Urinary levels of organophosphate pesticides and predictors of exposure in pre-school and school children living in agricultural and urban communities from south Spain

Environmental Research 186 (2020) 109459

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Urinary levels of organophosphate pesticides and predictors of exposure in pre-school and school children living in agricultural and urban communities from south Spain

T

B. González-Alzagaa,b,1, D. Romero-Molinab,c,1, I. López-Floresb,d, M.J. Giménez-Asensioa, A.F. Hernándeze,∗∗, M. Lacasañaa,b,f,∗ a

Andalusian School of Public Health (EASP), Granada, Spain Instituto de Investigación Biosanitaria, ibs.GRANADA, Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain Department of Statistics and Operational Research, Faculty of Sciences, University of Granada, Granada, Spain d Department of Genetics, Faculty of Sciences, University of Granada, Granada, Spain e Department of Legal Medicine and Toxicology, University of Granada School of Medicine, Granada, Spain f CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain b c

ARTICLE INFO

ABSTRACT

Keywords: Organophosphate pesticides Environmental exposure Children Predictors

Background. Despite the widespread use of organophosphate (OP) pesticides, information on predictors of children's exposure to such pesticides is scarce. Objective. To assess exposure to OP pesticides in children 3-11 year-old living in agricultural communities and urban areas from Andalusia (Southern Spain), and to identify the main determinants of exposure. Methods. A longitudinal study was conducted in children 3-11-year-old children living in agricultural communities and urban areas from the provinces of Almeria, Granada and Huelva (Andalusia, Spain) between 2010 and 2011. Urinary levels of six dialkylphosphate (DAP) metabolites were measured by UHPLC-QqQ-MS/MS at the periods of low and high pesticide use in the agriculture (LPU and HPU, respectively). Information on sociodemographic characteristics, parental occupation, residential history, lifestyle and diet, among other relevant factors, was obtained from questionnaires administered to the mothers. Results. A total of 559 and 597 children participated in LPU and HPU periods, respectively. The proportion of urine samples below LOD was 67.4% for ΣDMs (sum of dimethyl metabolites), 77% for ΣDEs (sum of diethyl metabolites) and 58.5% for ΣDAPs (sum of total dialkylphosphate metabolites) in LPU period, and 50.4% for ΣDMs, 65.3% for ΣDEs and 43.9% for ΣDAPs in HPU period. Significantly greater urinary ΣDAP, ΣDM and ΣDE levels were observed in HPU relative to LPU period. Maternal schooling years, proximity of the house to crops or greenhouses, use of insecticides at home, spraying the garden with pesticides, storage of pesticides at home, house cleaning frequency, as well as child's frequency of bath/shower, were found to be the major predictors of urinary levels of ΣDAP. Likewise, not washing fruit and vegetables before consumption and banana consumption were also identified as determinants of the exposure levels. Conclusions. Urinary levels of metabolites of OP pesticides found in this study were relatively lower compared to similar studies. DAP levels were significantly increased in HPU period. Maternal schooling years and variables related to residential environment and home exposures were identified as the most relevant determinants of DAP metabolites. Regarding diet, banana consumption and not washing fruit before consumption were also identified as determinants of the exposure levels. This study contributes to improve our knowledge on the main sources and determinants of children exposure to OPS, and given that children are more vulnerable than adults this information is essential to reduce children exposure and protect their health.

Corresponding author. School of Public Health (EASP), Campus Universitario de Cartuja, c/ Cuesta del Observatorio 4, 18080, Granada, Spain. Corresponding author. University of Granada School of Medicine, Av. de la Investigación, 11, 18016, Granada, Spain. E-mail addresses: [email protected] (A.F. Hernández), [email protected] (M. Lacasaña). 1 First and second authors have contributed equally to this work; therefore, they must be considered indistinctly as first authors. ∗

∗∗

https://doi.org/10.1016/j.envres.2020.109459 Received 28 November 2019; Received in revised form 12 March 2020; Accepted 27 March 2020 Available online 07 April 2020 0013-9351/ © 2020 Elsevier Inc. All rights reserved.

Environmental Research 186 (2020) 109459

B. González-Alzaga, et al.

Abbreviations DAPs DEP DETP DMDTP DMP DMTP

ΣDAPs dialkylphosphate metabolites (sum DMs + DEs) ΣDEs diethyl metabolites (sum DEP + DETP + DEDTP) ΣDMs dimethyl metabolites (sum DMP + DMTP + DMDTP) FFQ food frequency questionnaire HPU period period of high pesticide use in the agriculture LOD limit of detection LPU period period of low pesticide use in the agriculture OP organophosphate

dialkylphosphates diethyl phosphate diethyl thiophosphate dimethyl dithiophosphate dimethyl phosphate dimethyl thiophosphate

1. Introduction

Granada (Coast) and Huelva (Condado-Litoral) has the greatest contribution to the total vegetable and fruit production in Andalusia (Council of Agriculture and Fishing, 2014). For instance, almost 50% of the total surface area of the municipality of El Ejido (Almeria) is devoted to intensive agriculture (SIMA, 2014). Likewise, Andalusia ranked first in Spain in the use of agrochemical products, especially the provinces of Almeria and Granada. Despite this important farming activity, no official data are available on the use of specific pesticides in Andalusia and the scarce available information on human exposure comes from studies carried out on agricultural workers, but not from the general population or vulnerable groups such as children. However, it is known that OPs are usually used in Andalusia, as has been reported by previous studies carried out on greenhouse farm workers from this region (Hernández et al., 2005; Parrón et al., 2011). This study aimed to assess exposure to metabolites of OP pesticides in children 3–11 years old living in agricultural communities and in urban areas from Almeria, Granada and Huelva provinces. Further information on parent OP compounds producing dimethylphosphates and diethylphosphates can be found in Kavvalakis and Tsatsakis (2012). Because of the potential variability in the urinary concentrations of these compounds, their metabolites were measured at two time points of the same crop season, representing low and high pesticide use. Furthermore, the main predictors of exposure, including sociodemographic characteristics, environmental and residential exposures, lifestyle and diet were addressed as well.

Organophosphates (OPs) are non-persistent pesticides widely used after banning or restricting the use of persistent organochlorine pesticides. OP pesticides are characterized by a short half-life in the environment and in the human body, from where they are eliminated between 24 and 72 h after exposure (Fourth National Report on Human Exposure to Environmental Chemicals, 2009; Maroni et al., 2000). Although OP pesticides do not tend to accumulate in the body, their exposure has been associated with carcinogenic, neurological, reproductive, immunological and genotoxic effects in adults (Koureas et al., 2012). Despite the limited information on adverse effects of these compounds in children, several studies reported a greater risk of adverse reproductive effects (Eskenazi et al., 2004; Lacasaña et al., 2006; Rauch et al., 2012), delayed or deranged neurodevelopment (GonzálezAlzaga et al., 2014) and genotoxic effects (Sutris et al., 2016). Urinary levels of non-specific metabolites of OP pesticides, dialkylphosphates (DAPs), are generally measured to assess exposure to these substances in children (González-Alzaga et al., 2014; Egeghy et al., 2011). Biomonitoring studies measuring DAP metabolites showed a widespread human exposure to OP, which indicates that the general population, including children, is exposed to these compounds (Fourth National Report on Human Exposure to Environmental Chemicals, 2014; GerES IV, 2008; Report on Human Biomonitoring of Environmental Chemicals in Canada. Health Canada, 2010). Furthermore, elevated urinary levels of OP pesticide metabolites have been observed in children populations living in agricultural communities (Arcury et al., 2007; Bradman et al., 2005; Coronado et al., 2006; Fenske et al., 2005; Lu et al., 2000; Rohitrattana et al., 2014). This fact represents a major public health concern because of the high vulnerability of children to the potential adverse health effects of these compounds. Therefore, information on the main sources and determinants of exposure is essential to reduce children exposure and protect their health. A wide range of factors might contribute to children exposure to OP pesticides, with diet playing a significant role as reported by a few studies. Curl et al. (2003), Lu et al. (2008) and Bradman et al. (2015) explored the role of organic diets on urinary DAP levels in children and observed lower levels in those who consumed organic fresh products (fruits, vegetables and juices) as compared to children on conventional diet. Other studies found higher urinary DAP levels associated with a greater vegetables or fruits consumption in children (Bradman et al., 2011; Roca et al., 2014). However, a recent study carried out in rural communities from USA did not observe a trend towards increased urinary DAP levels with the intake of fruits and vegetables in children, and highlighted the need of considering other potential predictors of OPs exposure such as the use of insecticides at home, proximity to crop fields or occupational take-home pathway, among others (Holme et al., 2016). Thus, predictors of exposure to OP pesticides are still unclear and might vary depending on the characteristics of the study area and target population. Andalusia, southern Spain, is one of the most important agricultural areas in the country as around 40% of vegetables and 10% of fruits growing in Spain are produced in this region (SIMA, 2014). Farming activity in the Andalusian provinces of Almeria (area of El Poniente),

2. Methods 2.1. Study design A longitudinal study was conducted on children 3-11-year-old living in agricultural communities and in urban areas from the provinces of Almeria, Granada and Huelva (Andalusia, Spain). Since OP pesticides are rapidly metabolized and excreted from the body, more than one measurement is needed to obtain more accurate exposure levels. For this reason, children exposure was followed-up over two time points of the same crop season. 2.2. Study population Study participants were recruited from public schools of the study areas. Seventeen schools (11 in farming communities and 6 in urban areas) were randomly selected from the total of public schools in the study area. Authorizations from Ministries of Health and Education of Andalusia were obtained to gain access to these schools. The study was approved by the Ethics Committee of Hospital Virgen de las Nieves (Granada). Children 3–11 years of age registered in the aforementioned schools in December 2009 were invited to take part in the study. Children considered eligible for the study met the following criteria: being 3-11 year-old, Spanish-speaking, with at least one of their parents also Spanish-speaking, and living in selected towns from the study area until completion of the study. Exclusion criteria included presence of prenatal or perinatal medical conditions, and suffering from liver diseases 2

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at the time of recruitment. However, no children were excluded from the study for these reasons. Six hundred and forty children from those who met inclusion criteria and their parents had signed an informed consent were randomly selected for the study and followed during the one year. Urine samples were available for 559 and 591 children for the first and second sampling period of the study (corresponding to the low and high periods of use of pesticide, respectively). Parents who declined to participate in the study were invited to fill in a brief questionnaire for non-participants to collect basic information on sociodemographic characteristics and other variables potentially associated with pesticide exposure, such as current parental current job and whether they worked in agriculture, with the aim of assessing possible differences between participants and non-participants that could introduce a potential selection bias. These data were available for 604 non-participants.

laboratory authorised (A.359/l) and accredited (AC.463/III) by the Regional Ministry of Health of Andalusia. First-morning urine has been considered a proper surrogate for 24-h urine, as reported by Scher et al. (2007) in a validation study. Polar and non-polar pesticides were extracted simultaneously following the methodology described by Cazorla-Reyes et al. (2011). Briefly, five ml urine were extracted using C-18 Sep-Pak conditioned cartridges. Dibuthyl phosphate (DBP) was used as internal standard before extraction. The retained analytes were eluted with dichloromethane and the extracts were evaporated to dryness with a vacuum rotary evaporator. One mL was taken and evaporated under a gentle nitrogen stream and the concentrated extract was re-dissolved with a mixture 1:1 (v/v) of methanol and aqueous solution of formic acid (0.01%, v/v) prior analysis by ultrahigh performance liquid chromatography resolution associated a tandem triple quadrupole mass (UHPLC-QqQ-MS/MS). Separation of the six DAPs was performed on an Agilent 1290 infinity HPLC system (Waters, Milford, MA, USA) equipped with an Acquity UPLCTM BEH C18 column (100 mm × 2.1 mm, 1.7 μm particle size). The LC separation was performed at room temperature and at a flow rate of 300 μL/min with mobile phases consisting of acetonitrile/ 0.5 mM tetrabutyl-ammonium acetate (TBA) formate in water. To provide overall assessments of precision, accuracy, and reliability of the method, quality control (QC) samples were analyzed along with collected samples. QC samples were inserted blindly for each batch of study samples as follows: a) reagent and blank samples; b) a calibration curve; c) blank samples spiked at the LOQ. The limit of detection (LOD) was 0.1 μg/L (ppb) for all analytes measured. Inter- and intra-day variability measures yielded a result of less than 10%. The recovery percentages ranged from 80 to 110%. Creatinine concentration in children's urine was determined using Jaffe's Method in a Hitachi 917 automatic chemistry analyzer. Although urinary concentrations of DAP metabolites were adjusted for creatinine levels in the descriptive data shown in Table 1, urine creatinine

2.3. Exposure assessment Urinary levels of OPs metabolites (DMP, DMTP, DMDTP, DEP, DETP and DEDTP), were measured at two time points of the same crop season. Urine samples corresponding to period of low pesticide use (LPU) in agriculture were collected in January–February 2010 in the provinces of Almeria and Granada, and during June 2010 in the case of Huelva. The sampling period of high pesticide use (HPU) was conducted in October 2010 (in Almeria and Granada), and in April 2011 (in Huelva). Materials and instructions to collect a first-morning urine sample were previously given to children and parents by investigators. At the time of urine sample collection, children’ weight and height were measured at school in order to calculate their body mass index (BMI). After collection, urine samples were stored in cool-boxes at 2-3aC for an average period of 5 h and sent to the Laboratorio Analitico Bioclinico (LAB) in Almeria, where samples were aliquotted and stored frozen at −40 °C until analysis. LAB is an analytical chemistry

Table 1 Urinary concentrations (creatinine adjusted and unadjusted and molar) of DAP metabolites in the study population in the periods of low (LPU; n = 559) and high (HPU; n = 597) pesticide use. Metabolite

Unit

DMP

nmol/L μg/L μg/g nmol/L μg/L μg/g nmol/L μg/L μg/g nmol/L μg/L μg/g nmol/L μg/L μg/g nmol/L μg/L μg/g nmol/L μg/L μg/g nmol/L μg/L μg/g

DMTP DMDTP

ΣDMs

DEP DETP

ΣDEs

ΣDAPs

LPU % > LOD 15.2 23.8 8.6 32.6 18.4 18.1 23.1 41.5

GM < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 8.9 1,32 1,8

P50 < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD

HPU P75 < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 40.4 5.59 6.1 < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 59.9 8.4 11.0

P95 146.5 18.3 21.6 197.9 27.9 39.4 10.8 1.7 1.7 294.3 30.7 64.3 37.4 5.7 6.9 5.1 0.9 0.9 46.9 7.4 9.6 346.5 46.5 71.5

% > LOD 40.7 31.5 7.9 49.6 27.8 22.1 34.7 56.1

GM < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 7.9 1,1 1,2 < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 14.0 2,0 2,2

P50 < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD < LOD 17.5 2.6 2.6

P75 32.2 4.0 4.3 10.4 1.5 1.4 < LOD < LOD < LOD 60.1 7.8 7.9 4.5 0.7 0.8 < LOD < LOD < LOD 13.8 2.1 1.9 80.1 11.1 11.2

P95 182.9 18.3 26.4 85.5 12.1 12.3 2.5 0.4 0.4 231.2 29.8 36.0 35.7 5.5 6.2 18.7 3.1 3.1 44.4 6.9 8.8 252.1 34.2 41.7

p-valuea < 0.001 0.788 0.093 0.058 0.042 0.003 < 0.001 0.038

LOD: limit of detection (0.1 μg/L); GM: geometric mean; P50: percentile 50; P75: percentile 75; P95: percentile 95. DMP: dimethyl phosphate; DMTP: dimethyl thiophosphate; DMDTP: dimethyl dithiophosphate; DEP: diethyl phosphate; DETP: diethyl thiophosphate; ΣDMs: sum DMP + DMTP + DMDTP; ΣDEs: sum DEP + DETP + DEDTP; ΣDAPs: sum DMs + DEs. nmol/L: nanomol/liter; μg/L: microgram/liter; μg/g: microgram/gram creatinine. a Wilcoxon signed-rank test was used in paired samples with information on urinary concentrations of DAP metabolites in both study period (n = 496). 3

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B. González-Alzaga, et al.

concentration was handled as an adjusting variable for further multivariate analysis instead of correcting DAP concentrations by creatinine levels. According to previous reports, this approach allows for a more accurate estimation of the exposure (Mage et al., 2004; Barr et al., 2005; O’Brien et al., 2016).

variables: a) sociodemographic factors (child age and sex, province of residence, parental age at child birth, income levels (€/month), parental education, mother or father working in agriculture, mother or father involved in pesticide application); b) environmental and residential exposures: living near crops or greenhouses, having a garden or orchard at home, use of pesticides at home, pesticides storage at home, frequency of house cleaning, children in contact with orchards or gardens, use of insecticides over the last two months, pets at home, frequency of washing bed sheets; c) lifestyle: bath or shower frequency; d) diet (frequency of banana consumption and source of water consumption). Urinary creatinine concentrations were retained in the models, regardless of their statistical significance. Multivariable analyses were performed to identify predictors of urinary ΣDM and ΣDAP concentrations using a combination of backward and forward stepwise multiple linear regression, setting a significance level at p ≤ 0.05 (p ≤ 0.1 for borderline significant differences) and changes in R2. Once models were generated, residuals were analyzed to check whether they were independent, normal and homoscedastic. The regression coefficients (β) of multivariable models represented the average change in the urinary concentration of ΣDMs and ΣDAPs associated with an increase in one unit of the predictor variable (for continuous variables) or changes in the reference category (for categorical variables). Data were stored and processed using the R statistical computing environment v3.2.3 (http://www.r-project.org/).

2.4. Collection of information Mothers were asked to self-complete a brief questionnaire in each of the study periods, covering sociodemographic characteristics, residential history, and children's lifestyle. A food frequency questionnaire (FFQ) was distributed and self-administered to mothers to collect information on children's consumption of fresh products. The FFQ included the average weekly consumption and portion sizes for 23 food items grouped into vegetable and fruit categories (Supplementary Table 1). The FFQ had nine options according to individual frequency consumption of each item in the prior six months: never or < 1 time per month, 1 time per week; 2–4 times per week; 5–6 times per week; 1 time per day; 2–3 times per day; 4–5 times per day; > 6 times per day. The frequencies reported were then transformed to servings per week. In addition, information on the consumption of ecologic products and home-grown food, as well as on source of drinking water (tap, bottled, well, other origin) and place of purchase of vegetables and fruits, was collected. Mothers were also interviewed by trained personnel to obtain further information on: a) parental occupations; b) home characteristics and residential exposure to chemical compounds; c) pregnancy and birth; d) physical activity; e) nurturance.

3. Results

2.5. Data analysis

A total of 559 (290 boys and 269 girls) and 597 children (307 boys and 290 girls) participated in LPU and HPU periods, respectively. Table 1 shows the geometric mean and percentiles 50, 75 and 95 of five urinary dialkylphosphate metabolites and ΣDMs, ΣDEs and ΣDAPs in LPU and HPU periods. Urinary concentrations of the metabolite DEDTP are not shown in Table 1, since only 0.2% of the samples were above LOD in both study periods. Median levels for each DAP metabolite were below LOD (0.1 μg/L) in both study periods. The proportion of urine samples below LOD for ΣDMs, ΣDEs and ΣDAPs was 67.4%, 77% and 58.5%, respectively, in the period of LPU, and 50.4%, 65.3% and 43.9%, respectively, for the period of HPU. Overall, the percentage of samples below the LOD was higher in the LPU period than in the HPU period. Likewise, a significant increase in urinary ΣDAP, ΣDM and ΣDE levels was observed in the period of HPU relative to LPU. In order to rule out a potential selection bias, we explored the potential differences between participants and non-participants in the variables gathered in the non-participant questionnaire, i.e., child's age and sex, current parent's job labor situation, and residential proximity to crop fields or greenhouses. No significant differences were observed between participants and non-participants for child's sex and current parents' job (data not shown). Mean age among participants was significantly higher than in non-participants (7.7 yrs ± 2.4) than in participants (7.2 yrs ± 2.4); (p < 0.001), although this difference was only of 5 months. Likewise, a higher percentage of children living near crop fields or greenhouses (distance ≤1000 m) was observed among non-participants compared to participants (54.3% vs. 41.3%, respectively). Urine concentrations of ΣDM, ΣDE and ΣDAP for each study period compared by the main characteristics of the study population are shown in Table 2 (sociodemographic variables) and Table 3 (residential environment, exposures at home and lifestyle). Children living in the province of Granada tend to show the highest levels of ΣDM and ΣDE in the LPU period as compared to those living in Almeria o Huelva. Although this trend was also observed in the HPU period, the differences were not statistically significant. Furthermore, ΣDE metabolites were higher in urban areas than in agricultural communities. ΣDAP and ΣDM concentrations were positively and significantly associated with maternal and paternal education level in the

DAP levels were transformed to molar concentrations and expressed in nanomol per liter (nmol/L). As OP pesticides are metabolized to diethyl or dimethyl phosphate metabolites, molar concentrations of these metabolites were summed to obtain total concentration of diethyl (ΣDEs) and dimethyl (ΣDMs). In addition, the six dialkyl phosphate metabolites were summed to obtain total DAPs (ΣDAPs). Values below the limit of detection (LOD) were imputed, assigning the LOD divided by 2. The normality of the urine concentrations of DAP metabolites was checked using Kolmogorov-Smirnov test. As these variables showed a non-normal distribution, non-parametric tests were used for the bivariate statistical analysis. Urinary levels of DAPs, DMs and DEs (mean, geometric mean and percentiles 50, 75 and 95) were described for LPU and HPU periods, and potential differences between these two periods were assessed using Wilcoxon signed-rank test for related samples. ∑DAP, ∑DM and ∑DE levels were compared by the characteristics of the study population (i.e. sociodemographic variables, environmental and domestic exposures and lifestyle) and FFQ data for each study period using the Mann-Whitney U test and Kruskal-Wallis test. Urinary DAP concentrations were log10-transformed for multivariate analysis to reduce their skewed distribution. Predictors of urinary levels of ΣDAPs and ΣDMs were studied in both study periods (LPU and HPU) using Tobit models (also called censored regression models), due to the high percentage of samples below the LOD. These models are useful for modeling censored variables and, therefore, are suitable for measures that have a minimum detection limit below which the actual value is unknown (Lubin et al., 2004). Tobit models assume that there is a latent variable, uncensored, which depends linearly on the considered independent variables via a linear multivariable regression model. Nevertheless, this methodology could not be used for ΣDE urinary concentrations, since almost 70% of the samples were below the LOD. Predictor variables of interest were selected according to the results of the bivariate analysis (with p ≤ 0.2 in at least one of the study periods), as well as those identified in previous studies. Accordingly, the multivariable analyses were adjusted for the following independent 4

Age (years) 3–5 6–11 Sex Boys Girls Province Almeria Granada Huelva Area Rural Urban Time in the municipality ≤7 years > 7 years Monthly incomes (€/month) < 500 [500,1000] [1000,2000] > 2000 Mother'sage ≤35 years > 35 years Father's age ≤35 years > 35 years Maternal education No formal Primary studies Secondary studies Higher studies Paternal education No formal Primary studies Secondary studies Higher studies

< <

< <

< < <

< <

< <

< < < <

< <

< <

< < < 12.5

< < < 23.3

290 (52) 269 (48)

361 (65) 118 (21) 80 (14)

468 (84) 91 (16)

233 (43) 312 (57)

26 (5) 97 (19) 232 (45) 158 (31)

194 (36) 351 (64)

96 (20) 380 (80)

52 (9) 246 (45) 146 (27) 103 (19)

84 (17) 240 (48) 117 (23) 61 (12)

P50

72 (13) 487 (87)

n (%)

5 59.7 25.7 95.2 142.4

57.0 47.5 16.0 148.3

56.7 82.0

43.8 83.2

29.6 53.0 40.1 116.9

82.3 57.6

54.1 88.1

89.4 110.3 12.7

35.3 91.1

88.0 57.6

P75

178.9 288.0 401.0 876.8

266.1 262.1 268.1 860.0

242.4 384.8

248.7 387.4

261.6 280.1 435.3 380.0

328.3 380.6

329.1 469.7

355.5 602.8 46.2

385.2 292.0

313.2 363.7

P95 a

0.01b

0.00b

0.68a

0.22a

0.31b

0.53a

0.88a

0.16b

0.18a

0.99

p

< < < <

< < < <

< <

< <

< < < <

< <

< <

< < <

< <

< <

P50

54.0 6.8 89.7 122.0

46.0 40.4 2.6 130.5

16.0 47.3

11.6 56.2

13.3 50.7 13.3 96.7

54.8 33.3

30.4 66.3

58.6 88.1 1.1

19.7 60.9

56.3 31.9

P75

156.0 216.3 343.4 875.9

255.6 201.8 231.1 859.1

215.8 326.4

218.7 336.5

252.7 235.1 350.5 352.4

299.6 304.0

283.4 468.8

284.5 601.9 10.6

362.9 276.7

312.3 292.3

P95 a

0.00b

0.00b

0.28a

0.14a

0.18b

0.95a

0.45a

0.00b

0.30a

0.90

p

< < < <

< < < <

< <

< <

< < < <

< <

< <

< < <

< <

< <

P50

< < 1.5 <

< < < 1.5

< <

< <

5.1 1.7 < <

< <

< <

< < 8.8

< 1.5

< <

P75

65.7 48.4 44.7 88.8

48.9 50.3 32.3 105.8

88.8 46.8

60.5 43.1

34.2 58.6 53.7 43.2

45.7 53.4

58.8 30.9

51.0 66.6 30.1

42.3 68.4

46.0 47.9

P95 a

0.88b

0.75b

0.99a

0.49a

0.83b

0.85a

0.25a

0.00b

0.10a

0.12

p

84 (16) 264 (49) 116 (21) 75 (14)

47 (8) 266 (46) 147 (25) 122 (21)

92 (18) 422 (82)

204 (35) 376 (65)

42 (8) 117 (21) 239 (44) 149 (27)

304 (52) 277 (48)

524 (88) 73 (12)

411 (69) 100 (17) 86 (14)

307 (51) 290 (49)

144 (24) 453 (76)

n (%)

ΣDAPs

ΣDEs

ΣDAPs

ΣDMs

HPU

LPU

< 17.0 16.5 26.1

< 12.7 25.5 34.6

12.2 16.0

6.0 24.5

17.8 23.5 17.5 12.7

23.7 13.0

17.8 13.9

17.5 15.9 16.0

19.6 16.8

< 20.9

P50

59.4 74.1 92.3 118.9

33.7 61.8 84.0 117.2

87.4 79.0

74.1 83.9

76.3 106.0 68.8 81.8

94.9 60.3

77.7 127.7

64.5 179.8 97.0

85.4 78.1

79.8 81.9

P75

238.5 236.9 271.7 401.1

259.3 224.9 276.2 305.3

236.6 272.6

231.1 271.3

290.2 281.2 210.7 265.5

272.4 243.5

240.2 379.9

228.8 338.6 232.5

257.4 245.2

247.7 262.0

P95 a

0.17b

0.00b

0.94a

0.08a

0.97b

0.18a

0.59a

0.39b

0.32a

0.23

p

< < < 10.7

< < 5.2 24.6

2.9 <

< 8.3

< 3.4 4.1 <

3.7 <

2.3 <

6.8 < <

6.8 <

< 6.7

P50

ΣDMs

47.5 53.1 73.4 85.0

26.3 53.0 65.2 90.9

64.8 59.3

54.7 67.1

72.1 45.5 58.7 65.8

79.0 47.3

58.5 85.1

54.9 161.9 51.4

65.5 56.4

60.2 60.1

P75

230.9 210.1 255.7 369.1

241.3 208.4 249.3 290.4

205.8 253.9

218.5 251.3

287.5 266.2 184.2 243.4

231.0 236.6

223.5 361.7

187.8 302.1 219.4

247.1 215.6

219.1 237.7

P95

a

0.32b

0.01b

0.88a

0.14a

0.99b

0.12a

0.97a

0.54b

0.20a

0.39

p

Table 2 Distribution of urinary levels of ΣDAPs, ΣDMs and ΣDEs (nmol/L) in the study population by sociodemographic characteristics in the periods of low (LPU) and high (HPU) pesticide use.

9.0 12.3 12.2 22.9

< 13.7 14.7 19.7

14.2 12.6

10.3 14.8

8.6 14.4 12.0 14.3

15.5 11.8

11.4 21.9

8.8 20.5 20.0

11.3 16.4

8.7 14.5

P75

29.7 48.9 43.7 55.6

46.9 38.6 53.9 48.0

49.3 41.6

48.9 42.0

39.8 57.7 37.5 41.8

49.5 39.9

40.9 92.2

36.4 48.8 103.3

38.9 48.9

54.6 42.7

P95

0.12b

0.01b

0.54a

0.27a

0.84b

0.18a

0.01a

0.00b

0.07a

0.56a

p

(continued on next page)

< < < <

< < < <

< <

< <

< < < <

< <

< <

< < <

< <

< <

P50

ΣDEs

B. González-Alzaga, et al.

Environmental Research 186 (2020) 109459

Environmental Research 186 (2020) 109459

380.6 328.9

0.25a

< <

52.9 31.9

294.5 230.1

0.68a

< <

< <

58.3 47.5

0.32a

377 (70) 164 (30)

12.3 23.9

89.1 61.8

290.6 210.2

0.87a

< 13.3

62.4 55.1

277.0 183.5

0.68a

< <

13.7 12.5

44.8 12.4

0.88a

LPU period. A similar association was also observed for ΣDAP, ΣDM and ΣDE metabolites in the HPU period, but only for maternal education. Notwithstanding that, the higher the education level of children's parents, the greater levels of ΣDAP, ΣDM and ΣDE metabolites were observed in the HPU period. Children whose mothers did not work in agriculture showed higher urinary levels of ΣDAPs and ΣDM metabolites in the LPU period. In relation to residential environment and home exposures (Table 3), significantly higher ΣDM levels (and near-significant ΣDAP levels) were found in children living near crops or greenhouses (distance ≤1000 m) in the period of LPU. Likewise, children whose parents reported the use of pesticides in the garden or orchard showed higher ΣDAP and ΣDM levels than those not using pesticides at home (p = 0.07; p = 0.02, respectively), but only in the LPU period. Moreover, 25% of the children living in houses where pesticides are stored had levels of ΣDM and ΣDAP twice as high as children from families who did not report storage of these substances at home (p = 0.01; p = 0.02, respectively) in the LPU period. The use of insecticides at home was also associated with increased levels of ΣDM (p = 0.04) and ΣDAP (p = 0.07) only in LPU period. Significantly increased ΣDMs levels were observed in children with a lowest bath/shower frequency. Also frequency of house cleaning was significantly and inversely associated with ΣDAPs and ΣDMs levels in the HPU period. A descriptive analysis of urinary concentrations of ΣDAPs, ΣDMs and ΣDEs according to the frequency of food consumption in both study periods is shown in Supplementary Table 1. Overall, no significant differences were observed in either study period, with only banana consumption (i.e. more than 3 bananas per week) being significantly associated with increased ΣDAP and ΣDM levels in the LPU period. The multivariable predictive models of urinary ΣDAP and ΣDM levels in both exposure periods are listed in Tables 4 and 5. As mentioned above, multivariable models were not used for ΣDE concentrations since most of the samples were below the LOD. In the period of LPU, maternal schooling years, living near crops or greenhouses, storage of pesticides at home, and not washing fruit before consumption and frequency of banana consumption, were significantly and independently associated with ΣDM levels (Table 4). Regarding ΣDAP metabolites in the period of LPU (Table 5), maternal schooling years, living near crops or greenhouses, use of insecticides at home in the past two months, banana consumption (referred to units per week) and not washing fruit before consumption were identified as predictors of these metabolites, in line with results from the bivariate analysis (Tables 2 and 3). The variable “mother working in the agriculture” showed a strong correlation with “maternal schooling years” and, consequently, only the latter was retained in the final models for the period of LPU. Regarding the HPU period, maternal schooling years and house cleaning frequency remained significantly associated with ΣDAP and ΣDM, as was also observed in the bivariate analysis. Other variables that failed to be significantly associated in the bivariate analysis, such as spraying the garden, child's bathing/showering frequency or banana consumption (only for ΣDAP metabolites) and frequency of washing bed sheets (only for ΣDM metabolites), were identified as predictors of exposure levels of these metabolites in multivariate models.

< : Below limit of detection (0.1 μg/L). a: Mann-Whitney U test. b: Kruskal-Wallis test. o Agriculture as main occupation.

< < 356 (70) 150 (30)

82.0 47.5

0.66a

42.6 58.9 14.3 11.6 < <

0.63a

232.3 238.9 60.7 58.7 < 17.5

0.64a

259.4 248.9 83.8 74.3 17.3 22.5 517 (89) 65 (11)

0.33a

51.2 37.8 < < < <

0.02a

377.8 194.3 54.2 < < <

0.01a

377.8 194.3 < < 480 (88) 65 (12)

86.7 5.4

0.60a

45.1 42.3 13.7 12.2 < <

0.09a

230.8 266.7 51.3 82.5 < 8.4

0.10a

240.1 274.8 62.1 101.7 11.8 25.4 291 (56) 228 (44)

0.40a

50.8 58.2 < < < <

0.00a

214.0 509.7 12.9 89.7 < <

0.00a

275.7 568.5 < < 283 (58) 201 (42)

32.2 100.9

0.05a

48.6 45.8 12.4 14.8 < < 0.01a

195.9 264.4 44.5 76.3 < 10.7 0.01a

220.4 277.2 60.9 93.4 < 26.1 223 (40) 335 (60) 0.44a

46.8 82.2 < < < < 0.27a

201.7 351.1 31.9 56.2 < < 0.14a

251.5 407.2 45.0 89.6 < <

p P95 P75 P50 p P95 P75 p P95 P75 P50

231 (44) 299 (56)

P50 n (%)

ΣDEs

Table 2 (continued)

Maternal schooling years ≤9 years > 9 years Paternal schooling years ≤9 years > 9 years Working in agricultureo (mother) No Yes Working in agricultureo (father) No Yes

P50 P50

ΣDAPs ΣDMs ΣDAPs

n (%)

HPU LPU

P50

P75

P95

p

ΣDMs

P75

P95

p

ΣDEs

P75

P95

p

B. González-Alzaga, et al.

4. Discussion This study assessed exposure to OP pesticides in children living in agricultural and urban areas from Andalusia by measuring urinary DAP levels and their main predictors. These metabolites were measured at two time points of the same crop season, representing low and high pesticide use in the agriculture, in order to explore potential variability in the exposure levels between these two periods. These results complement those previously reported for persistent pesticides in this child population (González-Alzaga et al., 2018), and contribute to expand the 6

Living near greenhouses or crops No Yes Fumigate the garden No Yes Pesticides stored at home No Yes Child's bath or shower frequency Daily 4-6 times/week 1-3 times/week Pets sleeping in child's room No Yes House cleaning frequency More than once a week Once or less at week Bed sheets washing frequency Weekly Several at month Stuffed toys washing frequency Weekly Several at month Monthly Every several months Never Use of insecticides at home No Yes Use of repellent body lotionc No Yes Use of lice treatmentc No Yes Main source of water consumption Municipal water Water well Bottled water Others Washing fruit before consumption Unwashed Washed Washed and dried Packed fruit and vegetablesd Yes

< <

< 4.5

< 22.8

< < <

< <

< <

< <

< < < < 147.1

< <

< 8.5

< <

< 30.5 < <

171.7 < <

<

256 (47) 292 (53)

464 (86) 73 (14)

497 (94) 34 (6)

323 (59) 138 (25) 87 (16)

533 (98) 9 (2)

404 (73) 146 (27)

514 (94) 35 (6)

27 (8) 66 (20) 46 (14) 170 (52) 20 (6)

7

60 (11) 491 (89)

542 (98) 8 (2)

459 (85) 78 (15)

205 (38) 5 (1) 243 (45) 86 (16)

5 (1) 368 (68) 168 (31)

517 (94)

69.1

422.8 82.7 28.9

58.3 87.8 76.8 53.4

59.9 88.7

59.3 143.7

5.6 83.2

93.0 84.1 82.7 37.0 147.1

58.0 111.7

53.7 88.0

57.5 167.2

55.2 69.8 103.8

58.3 129.1

57.6 109.5

26.2 96.1

347.7

– 374.5 258.2

375.7 – 327.6 349.3

358.6 426.1

344.1 –

342.4 362.0

320.5 421.9 340.0 368.1 675.0

328.3 512.4

376.2 277.5

350.1 –

359.8 200.0 388.5

347.7 540.9

365.0 343.2

361.7 343.2

P95 a

0.23a

0.00b

0.64b

0.72a

0.48a

0.04a

0.72b

0.89a

0.08a

0.55a

0.12b

0.02a

0.07a

0.09

p

<

163.1 < <

< 14.8 < <

< <

< 7.6

< <

< < < < <

< <

< <

< <

< < <

< 12.9

< <

< <

40.4

415.1 49.6 9.8

51.9 82.7 22.0 28.7

40.4 60.3

35.4 131.3

< 46.1

66.7 50.5 58.9 12.7 145.4

40.8 14.1

38.8 51.0

32.8 166.3

33.7 47.5 75.2

37.1 112.8

38.3 99.1

5.0 68.7

P75

294.4

– 304.3 221.1

349.1 – 293.1 229.6

311.8 179.7

293.4 –

339.7 294.7

170.0 421.0 210.4 289.5 673.4

294.5 406.4

340.9 223.7

289.9 –

284.2 164.9 381.6

311.9 468.4

318.3 287.7

302.0 301.0

P95 a

0.42a

0.00b

0.60b

0.99a

0.28a

0.07a

0.73b

0.99a

0.43a

0.32a

0.04b

0.01a

0.02a

0.01

p

<

< < <

< 1.5 < <

< <

< <

< <

< < < < <

< <

< <

< <

< < <

< <

< <

< <

P50

<

23.3 < <

< 12.2 < <

< <

< <

< <

< < < 1.5 6.7

< 1.5

< 5.8

< <

< < <

< 5.1

< <

< <

P75

47.1

– 55.0 46.2

32.9 – 80.9 82.9

46.8 71.7

46.8 –

39.2 49.6

202.8 61.7 52.3 43.3 57.6

45.4 168.5

45.9 93.3

47.4 –

67.8 32.2 54.4

46.8 115.1

46.8 52.0

41.9 64.7

P95 a

0.52a

0.47b

0.15b

0.98a

0.56a

0.49a

0.43b

0.32a

0.01a

0.41a

0.55b

0.50a

0.81a

0.93

p

462 (94)

5 (1) 324 (67) 152 (32)

230 (40) 5 (1) 261 (46) 73 (13)

479 (81) 110 (19)

575 (98) 14 (2)

75 (13) 514 (87)

16 (4) 51 (14) 77 (21) 208 (55) 24 (6)

548 (93) 41 (7)

388 (66) 201 (34)

576 (98) 11 (2)

457 (78) 90 (15) 42 (7)

539 (94) 37 (6)

497 (86) 78 (14)

249 (42) 338 (58)

n (%)

P50

P75

n (%)

P50

ΣDAPs

ΣDMs

ΣDAPs

ΣDEs

HPU

LPU

24.6

< 24.9 16.9

24.0 2.0 13.9 2.0

16.1 26.0

17.5 19.3

33.0 16.3

< < 30.5 24.9 <

10.1 <

9.4 30.5

16.8 74.6

15.4 24.9 26.5

17.5 17.5

13.0 34.3

17.5 17.7

P50

89.1

36.2 91.7 85.1

105.4 131.3 64.2 65.2

77.2 103.8

83.6 41.4

121.5 77.0

15.2 47.7 101.6 92.7 81.8

83.9 49.5

74.1 102.6

80.2 130.2

75.0 99.7 92.9

80.1 104.2

83.8 86.2

99.5 75.0

P75

272.6

– 275.7 253.9

314.1 – 221.9 212.7

251.6 290.9

256.8 –

366.0 244.8

– 230.0 281.4 265.2 455.7

247.5 279.5

239.7 271.4

256.7 –

233.1 581.9 272.7

251.6 1108

251.6 396.2

262.6 256.5

P95 a

0.03a

0.48b

0.21b

0.42a

0.92a

0.14a

0.06b

0.24a

0.01a

0.19a

0.24b

0.45a

0.11a

0.70

p

9.0

< 8.5 3.7

8.2 < < <

< 12.2

< 4.6

19.0 <

< < 8.0 16.3 <

3.7 <

< 20.0

< 73.7

< 13.3 12.8

< 3.4

< 25.9

< <

P50

ΣDMs

66.7

25.9 72.0 62.9

69.1 99.6 59.4 49.0

58.7 77.8

60.5 40.4

79.3 57.6

14.0 41.1 78.0 67.3 80.9

60.7 28.0

56.8 78.0

59.4 115.8

54.6 79.9 81.2

59.4 88.0

59.7 67.6

65.3 59.6

P75

249.4

– 264.3 227.2

289.1 – 194.3 190.9

227.8 275.0

232.1 –

303.3 225.7

– 214.4 256.6 246.7 327.1

230.9 278.6

228.4 249.3

232.5 –

209.1 450.6 249.6

230.5 934.7

228.1 385.8

252.6 228.0

P95 a

0.10a

0.36b

0.40b

0.30a

0.90a

0.17a

0.13b

0.07a

0.02a

0.08a

0.20b

0.43a

0.13a

0.99

p

15.4

10.4 14.7 16.4

14.7 31.7 14.1 8.7

13.6 16.1

14.1 14.0

17.1 12.8

< < 18.5 15.8 8.5

14.1 16.5

13.7 14.9

14.3 12.4

12.5 19.4 15.7

13.6 18.1

12.7 18.1

16.4 11.6

P75

42.9

– 42.9 49.7

50.2 – 35.6 39.6

44.8 52.0

45.1 –

71.4 41.3

– 38.1 53.4 41.4 132.5

44.9 158.1

36.5 55.6

45.1 –

44.8 71.9 29.7

45.4 57.3

47.9 36.4

71.4 36.9

P95

0.19a

0.75b

0.73b

0.29a

0.88a

0.21a

0.02b

0.92a

0.13a

0.42a

0.28b

0.16a

0.19a

0.32a

p

(continued on next page)

<

< < <

< < < <

< <

< <

< <

< < < < <

< <

< <

< <

< < <

< <

< <

< <

P50

ΣDEs

Table 3 Distribution of urinary levels of ΣDAPs, ΣDMs and ΣDEs (nmol/L) in the study population by residential environment, exposures at home and lifestyle, in the periods of low (LPU) and high (HPU) pesticide use.

B. González-Alzaga, et al.

Environmental Research 186 (2020) 109459

Environmental Research 186 (2020) 109459

50.6 44.0 – 20.7 14.6 – < < 29.3 369.0 234.2 – 86.9 59.9 – 10.7 6.0 63.9 382.5 247.6 – 109.8 82.8 – 25.4 19.1 93.2 87 (18) 388 (81) 2 (1) 62.2 44.6 – 1.2 < – < < 34.8 304.2 294.9 – 46.4 31.5 – < < 160.7

<

8.0

319.2

0.27b

<

<

92.9

0.10b

27 (6)

<

25.4

313.7

0.35b

<

24.5

238.4

0.61b

<

0.9

75.3

0.06b

limited knowledge available so far on determinants of exposure to OP pesticides in children. Overall, the frequencies of detection of DAP metabolites were low in both study periods. The percentage of urine samples above LOD for ΣDMs, ΣDEs and ΣDAPs ranged from 23% to 41.5% in the period of LPU and from 34.7 to 56.1% in the period of HPU. Regardless this finding, ΣDMs, ΣDEs and ΣDAPs levels were higher in the period of HPU, as expected, due to the greater application of OP pesticides to crops. The higher percentage of children with ΣDEs below the LOD indicates a reduced exposure, because of a limited use of diethyl OP pesticides. This is a noteworthy finding as chlorpyrifos is the OP most often used in agriculture and the only diethyl OP pesticide approved as plant protection product in the EU. Overall, these results suggest that the magnitude of exposure to OP pesticides was less than originally thought. ΣDM, ΣDE and ΣDAP levels were lower than those observed in other studies carried out in children from agricultural areas from USA (Bradman et al., 2011; Holme et al., 2016; Lu et al., 2000; O’Brien et al., 2016; Marks et al., 2010), France (Cartier et al., 2016), Chile (MuñozQuezada et al., 2012) and Thailand (Rohitrattana et al., 2014). Likewise, DAP levels found in this study were also lower than those reported for children from non-agricultural areas from Greece (Myridakis et al., 2016), Japan (Osaka et al., 2016), Germany (GerES IV, 2008) and China (Wang et al., 2017; Zhang et al., 2015). In turn, similar exposure levels were observed in children 6–11 years from non-agricultural USA areas (Fourth National Report on Human Exposure to Environmental Chemicals, 2014). Similar urinary DM concentrations were observed between our study population and children living in agricultural and non-agricultural areas from other Spanish region (Valencia, Eastern Spain) (Roca et al., 2014), which strengthen the consistency of this research. However, DEP and DAP levels, as well as the frequency of detection of these metabolites in urine samples, where higher among children from Valencia, likely because of the different crops grown, vegetable crops in the study area and fruit trees in Valencia, which are also treated with OP pesticides to combat pests. On the other hand, our study area has increasingly been implementing the use of Integrated Pest Management techniques (IPM) to promote a reduction in the use of synthetic pesticides, which entails a lower risk of adverse effects for non-target organisms and humans (Lucchi and Benelli, 2018). In the study area, non-persistent pesticides have been gradually replaced by other biological methods since the second half of the 2000's, even before the implementation of the Directive 2009/128/EC on the sustainable use of pesticides in agriculture, which was intended to reduce the use of chemical pesticides, in order to decrease human and environmental exposure. This study also identified the main predictors of urinary DAP metabolites in our child population. Despite the growing research on determinants of prenatal exposure to OP pesticides (van den Dries et al., 2018; Llop et al., 2017; Sokoloff et al., 2016; Lewis et al., 2015), scarce information is available on potential predictors of postnatal exposure to OP pesticides in children, since most of the studies reviewed herein (Supplementary Table 3) reported only descriptive results for exposure levels, which do not allow for further comparisons. No significant differences in urine DAP concentrations were observed by child's sex or age, which is in line with previous studies (Myridakis et al., 2016; Roca et al., 2014; Rohitrattana et al., 2014; Osaka et al., 2016). Conversely, other studies found higher levels of exposure in younger children (Muñoz-Quezada et al., 2012), which might be accounted for metabolic and behavioural differences among children in different geographical areas (Clougherty, 2010). Regarding sociodemographic features, maternal education was significantly associated with DAP levels in the multivariable models, since children of mothers reporting more schooling years showed significantly increased levels of DMs and DAPs in the two study periods. Although this finding has not reported by other studies assessing postnatal exposure to OP (Muñoz-Quezada et al., 2012; Roca et al., 2014), it is in agreement with a previous study on persistent pesticides

< : below limit of detection (0.1 μg/L). -: data non available. a: Mann-Whitney U test. b: Kruskal-Wallis test. c Products applied to the child in the past two months. d Referred to the main type of fruit and vegetables consumed.

< < 209.2 109 (20) 426 (79) 3 (1)

71.1 55.8 –

356.9 348.7 –

0.35b 394.7 9.6 <

p P95 P75 P50 p P95 P75 p P95 P75 P50

32 (6)

P50 n (%)

ΣDEs

Table 3 (continued)

No Ripeness of fruit/vegetablesd Overripe Ripe Unripe

P50 P50

ΣDAPs ΣDMs ΣDAPs

n (%)

HPU LPU

P50

P75

P95

p

ΣDMs

P75

P95

p

ΣDEs

P75

P95

p

B. González-Alzaga, et al.

8

Environmental Research 186 (2020) 109459

B. González-Alzaga, et al.

Table 4 Predictors of concentrations of ΣDM metabolites in child urine in the periods of low (LPU; n = 479) and high (HPU; n = 540) pesticide use. TOBIT models. LPU

Creatinine concentration Maternal schooling years Living near crops or greenhouses No Yes Pesticides stored at home No Yes Washing fruit before consumption No Yes Yes, and dried too Banana consumptiona

a b

HPU β (95% CI)

p-Value

0.02 (0.0; 0.04) 0.18 (0.04; 0.32) Ref. 1.79 (0.62; 2.97)

0.022 0.001 0.003

Ref. 2.11 (0.01; 4.22)

0.049

Ref. −5.53 (−10.23; −0.83) −6.95 (−11.73; −2.18)

0.021 0.004

0.15 (0.02; 0.29)

0.022

β (95% CI)

p-Value

Creatinine concentration Maternal schooling years House cleaning frequencyb > once/week ≤ once/week Fumigate the garden No Yes Child's bath or shower frequency Daily Not daily

0.02 (0.01; 0.02) 0.14 (0.05; 0.23) Ref. 0.72 (0.0; 1.44)

< 0.001 0.002 0.051

Ref.0.98 (−0.11; 2.09)

0.079

Ref. 0.87 (0.04; 1.71)

0.039

Bed sheets washing frequency Weekly Several times/month

Ref. −1.75 (−3.20; −0.29)

0.018

referred to units per week general cleaning of the house.

conducted in the same child population (González-Alzaga et al., 2018). Likewise, recent studies carried out in birth cohorts from the Netherlands and Canada reported a positive association between DAP levels during pregnancy and maternal education (van den Dries et al., 2018; Sokoloff et al., 2016). This finding could be explained by the association between family status and other variables potentially associated with exposure to OP pesticides, such as dietary habits or living environment (see below). Residential exposures were also found to be associated with urinary DAPs. For example, living near agricultural crops or greenhouses was a predictor of urinary concentrations of ΣDM and ΣDAP metabolites, but only in the period of low pesticides use, which might indicate a residual use of these substances in that season. Likewise, the population living in agricultural communities might consider that there is no exposure to these substances in such period, thus making them more prone to take less prevention measures. The effect of the distance between children's residence to crops observed in the present study is in agreement with studies conducted in other agricultural areas, in which this distance was significantly and inversely associated with exposure to OP pesticides (Bradman et al., 2011; Roca et al., 2014; Muñoz-Quezada et al., 2012). The use and storage of pesticides at home, as well as the frequency of general cleaning at home were also significantly associated with greater

levels of ΣDM and ΣDAP metabolites, which suggest accumulation of OP residues at home, ultimately resulting in higher exposure of children living in those houses. However, none of the studies reviewed herein found similar findings (Bradman et al., 2011; Roca et al., 2014; Rohitrattana et al., 2014). These differences might be due to variation in self-reporting across studies, also influenced by local characteristics affecting housekeeping and the use of insecticides. Hence, residential factors should be better studied by using new approaches (i.e. time/ activity diary data, direct observations …) to discern their role in human exposure to OP pesticides. Based on the results of the multivariable predictive models, some of the predictors identified for ΣDM and ΣDAP metabolites in the LPU period differed from those identified for the HPU period. It would have been expected that predictors of DAP levels in the LPU period, such as living near crop fields or greenhouses, were also determinants of the exposure in the HPU period. However, the effect of these variables could be masked by residual confounding not assessed in this study. Controversial results have been observed regarding the role of dietary habits, especially vegetable and fruit consumption, on children exposure to OP pesticides. Some studies found positive associations between the intake of vegetables and fruits and urinary DAP levels (Roca et al., 2014; Bradman et al., 2011), as well as a reduction in

Table 5 Predictors of concentrations of ΣDAP metabolites in child urine in the periods of low (LPU; n = 498) and high (HPU; n = 520) pesticide use. TOBIT models. LPU

HPU β (95% CI)

p-Value

Creatinine concentration Maternal schooling years Living near crops or greenhouses No Yes Use of insecticides at home in the past two months No Yes Banana consumptiona

0.02 (0.0; 0.03) 0.13 (0.03; 0.23) Ref. 0.72 (−0.10; 1.54)

0.009 0.008 0.084

Ref. 1.52 (0.08; 2.96)

0.039

0.14 (0.05; 0.24)

0.002

Washing fruit before consumption No Yes Yes, and dried too

Ref. −4.31 (−7.81; −0.82) −5.35 (−8.90; −1.81)

0.015 0.003

a b

Referred to units per week. General cleaning of the house. 9

Creatinine concentration Maternal schooling years House cleaning frequencyb > once/week ≤ once/week Fumigate the garden No Yes Child's bath or shower frequency Daily Not daily Banana consumptiona

β (95% CI)

p-Value

0.01 (0.01; 0.02) 0.12 (0.05; 0.19) Ref. 0.58 (0.0; 1.17)

< 0.001 0.001 0.051

Ref. 0.98 (0.10; 1.87)

0.017

Ref. 0.55 (−0.75; 1.43)

0.105

0.09 (0.0; 0.20)

0.070

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exposure levels after replacing traditional fresh products by organic products (Lu et al., 2008). However, although pesticide residues are often found in fruits and vegetables due to their use for crop protection, no significant association was observed either with consumption of individual food commodities or with total intake of vegetables and fruits. This finding is in agreement with a study where fruit and vegetable consumption failed to be significantly associated with urinary DAP levels (Holme et al., 2016). In the current study, only banana consumption was significantly associated with greater urinary ΣDM levels in the period of LPU, and increased urinary ΣDAP levels in both study periods, which might be related with the high frequency of consumption of banana among children. In addition, banana may have high pesticide residues because farming is pesticide intensive as this fruit is grown in massive monocultures, without crop rotation, which render plants more vulnerable to insect pests.

fields or greenhouses were more likely to work in agriculture, which could limit their willingness to take part in the study because of the possible negative impact of the study results on their employment. Since living near crops fields or greenhouses was significantly associated with increased levels of urine DAP metabolites in the period of low exposure to pesticides, the participation of a lower number of children reporting residential proximity to crop fields or greenhouses, might have underestimated the measure of urinary DAP levels in our study population. Despite this potential underestimation, significantly increased DAP levels were observed among children living near crop fields, thus supporting our hypothesis. 6. Conclusions This study contributes to a better knowledge of the exposure levels to OP pesticides in children living in urban and agricultural areas from Andalusia (South Spain), since scarce information is available on this issue. Overall, the results showed low exposure levels to OP pesticides in our children population. It is worth noting that the agricultural areas studied count the largest greenhouse surface from Spain in which different pesticides classes are used, including OPs. This study also provides novel and valuable information on the main predictors of OP exposure in vulnerable populations, such as children Factors related to environmental and residential exposure (i.e. living near agricultural crops or greenhouses, the use and storage of pesticides at home, and house cleaning frequency), and sociodemographic characteristics (i.e. maternal education) were the major determinants of urinary DM and DAP metabolites in the two study periods assessed. By contrast, the impact of dietary habits, especially vegetable and fruit consumption, on children's exposure to OP pesticides was pretty limited, with banana consumption being the only food item identified as predictor of exposure to these compounds. This characterization of the exposure to OP pesticides could be of interest for the development of strategies aimed at reducing the exposure levels in the study population.

5. Strengths and limitations The major strength of this study is the valuable information provided on exposure levels to OP pesticides in children living in urban and agricultural areas from Andalusia (South Spain), as well as on their main determinants. To our knowledge, despite the widespread use of OP pesticides and the evidences on adverse effects on human health, no previous studies have studied these associations in children living in this region. This study also provides information on potential predictors of OP exposure in children, including sociodemographic features, home characteristics, residential exposures, and detailed information on dietary habits and lifestyles. Thereby, this study provides an opportunity to identify main predictors of children exposure to OP and contributes to enhance the limited information available to characterize this topic. Additionally, these results represent a major source of information to support the development of strategies aimed at reducing the exposure levels in vulnerable groups, such as children. One of the limitations of this study is the measurement of DAP metabolites in urine, as these represent non-specific metabolites of OPs pesticides. Certainly, the presence of DAPs in urine reflects recent exposure to many parent OPs, and also exposure to preformed DAPs in food or dust (Sudakin and Stone, 2011). Despite these limitations, urine DAPs have been widely used in epidemiological studies to assess OPs exposure (as shown in Supplementary Table 3) and to provide information on exposure to compounds in agricultural and urban communities where they are often used. Another limitation of this study is the high intra- and inter-individual variability in children's urinary DAP levels (Griffith et al., 2011), although measurements at two different time-points may partially account for these variations. Information on children's dietary habits, reported by mothers via a FFQ, might also introduce some bias, albeit the use of a validated questionnaire and the assumed good knowledge of the children's dietary habits contribute to reducing these potential biases. Despite the inherent bias of FFQ, to date it is the best approach to identify the main food commodities generally consumed by children in epidemiological studies, based on the cost-effectiveness principle (Pérez Rodrigo et al., 2015; Vioque et al., 2016). Regarding selection bias, this study explored potential differences between children participating, those who declined to participate, and those who did not provide urine samples concerning child's age and sex, proximity of the house to crops fields or greenhouses and parental education, labour status and main occupation. Although the mean age among participants was significantly higher than among non-participants, child's age was not identified as a predictor of urinary levels of DAP metabolites in the current study. Overall, we consider that these differences did not introduce a selection bias in our results and, therefore, did not modify the magnitude of the associations found. On the other hand, a higher percentage of children living near crop fields or greenhouses was observed among non-participants (54.3%) compared to the participants (41.3%). These differences might be due to the fact that parents of children living near crop

Funding This study was supported by grants from the Council of Economy, Innovation, Science and Employment of the Andalusian Government (reference number P08-CTS-04313, FEDER funds). Ethical implications Since this study involved human biological samples, it was approved by the Ethics Committee of Hospital Virgen de las Nieves (Granada). An informed consent signed by children’ parents was required for the participation in this study. Likewise, authorizations from Ministries of Health and Education of Andalusia were obtained to gain access to the schools where children recruitment took place. Category tasks author's contribution Conceptualization Ideas; formulation or evolution of overarching research goals and aims ML, AH, BGA, Methodology Development or design of Methodology; creation of models ML, AH, BGA, Validation Verification, whether as a part of the activity or separate, of the overall replication/ reproducibility of results/experiments and other research outputs ML, BGA, ILF Formal analysis Application of statistical, mathematical, computational, or other formal techniques to analyze or synthesize study data DR, BGA Investigation Conducting a research and Investigation process, specifically performing the experiments, or data/ evidence collection All authors Resources Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing Resources, or other analysis tools ML, BGA, ILF Writing - original draft Preparation, creation and/or presentation of the published work, specifically writing the initial draft (including 10

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substantive translation) BGA, DR Writing - review & editing Preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision – including pre-or postpublication stages All authors Visualization Preparation, creation and/or presentation of the published work, specifically Visualization/ data presentation BGA, DR, ILF, MJGA Supervision Oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team ML, AH Project administration Management and coordination responsibility for the research activity planning and execution ML Funding acquisition Acquisition of the financial support for the project leading to this publication ML.

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Declaration of competing interest The authors declare that they have no conflicts of interest. Acknowledgments This study was supported by grants from the Council of Economy, Innovation, Science and Employment of the Andalusian Government (reference number P08-CTS-04313, FEDER funds). The authors would also like to thank the schools staff and the children and families, without whom this study would not have been possible. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.envres.2020.109459. References Arcury, T.A., Grzywacz, J.G., Barr, D.B., Tapia, J., Chen, H., Quandt, S.A., 2007. Pesticide urinary metabolite levels of children in eastern North Carolina farmworker households. Environ. Health Perspect. 115 (8), 1254–1260. https://doi.org/10.1289/ehp. 9975. Barr, D.B., Wilder, L.C., Caudill, S.P., Gonzalez, A.J., Needham, L.L., Pirkle, J.L., 2005. Urinary creatinine concentrations in the U.S. population: implications for urinary biologic monitoring measurements. Environ. Health Perspect. 113 (2), 192–200. Bradman, A., Eskenazi, B., BarrD, B., Bravo, R., Castorina, R., Chevrier, J., et al., 2005. Organophosphate urinary metabolite levels during pregnancy and after delivery in women living in an agricultural community. Environ. Health Perspect. 113, 1802–1807. Bradman, A., Castorina, R., Barr, D.B., Chevrier, J., Harnly, M.E., EisenEA, McKoneTE., Harley, K., Holland, N., 2011. EskenaziB. Determinants of organophosphorus pesticide urinary metabolite levels in young children living in an agricultural community. Int. J. Environ. Res. Publ. Health 8, 1061–1083. Bradman, A., Quirós-Alcalá, L., Castorina, R., Schall, R.A., Camacho, J., Holland, N.T., et al., 2015. Effect of organic diet intervention on pesticide exposures in young children living in low-income urban and agricultural communities. Environ. Health Perspect. 123, 1086–1093. Cartier, C., Warembourg, C., Le Maner-Idrissi, G., Lacroix, A., Rouget, F., Monfort, C., Limon, G., Durand, G., Saint-Amour, D., Cordier, S., Chevrier, C., 2016. Organophosphate insecticide metabolites in prenatal and childhood urine samples and intelligence scores at 6 Years of age: results from the mother-child PELAGIE cohort (France). Environ. Health Perspect. 124, 674–680. Cazorla-Reyes, R., Fernández-Moreno, J.L., Romero-González, R., Frenich, A.G., Vidal, J.L., 2011. Single solid phase extraction method for the simultaneous analysis of polar and non-polar pesticides in urine samples by gas chromatography and ultra high pressure liquid chromatography coupled to tandem mass spectrometry. Talanta 85, 183–196. Clougherty, J.E., 2010. A growing role for gender analysis in air pollution epidemiology. Environ. Health Perspect. 118, 167–176. Coronado, G.D., Vigoren, E.M., Thompson, B., Griffith, W.C., Faustman, E.M., 2006. Organophosphate pesticide exposure and work in pome fruit: evidence for the takehome pesticide pathway. Environ. Health Perspect. 114 (7), 999–1006. https://doi. org/10.1289/ehp.8620. Curl, C.L., Fenske, R.A., Elgethun, K., 2003. Organophosphorus pesticide exposure of urban and suburban preschool children with organic and conventional diets. Environ. Health Perspect. 111 (3), 377–382. https://doi.org/10.1289/ehp.5754. Egeghy, P.P., Cohen, HubalEA., Tulve, N.S., MelnykLJ, Morgan, M.K., Fortmann, R.C., Sheldon, L.S., 2011. Review of pesticide urinary biomarker measurements from selected US EPA children's observational exposure studies. Int. J. Environ. Res. Publ. Health 8, 1727–1754. Eskenazi, B., Harley, K., Bradman, A., Weltzien, E., Jewell, N.P., Barr, D.B., et al., 2004.

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