International Journal of Hygiene and Environmental Health 248 (2023) 114105
Available online 21 December 20221438-4639/© 2022 The Authors. Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Assessment of exposure to pesticide mixtures in five European countries by 
a harmonized urinary suspect screening approach 
Ilse Ottenbros a,b, Erik Lebret a, Carolin Huber d,e, Arjen Lommen f, Jean-Philippe Antignac g, 
Pavel Cupr h, Libor Sulc h, Ondrej Mikes h, Tamas Szigeti i, Szilvia Kozepesy i, Inese Martinsone j, 
Zanna Martinsone j, Lasma Akulova j, Olga Pardo k,1,2, Sandra F. Fernandez k, Clara Coscolla k, 
Susana Pedraza-Diaz l, Martin Krauss d, Laurent Debrauwer m,n, Kevin Wagner m,n, 
Rosalie Nijssen f, Hans Mol f, Chiara Maria Vitale h, Jana Klanova h, Borja Garlito Molina o, 
Nuria Leon k, Roel Vermeulen a, Mirjam Luijten c, Jelle Vlaanderen a,* 
a Institute of Risk Assessment Sciences, Utrecht University, Yalelaan 2, 3584CM, Utrecht, the Netherlands 
b Center for Sustainability, Environment and Health, National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721MA, 
Bilthoven, the Netherlands 
c Center for Health Protection, National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721MA, Bilthoven, the Netherlands 
d Department of Effect-Directed Analysis, Helmholtz Centre for Environmental Research (UFZ), Permoserstr. 15, 04318, Leipzig, Germany 
e Institute for Ecology, Diversity and Evolution, Goethe University Frankfurt Biologicum, Campus Riedberg, Max-von-Laue-Str. 13, 60438, Frankfurt am Mein, Germany 
f Wageningen Food Safety Research, Part of Wageningen University and Research, Akkermaalsbos 2, 6708WB, Wageningen, the Netherlands 
g Oniris, INRAE, LABERCA, 44300, Nantes, France 
h RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, 61137, Czech Republic 
i National Public Health Center, Albert Florian út 2-6, 1097, Budapest, Hungary 
j Laboratory of Hygiene and Occupational Diseases, Rıga Stradin¸s University, Latvia 
k Foundation for the Promotion of Health and Biomedical Research of the Valencia Region, Av. Catalunya 21, Valencia, Spain 
l National Centre for Environmental Health, Instituto de Salud Carlos III (ISCIII), Majadahonda, Spain 
m Toxalim (Research Center in Food Toxicology), Toulouse University, INRAE UMR 1331, ENVT, INP-Purpan, Paul Sabatier University, 31027, Toulouse, France 
n Metatoul-AXIOM Platform, National Infrastructure for Metabolomics and Fluxomics: MetaboHUB, Toulouse, France 
o Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water (IUPA), Universitat Jaume I, Av. Sos Baynat S/N, 12071, Castello 
de la Plana, Spain   
A R T I C L E  I N F O   
Keywords: 
Pesticide exposure 
Suspect screening 
Mixtures 
Co-occurrence 
Correlation patterns 
HBM4EU 
A B S T R A C T   
Humans are exposed to a mixture of pesticides through diet as well as through the environment. We conducted a 
suspect-screening based study to describe the probability of (concomitant) exposure to a set of pesticide profiles 
in five European countries (Latvia, Hungary, Czech Republic, Spain and the Netherlands). We explored whether 
living in an agricultural area (compared to living in a peri-urban area), being a a child (compared to being an 
adult), and the season in which the urine sample was collected had an impact on the probability of detection of 
pesticides (-metabolites). 
In total 2088 urine samples were collected from 1050 participants (525 parent-child pairs) and analyzed 
through harmonized suspect screening by five different laboratories. Fourty pesticide biomarkers (either pesti-
cide metabolites or the parent pesticides as such) relating to 29 pesticides were identified at high levels of 
confidence in samples across all study sites. Most frequently detected were biomarkers related to the parent 
pesticides acetamiprid and chlorpropham. Other biomarkers with high detection rates in at least four countries 
related to the parent pesticides boscalid, fludioxonil, pirimiphos-methyl, pyrimethanil, clothianidin, fluazifop 
and propamocarb. In 84% of the samples at least two different pesticides were detected. The median number of 
Abbreviations: HBM, Human Biomonitoring; HBM4EU, European Human Biomonitoring Initiative; LC-HRMS, Liquid chromatography coupled to High Resolution 
Mass Spectrometry; SPECIMEn, Survey on PEstiCIde Mixtures in Europe; SS, Suspect Screening. 
* Corresponding author. 
E-mail address: J.J.Vlaanderen@uu.nl (J. Vlaanderen).   
1 Present Address: Department of Analytical Chemistry, University of Valencia, Doctor Moliner 50, 46100, Burjassot, Spain.  
2 Present Address: Public Health Laboratory of Valencia, Av. Catalunya 21, Valencia, 46020, Spain. 
Contents lists available at ScienceDirect 
International Journal of Hygiene and Environmental Health 
journal homepage: www.elsevier.com/locate/ijheh 
https://doi.org/10.1016/j.ijheh.2022.114105 
Received 15 July 2022; Received in revised form 24 November 2022; Accepted 12 December 2022   
International Journal of Hygiene and Environmental Health 248 (2023) 114105
2
detected pesticides in the urine samples was 3, and the maximum was 13 pesticides detected in a single sample. 
The most frequently co-occurring substances were acetamiprid with chlorpropham (in 62 urine samples), and 
acetamiprid with tebuconazole (30 samples). Some variation in the probability of detection of pesticides (-me-
tabolites) was observed with living in an agricultural area or season of urine sampling, though no consistent 
patterns were observed. We did observe differences in the probability of detection of a pesticide (metabolite) 
among children compared to adults, suggesting a different exposure and/or elimination patterns between adults 
and children. 
This survey demonstrates the feasibility of conducting a harmonized pan-European sample collection, com-
bined with suspect screening to provide insight in the presence of exposure to pesticide mixtures in the European 
population, including agricultural areas. Future improvements could come from improved (harmonized) quan-
tification of pesticide levels.   
1. Introduction 
Humans are typically exposed to pesticides through multiple sources, 
including diet, occupational or environmental exposures (Damalas and 
Eleftherohorinos, 2011; Deziel et al., 2015). Growing evidence indicates 
that living in an agricultural area where pesticides are applied contrib-
utes to higher exposure than residents living away from agricultural 
areas (Dereumeaux et al., 2020; Figueiredo et al., 2021; Teysseire et al., 
2020, 2021). Determinants contributing to this increased exposure 
include proximity to agricultural fields where pesticides are applied, 
crop acreage around the home, and season (Dereumeaux et al., 2020; 
Teysseire et al., 2021). Pesticide exposure has been linked to various 
short-term and chronic health effects such as respiratory or neurological 
development issues (Kim et al., 2017; Ntzani et al., 2013). Therefore a 
comprehensive characterization of the exposure to real-life mixtures of 
pesticides, which includes the contribution of living close to agricultural 
areas where pesticides are applied, is essential for human health risk 
assessment. 
Most non-occupational pesticide exposure studies focus on selected 
sets of targeted pesticides for human biomonitoring (HBM), often based 
on a priori selected biomarkers related to e.g. the spraying activities in a 
certain area, the health outcome of interest, or practical considerations 
such as the commercial availability of standards (Dereumeaux et al., 
2020; Teysseire et al., 2021). Currently, HBM for urinary pesticide 
biomarkers by targeted methods is limited to mostly pyrethroids and 
non-specific markers of organophosphorus pesticides. However, in 
real-life pesticide exposure often is already a mixture of multiple 
co-occurring compounds with repeated exposure timeframes (Crepet 
et al., 2019). With more than 450 active pesticides currently approved 
(plus 50 more currently pending) for use in the European Union (EU 
Database Pest, 2022), there is a growing need for information on the 
co-occurrence of these compounds in the human body. HBM of pesti-
cides in urine is a useful method to assess the aggregate exposure of 
pesticides from various exposure sources and routes, by measuring the 
parent pesticide and/or the corresponding biotransformation products 
(Bonvallot et al., 2021). However, as the list of registered pesticides is 
long and they occur often highly metabolized in urine, a large number of 
targeted assays would be required to assess presence of all urinary 
pesticides and their metabolites in each sample. This is currently not 
feasible since many human urinary biomarkers of exposure (typically 
phase I/II metabolites) are often unknown, and the analytical reference 
standards are not readily available. Suspect screening (SS) approaches 
based on full scan High Resolution Mass Spectrometry (HRMS) emerge 
as an innovative way to assess the presence of a broad range of exposure 
markers and better capture the complexity of pesticide mixtures (Andra 
et al., 2017; Pourchet et al., 2020; Huber et al., 2022). 
The study presented here, the Survey on PEstiCIde Mixtures in 
Europe (SPECIMEn), aimed to generate new pesticide exposure data in a 
harmonized pan-European setting (as part of the European Human 
Biomonitoring Initiative HBM4EU, www.hbm4eu.eu). This was done by 
analyzing 2088 urine samples collected in five countries through a 
multi-laboratory high-throughput SS approach. This study aimed at 
exploring co-occurrence (probability of exposure) of pesticide 
biomarkers across Europe and within each participating country. It also 
aimed at assessing differences of exposure patterns by location (living 
close to agricultural fields versus non-agricultural areas), seasons (dif-
ferences in spraying activities), as well as age groups (adults versus 
children, of which the latter are more sensitive to health effects and 
usually have higher internal exposure levels, due to e.g. a higher food 
intake/kg body weight (Eskenazi et al., 1999; Sapbamrer and Hon-
gsibsong, 2019). The study design therefore provides insight into local 
contributions, based on a broad combination of pesticides. Higher 
detection frequencies of pesticide markers might be expected for those 
pesticides applied on local crops during the spraying season in residents 
living close to the agricultural fields. 
2. Material and methods 
2.1. Sampling strategy 
To create geographical coverage across Europe, study sites from five 
countries were included to provide insight into variations of pesticide 
exposure patterns across Europe, namely the Czech Republic, Hungary, 
Latvia, Spain and the Netherlands. Within each country, urine samples 
were collected simultaneously at two locations: agricultural and non- 
agricultural areas. Each address in the agricultural area was located 
within 250 m from an agricultural field where pesticides were typically 
applied, mainly focusing on tree-crops or so-called ‘overhead cultures’ 
(except Latvia where tree-crops were hardly grown). These ‘overhead- 
cultures’ will result in potentially higher exposure concentrations in the 
air due to machine-drawn air blast or a hand-held overhead spray, which 
are more prone to drift (Willenbockel et al., 2022). The crop types 
differed slightly between countries due to e.g. differences in climate. A 
detailed description of the area selection in all five country can be found 
in Supplementary Material F. In summary, Spain focused on residential 
areas close to citrus fruits, Czech Republic on apples, vineyards, peach, 
plums and apricots, Hungary on apples, the Netherlands on apples and 
pears, and Latvia mostly on winter and summer rapeseed, summer 
wheat and barley. Non-agricultural areas were defined as sub-urban 
areas at least 500 m away from any agricultural fields. 
Per country at each agricultural and non-agricultural area, 50 
parent-child pairs (50 households) were included (total of 100 parent- 
child pairs per country). Each parent-child pair was composed of one 
child aged 6–11 years at the time of inclusion, accompanied by one of 
their parents or legal guardians living in the same household. Adults 
who worked in the agricultural sector (i.e. farmers) were excluded from 
recruitment, since the sample size was too limited to distinguish occu-
pational exposures. The same selection criteria were used in all five 
countries. 
A minimum of 100 parent-child pairs per country (200 individuals) 
provided a first morning void urine sample, and completed a harmo-
nized questionnaire. The admission of the questionnaire, sample 
collection procedures and timing of sampling was coordinated, and 
sampling materials such as cups and tubes were bought in bulk to avoid 
any batch differences. All collected urine samples were stored and 
transported refrigerated (at 4 C), until samples were aliquoted and 
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International Journal of Hygiene and Environmental Health 248 (2023) 114105
3
stored at  80 C (within 48 h of sample collection). Samples were 
transported to the laboratory of analysis after each season. 
All households were visited twice: the first visit was made in winter 
2019/2020 (season 1), the second in summer 2020 (season 2). The 
specific sampling dates (Supplementary Material A) differed slightly 
between study sites, partly due to differences in spraying season due to 
climate and the type of crop grown on the field. The sampling of the 
second season was slightly delayed (end of summer) due to the COVID- 
19 pandemic and accompanied uncertainties. The recruitment strategy 
differed between the study sites, a detailed description of the recruit-
ment strategy per country can be found in Supplementary Material F. In 
summary, the Hungarian partner involved local public health officers to 
get in touch with the participants, while others sent out letters (the 
Czech Republic and the Netherlands), contacted colleagues as study 
participants (Spain and the Netherlands), conducted an online campaign 
(the Czech Republic and the Netherlands), and/or contacted partici-
pants through schools (Spain and Latvia). A detailed questionnaire was 
completed during the first season by the parent, and a subset of ques-
tions was asked again during the second season (Supplementary Mate-
rial B). The joint questionnaire was developed in English, and 
subsequently translated to the local languages. The questionnaire 
covered personal and household characteristics, activities up to three 
days prior to sampling, potential pesticide exposure scenarios (occupa-
tional, usage of products containing pesticides), and the food con-
sumption pattern of the day prior to sampling (origin of consumed foods 
as well as a food frequency table for food consumption 24 h prior to 
sampling). 
All partner countries acquired approval from the appropriate local 
medical ethical committees, and written informed consent was obtained 
from all participants (parents and children separately). A description of 
the ethical approval procedure per country can be found in supple-
mentary material F. A harmonized informed consent form was used for 
all participants, which was evaluated by an internal HBM4EU review 
board. 
2.2. Suspect screening approach 
A SS methodology was applied to analyze the urine samples, of 
which a detailed description can be found in Huber et al. (2022). Briefly, 
the applied analytical workflow from sample preparation, instrumental 
analysis, and data processing was conducted under harmonized condi-
tions in five different laboratories across Europe, in the Netherlands, 
Germany, France, the Czech Republic and Spain (Vitale et al., 2022). 
Each laboratory analyzed approximately 400 urine samples originating 
from one of the five SPECIMEn study sites. Samples were analyzed after 
each season, and potential batch effects were addressed (Huber et al., 
2022). The suspect database generation, MS data analysis and confir-
mation procedures were performed in a centralized way. Several 
consolidated quality assurance/quality control (QA/QC) dispositions, 
parameters and criteria were first implemented to ensure the consis-
tency of the results obtained across the different participating labora-
tories as well as to document the applied method performances (Vitale 
et al., 2022). The applied analytical workflow was described in detail by 
Huber et al. (2022) and consists of i) SPE cleanup/concentration (5-fold) 
of the urine after pH adjustment, ii) measurement of the extracts by full 
scan liquid chromatography coupled to HRMS (LC-HRMS), iii) data 
pre-processing and analysis, iv) prioritization of putative detects, v) 
generation of a list of representative samples for follow up identification 
experiments using tandem mass spectrometry (MS/MS), and vi) final 
confirmation of putative detects by spectral comparison with reference 
standards either purchased/synthesized or generated in vitro by human 
liver S9 incubations. The curated suspect list of pesticides may include 
multiple metabolites originating from the same parent compound, 
resulting in a final datafile with potentially several metabolites that 
reflect exposure of the same parent compound. This redundancy is 
considered enhancing confidence. In the case of SPECIMEn, this list 
focused on pesticides and one aggregated list of known and predicted 
pesticide metabolites from all five laboratories was used as suspect 
database. ‘Fully identified’ were those with the highest level of confi-
dence: Schymanski level 1 if a reference standard material is commer-
cially available, or Schymanski 2 by diagnostic evidence acquired by 
human liver S9 incubation experiments (Schymanski et al., 2014). Bio-
markers which were identified at a lower tier will end up in lower 
confidence levels, reflecting the level of uncertainty about the identity of 
that feature. In the context of the present paper, only biomarkers iden-
tified with confidence levels 1 and 2 were considered. 
2.3. Statistical analysis 
In line with the basic principle of the SS approach, the data generated 
in SPECIMEn are ‘semi-quantitative’, i.e. quantitative signal intensities 
for each representative spectrometric mass are reported per sample, yet 
these intensities cannot be considered as urinary pesticide concentra-
tions and are not standardized across laboratories. The data was ana-
lysed by dichotomizing the intensities into ‘detected’ versus ‘non- 
detected’, which allows comparisons across study sites as well as in-
clusion of biomarkers with low detection rates in the statistical analysis. 
The detection rate was calculated as the number of samples in which 
a particular biomarker was detected and identified with confidence 
levels 1 and 2 over the total number of samples collected, expressed in 
percentage. Based on the parent pesticides (if multiple metabolites and 
the parent pesticide were measured, these were considered as one), the 
patterns of co-occurrences were explored. First, the total number of 
pesticides per urine sample was evaluated. Secondly, with the usage of 
an UpSet plot it was evaluated which parent pesticide combinations co- 
occurred and how frequent. Thirdly, the correlation pattern in the total 
set of parent pesticides was evaluated for each study site with a weighted 
correlation network using the IsingFit R package v0.3.1 (van Borkulo 
et al., 2015). This package estimates the network based on the Ising 
model: combining L1-regularized logistic regression with EBIC model 
selection (gamma 0.25). On this network a clustering algorithm was 
applied (walktrap), to detect communities of closely related features 
indicated by different colours in the network (Pons and Latapy, 2005). 
To assess the influence of co-variates, logistic mixed effects regres-
sion models were applied, with participant ID and household ID as 
random effects. Our main model includes fixed effects for season (season 
1/season 2), location (agriculural/non-agricultural) and age category 
(child/adult). We assessed the sensitivity for further adjustment for 
potential confounding by including body mass index (BMI) level, edu-
cation of the parent, consumption of homegrown foods (yearly average 
percentage), and a summary indicator for pesticide usage in an extended 
model. The pesticide usage indicator indicates whether pesticide con-
taining products were used up to three days prior to sampling either for 
human use, in the garden, indoors and/or for professional use. The es-
timates for season, location and age groups were transformed to Odds 
Ratios (OR) with 95% Confidence Intervals (CI) for both the main and 
extended models. 
3. Results 
3.1. Population characteristics 
The description of the study population for the five study sites of the 
SPECIMEn study is provided in Table 1. In total 2088 urine samples were 
collected, which were equally spread across the five study sites and 
areas. The loss to follow-up of individuals between seasons was low, 
varying from 0.9 to 2.9%. Reasons for loss to follow-up were loss of 
contact, divorce and/or move to another location. The adult samples 
mainly originated from the mothers, while gender was equally divided 
across the children’s samples. The mean age of the adults was compa-
rable across all study sites, varying from 38 to 44 years. The mean BMI 
(self-reported) of the adults originating from Latvia and Hungary was 
I. Ottenbros et al.                                                                                                                                                                                                                               
International Journal of Hygiene and Environmental Health 248 (2023) 114105
4
slightly higher compared to the adults from other study sites. Most of the 
participants did not smoke, although in the agricultural areas of Spain 
and Hungary there was a substantial group of current smokers 35% and 
45%, respectively (Supplementary Material B). Based on the total 
household income categories, participants of agricultural areas mostly 
earned less money than those living in non-agricultural areas. In all 
areas except the agricultural area in Hungary, the majority of the par-
ticipants had a university education level. In Spain and Hungary, about 
half of the households in agricultural areas used pesticide products 
during summer season, which includes the use of consumer products, 
usage indoors, in the garden and/or professional use. These different 
categories are presented separately in Supplementary Material B. 
Overall, the homegrown food consumption percentage was higher in 
households in agricultural areas than those in non-agricultural areas, 
mostly during summer. 
3.2. Annotations and detection rates 
The application and harmonization of the SS approach was per-
formed on 2088 urine samples using the method described in detail in 
Huber et al. (2022). A total number of 498 tentative annotations of 
pesticide biomarkers was obtained and prioritized, of which 40 pesticide 
biomarkers were annotated with confidence level 1 or 2 (Table 2). These 
40 related to a total of 29 parent pesticides. In addition to these 40, 54 
other pesticide biomarkers (either pesticide metabolites or the parent 
pesticides as such) were detected with a lower confidence level (Schy-
manski levels 3–5) which are detailed in Supplementary Material C. 
These 54 are not further described in this paper and not used in the 
analyses. 
For each annotated exposure marker (confidence levels 1 and 2), the 
overall detection rate per study site was calculated (Table 2). Overall, 
biomarkers were generally detected below 25% of the samples. The 
results evidenced a significant variability between study sites, with 
Latvia having generally the lowest number of detects and Spain the 
highest one. Overall, the metabolites related to the parent pesticides 
acetamiprid (N-demethylated metabolite) and chlorpropham (4-HSA 
metabolite) were most frequently detected in samples of all study sites. 
Other biomarkers that had detection rates of at least 10% (including 
both locations and both season) relate to the parent pesticides boscalid 
(not in Hungary), chlorpyrifos (only in Spain and Czech Republic), 
clothianidin (not in Latvia), cyprodinil (not in Latvia and Hungary), 
flonicamid (not in Latvia and Czech Republic), fluazifop (not in Latvia), 
fludioxonil (not in Hungary), imazalil (only in Spain and Latvia), imi-
dacloprid (only in Spain), pirimiphos-methyl (not in Hungary), prop-
amocarb (not in Latvia), pyrimethanil (not in Hungary), tebuconazole 
(not in Latvia), and thiamethoxam (only in Spain and Hungary). Bio-
markers that were detected at low frequencies (<10%) across all study 
sites include 2,4-dichlorophenoxy acetic acid (2,4-D), ametoctradin, 
Table 1 
Descriptive characteristics of the SPECIMEn study participants by study site and location.  
Study site ESa LVa HUa CZa NLa 
Area Agricultural Non- 
Agricultural 
Agricultural Non- 
Agricultural 
Agricultural Non- 
Agricultural 
Agricultural Non- 
Agricultural 
Agricultural Non- 
Agricultural 
Adult-child 
pairsb, n 
52 53 50 51 51 52 51 60 55 50 
Urine samples, n 206 212 200 202 201 208 204 238 219 198 
Season 1 104 106 100 102 102 104 102 120 110 100 
Season 2 102 106 100 100 99 104 102 118 109 98 
Gender, female, % 
Adults 50 87 90 82 94 85 71 60 71 66 
Children 54 49 58 47 49 52 43 43 53 46 
Mean age, years 
Adults 44 44 40 39 38 40 41 42 42 42 
Children 8.2 8.7 8.9 8.4 9.7 9.2 8.8 9.1 8.6 8.6 
Mean BMI 
Adults 25 24 26 26 26 26 24 24 24 23 
Children 17 17 17 17 18 19 16 16 16 16.0 
Educational level adult, % 
No or only 
primary 
education 
0 0 2.0 0 40 5.8 0 1.7 1.8 0 
Secondary 
education 
7.8 17 30 12 28 20 2.0 3.3 5.5 2.0 
Tertiary 
education 
(post- 
secondary) 
25 17 8.0 7.8 23 26 26 10.0 18 18 
University 
studies (BSc, 
MSc, PhD) 
67 66 60 77 8.0 48 71 83 71 76 
Don’t Know/ 
NA 
0 0 0 3.9 0 0 2.0 1.7 3.6 4.1 
Usage of pesticide (-products) up to 3 days prior to samplingc, n households 
Season 1 9 5 4 7 2 6 4 2 1 6 
Season 2 27 8 12 8 22 7 14 12 10 4 
Seasonal homegrown vegetables, fruit and/or herbs consumption, % of total consumption 
Winter 6.7 1.1 30 22 23 4.5 13 10 2.0 0.1 
Spring 10 3.4 28 19 21 9.4 22 12 4.8 2.2 
Summer 12 8.0 63 44 41 25 64 51 15 7.8 
Autumn 8.9 6.0 63 45 39 17 45 40 8.3 4.5  
a ES: Spain, LV: Latvia, HU: Hungary, CZ: Czech Republic, NL: the Netherlands. 
b Number of individuals included in season 1. 
c Summary indicator which includes: pesticides for human use, use indoors, use in garden, and professional use. For specification of the categories see Supplementary 
Material B. 
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International Journal of Hygiene and Environmental Health 248 (2023) 114105
5
chlorantraniliprole, clopyralid, fluopyram, flupyradifurone, fluvalinate, 
penconazole, propyzamide, thiabendazole, thiacloprid, trifloxystrobin, 
as well as the metabolite permethric acid (DCCA) (originated from 
parent pesticides cypermethrin, cyfluthrin, permethrin or transfluthrin). 
3.3. Co-occurrence of pesticides 
In order to assess how many pesticides were co-occurring within the 
same individual at a single time point, the number of detected parent 
pesticides per urine sample are presented in Fig. 1. In line with the 
detection ratios, the lowest number of detected pesticides were in 
samples originating from Latvia, with mostly less than 3 co-occurring 
pesticides per urine sample. Samples originating from Spain showed 
the highest numbers of co-occurring pesticides, with a median value of 
7. In the majority of the samples the number of parent pesticides per 
samples typically ranged from 2 to 5. The maximum number of different 
pesticides detected in the same urine sample was 13, which was the case 
for two samples. The samples with no (n ˆ 100) or only one (n ˆ 225) 
detected pesticide add up to 16% of the total amount of samples, indi-
cating that in a majority of the samples from the SPECIMEn study at least 
two different parent pesticides were detected. 
The next step was to evaluate which pesticides were co-occurring in 
each urine sample, for which the most frequently (in 5 or more urine 
samples) co-occurrent pesticides or mixtures are presented in an UpSet 
plot in Fig. 2. These most frequent co-occurrences consisted of 44 
different combinations based on 14 different pesticides. The majority of 
co-occurrences consisted of 2 or 3 pesticides, with minimal overlap 
across all study sites. The most common co-occurrence was acetamiprid 
with chlorpropham, detected in 62 samples although this combination 
was not detected in any sample originating from Spain. The second most 
frequently co-occurring pesticides were acetamiprid with tebuconazole, 
however this combination was not seen in the Netherlands. The only co- 
occurrence combination detected in all five study sites was acetamiprid 
with pirimiphos-methyl. The less frequent the co-occurrent pesticides 
the more variation in combinations were seen, which was even more 
pronounced when detected in just 2, 3 or 4 urine samples (see Supple-
mentary Material D for the extended UpSet plot). 
The stability of the co-occurrences at each study site can be evaluated 
with correlation networks, which are presented in Fig. 3A–E. Similar to 
the findings of Fig. 2, mostly small groups (two to four biomarkers) of 
Table 2 
Annotated pesticide biomarkers with Schymanski confidence levels 1 and 2 (p ˆ 40) and their overall detection frequency (%) per study site (Schymanski et al., 2014).  
ID Pesticide typea Parent pesticide Pesticide (metabolite) annotationb Confidence levelc Overall Detection Frequency (%) 
ES d LVd HUd CZd NLd 
P1 H 2,4-Dichlorophenoxyacetic acid Parent 1 4.1 0 2.2 2.7 0 
P2_a I Acetamiprid -CH2 1 99 33 94 98 93 
P3_a F Ametoctradin -C2H6 ‡2O 1 5.0 2.7 1.2 4.8 2.9 
P5_a F Boscalid ‡O ‡ SO3 e 2 36 18 3.9 23 33 
P5_b ‡O ‡ SO3 f 2 7.2 0 0 0.5 0.2 
P6 I Chlorantraniliprole ‡O 2 3.8 0.3 0.2 0 0.2 
P8_a H, GR Chlorpropham ‡O ‡ SO3 (4-HSA) 1 56 32 31 34 75 
P9_a I Chlorpyrifos (methyl) TCPy 1 1.7 0 0.2 0.2 0.2 
P9_b -CH2 1 36 0 6.9 21.7 6.5 
P10 H Clopyralid Parent 1 1.0 0 0 1.4 0.7 
P11_a I Clothianidin (can come from thiamethoxam) Parent 1 34 1.7 22 25 20 
P11_b -NO2 ‡H 1 0.5 0 0.2 0 0.2 
P11_c -CH2 2 21 0.8 9.8 6.6 3.1 
P12_a I Cypermethrin, cyfluthrin, permethrin, transfluthrin DCCA 1 0.5 0 0 0 0 
P13_a F Cyprodinil ‡O ‡ SO3 2 14 7.7 2.7 10 26 
P18_a I Flonicamid Parent 1 1.7 0.8 2.0 2.7 5.7 
P18_b -C2HN 2 15 0.3 27 0.2 57 
P19_a H Fluazifop Parent e 1 20 2.5 11 18 21 
P19_b Parent f 1 8.1 1.5 4.9 5.2 8.2 
P20 F Fludioxonil ‡O ‡ C6H8O6 2 16 15 2.0 14 27 
P21_a F Fluopyram ‡O ‡ SO3 2 3.6 0.5 0.2 1.1 1.0 
P21_b ‡O ‡ C6H8O6 2 2.4 0.8 0.5 3.2 4.8 
P21_c -2H 2 11 6.7 0.5 3.4 3.1 
P22_a I Flupyradifurone Parent 1 2.6 0.3 0.5 0.7 2.2 
P25_a I, Ac Fluvalinate -C14H9NO 2 1.0 0 0.7 0.2 0 
P27_a F Imazalil ‡C6H8O6 2 19 11 8.3 4.5 4.6 
P28_a I Imidacloprid -NO2 ‡H 1 17 1.7 4.2 0.7 9.4 
P32_a F Penconazole ‡O ‡ C6H8O6 2 6.5 1.7 2.2 2.0 2.4 
P34_a I, Ac Pirimiphos-methyl -CH2 1 85 10 6.6 24 48 
P35_a F Propamocarb Parent 1 9.6 1 11 5.0 23 
P35_b ‡O 2 21 5.5 18 12 43 
P37 H Propyzamide ‡H2O3 2 8.6 0 0.5 0.9 1.0 
P38_a F Pyrimethanil ‡O ‡ SO3 2 27 14 4.9 22 32 
P38_b ‡O 2 0.7 0 2.7 0 0.5 
P40_a F Tebuconazole -2H ‡2O 2 71 5.5 25 52 36 
P41_a F Thiabendazole ‡O ‡ C6H8O6 2 0 0.8 0.2 0 0.5 
P42_a I Thiacloprid ‡O 2 8.4 0.8 2.9 7.9 4.6 
P43_a I Thiamethoxam Parent 1 0.7 0 2.4 0 0.5 
P43_b -NO2 ‡H 1 23 0 15 0 0.2 
P46_a F Trifloxystrobin -CH2 -CH2 2 0.7 0.5 0 3.6 3.8  
a H: Herbicide, F: Fungicide, I: Insecticide, GR: Plant Growth Regulator, Ac: Acaricide. 
b Metabolite annotation: “-CH2” means the molecular formula of the metabolite is that of the parent minus CH2 (corresponding to demethylation). Similarly, “‡O” 
means the metabolite is the parent compound plus one oxygen atom (hydroxylation). “‡SO3” and “‡C6H8O6” indicate sulfation and glucuronidation, respectively. 
c Schymanski confidence level, ranging from 1 to 5, (Schymanski et al., 2014). 
d ES: Spain, LV: Latvia, HU: Hungary, CZ: Czech Republic, NL: the Netherlands. 
e Positive precursor ion. 
f Negative precursor ion. 
I. Ottenbros et al.                                                                                                                                                                                                                               
International Journal of Hygiene and Environmental Health 248 (2023) 114105
6
co-occurrent pesticides were found. Consistent across all study sites was 
the positive relation between cyprodinil (P13) and fludioxonil (P20), 
both fungicides, although sometimes together with other pesticides and/ 
or part of a different community. Also, in both Spain and the Czech 
Republic, imazalil (P27) was related to pyrimethanil (P38), which are 
both fungicides. Finally, in Spain and Hungary chlorpyrifos-methyl (P9) 
was related to pirimiphos-methyl (P34), which are both insecticides. 
Interestingly, the relations of acetamiprid (P2) with chlorpropham (P8) 
or tebuconazole (P40) were not detected in the networks. 
3.4. Changes in occurrence of pesticides by location, season, age category 
To explore the differences in occurrence of the pesticide biomarkers 
by location, season, and age category, logistic mixed effects models were 
constructed. The main model includes the covariates for location, season 
and age category, the extended model was also corrected for pesticide 
usage (self-reported), BMI, level of education and homegrown food 
consumption. Results of the models of the biomarkers detected in at least 
four study sites are shown in Table 3, the full table with estimates for all 
exposure markers associated with confidence levels 1 and 2 can be found 
in Supplementary Material E. 
In Spain, no effect of location was detected in the models, except for 
clothianidin which was less frequently detected in agricultural areas 
compared to non-agricultural areas. Chlorpropham, chlorpyrifos, clo-
thianidin, fluazifop, fludioxonil, imazalil, imidacloprid, pyrimethanil, 
and tebuconazole were most frequently detected during the first sam-
pling season. These effects were not influenced by inclusion of the 
additional predictors in the extended model. Between the group of 
parents and children in Spain, the biomarkers related to boscalid, and 
cyprodinil were most frequently detected among parents, while chlor-
propham, chlorpyrifos, chlothianidin, pirimiphos-methyl, tebuconazole, 
and thiacloprid were more frequently detected among children. The 
extended models confirmed most of these effects (not for clothianidin 
and cyprodinil). 
In Latvia, propamocarb was the only biomarker more frequently 
detected at the agricultural area. Acetamiprid, fluopyram, imazalil, and 
propamocarb were more frequently detected in the first season (winter), 
while pyrimethanil and tebuconazole were more frequently detected 
during the second season (summer). Only the effects related to prop-
amocarb and pyrimethanil were confirmed with the extended models. 
Chlorpropham, pirimiphos-methyl, and propamocarb were more 
frequently detected among the Latvian children compared to adults, 
while imazalil was more frequently detected within Latvian parents (not 
in extended model). 
In Hungary, both biomarkers related to clothianidin were more 
frequently detected at the agricultural areas. On the other hand, chlo-
pyrifos, pirimiphos-methyl, propamocarb, tebuconazole, and thiaclo-
prid were most frequently detected at the non-agricultural areas. 
Chlorpyrifos, chlothianidin, pirimiphos-methyl, propamocarb, and 
tebuconazole were most frequently detected during the second season. 
While, in contrary, chlorpropham and imazalil were most frequently 
detected during the first season. Acetamiprid, chlorpropham, chlorpyr-
ifos, chlothianidin, fluazifop, pirimiphos-methyl, propamocarb, and 
tebuconazole, were most frequently detected among the Hungarian 
children. Of which chlorpropham, fluazifop, pirimiphos-methyl, prop-
amocarb and tebuconazole were confirmed in both models. 
In the Czech Republic, the metabolite of ametoctradin was more 
frequently detected at the agricultural areas, although this effect dis-
appeared in the extended model. The biomarkers related to cyprodinil 
and fludioxonil were more frequently detected at the non-agricultural 
locations (only cyprodinil confirmed with the extended model). The 
chlorpropham metabolite (4-HSA) was more frequently detected during 
the second season. While the biomarkers related to ametoctradin, ima-
zalil, and pyrimethanil showed an opposite effect, and were more 
frequently detected during the first season. Of these three, only the effect 
of pyrimethanil was confirmed with the extended model. Seven different 
biomarkers were found to be more detected among children compared 
to adults: boscalid, chlorpropham, chlorpyrifos, flonicamid, pirimiphos- 
methyl, tebuconazole, and thiacloprid. The extended model confirmed 
the effects seen for chlorpropham and tebuconazole. 
Fig. 1. Number of parent pesticides (p ˆ 29) detected per urine sample (n ˆ
2088), with the five different study sites indicated in different colors 
(CZˆCzech Republic, ES ˆ Spain, HUˆHungary, LV ˆ Latvia, NLˆNether-
lands). Multiple metabolites and/or parent compounds related to the same 
parent pesticide were considered as one. 
Fig. 2. Frequency (number of urine samples, n ˆ
2088) of co-occurrent parent pesticides; the most 
frequent (in 5 or more urine samples) co-occurrences 
are shown. Different study sites are indicated by 
colors (CZˆCzech Republic, ES ˆ Spain, HUˆHun-
gary, LV ˆ Latvia, NLˆNetherlands), the detection 
frequency (%) of the listed parent pesticides is given 
on the right. Pesticides are co-occurring in the same 
sample when both have a black connected dot. Mul-
tiple metabolites and/or parent compounds related to 
the same parent pesticide were considered as one.   
I. Ottenbros et al.                                                                                                                                                                                                                               
International Journal of Hygiene and Environmental Health 248 (2023) 114105
7
Finally, in the Netherlands, the metabolites of chlorpropham were 
most frequently detected at agricultural areas. While biomarkers related 
to cyprodinil and pyrimethanil had higest detection frequencies at the 
non-agricultural areas. Chlorpropham, fluazifop, and thiacloprid were 
more frequently detected during the second season, while acetamiprid, 
chlorpyrifos, clothianidin, imazalil, and pyrimethanil had highest 
detection rates during the first season. The biomarkers related to ame-
toctradin, cyprodinil, flonicamid, fludioxonil, pirimiphos-methyl, and 
tebuconazole were more frequently detected among children. Of these, 
the effects seen for pirimiphos and tebuconazole were confirmed in the 
extended models. While on the other hand, propamocarb was more 
frequently detected among adults (only in extended model). 
Overall, almost no biomarkers were more frequently detected in both 
agriculture areas and (summer) season 2. Only exceptions were chlor-
propham (4-HSA metabolite) in the Netherlands, and clothianidin 
(parent compound and the N-demethylated metabolite) in Hungary. 
4. Discussion 
This study reports on the co-occurrence patterns of 40 different 
pesticide biomarkers at study sites from five European countries, and 
identifies whether proximity to agrigultural fields, season, and age 
category impacted the probability of detection of these biomarkers. The 
developed application of a harmonized SS methodology allowed 
screening for 1000s of suspects (pesticides and their known/predicted 
phase I/II metabolites), and enabled detection of many pesticides/me-
tabolites at different levels of confidence in urine. As such, this study 
should be seen as the first step towards a more complete assessment of 
the pesticide mixture exposure in the general European population. 
4.1. Detected pesticides and the impact of location, season and age 
category 
The most frequently detected biomarkers across all study sites were 
related to the parent pesticides acetamiprid and chlorpropham. Acet-
amiprid is a neonicotinoid (insecticide), is approved in the EU and 
commonly used on fruit trees such as apples, pears and citrus, but also on 
e.g. potatoes and rapeseed (Allema et al., 2017; EU Database Pest, 
2022). All study sites included agricultural areas where these crops are 
grown. However since we did not find a difference between areas for 
acetamiprid, this high detection frequency is likely due to other factors 
such as diet. For Latvia and the Netherlands, acetamiprid was less 
frequently detected during the second season (summer), arguing that 
additional exploration is needed on for example the change of diet be-
tween seasons. Chlorpropham is a plant growth regulator and herbicide, 
commonly used on e.g. onions and potatoes to prevent sprouting. In the 
Netherlands only, chlorpropham had a higher probability of detection in 
the summer season, which is consistent with an earlier study on flower 
bulb fields in the Netherlands (Gooijer et al., 2019; Oerlemans et al., 
2021). Although chlorpropham has no longer been approved as pesti-
cide since 2019 in the EU, still high probabilities of detection were seen 
in both seasons (EU Database Pest, 2022). This is not unexpected due to 
periods of grace until October 2020, which overlaps with both sampling 
periods of the current study. Interestingly, in Spain and Hungary 
chlorpropham was more frequently detected during the first season, 
while in Czech Republic and the Netherlands highest frequencies were 
seen in the second season. Chlorpropham also had higher probabilities 
of detection in children compared to adults, which could be related to 
food consumption: children have a larger food intake per kg of body-
weight; also, biological elimination mechanisms may differ between 
children and adults (Arena et al., 2017). 
Also, high detection rates in SPECIMEn were found for the bio-
markers related to pirimiphos-methyl and tebuconazole, which are in 
good agreement with other targeted studies (Noren et al., 2020; Yusa 
et al., 2022). For these and other highly detected pesticides, no consis-
tent effect across all countries of season or location was found, in 
contrast with expectations based on previous findings (Dereumeaux 
et al., 2020; Teysseire et al., 2020). Differences in study sites might occur 
due to different crop types. Detected differences are most likely influ-
enced by a set of other covariates not included in the current regression 
models, such as diet. Dietary habits of participants may differ between 
the countries, locations within countries, seasons and age groups. Also, 
there might be differences in percentage of consumption of imported 
foods, and percentage of homegrown food consumption. These aspects 
make the variety of exposure due to diet complex and subject to many 
changes; therefore future work needs to focus on the actual consumed 
Fig. 3. Weighted correlation networks per study site based on the parent pesticides. Relationships between markers are indicated by a line (green ˆ positive, red ˆ
negative). The colours indicate the different communities or groups of more closely related markers. ES) Spain (p ˆ 28), LV) Latvia (p ˆ 21), HU) Hungary (p ˆ 26), 
CZ) Czech Republic (p ˆ 25), NL) the Netherlands (p ˆ 26). See Table 2 for a description of the used ID numbers for each pesticide. Multiple metabolites and/or 
parent compounds related to the same parent pesticide were considered as one. 
I. Ottenbros et al.                                                                                                                                                                                                                               
International Journal of Hygiene and Environmental Health 248 (2023) 114105
8
Table 3 
Results of logistic mixed effects models, main and extended. Results are presented as Odds Ratios (OR) with 95% confidence intervals (CI). Significance levels based on 
p-value: ‘***’ <0.001, ‘**’ <0.01, ‘*’ <0.05. Random effects are household and participant ID. Main model includes the predictors: location, season, and age category. 
Extended model includes additional predictors for pesticide usage, BMI, level of education and homegrown food consumption. Results are shown of features detected in 
at least 4 study sites.  
ID Parent pesticide Category SP LV HU CZ NL 
Main 
OR 
(95% 
CI)a 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
P2_a Acetamiprid Agricultural vs 
Non- 
Agriculturalb 
NAc NAd 1.0 
(0.6; 
1.6) 
1.1 (0.7; 
1.9) 
1.2 
(0.1; 
2.7) 
1.4 (0.5; 
3.5) 
0.5 
(0.1; 
2.2) 
0.6 (0.1; 
3.0) 
2.0 
(0.3; 
14) 
2.4 (0.9; 
6.2) 
Season 2 vs 1b 0.5 
(0.1; 
2.7) 
0.5 (0.1; 
2.7) 
0.6 
(0.4; 
1.0) 
0.6 (0.4; 
1.0) * 
1.2 
(0.5; 
2.7) 
1.3 (0.5; 
3.1) 
0.6 
(0.1; 
2.5) 
0.8 (0.2; 
3.9) 
0.2 
(0.0; 
0.8) * 
0.5 (0.2; 
1.2) 
Parent vs 
Childb 
0.2 
(0.0; 
1.7) 
0.2 (0.0; 
2.8) 
0.8 
(0.5; 
1.3) 
0.7 (0.4; 
1.5) 
0.4 
(0.2; 
1.0) * 
0.5 (0.2; 
1.5) 
1.0 
(0.2; 
4.1) 
12 (1.0; 
149) 
1.3 
(0.2; 
9.0) 
1.7 (0.3; 
8.0) 
P3_a Ametoctradin Agri. vs Non- 
Agri. 
0.3 
(0.0; 
2.8) 
0.4 (0.1; 
1.0) 
1.8 
(0.5; 
6.3) 
1.3 (0.3; 
5.3) 
1.6 
(0.3; 
9.5) 
NAd 3.2 
(1.1; 
9.5) * 
3.0 (1.0; 
9.4) 
0.8 
(0.03; 
20) 
NAd 
Season 2 vs 1 0.4 
(0.1; 
1.5) 
0.6 (0.2; 
1.4) 
0.6 
(0.2; 
2.0) 
0.5 (0.2; 
1.9) 
0.7 
(0.1; 
4.1) 
NAd 0.4 
(0.1; 
1.0) * 
0.4 (0.1; 
1.1) 
0.3 
(0.1; 
1.6) 
NAd 
Parent vs Child 2.1 
(0.3; 
17) 
3.0 (0.9; 
10) 
0.8 
(0.2; 
2.8) 
2.3 (0.3; 
15) 
4.0 
(0.4; 
37) 
NAd 0.6 
(0.2; 
1.5) 
0.2 (0.0; 
1.3) 
0.1 
(0.02; 
0.9) * 
NAd 
P5_a Boscalid Agri. vs Non- 
Agri. 
1.0 
(0.6; 
1.9) 
1.0 (0.5; 
1.8) 
1.2 
(0.5; 
2.6) 
1.4 (0.6; 
3.1) 
0.8 
(0.2; 
2.2) 
0.5 (0.1; 
1.5) 
1.4 
(0.8; 
2.5) 
1.4 (0.8; 
2.7) 
0.6 
(0.3; 
1.0) 
0.5 (0.3; 
1.0) 
Season 2 vs 1 0.7 
(0.4; 
1.0) 
0.6 (0.4; 
1.0) 
0.9 
(0.5; 
1.6) 
0.9 (0.5; 
1.6) 
0.6 
(0.2; 
1.7) 
0.6 (0.2; 
1.9) 
0.7 
(0.4; 
1.2) 
0.8 (0.5; 
1.3) 
1.4 
(0.9; 
2.2) 
1.4 (0.9; 
2.2) 
Parent vs Child 2.9 
(1.8; 
4.6) 
*** 
2.5 (1.3; 
4.9) ** 
1.3 
(0.7; 
2.4) 
1.0 (0.4; 
2.6) 
2.1 
(1.0; 
9.9) 
2.4 (0.6; 
9.3) 
2.1 
(1.3; 
3.5) ** 
2.5 (1.0; 
6.1) 
1.4 
(0.9; 
2.1) 
1.1 (0.4; 
2.6) 
P8_a Chlorpropham Agri. vs Non- 
Agri. 
0.7 
(0.4; 
1.3) 
0.7 (0.4; 
1.3) 
1.3 
(0.7; 
2.7) 
1.2 (0.6; 
2.5) 
1.3 
(0.7; 
2.5) 
1.5 (0.7; 
3.2) 
1.0 
(0.6; 
2.0) 
1.0 (0.5; 
2.0) 
2.1 
(1.1; 
3.9) * 
2.1 (1.1; 
4.1) * 
Season 2 vs 1 0.4 
(0.3; 
0.7) 
*** 
0.4 (0.3; 
0.7) *** 
1.6 
(1.0; 
2.6) 
1.5 (0.9; 
2.4) 
0.5 
(0.3; 
0.8) ** 
0.5 (0.3; 
0.9) * 
2.1 
(1.3; 
3.3) ** 
1.9 (1.2; 
3.1) *e 
2.8 
(1.7; 
4.7) 
*** 
2.7 (1.6; 
4.6) *** 
Parent vs Child 0.4 
(0.2; 
0.6) 
*** 
0.3 (0.2; 
0.6) *** 
0.3 
(0.2; 
0.6) 
*** 
0.4 (0.2; 
1.0) * 
0.5 
(0.3; 
0.7) ** 
0.4 (0.2; 
0.7) ** 
0.4 
(0.2; 
0.6) 
*** 
0.3 (0.1; 
0.8) * 
0.6 
(0.4; 
1.1) 
0.5 (0.2; 
1.2) 
P9_a Chlorpyrifos 
(/methyl) 
Agri. vs Non- 
Agri. 
0.8 
(0.5; 
1.3) 
0.8 (0.5; 
1.3) 
NDf NDf 0.2 
(0.1; 
0.7) * 
0.3 (0.1; 
1.0) 
1.3 
(0.7; 
2.4) 
1.3 (0.7; 
2.4) 
1.2 
(0.4; 
3.2) 
1.2 (0.4; 
3.3) 
Season 2 vs 1 0.2 
(0.1; 
0.4) 
*** 
0.2 (0.1; 
0.4) *** 
NDf NDf 2.5 
(1.0; 
6.1) * 
2.7 (1.1; 
6.5) * 
0.6 
(0.4; 
1.0) 
0.6 (0.4; 
1.0) 
0.5 
(0.2; 
1.1) 
0.4 (0.1; 
1.0) * 
Parent vs Child 0.5 
(0.3; 
0.7) 
*** 
0.4 (0.2; 
0.7) ** 
NDf NDf 0.5 
(0.2; 
1.1) 
0.2 (0.1; 
0.8) * 
0.5 
(0.3; 
0.7) ** 
0.7 (0.3; 
1.7) 
0.8 
(0.3; 
1.8) 
0.9 (0.2; 
5.2) 
P11_a Clothianidin (can 
come from 
thiamethoxam) 
Agri. vs Non- 
Agri. 
0.5 
(0.3; 
0.8) ** 
0.4 (0.3; 
0.7) *** 
1.4 
(0.3; 
6.3) 
0.9 (0.2; 
4.6) 
2.8 
(1.6; 
4.7) 
*** 
2.8 (1.5; 
5.1) ** 
1.3 
(0.8; 
2.2) 
1.4 (0.8; 
2.5) 
1.3 
(0.7; 
2.3) 
1.4 (0.8; 
2.7) 
0.6 
(0.4; 
0.9) ** 
0.5 (0.3; 
0.8) ** 
6.3 
(0.7; 
53) 
5.7 (0.7; 
50) 
3.1 
(1.8; 
5.2) 
*** 
3.5 (2.0; 
6.1) *** 
0.6 
(0.4; 
1.0) 
0.7 (0.4; 
1.1) 
0.6 
(0.4; 
1.0) 
0.6 (0.4; 
1.0) 
Season 2 vs 1 0.6 
(0.5; 
0.9) * 
0.6 (0.3; 
1.0) 
0.2 
(0.0; 
1.4) 
0.3 (0.0; 
5.7) 
0.6 
(0.4; 
1.0) 
0.4 (0.2; 
0.7) ** 
1.0 
(0.6; 
1.5) 
1.1 (0.5; 
2.7) 
0.9 
(0.5; 
1.5) 
1.7 (0.6; 
4.6) 
P11_c 1.4 
(0.8; 
2.8) 
1.4 (0.7; 
2.8) 
NDf NDf 4.4 
(1.8; 
11) *** 
5.5 (2.1; 
14) *** 
1.3 
(0.6; 
2.7) 
1.3 (0.6; 
2.8) 
1.5 
(0.0; 
38) 
1.4 (0.7; 
2.6) 
Parent vs Child 0.9 
(0.5; 
1.5) 
0.8 (0.5; 
1.4) 
NDf NDf 1.9 
(0.9; 
3.9) 
2.5 (1.2; 
5.5) * 
0.5 
(0.2; 
1.1) 
0.5 (0.2; 
1.1) 
0.02 
(0.0; 
0.3) ** 
0.6 (0.4; 
1.0),e and g 
(continued on next page) 
I. Ottenbros et al.                                                                                                                                                                                                                               
International Journal of Hygiene and Environmental Health 248 (2023) 114105
9
Table 3 (continued ) 
ID Parent pesticide Category SP LV HU CZ NL 
Main 
OR 
(95% 
CI)a 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
0.7 
(0.4; 
1.3) 
0.8 (0.4; 
1.8) 
NDf NDf 0.8 
(0.4; 
1.5) 
0.3 (0.1; 
0.8) * 
0.9 
(0.4; 
2.0) 
1.2 (0.3; 
4.6) 
1.6 
(0.0; 
42) 
1.8 (0.7; 
4.9) 
P13_a Cyprodinil Agri. vs Non- 
Agri. 
1.4 
(0.7; 
2.7) 
1.4 (0.7; 
2.7) 
1.1 
(0.4; 
2.8) 
1.1 (0.4; 
2.9) 
1.9 
(0.1; 
44) 
1.8 (0.1; 
61) 
0.3 
(0.1; 
0.6) ** 
0.3 (0.1; 
0.6) ** 
0.6 
(0.3; 
0.9) * 
0.5 (0.3; 
0.9) * 
Season 2 vs 1 1.2 
(0.7; 
2.1) 
1.2 (0.6; 
2.2) 
0.7 
(0.3; 
1.5) 
0.7 (0.3; 
1.5) 
0.3 
(0.1; 
2.0) 
0.4 (0.1; 
2.9) 
0.9 
(0.5; 
1.6) 
0.9 (0.5; 
1.7) 
0.8 
(0.5; 
1.2) 
0.8 (0.5; 
1.2) 
Parent vs Child 2.0 
(1.6; 
5.6) 
*** 
2.3 (1.0; 
5.4) 
0.9 
(0.4; 
2.1) 
1.5 (0.5; 
5.1) 
6.2 
(0.9; 
40) 
2.9 (0.3; 
26) 
0.8 
(0.4; 
1.5) 
1.0 (0.3; 
3.0) 
0.5 
(0.3; 
0.8) ** 
1.2 (0.5; 
2.8) 
P18_a Flonicamid Agri. vs Non- 
Agri. 
2.6 
(0.5; 
14) 
2.6 (0.4; 
15) 
NDf NDf 1.0 
(0.3; 
4.3) 
2.3 (0.5; 
11) 
2.7 
(0.1; 
84) 
NAd 0.4 
(0.1; 
1.5) 
0.4 (0.1; 
1.3) 
Season 2 vs 1 1.4 
(0.3; 
6.3) 
1.4 (0.3; 
7.1) e 
NDf NDf 3.1 
(0.6; 
16) 
3.8 (0.7; 
20) 
1.0 
(0.2; 
5.3) 
NAd 1.6 
(0.6; 
4.0) 
1.5 (0.6; 
3.8) 
Parent vs Child 6.2 
(0.7; 
52) 
7.8 (0.6; 
96) 
NDf NDf 0.3 
(0.1; 
1.6) 
0.2 (0.0; 
1.5) 
0.1 
(0.0; 
0.4) ** 
NAd 0.3 
(0.1; 
0.8) * 
0.3 (0.0; 
2.3) 
P19_a Fluazifop Agri. vs Non- 
Agri. 
1.0 
(0.6; 
1.9) 
1.1 (0.6; 
2.1) 
4.2 
(0.9; 
20) 
3.1 (0.6; 
16) 
1.1 
(0.5; 
2.2) 
0.9 (0.4; 
2.1) 
1.1 
(0.6, 
2.1) 
1.3 (0.7; 
2.4) 
1.4 
(0.7; 
3.0) 
1.5 (0.7; 
3.1) 
0.5 
(0.3; 
0.8) ** 
0.5 (0.3; 
0.9) * 
0.7 
(0.2; 
2.4) 
0.6 (0.1; 
2.3) 
1.1 
(0.6; 
2.0) 
0.9 (0.4; 
1.7) 
0.9 
(0.5; 
1.4) 
0.8 (0.5; 
1.4) 
1.0 
(0.6; 
1.6) 
1.0 (0.6; 
1.7) 
Season 2 vs 1 1.0 
(0.6; 
1.7) 
0.7 (0.3; 
1.5) 
1.0 
(0.3; 
3.5) 
1.2 (0.2; 
7.8) 
0.6 
(0.3; 
1.2) 
0.4 (0.2; 
1.0) * 
0.7 
(0.4; 
1.2) 
0.5 (0.2; 
1.3)e 
0.7 
(0.4; 
1.2) 
0.5 (0.2; 
1.5) 
P19_b 1.6 
(0.7; 
3.6) 
1.6 (0.6; 
4.1) 
5.2 
(0.6; 
45) 
4.6 (0.5; 
44) 
2.1 
(0.3; 
17) 
2.4 (0.8; 
7.2) 
0.9 
(0.3; 
2.3) 
0.9 (0.3; 
2.5)e 
1.4 
(0.2; 
10) 
3.1 (0.3; 
29) 
Parent vs Child 0.8 
(0.4; 
1.6) 
0.7 (0.3; 
1.6) 
0.5 
(0.1; 
2.7) 
0.4 (0.1; 
2.6) 
1.4 
(0.1; 
4.5) 
1.0 (0.3; 
2.6) 
2.5 
(1.0; 
6.3) 
2.4 (0.9; 
6.3) 
7.2 
(1.6; 
32) ** 
4.5 (1.1; 
17) * 
1.0 
(0.5; 
2.1) 
0.4 (0.1; 
1.2) 
0.5 
(0.1; 
2.7) 
1.9 (0.1; 
29) 
0.2 
(0.0; 
0.9) * 
0.3 (0.1; 
0.9) * 
1.3 
(0.6; 
3.2) 
1.0 (0.2; 
4.6) 
0.6 
(0.1; 
4.1) 
0.0 (0.0; 
2.9) 
P20 Fludioxonil Agri. vs Non- 
Agri. 
1.1 
(0.6; 
2.1) 
1.0 (0.5; 
2.0) 
0.8 
(0.4; 
1.7) 
0.9 (0.4; 
1.9) 
0.6 
(0.1; 
2.6) 
0.9 (0.2; 
4.8) 
0.5 
(0.3; 
0.9) * 
0.6 (0.3; 
1.1) 
0.9 
(0.5; 
1.6) 
0.8 (0.5; 
1.4) 
Season 2 vs 1 0.5 
(0.3; 
0.9) * 
0.5 (0.3; 
0.9) * 
0.8 
(0.4; 
1.4) 
0.8 (0.4; 
1.4) 
1.7 
(0.4; 
7.2) 
1.7 (0.4; 
7.5) 
0.7 
(0.4; 
1.2) 
0.7 (0.4; 
1.2) 
0.6 
(0.3; 
0.9) * 
0.6 (0.4; 
0.9) * 
Parent vs Child 1.5 
(0.8; 
2.8) 
0.9 (0.4; 
2.2) 
1.1 
(0.6; 
2.1) 
0.8 (0.3; 
2.1) 
1.0 
(0.2; 
4.0) 
0.9 (0.2; 
5.5) 
0.7 
(0.4; 
1.2) 
1.1 (0.4; 
3.1) 
0.5 
(0.3; 
0.9) * 
0.8 (0.3; 
1.8) 
P21_c Fluopyram Agri. vs Non- 
Agri. 
1.4 
(0.6; 
3.4) 
1.5 (0.6; 
4.0) 
0.6 
(0.1; 
4.3) 
0.7 (0.2; 
2.2) 
NDf NDf 1.3 
(0.2; 
10) 
1.9 (0.2; 
20) 
1.0 
(0.1; 
22) 
NAd 
Season 2 vs 1 1.0 
(0.5; 
1.9) 
1.1 (0.5; 
2.2) 
0.2 
(0.0; 
0.8) * 
0.4 (0.2; 
1.1) 
NDf NDf 1.8 
(0.5; 
6.4) 
1.8 (0.5; 
7.2) 
4.3 
(0.5; 
36) 
NAd 
Parent vs Child 1.5 
(0.8; 
3.0) 
0.9 (0.3; 
2.6) 
0.9 
(0.1; 
6.1) 
1.1 (0.3; 
4.3) 
NDf NDf 0.8 
(0.2; 
2.8) 
1.0 (0.1; 
12) 
0.5 
(0.2; 
12) 
NAd 
P27_a Imazalil Agri. vs Non- 
Agri. 
1.0 
(0.5; 
2.0) 
1.1 (0.6; 
2.2) 
1.7 
(0.6; 
4.9) 
0.7 (0.2; 
2.2) 
0.6 
(0.2; 
2.0) 
0.7 (0.2; 
2.3) 
0.9 
(0.1; 
11) 
1.0 (0.4; 
2.6) 
1.6 
(0.6; 
4.2) 
1.6 (0.6; 
4.1) 
Season 2 vs 1 0.2 
(0.1; 
0.3) 
*** 
0.2 (0.1; 
0.4) *** 
0.4 
(0.2; 
0.8) * 
0.4 (0.2; 
1.1) 
0.2 
(0.1; 
0.5) 
*** 
0.2 (0.1; 
0.5) ** 
0.1 
(0.0; 
0.6) * 
0.4 (0.2; 
1.2) e 
0.3 
(0.1; 
1.0) * 
0.4 (0.1; 
1.0) e 
Parent vs Child 1.1 
(0.7; 
2.0) 
0.8 (0.4; 
1.9) 
2.4 
(1.1; 
5.2) * 
1.1 (0.3; 
4.3) 
0.5 
(0.2; 
1.2) 
0.3 (0.1; 
1.1) 
2.9 
(0.2; 
44) 
3.1 (0.6; 
15) 
1.4 
(0.6; 
3.6) 
2.2 (0.4; 
11) 
P28_a Imidacloprid Agri. vs Non- 
Agri. 
1.5 
(0.7; 
3.1) 
1.2 (0.6; 
2.6) 
1.4 
(0.3; 
6.2) 
1.1 (0.2; 
5.7) 
0.9 
(0.2; 
3.3) 
1.0 (0.3; 
3.7) 
NDf NDf 1.2 
(0.6; 
2.5) 
1.4 (0.6; 
2.9) 
(continued on next page) 
I. Ottenbros et al.                                                                                                                                                                                                                               
International Journal of Hygiene and Environmental Health 248 (2023) 114105
10
Table 3 (continued ) 
ID Parent pesticide Category SP LV HU CZ NL 
Main 
OR 
(95% 
CI)a 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Main 
OR 
(95% 
CI) 
Extended 
OR (95% 
CI) 
Season 2 vs 1 0.6 
(0.3; 
1.0) * 
0.5 (0.3; 
1.0) * 
0.2 
(0.0; 
1.4) 
0.2 (0.0; 
1.4) 
0.9 
(0.3; 
2.5) 
1.1 (0.4; 
3.0) 
NDf NDf 1.1 
(0.5; 
2.1) 
1.1 (0.5; 
2.1) 
Parent vs Child 1.4 
(0.8; 
2.5) 
1.8 (0.8; 
4.2) 
1.4 
(0.3; 
6.1) 
0.7 (0.1; 
6.2) 
0.9 
(0.3; 
2.4) 
0.5 (0.1; 
2.0) 
NDf NDf 0.8 
(0.4; 
1.7) 
0.9 (0.2; 
3.3) 
P32_a Penconazole Agri. vs Non- 
Agri. 
0.7 
(0.3; 
1.7) 
0.8 (0.3; 
1.9) 
1.4 
(0.3; 
6.1) 
1.6 (0.3; 
7.7) 
0.5 
(0.1; 
2.1) 
0.7 (0.0; 
38) 
2.4 
(0.6; 
9.7) 
2.2 (0.5; 
9.3) 
0.9 
(0.3; 
3.2) 
0.8 (0.2; 
3.0) 
Season 2 vs 1 1.1 
(0.5; 
2.5) 
1.2 (0.5; 
2.6) 
0.8 
(0.2; 
3.4) 
0.8 (0.2; 
3.4) g 
3.6 
(0.7; 
18) 
9.5 (1.0; 
93) 
0.5 
(0.1; 
2.0) 
0.6 (0.1; 
2.5)e,g 
1.0 
(0.3; 
3.6) 
1.0 (0.3; 
3.6) g 
Parent vs Child 2.2 
(0.9; 
5.0) 
2.9 (0.9; 
9.0) 
0.8 
(0.2; 
3.4) 
0.6 (0.1; 
6.0) 
1.3 
(0.3; 
4.8) 
0.7 (0.0; 
12) 
2.0 
(0.5; 
8.3) 
2.9 (0.3; 
30) 
1.5 
(0.4; 
5.5) 
0.5 (0.1; 
4.5) 
P34_a Pirimiphos- 
methyl 
Agri. vs Non- 
Agri. 
0.7 
(0.3; 
1.4) 
1.1 (0.5; 
2.5) 
1.0 
(0.5; 
1.9) 
1.5 (0.3; 
7.4) 
0.1 
(0.02; 
0.4) 
*** 
0.2 (0.1; 
0.8) * 
1.5 
(0.8; 
2.7) 
1.3 (0.7; 
2.5) 
1.4 
(0.8; 
2.7) 
1.4 (0.8; 
2.7) 
Season 2 vs 1 0.8 
(0.4; 
1.4) 
1.0 (0.5; 
2.0) 
1.5 
(0.8; 
2.9) 
0.8 (0.2; 
3.8) 
3.6 
(1.4; 
9.4) ** 
4.1 (1.5; 
11) ** 
0.6 
(0.4; 
1.0) 
0.6 (0.4; 
1.1) 
0.7 
(0.4; 
1.0) 
0.7 (0.4; 
1.0) 
Parent vs Child 0.4 
(0.2; 
0.9) * 
0.2 (0.1; 
0.7) * 
0.4 
(0.2; 
0.9) * 
0.6 (0.1; 
5.4) 
0.2 
(0.1; 
0.5) ** 
0.1 (0.0; 
0.3) *** 
0.5 
(0.3; 
0.8) ** 
0.6 (0.2; 
1.5) 
0.3 
(0.2; 
0.5) 
*** 
0.3 (0.1; 
0.8) * 
P35_a Propamocarb Agri. vs Non- 
Agri. 
1.3 
(0.5; 
3.1) 
1.0 (0.4; 
2.4) 
NDf NDf 0.5 
(0.2; 
1.0) 
0.5 (0.2; 
1.2) 
0.5 
(0.1; 
4.5) 
0.5 (0.2; 
1.9) 
1.6 
(0.8; 
3.0) 
1.9 (1.0; 
3.6) 
1.9 
(0.9; 
4.1) 
1.7 (0.8; 
3.5) 
NDf NDf 1.1 
(0.6; 
2.1) 
1.1 (0.6; 
2.2) 
1.5 
(0.4; 
6.0) 
1.4 (0.5; 
3.5) e 
1.4 
(0.9; 
2.4) 
1.5 (0.9; 
2.5) 
Season 2 vs 1 1.1 
(0.5; 
2.5) 
1.2 (0.4; 
3.4) 
NDf NDf 0.4 
(0.2; 
1.0) * 
0.3 (0.1; 
0.9) * 
0.5 
(0.1; 
3.9) 
1.5 (0.2; 
10) 
1.0 
(0.6; 
1.6) 
3.1 (1.1; 
8.5) * 
P35_b 1.3 
(0.7; 
2.4) 
1.2 (0.7; 
2.2) 
4.2 
(1.1; 
16) * 
4.0 (1.0; 
17) 
0.5 
(0.3; 
0.9) * 
0.5 (0.2; 
1.0) * 
0.7 
(0.3; 
1.9) 
0.8 (0.3; 
2.1) 
1.2 
(0.7; 
1.9) 
1.3 (0.8; 
2.2) 
Parent vs Child 1.2 
(0.7; 
1.9) 
1.1 (0.7; 
1.9) 
0.3 
(0.1; 
0.9) * 
0.3 (0.1; 
0.8) * 
2.1 
(1.2; 
3.6) * 
2.3 (1.3; 
4.1) ** 
1.4 
(0.7; 
2.6) 
1.4 (0.7; 
2.8) 
1.0 
(0.6; 
1.5) 
1.0 (0.7; 
1.5) 
1.1 
(0.7; 
1.9) 
1.1 (0.5; 
2.3) 
0.4 
(0.1; 
1.1) 
0.2 (0.0; 
1.0) * 
0.5 
(0.3; 
0.9) * 
0.4 (0.2; 
0.8) * 
0.5 
(0.3; 
1.0) 
0.9 (0.2; 
3.3) 
1.2 
(0.8; 
1.8) 
3.2 (1.4; 
7.3) ** 
P38_a Pyrimethanil Agri. vs Non- 
Agri. 
0.8 
(0.5; 
1.3) 
0.8 (0.5; 
1.3) 
1.2 
(0.6; 
2.4) 
1.1 (0.5; 
2.3) 
0.4 
(0.1; 
2.0) 
0.3 (0.0; 
3.1) 
0.9 
(0.5; 
1.7) 
1.0 (0.5; 
1.8) 
0.6 
(0.4; 
1.0) * 
0.6 (0.4; 
0.9) * 
Season 2 vs 1 0.6 
(0.4; 
1.0) * 
0.7 (0.4; 
1.0) 
2.1 
(1.1; 
3.8) * 
2.1 (1.1; 
3.8) * 
1.3 
(0.5; 
3.8) 
1.6 (0.5; 
5.2) 
0.4 
(0.3; 
0.7) ** 
0.5 (0.3; 
0.8) ** 
0.6 
(0.4; 
1.0) * 
0.6 (0.4; 
1.0) * 
Parent vs Child 0.8 
(0.5; 
1.2) 
0.7 (0.3; 
1.3) 
1.0 
(0.6; 
1.8) 
1.3 (0.5; 
3.2) 
2.3 
(0.8; 
7.2) 
1.7 (0.8; 
7.4) 
1.4 
(0.9; 
2.3) 
2.1 (0.9; 
5.3) 
0.8 
(0.5; 
1.2) 
1.1 (0.5; 
2.4) 
P40_a Tebuconazole Agri. vs Non- 
Agri. 
0.9 
(0.5; 
1.7) 
0.7 (0.4; 
1.4) 
1.5 
(0.4; 
5.3) 
1.5 (0.6; 
4.0) 
0.5 
(0.3; 
1.0) 
0.5 (0.2; 
1.0) * 
1.0 
(0.6; 
1.5) 
1.1 (0.7; 
1.7) 
0.8 
(0.4; 
1.5) 
0.8 (0.4; 
1.6) 
Season 2 vs 1 0.5 
(0.3; 
0.9) * 
0.5 (0.3; 
0.8) ** 
3.3 
(1.1; 
9.3) * 
2.8 (1.1; 
7.4) 
2.1 
(1.2; 
3.5) ** 
2.2 (1.3; 
3.8) ** 
0.7 
(0.5; 
1.0) 
0.7 (0.4; 
1.0) 
0.6 
(0.4; 
1.0) 
0.6 (0.4; 
1.1) 
Parent vs Child 0.3 
(0.2; 
0.4) 
*** 
0.2 (0.1; 
0.4) *** 
0.8 
(0.3; 
2.1) 
2.9 (0.7; 
12) 
0.3 
(0.2; 
0.5) 
*** 
0.5 (0.2; 
0.9) * 
0.2 
(0.2; 
0.4) 
*** 
0.4 (0.2; 
0.7) ** 
0.1 
(0.1; 
0.2) 
*** 
0.2 (0.1; 
0.5) *** 
P42_a Thiacloprid Agri. vs Non- 
Agri. 
1.6 
(0.6; 
4.4) 
1.6 (0.6; 
4.5) 
NDf NDf 0.1 
(0.0; 
0.7) * 
0.1 (0.0; 
0.5) * 
1.6 
(0.6; 
4.7) 
2.1 (0.8; 
5.8) 
0.4 
(0.1; 
3.5) 
0.5 (0.1; 
4.0) 
Season 2 vs 1 0.9 
(0.4; 
2.0) 
1.1 (0.5; 
2.4) 
NDf NDf 3.2 
(0.8; 
12) 
3.5 (0.9; 
14)g 
0.9 
(0.4; 
2.0) 
1.2 (0.6; 
2.7) 
6.5 
(1.5; 
29) * 
6.3 (1.3; 
30) *e 
Parent vs Child 0.4 
(0.2; 
1.0) * 
0.3 (0.1; 
1.2) 
NDf NDf 0.3 
(0.1; 
1.2) 
0.6 (0.1; 
3.3) 
0.3 
(0.1; 
0.7) ** 
0.2 (0.1; 
1.1) 
0.6 
(0.2; 
2.1) 
2.4 (0.2; 
27)  
I. Ottenbros et al.                                                                                                                                                                                                                               
International Journal of Hygiene and Environmental Health 248 (2023) 114105
11
diet and their pesticide residue levels versus the suspect screening pat-
terns. For example, the consumption of organic foods has been linked to 
lower exposure concentrations of several pesticides such as organo-
phosphates and pyrethroids (Baudry et al., 2019; Hyland et al., 2019). 
As a final remark on the detected pesticides, the SS methodology is 
only recently being applied in large scale studies to assess exposure to 
pesticides, and only a few HBM studies have previously applied SS ap-
proaches to complement for example targeted monitoring programs 
(Gerona et al., 2018; Pellizzari et al., 2019; Plassmann et al., 2015; Wang 
et al., 2018). Within a cohort of approximately 300 pregnant women in 
France, Bonvallot et al. (2021) performed a large targeted pesticide 
exposure study which was extended with the application of suspect 
screening. This SS approach resulted in the most frequent detection of 
the parent pesticides azoxystrobin, fenpropimorph, phenmedipham, 
fluazifop(/butyl) and chlorpyrifos. From these, only the metabolites of 
fluazifop(/butyl) and chlorpyrifos overlapped and were also detected in 
the samples of the SPECIMEn study. This is due to among others dif-
ferences in the suspect database, for example fenpropimorph was not 
included in our current study because it didn’t contain Cl, Br or PO3 
(Huber et al., 2022). Another interesting point is the difference in 
detection frequency between the TCPy and -CH2 biomarkers of chlor-
pyrifos (methyl) in Spain and Czech Republic. TCPy can originate from 
both parent compounds chlorpyrifos and methyl-chlorpyrifos. -CH2 is 
not a human metabolite of chlorpyrifos, and its detection is likely due to 
exposure through diet. Also, a higher sensitivity for -CH2 compared to 
TCPy at an individual instrument level might have contributed to this 
difference. 
4.2. Co-occurrence 
To explore the exposure to pesticide mixtures in the general popu-
lation, it was assessed which parent pesticides co-occurred in the same 
urine sample. With the current work we were able to assess the proba-
bility of detection of 29 different parent pesticides simultaneously. In a 
large majority of the samples (84%) two or more different pesticides 
were detected. Our findings confirm the presence of mixtures and the 
necessity of assessing co-occurrent exposures, which is a topic of high 
concern in risk assessment (European Commission, 2020; Socianu et al., 
2022; Luijten et al., 2022). The number of co-occurring pesticides 
typically ranged from 2 to 5, with a maximum of 13 different pesticides 
(2 urine samples). These two urine samples originate both from the 
Spanish non-agricultural area, one from a child of the first season, the 
other of an adult of the second season. Both individuals had a lower 
number of co-occurring pesticides during the other season, resepectively 
8 and 10 pesticides. 
Based on the 14 most frequent co-occurrent pesticides, 44 different 
combinations could be made, resulting in highly individualized expo-
sure profiles. The most common combination of acetamiprid with 
chlorpropham, occurred in just 3% (n ˆ 62) of the urine samples. Also, 
assessment of the co-occurrence patterns at country level (network 
analysis), did not result in strong relations and hardly any overlap across 
countries was seen. The underlying correlations between these proba-
bilities of detection were also low, generally below 0.3. These results 
indicate that, qualitatively, pesticide mixtures might be highly variable 
between individuals. Nevertheless, the combined exposures may still 
pose a concern in terms of public health, especially when the different 
components of a chemical mixture share modes of action underlying 
toxicity (Rotter et al., 2018). Acetamiprid and chlorpropham seem to 
induce different toxicological effects (Arena et al., 2017; EFSA, 2016). 
Acetamiprid has been reported to mainly target the liver (EFSA, 2016), 
where it may cause, at least in rodents, oxidative stress leading to 
mitochondrial dysfunction (EL-Hak et al., 2022; S. Li et al., 2021). 
Exposure to chlorpropham rather leads to adverse effects on the he-
matopoietic system (Arena et al., 2017; Fujitani et al., 2000, 2004). 
Hemotoxicity such as hemolytic anemia, however, is considered to be 
due to oxidative stress (Rokushima et al., 2007; Sivilotti, 2004). Chlor-
propham belongs to the family of carbamates, which have been reported 
to induce oxidative stress in occupationally exposed workers (Saad--
Hussein et al., 2022). The other frequently observed combination of 
co-occurring substances involved acetamiprid and tebuconazole. Tebu-
conazole is a fungicide that mainly affects the liver and the adrenals 
(EFSA, 2014). Additionally, it has been reported to induce oxidative 
stress in the liver and endocrine disruption including anti-androgenic 
effects (Taxvig et al., 2007; Yang et al., 2018). Follow-up studies 
involving a larger number of participants and targeted biomarkers for 
these substances are needed to better assess the composition of the 
relevant mixtures and associated health risks. 
4.3. Strengths and limitations 
With the uniform design of our study a comparison could be made 
across Europe between agricultural and non-agricultural areas, seasons, 
and adults and children. Close collaborations with partners from all five 
countries resulted in the harmonized data collection, with little loss to 
follow up. The collection of the urine samples required a minimal 
invasive protocol, reducing the burden of citizens to participate in this 
survey, and opening up possibilities for scaling-up studies in future en-
deavours. A novel SS approach was harmonized and standardized across 
laboratories, with extensive QA/QC procedures (Vitale et al., 2022). 
Such harmonization is crucial to compare SS data and results coming 
from different laboratories and countries, a situation that is often un-
avoidable in large-scale studies. The applied SS approach allows for a 
relatively cost-effective way of providing semi-quantitative measure-
ments of a large number of pesticides. A clear strength of the SPECIMEn 
study is that information is obtained on (putative) internal exposure to 
pesticides not or hardly monitored before, and on simultaneous expo-
sure to multiple pesticides. Across countries, different pesticides tar-
geted to different controls on different crops are likely to have been 
applied at the time of sampling, of which the variation is covered with 
the SS approach. As such, this study should be seen as the first step to-
wards a more complete assessment of the pesticide mixtures that the 
general European population is exposed to. Further in-depth screening 
of the collected data and further methodological developments will in-
crease the number of biomarkers that can be detected in the collected 
urine samples. This allows an increasingly more complete coverage of all 
pesticides that are present in these samples as well as the detection of 
other biomarkers that might potentially interact with the pesticide 
mixture. Also, future more quantitative analysis of signal intensities will 
allow for a semi-quantitative interpretation, both in co-occurrence 
patterns and in the role of determinants of pesticide levels. 
Although the current study yields many new insights and perspec-
tives on pesticide occurrence and mixtures, several limitations need to 
be addressed. From an analytical methodology point of view, the suspect 
screening approach is less sensitive than targeted methods (Pourchet 
et al., 2020), and the data mining was biased towards halogenated 
pesticides (Huber et al., 2022). Despite harmonized methods between 
a OR: Odds Ratio, CI: Confidence Interval. 
b First mentioned is the reference category. 
c 100% detected in one of the categories, no estimate could be provided. 
d Due to low detection rate no extended model possible. 
e Model not corrected for level of Education, separation issue. 
f ND: Not detected or low detection rate (<1%), no model possible. 
g Model not corrected for Pesticide usage, separation issue. 
I. Ottenbros et al.                                                                                                                                                                                                                               
International Journal of Hygiene and Environmental Health 248 (2023) 114105
12
the involved laboratories, differences in sensitivity between the in-
struments used by the labs did occur, potentially introducing variability 
between countries which should be interpreted with care (Vitale et al., 
2022, Huber et al., 2022). Importantly, data generated by the SS 
approach applied in the SPECIMEn can currently not be related to uri-
nary pesticide concentration levels in the traditional quantitative way as 
in targeted analysis, but rather as semi-quantitative intensities as in-
dicators of exposure. 
With regards to the sample collection, it should be kept in mind that 
samples of the second season were collected during the COVID-19 
pandemic, while the first sample collection was not affected by the 
pandemic. Activity patterns or diet of participants might have been 
altered, and differences between seasons should be interpreted with 
caution. Also, the different seasons cannot be interpreted as ‘non- 
spraying’ and ‘spraying’, since the timing of the actual spraying activ-
ities (and spraying techniques) most likely differed between countries 
and crop types. Since the applied study design was not timed with an 
actual spraying activity, the detected exposures might be an underesti-
mation as compared to what has been reported in the literature (Der-
eumeaux et al., 2020; Teysseire et al., 2020). Agricultural areas were 
selected based on national databases on land-use (see Supplementary 
Material F for a description of the area selection per country), due to 
which the application of pesticides during the time of sampling could 
not be confirmed. Within SPECIMEn, only first morning void urines were 
collected. Due to the rapid excretion of many pesticides, the detected 
pesticides in the morning voids likely do not reflect the total daily 
exposure (A. J. Li et al., 2019; Scher et al., 2007). Finally, with respect to 
the performed logistic regression models, no correction for multiple 
testing was performed, since we wanted to detect any possible effects, 
accepting the risk of false-positive results. The inclusion of both location 
and season could have lead to an over-correction, especially since no 
difference between seasons at the non-agricultural locations would be 
expected due to any spraying activity (although diet might still differ 
between the seasons). 
5. Conclusions 
The current survey demonstrates the feasibility of conducting a 
harmonized pan-European sample collection combined with suspect 
screening (SS) to provide insight in the co-occurrence of pesticide mix-
tures in European agricultural areas. The application of a novel LC- 
HRMS based SS approach harmonized between different laboratories, 
resulted in detection of 40 biomarkers related to 29 parent pesticides 
with high levels of confidence. Some effects of living close to agricultural 
fields or season were detected, but these effects were not common at a 
European level. This study is a first step in addressing pesticide mixture 
exposure under real-life conditions. Combined with a suspect screening 
approach, this approach is a promising strategy for pesticide mixture 
risk assessment in the European population, that can guide the priori-
tization of pesticide (metabolites) to be measured using quantitative 
targeted methods. 
Credit author statement 
Conceptualization and design (IO, JV, EL, RV, JA); Investigation (IO, 
JV, EL, PC, LS, OM, TS, SK, IM, ZM, LA, OP, SF, CC, SP)); Analytical 
methodology (JA, CH, AL, OP, SF, MK, LD, KW, RN, HM, CM, JK, BG, 
NL); Formal analysis (IO, JV); Writing - Original Draft (IO, JV, EL); 
Writing – Review and Editing (all authors); Visualization (IO); Super-
vision (ML, RV); Project administration (IO, JV, EL); All authors read 
and approved the final manuscript. 
Declaration of competing interest 
Declarations of interest: none. 
Acknowledgements 
This project has received funding from the European Union’s Hori-
zon 2020 research and innovation program under grant agreement No 
733032 HBM4EU, No 857560and No 857340. P.C., O.M. & L.S. 
acknowledge the RECETOX research infrastructure supported by the 
Ministry of Education, Youth and Sports of the Czech Republic 
(LM2018121) and the Ministry of Education, Youth and Sports of the 
Czech Republic (CZ.02.1.01/0.0/0.0/17_043/0009632). This publica-
tion reflects only the author’s view and the European Commission is not 
responsible for any use that may be made of the information it contains. 
BG was supported by the Margarita Salas postdoctoral contract MGS/ 
2021/25 (UP2021-021) financed by the European Union- 
NextGenerationEU. 
Appendix A. Supplementary data 
Supplementary data to this article can be found online at https://doi. 
org/10.1016/j.ijheh.2022.114105. 
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