and October 2018 (Curchod et al., 2019) and their long half-lives in the soil of up to a year (Lewis et al., 2016)). The two detected pyrethroids were previously reported to be used in households (deltamethrin; Tolosana et al., 2009) and on farms (cypermethrin; recorded on apple and table grape farm spray records in October 2018; Curchod et al., 2019). However, no association was found in our study between self-reported household pesticide use and deltamethrin, which could be due to the low sample size as a broad set of different pyrethroids insecticides are used in households. Interestingly, also pesticides banned for agricultural use (DDT and endosulfan and their degradation products) were detected in 50% of the wristbands. The detection of both DDT and endosulfan could be the result of historical use and contaminated soils around apple and grape farming areas respectively. Indeed, in the year before this study, our yearly passive air sampling indicated highest DDT levels in Grabouw and higher endosulfan levels in Hex River Valley during the same time of the year (Veludo et al., 2021). The high prevalence of detected DDT could also be explained by the use of obsolete stocks on farmers or leaking buried containers (Dalvie et al., 2006) or long-range transport from other areas in Africa, where it is still used for malaria control (Murray et al., 2018).

Other studies also reported on pesticides detected on wristbands; besides differences in their analysis techniques and pesticide use pattern, there are interesting comparisons to be made as similar pesticides were targeted and detected. Analogous to a study conducted in girls living in agricultural areas in the US with comparable exposure time (seven days) and LOQ (about 1 ng/g), our study showed the same prevalence of cypermethrin (in both studies 57%) (Harley et al., 2019), but a higher prevalence of DDT, deltamethrin and chlorpyrifos. Compared to a study using a
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Adjusted multivariable generalized least square models (GLSM) for the imputed exp(levels (ng/g)) of eight pesticides across all 76 participants. Pesticide use at home (reference (ref) 1: never used); area H: Hex River Valley (ref 1: Grabouw); farm (ref 1: village).</figcaption></figure>

## Funding source

SF's effort was also supported by a fellowship of the Swiss National Science Foundation (SNSF; grant number: P4P4PM_199228 and P400PM_180757). HM and JD acknowledges partial funding from the Dutch Ministry of Agriculture, Nature and Food Quality through project KB-37-002-014. CD's contribution was supported by RECETOX (LM2018121) and ACTRIS-CZ (LM2018122) research infrastructures funded by the Czech Ministry of Education, Youth and Sports of the Czech Republic and the European Structural and Investment Funds (CZ.02.1.01/0.0/0.0/16_013/0001761, CZ.02.1.01/0.0/0.0/16_013/

0001315 and CZ.02.1.01/0.0/0.0/17_043/0009632). 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), the Swiss State Secretariat for Education, Research and Innovation (SERI), the University of Basel and the Swiss TPH.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2021.152330.
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