0000098923_wcag.pdf

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Environment International 145 (2020) 106119

Available online 17 September 2020

(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Contents lists available at ScienceDirect

Environment International

journal homepage: www.elsevier.com/locate/envint

Evaluation of 1-year urinary excretion of eight metabolites of synthetic pyrethroids, chlorpyrifos, and neonicotinoids

Anna Klimowska

^a

, Katarzyna Amenda

^a

, Wojciech Rodzaj

^a

, Malwina Wilenska ´

^a

, Joanna Jurewicz

^b

, Bartosz Wielgomas

^a^,^*

a Department of Toxicology, Medical University of Gdansk, ´ Al. Gen. Hallera 107, 80-416 Gdansk, ´ Poland

b Departament of Chemical Safety, Nofer Institute of Occupational Medicine, 8 Teresy St, 91-348 Ł´ od´ z, Poland

A R T I C L E I N F O Handling Editor: Adrian Covaci Keywords:

Daily excretion Human biomonitoring Insecticides Long-term variability Urine

A B S T R A C T

Synthetic pyrethroids, chlorpyrifos, and neonicotinoids are representatives of non-persistent insecticides ubiq- uitously used against insects all over the world. Their widespread use causes prevalent exposure to these compounds, which may be hazardous to human health. The insecticides have short biological half-lives and are mostly excreted in urine within 24 h after entering the human body; thus, the urinary concentration of their metabolites is highly dependent on the time elapsed between exposure and sample collection. Considering the within-day fluctuations in urinary concentration, one randomly collected sample may cause misclassification of long-term exposure. We evaluated the variability of excretion of eight insecticide metabolites in 24-h urine samples collected from 14 volunteers once or twice per month over 12 consecutive months. High detection frequency above 70% for non-specific metabolites of pyrethroid, chlorpyrifos, and neonicotinoids confirmed widespread exposure to these insecticides in the studied population. A long-term variability of exposure was assessed based on intraclass correlation coefficient (ICC). We found relatively low variability of excretion for non-specific pyrethroid metabolites and 3,5,6-trichloro-2-pyridinol (ICC > 0.75), but poor repeatability for 6- chloronicotinic acid. Constantly higher ICCs were observed for daily excretion than for unadjusted concentrations. Seasonal differences were observed for 3,5,6-trichloro-2-pyridinol and 6-chloronicotinic acid, with the highest and the lowest median concentration, respectively, in the summer. Due to high ICC values and lack of seasonal variations, one 24-h urine sample was considered sufficient to characterize long-term excretion of non- specific pyrethroid metabolites in non-occupationally exposed population. In addition, we calculated the daily intake (DI) for cypermethrin, permethrin, deltamethrin, and chlorpyrifos. The estimated DI values were mostly below the acceptable daily intake, which indicates that the evaluated exposure is non-hazardous to the population.

1. Introduction

Insecticides are compounds with a very diverse chemical structures, and they are widely used in pest control around the world. They are increasingly being used in Poland, as confirmed by the increase in the sales volume from 2945 tonnes in 2010 to 5207 tonnes in 2017 (Sta- tistics Poland, 2018). Insecticides are commonly used in agriculture, public health, and household sectors. They are classified according to their structure, and currently, organophosphates, neonicotinoids, car- bamates, phenyl-pyrazole, and pyrethroids are of the highest relevance due to the frequency of their use. The widespread use of insecticides

*Corresponding author.

E-mail addresses: anna.klimowska@gumed.edu.pl (A. Klimowska), k.amenda@gumed.edu.pl (K. Amenda), wojciech.rodzaj@gumed.edu.pl (W. Rodzaj), malwina.

wilenska@gumed.edu.pl (M. Wile´nska), Joanna.Jurewicz@imp.lodz.pl (J. Jurewicz), bartosz.wielgomas@gumed.edu.pl (B. Wielgomas).

https://doi.org/10.1016/j.envint.2020.106119

Received 18 May 2020; Received in revised form 3 September 2020; Accepted 3 September 2020

causes chronic exposure to low doses that can adversely affect human health.

Neonicotinoids are new class of insecticides with high selectivity against insects (Tomizawa and Casida, 2005). They are frequently used for seed treatment and crop protection, but they are also common in veterinary applications, such as tick control products and flea collars for pets. The growing popularity of neonicotinoids is related to their properties, such as relatively high solubility in water, long lasting insecticide activity, low initial pest resistance, and systemic action (Simon-Delso et al., 2015). The latter means that they are absorbed by and distributed in the entire plant, which provides them protection against pests.

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However, due to the incorporation of neonicotinoids into the plant tis- sues, it is impossible to remove them by rinsing plant surface with water before consumption or food processing (Zhang et al., 2019). Hence, they are considered environmental contaminants and may pose potential health threat to humans. The European Union has approved the use of five neonicotinoids—clothianidin, imidacloprid, thiamethoxam, acet- amiprid, and thiacloprid, in plant protection products (European Com- mission, 2011; The European Parliament and the Council of the European Union, 2009). In 2018, European Commission completely banned the outdoor use of imidacloprid, clothianidin, and thiamethoxam (European Commission, 2018a, 2018b, 2018c) due to their high toxicity to honeybees. The selectivity factor of neonicotinoids is >400, and hence, their toxicity in mammals is considered relatively low (Tomizawa and Casida, 2005). However, some recent scientific reports have indicated their toxicity in humans (Carmichael et al., 2014;

Hernández et al., 2008; Koureas et al., 2014); although, the available biomonitoring data remains limited (Göen et al., 2017; Osaka et al., 2016; Ospina et al., 2019; Song et al., 2020; Tao et al., 2019; Ueyama et al., 2014; Wang et al., 2015). 6-Chloronicotinic acid (6-CNA) is a metabolite of chloropyridinyl compounds, such as imidacloprid, acet- amiprid, and thiacloprid (Casida, 2011; Ford and Casida, 2006) and a biomarker of exposure to these insecticides (Göen et al., 2017; Tao et al., 2019; Wang et al., 2015).

Synthetic pyrethroids were developed as more persistent substitutes of natural pyrethrins (Casida, 1980; WHO, 2005). Synthetic pyrethroids are widely used against insects in agriculture for crop protection as well as for human health and pest control. Synthetic pyrethroids are highly toxic to insects and aquatic organisms; nevertheless, they pose low toxicity to humans (Bradbury and Coats, 1989). After entering the human body, pyrethroids are rapidly metabolized and mostly excreted in urine as non-specific or specific metabolites (Kaneko, 2010). 3-phenoxybenzoic acid (3-PBA), a non-specific metabolite of several synthetic pyrethroids, is frequently used as a biomarker in numerous biomonitoring studies evaluating exposure to pyrethroids in the general populations (Barr et al., 2010; Garí et al., 2018; M. K. Morgan et al., 2016; M. Morgan et al., 2016; Morgan et al., 2007; Saieva et al., 2004;

Wielgomas and Piskunowicz, 2013). The common metabolites of permethrin, cypermethrin, and cyfluthrin are cis-3-(2,2-dichlorovinyl)- 2,2-dimethylcyclopropane-1-carboxylic acid, and trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane-1-carboxylic acid (cis- and trans- DCCA). Lambda-cyhalothric acid (BIF) is a common metabolite of bifenthrin and cyhalothrin, and 4-fluoro-3-phenoxybenzoic acid (4-F-3- PBA) may indicate exposure to cyfluthrin or flumethrin. Nonetheless, there are also specific biomarkers of pyrethroids such as 2-methyl-3-phe- nylbenzoic acid, which is a specific metabolite of bifenthrin, or cis-3- (2,2-dibromovinyl)-2,2-dimethylcyclopropane-1-carboxylic acid (cis- DBCA), a metabolite of deltamethrin. Many biomonitoring studies have demonstrated very high detection frequency for pyrethroid metabolites, which indicate widespread exposure to this group of insecticides (Barr et al., 2010; Garí et al., 2018; M. K. Morgan et al., 2016; M. Morgan et al., 2016; Wielgomas et al., 2013; Wielgomas and Piskunowicz, 2013;

Oulhote and Bouchard, 2013). Pyrethroids are considered relatively safe to mammals, but there is growing evidence of their negative influence on human health after exposure to low environmental doses (Jurewicz et al., 2016; Meeker et al., 2009; Oulhote and Bouchard, 2013; Radwan et al., 2015). Pyrethroids have been detected not only in human biological materials, but also in both outdoor (soil, outdoor air) and indoor samples (carpet dust, floor wipes) (Leng et al., 2005; Morgan et al., 2007). They are more stable in households; therefore, indoor environment can induce long-term exposure through inhalation (adsorbed on particulate matter). Dietary intake is also a significant source of exposure to synthetic pyrethroids (Goen et al., 2017; Hyland et al., 2019; ¨ Riederer et al., 2008; Ye et al., 2015).

Organophosphates are another group of commonly used insecticides.

They act through the inhibition of acetylcholinesterase, leading to the accumulation of acetylcholine in organisms responsible for cholinergic

syndrome (Timchalk, 2006). The toxicity of organophosphates is high against insects and mammals, and thus, they have been removed from the market and replaced with safer alternatives in many countries. Due to the well documented toxicity chlorpyrifos-ethyl and chlorpyrifos- methyl are representatives of organophosphate pesticides banned by European Commission at the beginning of 2020. They can enter human body by ingestion or inhalation, or through the dermal route. Chlor- pyrifos is firstly metabolized to an oxon, which is responsible for the toxic effect; however, it is rapidly hydrolyzed to 3,5,6-trichloro-2-pyridinol (3,5,6-TCPyr) and excreted in the urine (Choi et al., 2006; Roberts and Hutson, 1999). About 70% of ingested dose of chlorpyrifos is excreted in the urine as 3,5,6-TCPyr (Nolan et al., 1984). 3,5,6-TCPyr is a metabolite of chlorpyrifos and chlorpyrifos-methyl and has been used in many biomonitoring studies for the assessment of human exposure (Barr et al., 2005; Koch et al., 2001; MacIntosh et al., 1999, 2001;

Meeker et al., 2005; Saieva et al., 2004). Triclopyr, a systemic herbicide, is also metabolized to 3,5,6-TCPyr, but a negligible fraction of the ingested dose is excreted in the urine as 3,5,6-TCPyr (Carmichael et al., 1989). 3,5,6-TCPyr can be found in the environment as a chlorpyrifos degradant (Morgan et al., 2005), and hence, its urinary concentration may indicate exposure to chlorpyrifos as well as its metabolites or degradants; therefore, the results of biomonitoring studies have to be interpreted with caution.

Non-persistent xenobiotics have short biological half-lives; thus, the concentration of their urinary biomarkers may vary depending on time elapsed between exposure and sample collection. Mostly, they are completely excreted through the kidney within 24 h after intake and the measured concentration in the urine reflects a very recent exposure (Koch et al., 2014; Moos et al., 2016; Ratelle et al., 2015a, 2015b; Sams and Jones, 2012; Sandborgh-Englund et al., 2006; Shin et al., 2019;

Teeguarden et al., 2011; V¨olkel et al., 2002). Therefore, it is important that short- and long-term variability in the amounts of excreted biomarkers is considered in the scheduling of biomonitoring studies to correctly interpret the data. Due to the simplicity of collection, single spot urine samples or first morning voids are used most frequently;

however, variation in the concentration of samples may be noticeable. In the case of non-persistent chemicals, 24-h urine sample represents total daily intake (DI) and should be less susceptible to short-term changes in exposure. Nonetheless, the collection of 24-h sample is more demanding, as it is necessary to pool all voids during the entire day, which requires a strong commitment of the study participants. Several reports have described the short- and long-term variability of bisphenol A and phthalate metabolites in urine samples (Aylward et al., 2017;

Dewalque et al., 2015; Koch et al., 2014; Lassen et al., 2013; Sun et al., 2017; Ye et al., 2011). However, the amount of data on other non- persistent chemicals remains scant. The short- and long-term variabil- ities of metabolites of synthetic pyrethroids (Attfield et al., 2014; Bar- koski et al., 2018; Morgan et al., 2016; Wielgomas, 2013), organophosphates (Attfield et al., 2014; Barkoski et al., 2018; Bradman et al., 2013; Egeghy et al., 2005), polycyclic hydrocarbons (Li et al., 2010), parabens, triclosan, and benzophenone 3 (Dewalque et al., 2015;

Koch et al., 2014) have been evaluated.

The aim of this study was to evaluate long-term exposure to commonly used insecticides (synthetic pyrethroids, neonicotinoids, and chlorpyrifos) by measuring the concentration of eight insecticide metabolites in 24-h urine samples donated by non-occupationally exposed volunteers.

2. Materials and methods

2.1. Study population and 24-h urine sample collection

The participants were recruited from Gdansk ´ (northern part of Poland) between 2014 and 2017. Sample collections started immediately after the enrolment of each participant and proceeded for 12 consecutive months; thus, the start and the end time points of the

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sampling period were different for each participant. Each volunteer had to sign informed written consent to participation and declare lack of occupational exposure to target compounds. There were no restrictions regarding the age and gender of the study participants. A child’s participation in the study was confirmed with a written informed consent signed by a parent. Polish ethical guidelines do not require a child to provide assent to participate in the study. According to the study aims, the subjects collected 24-h urine samples, which were used to evaluate total daily excretion of non-persistent environmental chemicals during the 1-year period. Each study participant submitted one or two 24-h urine samples monthly for 12 consecutive months. A collection day was selected according to personal preferences. It was recommended that the volunteers provide one sample during a working day and the other one during the weekend. However, because of the comfort, ma- jority of the samples were collected during the weekends.

An instruction with detailed guidelines on 24-h urine collection and storage was given to the participants before sample collection. They had to record a time of beginning and completion of collection, as well as the final volume of sample. Samples were collected into pre-cleaned 2000 mL polypropylene (PP) containers and stored in a refrigerator after sample collection without preservatives. After urine collection was complete and careful mixing, two 20-mL aliquots were transferred into PP scintillation vials and stored at �20 ^◦C until analysis. Samples were mostly delivered to the laboratory as a 24-h urine sample the day after collection, and they were mixed and aliquoted by laboratory staff. In cases where the samples could not be delivered to the laboratory the next day, the participants were allowed to mix and aliquot the samples at home. Participants were instructed on how to avoid external contamination and assure sample homogeneity. Participants always had one extra clean 2000 mL container for urine samples at home.

The study was approved by Independent Bioethics Commission for Research by Medical University of Gdansk ´ (NKBBN/416/2014).

2.2. Chemicals

The following metabolites: lambda-cyhalothric acid (BIF, cis-2,2- dimethyl-3-(2-chloro-3,3,3-trifluoro-1-propenyl)-cyclo-

propanecarboxylic acid) and cis/trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane-1-carboxylic acids (cis/trans-DCCA) were purchased from Toronto Research Chemicals (Canada). cis-(2,2-dibromovinyl)-2,2- dimethylcyclopropane-1-carboxylic acid (cis-DBCA) was procured from Roussel Uclaf (France); 3-phenoxybenzoic acid (3-PBA), and 6-chloronicotinic acid (6-CNA) was purchased from Sigma Aldrich (Germany);

3,5,6-trichloro-2-pyridinol (3,5,6-TCPyr) was procured from Riedel-de Ha¨ en (France). 4-fluoro-3-phenoxybenzoic acid (4-F-3-PBA) and the internal standards, 1, carboxyl-¹³C2, 1-D (cis-DCCA), phenoxy ¹³C6 (3- PBA), and 3,5,6-trichloro-2-pyridinol (4,5,6-¹³C3) were obtained from Cambridge Isotope Laboratories (USA).

Other reagents: n-hexane, sodium acetate, sodium hydroxide, and hydrochloric acid were obtained from POCH (Poland). Formic acid, magnesium sulfate, decane, 1,1,1,3,3,3-hexafluoroisopropanol (HFIP), diisopropylcarbodiodiimide (DIC), BSTFA:TMCS (99:1), and β-glucuronidase type HP-2 from Helix pomatia (activity: 125,255 U/mL of glucuronidase and 1095 U/mL of sulfatase) were purchased from Sigma Aldrich (Germany). Methyl tert-butyl ether was purchased from Merck Millipore (Darmstadt, Germany), and primary secondary amine (PSA) was from Scharlau (Spain).

Standard stock solutions (1 mg/mL) and working solutions as sequential dilution of stock solutions were prepared in acetonitrile and stored at �20 ^◦C.

2.3. Urine analysis

The analyses of urinary metabolites of synthetic pyrethroids were conducted using a combination of the methods reported by Schettgen et al. (Schettgen et al., 2002) and Arrebola et al. (Arrebola et al., 1999)

and included a slight modification of the extraction and derivatization steps. Briefly, 3 mL of urine was hydrolyzed with 0.6 mL of concentrated hydrochloric acid (95 ^◦C, 90 min) and extracted twice with 4 mL of n- hexane and methyl tert-butyl ether (3:1, v:v) mixture. Next, the organic phases were combined, and the analytes were re-extracted with 0.1 M NaOH. The organic layer was discarded, and the aqueous phase was acidified and extracted with 2 mL of n-hexane. Subsequently, the n- hexane layer was evaporated under gentle stream of nitrogen and 10 µL of HFIP, 20 µL of DIC, and 250 µL of n-hexane were added to the dry residue in that order. Following 10-min incubation, the reaction mixture was washed with 5% aqueous solution of K2CO3 to remove the excess of DIC. Next, 200 µL of hexane layer was transferred into a vial equipped with micro-insert and 2 µL of the sample was injected into gas chro- matography and mass spectrometry (GC-MS) system.

The concentrations of 3,5,6-TCPyr and 6-CNA in the urine samples were measured by liquid–liquid extraction and GC-MS/MS analysis (Klimowska and Wielgomas, in preparation). The urine samples (3 mL) were mixed with 750 µL of 1.0 M sodium acetate buffer (pH 5.0), including approximately 230 U of β-glucuronidase, and incubated overnight at 37 ^◦C. The enzymatic reaction was stopped by the addition of 450 µL of concentrated formic acid. Afterwards, the hydrolyzed sample was extracted twice with 3 mL of n-hexane and tert-butyl ether mixture (3:1, v:v), and the organic phase was then cleaned-up using dispersive solid phase extraction with 200 mg MgSO4 and 10 mg PSA.

After centrifugation, the extract was placed in the fresh tube, mixed with 10 µL of decane (keeper) and then evaporated under a gentle stream of nitrogen. The almost-dry residue was reconstituted in 50 µL N,O-bis (trimethylsilyl)trifluoroacetamide: trimethylchlorosilane (BSTFA:

TMCS, 9:1) and derivatized for 30 min at 60 ^◦C. The extract (1 µL) was injected into a GC-MS/MS system for analysis.

2.4. GC-MS/MS analysis

Analyses were performed using gas chromatograph Varian 450-GC equipped with VF-5 ms low bleed column (30 m × 0.25 mm × 0.25 µm +10 m EZ-guard) and 1177 split/splitless injector. Gas chromatograph was coupled with ion trap mass spectrometer Varian 220-MS.

Helium was used as a carrier gas at a flow rate of 1 mL/min. Injection was performed at 280 ^◦C in splitless mode, and after 1.5 min the valve was open (split ratio 20:1).

The following column program was used for pyrethroid metabolites analysis: 60 ^◦C (hold 1 min), 60–150 ^◦C (8 ^◦C/min), and 150–280 ^◦C (30 ^◦C/min, hold 5 min). The mass spectrometer worked in a single ion storage mode, and the monitored selected m/z ions were 357, 197 for BIF; 323, 163, 127 for cis-and trans-DCCA; 326, 127 for labeled cis- DCCA; 369, 367, 174 for cis-DBCA; 382, 215, 187 for 4-F-3PBA; 364, 197, 169 for 3-PBA; and 370, 203, 175 for labeled 3-PBA. The underlined values represent the quantitation ions.

For 3,5,6-TCPyr and 6-CNA, the column program was 60 ^◦C (hold 3 min), 60–155 ^◦C (100 ^◦C/min), 155–185 ^◦C (10 ^◦C/min, hold 2 min), and 185–300 ^◦C (20 ^◦C/min, hold 12 min). Tandem mass spectrometry was used to quantify the metabolites of chlorpyrifos and neonicotinoids.

Precursor and product m/z ions for 3,5,6-TCPyr, 6-CNA, and labeled 3,5,6-TCPyr were 254 and 219, 218; 214 and 170; and 257 and 222, 221, respectively. The quantitation analysis m/z ions for 3,5,6-TCPyr, 6- CNA, and labeled 3,5,6-TCPyr were 219, 170, and 222, respectively.

2.5. Quality control (QC)

The validation of analytical methods was performed based on matrix-matched calibration curves prepared in the range 0.1–25 ng/mL for pyrethroid metabolites and 0.1–50 ng/mL for 3,5,6-TCPyr and 6- CNA. Labeled cis-DCCA was used as internal standard for BIF, cis/

trans-DCCA, and cis-DBCA; labeled 3-PBA for 4-F-3-PBA and 3-PBA; and labeled 3,5,6-TCPyr for 3,5,6-TCPyr and 6-CNA. Linearity was verified by determining the regression coefficients (r²> 0.99 for most of the

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analytes, except 3,5,6-TCPyr, where r ² = 0.98). The quality of measurements was monitored by the analysis of control urine samples.

Human urine (pooled samples from 6 donors, confirmed previously to contain non-detectable or very low levels of studied metabolites) was spiked with analytes at two concentration levels (0.15 and 1.5 ng/mL for pyrethroid metabolites; 2.0 and 6.0 ng/mL for 3,5,6-TCPyr and 6-CNA) and stored at �20 ^◦C as 3-mL aliquots. QC samples were characterized by multiple analyses (>20) over a specified time (>1 month). Mean and standard deviation were calculated to determine the acceptance limits to be used with the Westgard rules. Daily analytical batch usually included 48 samples: 42 unknown, 2 low-QC, 2 high-QC, blank urine sample, and procedural blank. All the collected 24-h urine samples were prepared in two 7-day series (separately for each method) and analyzed immediately after preparation. The limit of detection (LOD) in our methods is defined as the lowest point on the calibration curve which can be determined with sufficient accuracy (80–120%) and precision (<25%). All samples with concentrations close to LOD were visually inspected to ensure proper ions ratio in the spectrum. Deviation in ion ratio lower than 30%

in comparison to standard is considered as a criterion of acceptance.

LODs were equal to 0.1 ng/mL for all pyrethroid metabolites and 0.2 ng/

mL for 3,5,6-TCPyr and 6-CNA. The between-day precision and accuracy were calculated based on spiked QCs (n = 28) and were in the range of 4–13 % and 80–120%, respectively. Samples with concentration above the highest calibration level were re-analyzed using 1.5 mL of urine.

The external QC of the analytes (except BIF) was examined by using The German External Quality Assessment Scheme for Toxicological Analyses in Biological Materials (G-EQUAS). Our results for all the tested metabolites were within the established tolerance ranges of the reference values for the G-EQUAS samples (Table S1 in supplementary material).

2.6. Data analysis

Non-adjusted concentrations as well as daily excretion rates were subjected to statistical analyses using Statistica 13.3 (TIBCO Software Inc, USA) and Microsoft Excel (Microsoft, USA) software. To evaluate daily excretion, the total volume of 24-h urine sample was determined.

Missing values were replaced by the mean volume of all the other samples from the same volunteer. The evaluation of daily excretion was determined by multiplying unadjusted concentration by total volume of sample. For spot urine samples, hydration status may significantly impact volumetric concentration (e.g. highly diluted or concentrated samples). To make samples comparable, measured concentrations were corrected using specific gravity or creatinine content in the urine samples. Creatinine correction is commonly used because the excretion rate of creatinine is fairly constant; however, it is highly dependent on age, sex, and health state (Barr et al., 2005b). Barr et al. reported lower concentration of creatinine in the urine samples of children and elderly individuals (Barr et al., 2005b). Although correction methods are very common, the collection of 24-h urine sample is still considered the ‘gold standard’ for biomonitoring studies, as it represents the total mass of biomarker excreted during an entire day.

Concentrations below LOD were replaced with LOD divided by the square root of two (Hornung and Reed, 1990). Analytes with detection frequency below 60% were excluded from statistical analyses and variability evaluation. The results were not normally distributed, and hence, the Kruskal-Wallis test and Spearman’s rank correlation coefficient were applied. The results with p < 0.05 were considered significant. The repeatability of excretion was assessed as intraclass correlation coefficient (ICC) with 95% confidence interval. The calculation of ICC was performed with SPSS Statistic Subscription software (IBM Corpo- ration, USA) using one-way random effects model and log-transformed results. In theory, ICC is defined as the ratio of between-subject variance to total variance (sum of between and within variance), and a value of 1 means perfect agreement of repeated measurements, whereas 0 represents no agreement between data. According to Rosner’s criteria

(Rosner, 2016), a value of ICC below 0.40 demonstrates poor, 0.40–0.75 demonstrates fair to good, and > 0.75 demonstrates excellent repeatability of serial measurements.

DI was calculated using the following equation (Koch et al., 2007):

( )

ng C(ng/mL) × V24h(mL) MW

DI BW per day = × ^parent

kg Fue × BW(kg) MWmetabolite

where C is the measured urinary concentration of metabolite; V24h is the total volume of 24-h urine sample; Fue is the fraction of excreted metabolite related to the insecticide; BW is body weight; and MWparent

and MWmetabolite are molecular weights of parent compound and metabolite, respectively. Fue values were adapted from human toxicokinetic studies. Urinary fractions used for DI calculation were 36% as total DCCA (sum of cis- and trans-DCCA) for cypermethrin (Ratelle et al., 2015a) and permethrin (Ratelle et al., 2015b); 45% as cis-DBCA for deltamethrin (Sams and Jones, 2012); and 70% as 3,5,6-TCPyr for chlorpyrifos (Nolan et al., 1984).

3. Results

The concentrations of eight insecticide metabolites were measured in 24-h urine samples collected continuously from non-occupationally- exposed volunteers for 12 consecutive months. We recruited 14 volunteers (7 males and 7 females) in the age range between 7 and 68 years.

Two participants were male children, two were retirees, and the others were between 25 and 39 years old. Nine out of 14 participants were members of four families living in the same households. All the volunteers were living in the urban area during the sampling period. The basic characteristics of the subjects are presented in Table 1. The participants, except two (a vegetarian (P9) and a vegan (P13)), consumed a con- ventional diet. None of the participants reported organic food consumption during the study period. Each volunteer provided 12–24 samples during the collection period and the total number of samples was 295. One person (P10) withdrew from the study after 6 months, but we decided to include the samples because they still represented long- term exposure. Information about the total volume of 24-h urine sample was not available for 19 samples, and hence, the missing values were replaced with the average volume of the remaining samples collected from the same participant. All missing sample volumes were related to samples aliquoted by the study participants, and it is highly recommended that this procedure be avoided in future studies.

At the beginning of the study, each adult participant received a questionnaire to fill during the entire study course. The questionnaire involved general information (e.g. age, weight, and height), as well as questions to characterize each collection, e.g. diet and usage of personal products. However, because of too many gaps in the information recorded in the questionnaires, we had to exclude the data from the study.

3.1. Descriptive statistics

The descriptive statistics are presented in Table 2. Among the pyrethroid metabolites, the highest detection frequency was found for non- specific biomarkers. Concentrations above LOD were measured in 93.9% (range among individuals: 67–100%), 85.4% (42–100%), and 81.0% (25–100%) of samples for trans-DCCA, cis-DCCA, and 3-PBA, respectively. The other more specific metabolites—cis-DBCA, BIF, and 4-F-3-PBA, were detected less frequently in 44.7% (17–67%), 39.3%

(4–67%), and 5.1% (0–17%) of samples.

The median concentrations of cis-DCCA, trans-DCCA, and 3-PBA were 0.20, 0.35, and 0.25 ng/mL, respectively. The high exposure levels represented by the 90th percentiles were in the range of 0.41–1.17 ng/mL and 0.68–1.50 µg per day, where the lowest and the highest values were estimated for cis-DCCA and 3-PBA, respectively.

After converting total daily excretion to excretion per kg of body weight,

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Table 1

Characteristics of participants.

Sex Age Weight Height Number of collected Mean volume of 24-h urine sample [mL] Household Occupation

[years] [kg] [cm] samples (SD) number

P1 P2 P3

M M F

37 7 68

94 24 65

186 134 165

22 21 24

1476 (3 1 6) 529 (2 0 1) 1117 (2 3 1)

1 1 2

Academic teacher Primary school student Retiree

P4 P5 M

M 68

39 80

76 180

185 24

24 1067 (2 0 3)

1537 (2 4 7) 2

3 Retiree

Medical doctor P6 P7 F

M 37

7 72

22 180

128 24

24 1551 (2 6 2)

1083 (2 8 8) 3

3 Academic teacher

Primary school student P8 P9

P10 F M F

25 30 25

56 55 62

166 170 165

24 24 12

1414 (2 9 6) 1647 (5 0 1) 1858 (3 7 1)

4 4 5

Student Academic teacher PhD student/

Pharmacist P11 P12 M

F 26

25 76

51 182

169 12

24 1235 (3 3 0)

691 (1 9 7) 6

7 Underwriter

Pharmacist

P13 F 26 65 170 24 2223 (3 7 4) 8 PhD student

P14 F 27 48 152 12 2600 (1 7 1) 9 PhD student/

Sportsman

the highest exposure level was observed for 3-PBA (28.1 ng/kg BW per day) and trans-DCCA (25.3 ng/kg BW per day). The median and maximum concentrations for all the participants are presented in the supplementary material (Table S2).

3,5,6-TCPyr was detected in 97.3% (83–100%) of the urine samples.

The median concentration was 2.32 ng/mL, whereas the highest exposure levels assessed for chlorpyrifos metabolite were 6.27 ng/mL and 9.68 µg per day (156 ng/kg BW per day).

In addition, a high detection rate of 6-CNA was observed. Concen- trations above LOD were measured in 77.4% (58–100%) of the samples.

The 50th and 90th percentiles of 6-CNA concentrations were 0.42 ng/

mL and 1.65 ng/mL, respectively. The highest daily excretion for neonicotinoids metabolite was 260 µg per day (4006 ng/kg BW per day).

Plots with the distribution of measured urinary biomarker concentrations for each participant are presented in supplementary material (Figure S1).

3.2. Spearman’s rank correlation coefficients

The Spearman’s rank correlation coefficients between biomarkers were calculated. We found a significant correlation between two isomers of DCCA and 3-PBA. For volumetric concentrations, the correlation was very strong for DCCA isomers (r = 0.915) and relatively strong for 3-PBA and DCCA isomers (cis-DCCA: r = 0.727 and trans-DCCA: r = 0.762).

Correlation between the remaining biomarkers (except for 6-CNA) were significant but weak (r < 0.45). Similar correlations were evaluated for daily excretion rates. The calculated Spearman’s coefficients are shown in Table 3.

3.3. Intraclass correlation coefficient

To estimate the variation of excretion for each biomarker, including unadjusted concentrations and daily excretion, the intraclass correlation coefficient was calculated (Table 4). Consistently higher ICCs were evaluated for daily excretion than for non-adjusted concentrations.

Excellent repeatability was found for DCCA isomers with ICC above 0.8;

however, considering the confidence interval (95% CI), the interpretation of results should be rather different. The lower value of CI was 0.69 for cis-DCCA and 0.68 for trans-DCCA, and the upper values were 0.96 for both metabolites; hence, the repeatability of measurements ranged from quite good to excellent. Regardless of the ICC values for DCCA isomers, we can conclude that their urinary concentrations were constant during the studied period. We also observed a low variability for 3- PBA (0.75). However, in case of 3-PBA, we noticed a wide range of CI with the bottom level representing merely fair repeatability (0.45). The highest ICC value (0.91), which represents relatively constant exposure

measure was observed with 3,5,6-TCPyr. The results of 6-CNA differed from those of the mentioned biomarkers. Considering the results of the volumetric concentrations, the evaluated repeatability of excretion was poor (0.39). However, the results were better when we considered the daily excretion rates. The assessed ICC for daily excretion was 0.71, but a broad range of CI (0.35–0.92) indicates a higher variability of exposure measure than that of the calculated ICC values.

3.4. Seasonal variation

The seasonal variation of biomarkers with total detection rate above 60% was evaluated, and the results are presented in the supplementary material (Table S3). There were no significant differences in the concentration of the metabolites in the urine samples collected at different times of the year in the case of synthetic pyrethroids (cis-DDCA, trans- DCCA, and 3-PBA). Interestingly, the daily excretion of 3,5,6-TCPyr was the highest during the summer, whereas at the same time of the year, we observed the lowest concentrations of 6-CNA (Fig. 1). Variation in the concentrations between seasons or months might be crucial for the correct classification of exposure level and should be considered in the scheduling of sample collection. The distribution of unadjusted concentrations within 12 months is presented in Fig. 2. Due to the low detection rates of BIF, cis-DBCA, and 4-F-3-PBA, we only compared the percentage of samples with concentration above LOD between months (Fig. 2). The highest detection frequencies were found in April and October for BIF (50.0%), in April for cis-DBCA (73.1%), and in July for 4- F-3-PBA (23.8%).

3.5. DI

We used the available toxicokinetic data to estimate the DI of permethrin, cypermethrin, deltamethrin, and chlorpyrifos following the conversion of parent compounds to metabolites (Table 5). Permethrin, cypermethrin, and cyfluthrin are metabolized to cis- and trans-DCCA, whereas 4-F-3-PBA is a metabolite of cyfluthrin and flumethrin. Owing to the low detectability of 4-F-3-PBA, we assumed that DCCA isomers represent exposure to permethrin or cypermethrin. Due to the similar toxicokinetic data (Ratelle et al., 2015a, 2015b), we estimated the DI for both metabolites. The median DI of permethrin and cypermethrin was 60.10 and 63.9 ng/kg BW per day, respectively for all the study participants, with 148.6 and 158.1 ng/kg BW per day as the highest individual median values, respectively. The highest calculated DI of permethrin was 1.15 µg/kg BW per day and was approximately 50-fold lower than the acceptable daily intake (ADI = 50 µg/kg BW per day) (JMPR, 1999). For cypermethrin, the maximum DI was 1.23 µg/kg BW per day with ADI of 20 µg/kg BW per day (JMPR, 2006). One of the

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Table 2

Descriptive statistics for unadjusted concentrations and daily excretion of urinary biomarkers.

% > LOD Unit GM Range Percentile

(n = 295) (+/- 95% CI) (min–max) 10th 25th 50th 75th 90th

BIF 39.3 ng/mL NC LOD–3.68 <LOD <LOD <LOD 0.17 0.41

ng/kg BW per day NC LOD–68.6 <LOD <LOD <LOD 4.06 10.8

µg/24 h NC LOD–3.50 <LOD <LOD <LOD 0.23 0.68

cis-DCCA 85.4 ng/mL 0.22 LOD–4.24 <LOD 0.13 0.20 0.31 0.58

(0.20–0.24)

ng/kg BW per day 4.81 LOD–80.8 <LOD 2.74 4.32 7.65 13.2

(4.38–5.28)

µg/24 h 0.27 LOD–4.96 <LOD 0.15 0.26 0.48 0.80

(0.24–0.30)

trans-DCCA 93.9 ng/mL 0.36 LOD–12.3 0.13 0.20 0.35 0.56 1.01

(0.32–0.40)

ng/kg BW per day 7.99 LOD–179 2.66 4.38 7.27 12.7 25.3

(7.21–8.85)

µg/24 h 0.45 LOD–10.7 0.14 0.24 0.41 0.84 1.41

(0.40–0.50)

cis-DBCA 44.7 ng/mL NC LOD–14.5 <LOD <LOD <LOD 0.24 0.61

ng/kg BW per day NC LOD–380 <LOD <LOD <LOD 5.57 15.1

µg/24 h NC LOD–24.7 <LOD <LOD <LOD 0.29 0.88

4-F-3-PBA 5.1 ng/mL NC LOD–1.40 <LOD <LOD <LOD <LOD <LOD

ng/kg BW per day NC LOD–25.4 <LOD <LOD <LOD <LOD <LOD

µg/24 h NC LOD–1.83 <LOD <LOD <LOD <LOD <LOD

3-PBA 81.0 ng/mL 0.27 LOD–11.9 <LOD 0.12 0.25 0.49 1.17

(0.24–0.31)

ng/kg BW per day 6.04 LOD–379 <LOD 2.63 5.35 11.3 28.1

(5.32–6.85)

µg/24 h 0.34 LOD–18.2 <LOD 0.15 0.30 0.66 1.50

(0.30–0.39)

3,5,6-TCPyr 97.3 ng/mL 2.13 LOD–50.3 0.50 1.30 2.32 3.89 6.27

(1.90–2.39)

ng/kg BW per day 47.6 LOD–1401 16.1 26.6 46.4 77.5 156

(42.6–53.1)

µg/24 h 2.67 LOD–108 0.55 1.55 2.96 5.25 9.68

(2.35–3.03)

6-CNA 77.4 ng/mL 0.47 LOD–104 <LOD 0.22 0.42 0.77 1.65

(0.41–0.52)

ng/kg BW per day 10.9 LOD–4006 <LOD 4.49 9.73 19.4 39.4

(9.12–11.8)

µg/24 h 0.58 LOD–260 <LOD 0.25 0.52 1.13 2.33

(0.51–0.67)

NC – not calculated, detection below 60%; LOD – limit of detection; CI – confidence interval, GM – geometric mean

Table 3

Spearman’s correlation coefficients for unadjusted concentrations and daily excretion.

Table 4

Intraclass correlation coefficient (±95% CI) for non-specific metabolites of synthetic pyrethroids, 3,5,6-TCPyr, and 6-CNA in urine samples from 14 volunteers within a 1-year period.

cis-DCCA trans-DCCA 3-PBA 3,5,6-TCPyr 6-CNA

cis-DCCA — 0.91* Â 0.73* Â 0.39* Â �0.11 Â

trans-DCCA 0.92* ^B — 0.76* Â 0.40* Â �0.13* Â

3-PBA 0.76* ^B 0.78* ^B — 0.35 ^A �0.05 ^A

3,5,6-TCPyr 0.49* ^B 0.49* ^B 0.43* ^B — �0.01 ^A

6-CNA 0.12* ^B 0.07^B 0.10^B 0.18* ^B —

*– results significant with p < 0.05; ^A– ng/mL; ^B– µg per day.

Unadjusted (ng/mL) 24-h excretion (ng per day) cis-DCCA

trans-DCCA 3-PBA 3,5,6-TCPyr 6CNA

0.81 (0.69–0.96) 0.86 (0.68–0.96) 0.75 (0.45–0.93) 0.91 (0.80–0.98) 0.39 (0.00–0.83)

0.91 (0.816–0.98) 0.91 (0.79–0.98) 0.81 (0.58–0.95) 0.94 (0.86–0.98) 0.71 (0.35–0.92)

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p=0.0534

1 2 3 4

0.1 1 10 100

3,5,6-TCPyr [ ng mL-1]

1 2 3 4

0.01 0.1 1 10

100

*

3,5,6-TCPyr [µg 24h-1 ]

1 2 3 4

0.1 1 10

100

*

6-CNA [ ng mL-1]

p=0.0687

1 2 3 4

0.01 0.1 1 10 100

6-CNA [µg 24h-1]

Fig. 1. Seasonal variation of excretion evaluated for 3,5,6-TCPyr and 6-CNA. Line – median; box – 25th and 75th percentile; whiskers – 10th and 90th percentile;

dots – results below 10th and above 90th percentile. Seasons: 1 – spring, 2 – summer, 3 – fall, 4 – winter. Kruskal-Wallis test was used for determination of seasonal variation. * - significant results at p < 0.05.

Fig. 2.Monthly distribution of unadjusted concentrations and detection frequencies of eight insecticide metabolites. Line – median; box – 25th and 75th percentile;

whiskers – 10th and 90th percentile; dots – results below 10th and above 90th percentile. Numbers above plots report the percentage of samples with concentration higher than the LOD.

highest DI for cypermethrin was calculated for one of two male children (P2; 1.168 µg/kg BW per day); however, it represents only 6% of the ADI value. The daily intake of deltamethrin was estimated based on urinary excretion of its specific biomarker (cis-DBCA) (Sams and Jones, 2012).

The highest individual median and maximum DI for deltamethrin among all the participants were 20.4 ng/kg BW per day and 1.40 µg/kg BW per day, respectively. In comparison with the ADI (10 µg/kg BW per day) (JMPR, 2000), the highest DI for deltamethrin was about 7-fold lower than the reference value.

Due to the fact that the concentration of 3,5,6-TCPyr in urine may result from exposure to both chlorpyrifos and chlorpyrifos-methyl, as well as to preformed 3,5,6-TCPyr in the environment, chlorpyrifos DI calculated on the basis of excreted 3,5,6-TCPyr may be overestimated. It is notable that in two cases, the calculated DI values for chlorpyrifos

exceeded the ADI (1 µg/kg BW per day), but were lower than the acute reference dose (5 µg/kg BW per day) (EFSA, 2014). The median DI for chlorpyrifos was 57.3 ng/kg BW per day, whereas the two highest values were 1.42 and 1.73 µg/kg BW per day for P1 and P13, respectively.

4. Discussion

Human biomonitoring is a universal approach used for the assessment of exposure to occupational and environmental chemicals, and it relies on the measurement of the level of a parent compound or its metabolite in biological samples. Usually, due to convenience and low cost, exposure to chemicals is estimated based on a single measurement of the biomarker concentration in a biological material obtained from one participant. This strategy can lead to improper classification of

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Table 5

Estimated daily intake (ng/kg BW per day) for permethrin, cypermethrin, deltamethrin, and chlorpyrifos among 14 volunteers.

Permethrin Cypermethrin Deltamethrin Chlorpyrifos

Median â Range ^b Median â Range ^b Median â Range ^b Median â Range ^b

(min–max) (min–max) (min–max) (min–max)

ng/kg BW per day

P1 38.3 NC–105 40.7 NC–111 15.4 NC–71.5 69.2 19.9–1424

P2 53.7 NC–1098 57.2 NC–1168 10.7 NC–143 53.4 NC–543

P3 45.7 NC–246 48.6 NC–261 NC NC–109 51.1 NC–361

P4 85.0 23.4–693 90.4 24.9–737 NC NC–308 50.9 8.39–255

P5 108 47.2–1073 115 50.2–1142 NC NC–406 82.1 26.6–423

P6 71.6 36.9–379 76.5 39.2–404 NC NC–369 51.0 7.48–208

P7 149 NC–682 158 NC–725 NC NC–387 28.0 NC–352

P8 55.6 NC–193 59.2 NC–205 9.52 NC–64.7 67.4 33.4–462

P9 62.9 NC–1149 66.9 NC–1223 NC NC–90.0 67.4 12.1–502

P10 NC NC–101 NC NC–107 NC NC–51.2 26.8 NC–92.5

P11 37.0 NC–102 39.3 NC–109 NC NC–157 58.8 36.7–96.0

P12 26.3 NC–1157 28.0 NC–1231 5.19 NC–360 45.9 16.2–572

P13 79.5 NC–266 84.6 NC–283 20.4 NC–1401 125 NC–1733

P14 70.2 NC–388 74.7 NC–413 NC NC–75.3 115 23.0–882

All 60.1 NC–1157 63.9 NC–1231 NC NC–1401 57.3 NC–1733

Underlined results exceed the acceptable daily intake value (two daily intake values for chlorpyrifos were higher than the acceptable daily intake value).

a – NC: not calculated, detection below 60%.

b – NC: not calculated, the lowest result below the limit of detection.

exposure, especially in the case of non-persistent pollutants which are usually excreted in urine within 24 h after entering the human body.

Short biological half-life of chemicals excreted in the urine is one of the reasons for high variability in concentrations even within 1 day. Hence, the urinary concentrations of metabolites might be highly variable and depend on time elapsed between exposure and sample collection. Non- persistent chemicals are characterized by a short biological half-life, and exposure to these chemicals can be continuous. It has been demonstrated that the urinary levels of phthalate metabolites or BPA may differ by two folds even within 24 h (Preau et al., 2010; Ye et al., 2011). The main goal of this study was to assess the long-term exposure to common insecticides based on the levels of their metabolites in 24-h urine samples collected during 12 consecutive months.

4.1. Concentration of insecticide metabolites in urine

The exposure to synthetic pyrethroids in various populations in Poland has been previously described (Wielgomas, 2013; Wielgomas and Piskunowicz, 2013). In previous studies, 3-PBA was the most frequently detected metabolite (80–82%) with a median concentration of 0.252–0.263 ng/mL. The levels and the detection rate of 3-PBA in repeated 24-h urine samples are consistent with observed in those studies. We found detectable concentrations of 3-PBA in 81% of the samples (0.25 ng/mL). A stable urinary concentration of 3-PBA concentration over time might indicate a constant level of exposure among Polish population during 2010–2017. Higher detection frequency was observed for cis-DCCA (94%) and trans-DCCA (86%) in this study, compared with previously reported values (41–60%) (Wielgomas, 2013;

Wielgomas and Piskunowicz, 2013). As expected, the remaining pyrethroid metabolites, cis-DBCA (45%), BIF (39%), and 4-F-3-PBA (5%), were detected less frequently, probably due to the occasional and lower exposure to corresponding parent compounds. The highest detection rate of deltamethrin metabolite (>60%) was found in two participants (P1 and P2) living in the same household, whereas the range among other participants was 17–54%. To the best of our knowledge, this is the first study to report urinary excretion of BIF and 4-F-3-PBA in a Polish population. Detection rate was in the range of 4–67% and < LOD–17%

for BIF and 4-F-3-PBA, respectively, in the study participants.

A significant and relatively strong correlation was noted between the DCCA isomers (r > 0.9) and between the isomers and 3-PBA (r > 0.7).

This observation is consistent with the toxicokinetic data showing that both DCCA and 3-PBA are common metabolites of permethrin and

cypermethrin (Ratelle et al., 2015a, 2015b).

The urinary levels of pyrethroid metabolites measured in this study are similar to those observed among the general population in the USA (Barr et al., 2010; Morgan, 2015; Morgan et al., 2007) and Canada (Ye et al., 2015), but lower than the median concentrations reported for 50 adults in North Carolina (Morgan et al., 2016b) and Japanese children (Osaka et al., 2016). It should be however noted that LODs/LOQs of analytical methods used in mentioned studies vary from 0.006 to 0.01 ng/mL (Ye et al., 2015) to 0.4 ng/mL (Barr et al., 2010; Morgan, 2015) for different pyrethroid metabolites. On the other hand, children tend to have higher concentrations of non-persistent chemicals, possibly due to specific activities (e.g., hand-to-mouth) and different metabolic activity (Oulhote and Bouchard, 2013).

3,5,6-TCPyr is widely used for the evaluation of chlorpyrifos exposure. The high detection rate (97.3%) of 3,5,6-TCPyr is consistent with the data obtained in other populations around the world (Barr et al., 2005; Garí et al., 2018; Koch et al., 2001; Morgan et al., 2005; Saieva et al., 2004). The median concentration measured in this study was 2.32 ng/mL, which was similar to that reported for Spanish non-farmworkers (2.2 ng/mL, LOQ 0.02 ng/mL) (Garí et al., 2018), lower than that reported for children in North Carolina (5.3 ng/mL, LOQ 2 ng/mL) (Morgan et al., 2005), and higher than the median concentration among adults in Germany (1.4 ng/mL, LOQ 0.1 ng/mL) (Koch et al., 2001).

However, the measured urinary concentration of 3,5,6-TCPyr may have resulted from exposure to chlorpyrifos and its metabolite. Morgan et al.

(2005) reported that indoor air, dust samples, solid food samples, and diet are the major source of exposure to 3,5,6-TCPyr. The authors inferred that the aggregate absorbed dose of 3,5,6-TCPyr was 10-fold higher than that of chlorpyrifos. Conversely, in another study by Mor- gan et al. (2018) pyrethroid degradates were likely not present in sufficient levels in the diet to substantially impact the adults’ urinary biomarker concentrations.

Data on exposure to neonicotinoids, which are currently one of the most commonly applied insecticides all over the world, are scarce. The levels of neonicotinoids and their metabolites have been measured in urine samples from Japanese, Chinese, and American populations (Osaka et al., 2016; Ospina et al., 2019; Song et al., 2020; Tao et al., 2019; Ueyama et al., 2014; Wang et al., 2015). The high detection rate of the parent compounds (mostly above 50%) suggests the widespread exposure of the Japanese and Chinese populations to neonicotinoids (Osaka et al., 2016; Tao et al., 2019; Ueyama et al., 2014; Wang et al., 2015). Tao et al. (Tao et al., 2019) assessed the urinary concentrations of

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imidacloprid and 6-CNA before and after the application of imidacloprid among volunteers living in nine orchard areas in China. The study group included pesticide applicators and their family members. Samples collected from urban volunteers were used as a control. Both compounds were detected in all the collected urine samples; however, the urinary concentrations of 6-CNA were higher among rural residents before (median 0.96 ng/mL) and 2 days after spraying (1.15 ng/mL). On the third day after pesticide application, the concentrations in both groups were similar (rural, 0.45 ng/mL and urban, 0.53 ng/mL). Moreover, the authors found a positive correlation between the measured amount of imidacloprid and that of its metabolite after pesticide spraying. On the contrary, Wang et al. (Wang et al., 2015) reported no increase in the urinary excretion of 6-CNA following imidacloprid application among imidacloprid sprayers and members of their families (wives, children, and elders). We detected 6-CNA in 77.4% (range 58–100% in all study participants) of samples with a median value of 0.42 ng/mL, which is comparable with the concentrations measured among urban residents in China (Tao et al., 2019) and 19 non-occupationally exposed volunteers in the USA (0.445 ng/mL) (Li et al., 2020). There are no available data describing the metabolism of neonicotinoids to 6-CNA in humans;

nevertheless, due to inconsistent biomonitoring data, it is not clear if 6- CNA is the right biomarker to evaluate imidacloprid exposure (Tao et al., 2019; Wang et al., 2015).

Only a limited number of reports have provided measurement results based on 24-h urine collection. The daily excretion of pesticide metabolites was assessed by Saieva et al. (Saieva et al., 2004) by analyzing 24- h urine samples from 69 adult residents of two Italian cities. The authors found that the median concentrations of 3-PBA and 3,5,6-TCPyr were 5.6 nmol per day (1.20 µg per day) and 29.5 nmol per day (5.85 µg per day), respectively. The values were higher than those obtained in this study, where the median values for 3-PBA and 3,5,6-TCPyr were 297 ng per day and 2.96 µg per day, respectively. Similar daily excretion of 3- PBA with a median excretion of 362 ng per day was reported by Wiel- gomas in 24-h urine samples collected for 1 week (Wielgomas, 2013).

4.2. Long-term variability

Considering the usefulness of human biomonitoring in environmental epidemiology, we expect that single urine measurement represents long-term exposure; however, available data on long-term variability of excretion of non-persistent chemicals are limited. Several authors have questioned the reliability of single measurement of metabolites in urine for the characterization of long-term exposure (Attfield et al., 2014; Aylward et al., 2017; Barkoski et al., 2018; Morgan et al., 2016). With regard to non-persistent chemicals that are rapidly metabolized in the human body, the concentrations of their metabolites in spot urine samples are highly dependent on the time elapsed between exposure and sample collection. To reduce within-day fluctuations in the urinary excretion of target compounds, a 24-h urine sample may be collected (Aylward et al., 2017; Wielgomas, 2013).

The main outcome of this study is the evaluation of exposure variability to insecticides over a 1-year period, which was reported as ICC values. High ICC value indicates single urine collection, which reliably describes long-term exposure. The ICC values of 3-PBA in this study are consistent with the results of short-term repeatability evaluation by Wielgomas (Wielgomas, 2013) among seven participants over 1 week.

The ICC calculated for 24-h urine samples was 0.68, compared with 0.75 in this study. Both studies showed fairly good repeatability of measured concentrations, which can be characteristics of the Polish population.

Nevertheless, it is important to keep in mind that results can vary between studied populations. The available data for different populations in the USA (Attfield et al., 2014; Barkoski et al., 2018; Morgan et al., 2016) are contrary to the results of this study and showed high variability with the ICC values in the range of 0.00–0.33. We also evaluated long-term variability for cis- and trans-DCCA, and observed a constant daily excretion with ICC values above 0.8. Attfield et al. (Attfield et al.,

2014) reported high variability of trans-DCCA over a 1-year period for first and last urine voids with ICC values of 0.34 and 0.35, respectively.

The study group was 23 children in the age range of 3–11 years. Children participated in four sampling rounds (one round for each season), with two daily samples (the first void in the morning and the last void before bedtime) over 1 or 2 weeks. Long-term study in the USA also showed poor repeatability for 3,5,6-TCPyr urinary excretion (ICC < 0.4) (Att- field et al., 2014; Barkoski et al., 2018; Egeghy et al., 2005; Meeker et al., 2005). In our study, the repeatability for 3,5,6-TCPyr over 1 year was very good (ICC > 0.9). Li et al. (Li et al., 2020) reported the ICC for 6- CNA among 19 non-occupationally exposed volunteers in the USA, with samples collected for 44 consecutive days. The authors found very low repeatability with ICC value of 0.08 for spot samples and 0.02 for first morning voids. We also observed poor repeatability for the neonicotinoids metabolite, which is represented by low ICC value for unadjusted concentration (0.39). Daily excretion showed excellent reproducibility (ICC = 0.71); nevertheless, a wide range of 95% CI (0.35–0.92) means that the true value of ICC was within this range, which indicates very poor to excellent repeatability. The lower variability in our study, compared with other studies, may be due to 24-h urine collection which helped to overcome the challenge of within-day fluctuations. The proper interpretation of variability studies requires not only the ICC values but also the 95% CI values. However, it is worth noting that the comparison of ICCs has some limitations. As earlier indicated, ICC values for a specific chemical may significantly differ between studies. These variations may arise from not only study sampling strategies (e.g. sample type, sampling period, and frequency of sample collection), but also from chemical properties and exposure pattern (LaKind et al., 2019). Regardless of the ICC value, it is highly recommended to collect multiple urine samples to evaluate exposure to non-persistent chemicals. The number of collected samples and the frequency of sampling should be related to the period of interest.

Seasonal variation is an important factor in epidemiological studies.

The variation in the concentrations between months or seasons can lead to the misclassification of general exposure. However, a limited number of studies have assessed seasonal variability of exposure. Seasonal differences in exposure may result from several factors, such as time spent indoor, fresh fruit and vegetable consumption, and the use of pet care products. These factors should be monitored in epidemiological studies.

We observed stable concentrations across four seasons for non-specific pyrethroid metabolites, with the highest and the lowest median concentration in the summer for 3,5,6-TCPyr and 6-CNA, respectively. More specific pyrethroid metabolites were detected less frequently, and hence, we compared the detection rate between months. The highest fluctuations over 12 months were observed for cis-DBCA, with 3.5-fold higher number of samples above LOD in April (73.1%) than in October (20.8%). This difference suggests high variability of exposure to deltamethrin for the entire year. Lu et al. (Lu et al., 2009) evaluated seasonal variation for repeated urine samples from elementary school children, and they reported lower concentration of 3-PBA as well as cis- and trans-DCCA in the summer. The highest concentration of 3-PBA was observed in the spring among adults in Ohio (Morgan, 2015), whereas the sampling season had no effect on urinary 3-PBA in North Carolina (Morgan et al., 2016a). The highest concentrations of 3,5,6-TCPyr (MacIntosh et al., 1999; Morgan, 2015) and neonicotinoids (Osaka et al., 2016) in the summer have also been reported.

4.3. DI

Due to the collection of 24-h urine samples, we were able to back- calculate the DI for some of the targeted insecticides. Based on the available toxicokinetic data from human studies, we were able to estimate the DI of permethrin, cypermethrin, and deltamethrin, while assuming that these compounds are the sole source of the metabolites assayed. However, it should be noted that the other insecticides from this group also may be the source of these metabolites (e.g. transfluthrin