Isabelle Claudet
email: claudet.i@chu-toulouse.fr

MD Sophie Breinig

MD Michel Lavit

MD Lucas Ricco

MD Camille Brehin

MD Souleiman El Balkhi

Prof. Isabelle Claudet

Is there an interest for hair analysis in non-intentional pediatric cannabis intoxication?

Keywords: hair testing, cannabis, hashish, intoxication, pediatric

Background and Objectives:

The incorporation of drugs in the hair of young children differs from that of adults and the metabolism of cannabis cannot be the same. Our primary objective was to analyze the distribution of the different cannabinoids in children's hair samples. The secondary objective was to correlate the intensity of toxic environmental exposure to cannabinoid metabolite levels.

Methods: This was a prospective, single-center, observational pilot study of a pediatric cohort. Included subjects were all children less than 6 years of age admitted to a tertiary pediatric emergency unit for proven cannabis intoxication during the reference period. A hair strand was sampled within 12 hours of emergency admission.

Results: Forty-one pediatric patients were consecutively enrolled. Hair analysis showed that 34 children were positive for Δ9-THC (range 0.06-284.4 ng/mg); 41% of them were also positive for THC-COOH (range 0.26-2.76 pg/mg). Depending on the Δ9-THC concentration (>1 ng/mg), 39% of the children could be considered exposed to an intensely toxic environment. The rate of THC-COOH detection steadily increased from 2015 to 2018 (18%, 40%, 50%, 58% for each consecutive year). Children intensely exposed weighed less on admission (p=0.02), had more comatose presentations (p=0.02), and more previous social issues (75% versus 12%, OR 22.0, p=0.0002).

Conclusion:

Hair testing in this context indirectly shows the intensity of children's toxic environmental exposure by the cannabinoid metabolite threshold. This was very helpful during the collegial examination of the toddlers' environment and led to a full investigation and to appropriate decisions concerning social measures.

Introduction

France has one of the highest rates of cannabis consumption in the world despite its prohibition. Users are mainly young adults who consume the resin form (hashish). Cannabis resin remains readily accessible and is becoming increasingly potent due to the increasing Δ9tetrahydrocannabinol (Δ9-THC) concentrations (12% in 2011, 25% in 2016) (Graph 1). We previously reported in a French national study that this change led to an increase in the presentation of non-intentionally intoxicated infants to pediatric emergency and intensive care units [START_REF] Claudet | Unintentional Cannabis Intoxication in Toddlers[END_REF]. Over the past 15 years, these pediatric admissions have become worryingly more frequent and more severe. Consequently, we think that it is important to find a way to detect children with intensely toxic environmental and/or daily exposure. The assessment of this risk is possible only when intoxicated toddlers are referred to child protective services. Parents must be informed of the procedure and that social workers will conduct an in-depth, long-term investigation. This investigation is usually followed by a home visit by a pediatric nurse.

However, parents have the right to refuse this intervention. Many emergency physicians or pediatricians do not even report such cases of intoxication, arguing that they have insufficient proof of parental consumption levels. For these reasons, we decided to conduct a study to determine whether hair analysis could help to improve the assessment of at-risk environments (heavy consumers) and the implementation of appropriate social measures.

Objectives -Considering that the absorption of drugs in young children's hair differs from adults' hair and that the metabolism of cannabis cannot be the same in this particular population, our primary objective was to analyze the distribution of the different cannabinoids in the hair samples of presumably exposed children. The secondary objective was to correlate the intensity of toxic environmental exposure to the Δ9-THC level and the concomitant detection of 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH). We also aimed to describe the influence of hair testing on the choice of social measures in terms of child protection.

Study design -This is a single-center, prospective, observational study of a pediatric cohort.

Setting and study participants -Between December 6, 2014 and December 11, 2018, all children under 6 years of age admitted with proven cannabis intoxication (compatible clinical symptoms and positive toxicological screening) to our tertiary pediatric emergency department during the reference period were eligible. Compatible clinical symptoms for the diagnosis of "intoxicated" children were any acute neurological symptom(s) (drowsiness, ataxia, hypo-or hypertonia, seizures, coma, altered consciousness, agitation, euphoria ± mydriasis) occurring in a previously healthy, afebrile toddler with no prior medical history.

Patients over 6 years of age, asymptomatic patients (exposed but not intoxicated) and those with suspected but unproven cannabis intoxications were not included.

As of December 2014, the hair of all children with proven cannabis intoxication was analyzed. Within 12 hours after emergency admission, a hair strand was sampled from the posterior vertex of each patient. Twenty milligrams of hair are required to measure cannabinoid levels. The hair sample was mailed to the regional toxicology laboratory with hair analysis expertise (Limoges). A month later, a joint consultation (pediatric emergency physician and a social worker) was systematically held in order to present hair results to the parents and to make the most appropriate decision in terms of child protection and the need to resort to an addiction team. [START_REF] Persson | Poisoning severity score. Grading of acute poisoning[END_REF]. For hair analysis, specific collected data were hair strand length, type of analysis (global or by segmentation), the presence of cannabis metabolites (11-nor-9-carboxy-Δ9-tetrahydrocannabinol [THC-COOH], Δ9-tetrahydrocannabinol [Δ9-THC], cannabinol [CBN], cannabidiol [CBD]) and their concentrations. Children were considered positive when Δ9-THC and THC-COOH values were over the limits of quantification (respectively, 50 pg/mg and 0.2 pg/mg) as recommended by the Society of Hair Testing (SoHT). By applying the results of a preliminary study that determined the threshold of exposure intensity and significant systemic absorption [START_REF] Winckel | CANHAIRKID (evaluation of hair analysis in the diagnosis of significant cannabis exposure among children): preliminary results[END_REF], patients were divided in two groups: intense environmental exposure (Δ9-THC > 1 ng/mg) and low to moderate environmental exposure (Δ9-THC < 1 ng/mg).

Laboratory analysis strategies -Urine drug testing could be performed by automated immunochemical (IC) methods as soon as urine samples were available. IC screening methods detected THC-COOH, with a cut-off set at 50 µg/L. Positive results were confirmed with chromatography methods (LC-MS/MS with a limit of quantification [LOQ] of 1 µg/L).

With the LC-MS/MS method, the detection of the most important cannabinoids was possible (namely, THC, 11-OH-THC and THC-COOH). This improved the estimation of the period between the intake of cannabis and the sampling moment. In some cases, a general unknown screening with GC-MS or LC-MS/MS was performed to rule out the presence of other drugs besides cannabis.

Hair analysis

The hair strand was analyzed globally or divided in two to four segments depending on the hair length. Hair samples were decontaminated by a gentle mixing of samples in two glass tubes containing 10 ml water for 2 min each, followed by two 1-min baths in 10 ml of dichloromethane. The hair samples were then washed and finely cut (1-2 mm long, using perfectly decontaminated scissors) before being introduced in a 15 ml glass tube to which the internal standards solutions were added. After the addition of 1 ml of NaOH 1N to approximately 20 mg of the samples, 50 µL of internal standards were added and the tube was heated in a boiling water bath for 10 min. After total hydrolysis, samples were cooled and 1 mL of acetic acid 20% was added. Samples were left at room temperature for 5 min and 7 mL of hexane/ethyl acetate (90/10 ; v/v) were added. Hair samples were vortex-mixed for ten seconds and centrifuged at 3000 rpm for 5 min. Supernatants were then dry evaporated à 30°C under N 2 flux before reconstitution with 20 μl of a mixture of acetonitrile and formate buffer and injection into the LC-ESI-MS/MS system. A hybrid API 5500 QTRAP (Quadrupole/Quadrupole/Ion Trap) mass spectrometer (AB Sciex, Courtaboeuf, France) was used for detection and quantification with negative electrospray ionization mode was used for the analysis of cannabinoids. The limit of quantification (LOQ) was 0.05 ng/mg for THC, cannabidiol (CBD) and cannabinol (CBN). THC-COOH had a lower LOQ at 0.0002 ng/mg. Briefly, the method was successfully validated with a correlation coefficients higher than 0.99 (for the following ranges: 0.0002-20,000 ng/mg for THC-COOH; 0.05-10 ng/mg for THC, CBD and CBN) and intra and inter-assay accuracies below 15% for the all compounds. This method has already been published [START_REF] Dulaurent | Simultaneous determination of Δ9-tetrahydrocannabinol, cannabidiol, cannabinol and 11-nor-Δ9-tetrahydrocannabinol-9carboxylic acid in hair using liquid chromatography-tandem mass spectrometry[END_REF].

Statistical analysis -For statistical analysis, data were entered in Microsoft Excel tables (Microsoft Corp, Redmond, WA). The analysis was performed with StatView 5.1 (SAS Institute Inc., Cary, NC) and EpiInfo 6.04fr (VF, ENSP-Epiconcept, Paris, France). In the descriptive analysis, data are presented as mean ± SD, median with extreme values, or with 95% confidence intervals where appropriate, unless otherwise indicated. To compare qualitative variables, a 2 test (Mantel-Haenszel) was used and a 2-tailed Fischer's exact test if the expected value was <5. For quantitative independent variables, a paired student t test was used. A nonparametric Kruskall-Wallis test was performed in case of non-normal distribution. Statistical significance was considered at p < 0.05.

Ethical and regulatory considerations -The data recorded during this research were subject to electronic processing at the Toulouse University Hospital Pediatric Emergency Department (PED) in accordance with law no. 78-17 of January 6, 1978 regarding Data Processing, Data Files and Individual Liberties, amended by law 2004-801 of August 6, 2004. At the time of the study, toxicological analysis of hair strands were excluded from the French bioethics law and did not require approval from an ethics committee, parental acceptance or non-opposition.

Results -Main characteristics -During the study period, 41 pediatric patients were consecutively enrolled. The male/female ratio was 1:1.4 (male, n=17). The mean age was 1.5 ± 0.5 years; the mean weight was 10.9 ± 1.3 kg. When the time of intoxication was known (83%), it occurred most often during the afternoon between 12:00 AM and 6:00 PM (53%). The mean time period between intoxication and hospital admission was 5h55min ± 3h31min. The principal place of intoxication was the parental home (73%). The main form of cannabis that was ingested was hashish (97%). One child ate a "space cake". Table 1 summarizes the main characteristics of the population and the features of intoxication.

Forty-four percent of the children were in a coma on admission; six patients had prehospital seizures. Eight patients were diagnosed with respiratory failure. Basic metabolic panel blood tests performed on all patients revealed abnormalities (n=19). These included high blood glucose levels (n=10), hyponatremia (≤130 mmol/l) (n=6), and functional kidney failure (n=7). Additional procedures included a head CT scan (n=14), lumbar puncture (n=6), EEG (n=2), and abdominal ultrasound (n=1). In addition to cannabis detection by IC assays, blood and/or urine toxicological screening was performed in 33 cases. Three other molecules were isolated in three children (diazepam, acetaminophen and caffeine). Paracetamol was allegedly being given to treat hyperthermia, and benzodiazepines were detected in a child who had received diazepam to stop seizures. Parents could not explain the caffeine detection.

Most of the children were hospitalized (90%). Four patients were admitted to the pediatric intensive care unit and one child required assisted ventilation for 12 hours. Twenty-two (54%) different children met at least one severity criteria. Forty-one percent of the children had a PSS score of 3 (2015-2016: 41%; 2017-2018: 46%). Sixteen families initially conceded that they were cannabis consumers (father n=10, mother n=3, both parents n=3). During the hospital stay, the social worker met all families and social issues were identified in 15 families (37%). These included social deprivation (n=4), previous interaction with child protective services (CPS) in 6 other families (with effective educative measures for 4 of them), and isolated or single-mother households for 5 children. At the time of the follow-up consultation, families who initially denied being cannabis consumers or who gave no explanation for the cause of the intoxication finally admitted cannabis use when confronted with the hair results. Social or legal measures included a referral to CPS for all cases; a written report of special concern was made for 32 children, 13 cases were reported to the juvenile court judge, 4 children were placed in foster care by court order and 3 families were subject to a complaint filed with the police department. 2 summarizes the hair results with the different metabolites present and their concentrations. Considering the consensus of the SoHT and the recommended limit of quantification (LOQ), 34 children (83%) were positive for Δ9-THC (LOQ >0.05 ng/mg, range 0.06-284.4 ng/mg) and 14 (41%) were also positive for THC-COOH (LOQ >0.2 pg/mg, range 0.26-2.76 pg/mg). Patients were divided into two groups according to the Δ9-THC concentration (over or below 1 ng/mg) (Table 3), and 39% of the children could be considered exposed to an intensely toxic environment (Δ9-THC > 1 ng/mg).

Results of hair analysis -Table

Fifteen children had blood measurements of cannabis metabolites and they were all positive for THC-COOH: THC blood concentration (range 0.5 -138 ng/ml), 11-OH-THC (range 0.7 -34.6 ng/ml) and THC-COOH (range 11 -122.6 ng/mL).

Comparative analysis -Comatose and non-comatose patients did not differ when their main characteristics and the hair distribution of cannabinoids were compared. In terms of the level of environmental exposure, children with intense environmental exposure (Δ9-THC > 1 ng/mg, n=16) weighed less on admission (10.1 ± 0.9 kg versus 11.4 ± 1.2 kg, p=0.006), had more comatose presentations (56% versus 20%, OR 5.1 (1.3-20.7), p=0.02) and had more previous social issues (75% versus 12%, OR 22.0 (4.2-115.0) p=0.0002). The prematurity rate of our cohort was higher (12%) than the French national prematurity rate (7.5%), without any significant difference. Discussion -Currently, the increase of admissions in emergency services for pediatric nonintentional cannabis intoxication is alarming. This phenomenon is observed in all countries, regardless of whether cannabis has been legalized or not [START_REF] Richards | Unintentional Cannabis Ingestion in Children: A Systematic Review[END_REF][START_REF] Wang | Unintentional Pediatric Exposures to Marijuana in Colorado, 2009-2015[END_REF][START_REF] Heizer | Marijuana Misadventures in Children: Exploration of a Dose-Response Relationship and Summary of Clinical Effects and Outcomes[END_REF][START_REF] Noel | Increase in emergency department visits related to cannabis reported using syndromic surveillance system[END_REF][9]. In terms of child protection, it is therefore essential to use all available resources to determine whether children are regularly and intensively subjected to a toxic environment, especially to avoid resultant health risks, such as potential neurological sequela.

Compared with adults, children's hair is thinner and more porous, and the risk of contamination from sweat and environmental smoke is greater [START_REF] Dulaurent | Simultaneous determination of Δ9-tetrahydrocannabinol, cannabidiol, cannabinol and 11-nor-Δ9-tetrahydrocannabinol-9carboxylic acid in hair using liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Berthet | A systematic review of passive exposure to cannabis[END_REF][START_REF] Wang | Review: Interpretation of drug presence in the hair of children[END_REF]. During certain periods, the growth rate of children's hair can differ from the usual growth of 1 cm per month, which starts at the age of one year [START_REF] Kintz | Contribution of in utero drug exposure when interpreting hair results in young children[END_REF]. Due to children's hair properties, hair analysis interpretation that has been proven in adults cannot always be applied to children. On the other hand, the maturity of liver enzymes is gradually acquired after birth. Cytochromes P450, involved in the conversion of THC into THC-COOH, are partially or completely immature (e.g. CYP2C activity is not functional at birth and reaches 30% after the first year of life), and this can lead to an underestimation of toxic exposure. The immaturity of enzyme activity can also affect UDP-glucuronyltransferase, leading to a decrease in the conjugation of THC-COOH and can facilitate hair absorption, with a theoretically higher concentration for the same amount of THC absorbed when compared with adults [START_REF] Hakkola | Developmental expression of cytochrome P450 enzymes in human liver[END_REF][START_REF] Lucas | The pharmacokinetics and the pharmacodynamics of cannabinoids[END_REF].Therefore, before interpreting hair analysis results, physicians should be aware of these differences and be warned about the difficulties interpreting blood or hair analysis in young children [15][START_REF] Moosmann | Finding cannabinoids in hair does not prove cannabis consumption[END_REF][START_REF] Moosmann | Cannabinoid findings in children hair -what do they really tell us? An assessment in the light of three different analytical methods with focus on interpretation of Δ9tetrahydrocannabinolic acid A concentrations[END_REF][START_REF] Cuypers | The interpretation of hair analysis for drugs and drug metabolites[END_REF][19][20][START_REF] Alvarez | Hair analysis does not allow to discriminate between acute and chronic administrations of a drug in young children[END_REF][START_REF] Chatterton | Interpretation of pharmaceutical drug concentrations in young children's head hair[END_REF].

In adults, it has been proven that the presence of THC alone in hair indicates that the subject lives in a contaminated environment, but the detection of THC-COOH argues for active use of cannabis [9,15]. To reduce contamination from passive exposure, the SoHT has recommended applying a decontamination procedure to wash out external pollutants (including an initial organic solvent followed by aqueous washes) [START_REF] Cooper | Society of Hair Testing. Society of Hair Testing guidelines for drug testing in hair[END_REF][START_REF] Duvivier | Evidence based decontamination protocols for the removal of external Δ9-tetrahydrocannabinol (THC) from contaminated hair[END_REF]. Even with the SoHT recommended application to decontaminate hair strands before analysis [START_REF] Cooper | Society of Hair Testing. Society of Hair Testing guidelines for drug testing in hair[END_REF], it has been established that contamination issues should still be considered in the interpretation of the final results.

Environmental exposure in children should lead to a profound investigation of the contextual circumstances [START_REF] Pélissier | Parental cannabis abuse and accidental intoxications in children: prevention by detecting neglectful situations and at-risk families[END_REF]. THC-COOH screening has been suggested for differentiating passive and active users (in children it was thought to be an indirect indication of caregiver or parental use) [15,[START_REF] Cuypers | The interpretation of hair analysis for drugs and drug metabolites[END_REF][START_REF] Cooper | Society of Hair Testing. Society of Hair Testing guidelines for drug testing in hair[END_REF]. In intoxicated children, the presence of THC-COOH in hair is inconsistent, even when the intoxication is proven by blood and/or urine detection of cannabinoid metabolites with chromatography/mass tandem spectrometry analysis. The absence of THC-COOH could be explained by the absence of cannabis intake over the past months. A segmented analysis is of no interest in such non-intentional intoxications since the purpose is more to determine the risk of high environmental exposure and not to date or prove chronic drug administration, as in pediatric forensics or cases of abuse []11 ,21,26,27].

In our study, the majority of patients' hair tested positive for Δ9-THC (83%), which indicates that they were living in contaminated areas and/or surrounded by cannabis users. THC-COOH was detected in 41% of the cases, which is not very uncommon. There was no significant relation between the findings of THC-COOH in hair and the severity of presentation (47% in comatose children vs. 36% in non-comatose patients, p=0.48). However, we observed that the rate of THC-COOH detection increased steadily from 2015 to 2018 (18%, 40%, 50%, 58% for each consecutive year, p=0.09, OR 3.1 [0. 8-11.8]). This could be related to the increase in resin THC content (cf. Graph 1).

From a previous study conducted in Limoges, France, the authors were able to determine that the hair THC concentration threshold (> 1ng/mg) could reflect intense environmental exposure [START_REF] Winckel | CANHAIRKID (evaluation of hair analysis in the diagnosis of significant cannabis exposure among children): preliminary results[END_REF]. When this threshold was reached and associated with the presence of THC-COOH, this meant significant systemic absorption [START_REF] Winckel | CANHAIRKID (evaluation of hair analysis in the diagnosis of significant cannabis exposure among children): preliminary results[END_REF]. Our study is the first to apply such thresholds, finding that 39% (n=16) of our intoxicated patients were living in an environment with intense exposure. Compared with other intoxicated patients, children who met intense environmental exposure criteria (n=16) weighed less on admission, but their birth weight was not lower. Compared with the French national rate (7.5%), we also had a greater proportion of premature children (12%) in our cohort, which could be related to the mothers' exposure to cannabis smoke during pregnancy, as it was the father who conceded use [START_REF] Grant | Cannabis use during pregnancy: Pharmacokinetics and effects on child development[END_REF]. However, we cannot completely rule out that the mothers were also users during pregnancy. In case of suspected child abuse, it is very difficult to distinguish a child with environmental cannabis contamination from one to whom cannabis is being administered for purposes of sedation or who is being self-poisoned by consuming remaining hashish present at home or in the living area [START_REF] Moosmann | Finding cannabinoids in hair does not prove cannabis consumption[END_REF][START_REF] Cooper | Society of Hair Testing. Society of Hair Testing guidelines for drug testing in hair[END_REF]. Some authors have suggested screening for the non-psychoactive precursor of THC (THCA-A), which is a marker of external contamination, while THC-COOH is an indicator of THC absorption [START_REF] Wang | Review: Interpretation of drug presence in the hair of children[END_REF]. In case 28, the hair THC level was very high with the absence of THC-COOH, suggesting several manual contaminations by caregivers/parents. THCA-A is found in cannabis smoke, but it is not present following absorption due to the extensive metabolization to THC and THC-COOH. Therefore, its detection suggests external contamination [START_REF] Wang | Review: Interpretation of drug presence in the hair of children[END_REF].

Positive results should be very cautiously interpreted. They should be taken into consideration by a cross-disciplinary study group including: 1) physicians or pediatricians who are familiar with the medical file (clinical presentation) and past medical history, especially previous admission for a domestic accident; 2) social workers who have information concerning the socioeconomic situation and preexisting social measures; and 3) analytical and clinical toxicologists with expertise in hair analysis.

Bias and limitations -This study concerned our center, which does not reflect what is being done on a national level. However, during the Marie-Jeanne study [START_REF] Claudet | Unintentional Cannabis Intoxication in Toddlers[END_REF], we had interviewed each center regarding their practices concerning hair analysis in such cases and it appeared that we were by far the largest center in terms of annual demands (80%).

Among the 41 hair samples analyzed in our study, disparities in hair color were observed.

Although several studies have shown that melanin in hair appears to play a binding role for at least some commonly abused drugs, these findings have not been sustained when larger numbers of subjects were analyzed. This has been demonstrated for THC-COOH and cocaine [START_REF] Mieczkowski | Assessing the Potential of a "Color Effect" for Hair Analysis of 11nor-9-carboxy-delta(9)-tetrahydrocannabinol: Analysis of a Large Sample of Hair Specimens[END_REF][START_REF] Mieczkowski | Assessing the potential for racial bias in hair analysis for cocaine: examining the relative risk of positive outcomes when comparing urine samples to hair samples[END_REF].

Conclusion

-Without hair analysis, many pediatric forensic or abuse cases would not have been resolved. Rejecting hair testing because of some drawbacks would be a loss of opportunity in many cases. This paper highlights that hair testing in this context leads to parental admission of cannabis use and that the intensity of toddlers' exposure can be indirectly predicted by hair cannabinoid concentrations. Hair testing results are also helpful during the follow-up consultation with parents, as an appointment with an addictions team could be suggested. We also demonstrated that intensely exposed children had more serious clinical and biological manifestations and weighed less, leading to more severe presentations (comatose status). Collegial examination of the toddler environment through a full Graph 1 -Change in hashish THC concentration (results obtained from the analysis of products seized by French customs https://www.ofdt.fr/BDD/sintes/LePointSINTES04.pdf ), and comatose presentations among non-intentional cannabis intoxicated children

Location of intoxication

  The general data collected per patient were: demographic data (age, gender, weight, date and time of admission, mode of transportation); clinical data (vital signs on admission, Glasgow

	[mechanical ventilation required]); notion of parental consumption and outcome (date and
	time of discharge from the emergency unit, total hospitalization period, social issues [isolated
	parent, family violence, social deprivation, previous social measures], social measures [child
	protective services, alert information, reporting to the juvenile/family court judge, foster
	care]). The severity criteria were: coma (unarousable, unresponsive), seizures, respiratory
	failure (apnea and/or respiratory rate < 10 th percentile for age, and/or tracheal intubation),
	hypotension (systolic blood pressure < 5 th percentile for age), hypertension (systolic blood
	pressure > 95 th percentile for age), bradycardia (pulse rate < 80 bpm [age ≤ 1 year]; pulse rate
	< 60 bpm [age 1-6 years]), and a Poisoning Severity Score (PSS) of 3
	Coma Scale [GCS] score); clinical symptoms; intoxication-related data (time and mode of
	exposure, estimated ingested amount, place of intoxication, form of cannabis [resin, cannabis
	cigarette, edible products ("space cake", "space cookies", candies, chocolate bars)], liquid [e-
	cigarette]); data related to examinations: blood tests, lumbar puncture, head CT scan,
	electrocardiogram (EKG), electroencephalogram (EEG), toxicological tests (blood, urine,
	hair); disposition (home, general pediatric ward, intensive care or resuscitation unit

Table and graph legends Graph 1.

  Change in THC concentration (results obtained from the analysis of products seized by French customs), and comatose presentations among non-intentional cannabis intoxicated children.

Table I .

 I Main characteristics of non-intentional cannabis intoxicated pediatric patients

	(n=41).

Table II .

 II Results of hair analysis with cannabis metabolite concentrations.

Table III .

 III Hair detection of cannabinoid metabolites in comatose and non-comatose patients (percentage in column).

Table 1 -

 1 Main characteristics of non-intentional cannabis intoxicated pediatric patients (n=41)

	Population characteristics, n (%)		p
	Males	17 (41)
	Mean age, years (SD) [range]	1.5(0.5) [0,9-4.0]
	-male (n=17)	1.7(0.7) [0.9-4.0]	0.12
	-female (n=24)	1.3(0.3) [0.9-2.8]
	Age group
	0-11 months	9(22)
	12-23 months	27(66)
	24-35 months	3(7)
	≥36 months	2(5)
	Mean weight, Kg(SD) [range]	10.9(1.3)[8.4-17.5]	0.15
	-male	11.4(1.2) [9.0-17.5]
	-female	10.6(1.2) [8.4-14.8]
	Term of birth
	Term≥ 37 weeks of gestation	26(63)
	Prematurity	5(12)
	Non specified (Ns)	10(25)
	Referral mode
	-spontaneous	30(73)
	-family physician, pediatrician	11(27)
	Transportation mode
	-sanitary (firefighters, ambulances)	12(29)
	or medical transportation (physician
	on board)
	Weekend admission	16(39)
	Time of admission
	8:00AM-11:00AM	3(5)
	12:00AM-5:00PM	19(46)
	6:00PM-11:00PM	18(44)
	12:00PM-7:00AM	3(5)

investigation leads to appropriate decisions concerning the social measures that are in the best interest of the child [START_REF] Tsanaclis | Hair analysis when external contamination is in question: a review of practical approach for the interpretation of results[END_REF].

Contributor's statement page

X developed the project and the study design, analyzed results, interpreted data, and drafted the initial manuscript.

X collected data and critically reviewed and revised the manuscript.

X did the follow-up consultations.

X performed toxicological analyses and critically reviewed and revised the manuscript.

All the listed authors contributed to data collection and substantially participated in data analysis. All the authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.


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niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Water resources contamination is one of the greatest and most current challenges. It is a serious problem which can lead to dangerous risks on human health, flora, wildlife and also environment. Discharge of effluents from industrial runoff, wastewater, agricultural fields and also road runoff are the major sources of water bodies pollution, that release high levels of toxic pollutants [START_REF] Jern | Industrial wastewater treatment[END_REF]. These latter include essentially (1) total suspended solids as silts and clays and organic matters, (2) heavy metals such as lead, cadmium, chromium, mercury, zinc, nickel and copper ions, (3) nutrients, mainly phosphate and azote, (4) antibiotics and also (5) polycyclic aromatic hydrocarbons (PAHs) [START_REF] Jern | Industrial wastewater treatment[END_REF][START_REF] Flanagan | Stochastic Method for Evaluating Removal, Fate and Associated Uncertainties of Micropollutants in a Stormwater Biofilter at an Annual Scale[END_REF]. All these pollutants are very persistent in the environment, especially heavy metals, due to their toxicity nature and non-biodegradability [START_REF] Jern | Industrial wastewater treatment[END_REF]. Therefore, various technologies have been developed for metal ions removal. Among these, biosorption is one of the most attractive methods, based on metal binding to biological adsorbents. Different types of inexpensive biosorbents have been explored, they include dead biomass like agricultural waste and vegetative fibers, as well as living biomass such as microbial and bacterial organisms. Among these biosorbents, we list wool fibers [START_REF] Balköse | Adsorption of heavy metal cations from aqueous solutions by wool fibers[END_REF], modified jute fibers [START_REF] Shukla | Adsorption of Cu (II), Ni (II) and Zn (II) on modified jute fibres[END_REF], rice husk [START_REF] Krishnani | Biosorption mechanism of nine different heavy metals onto biomatrix from rice husk[END_REF], pomegaranate peel [START_REF] El-Ashtoukhy | Removal of lead (II) and copper (II) from aqueous solution using pomegranate peel as a new adsorbent[END_REF], hazelnut and almond shell (Pehlivan et al., 2009b), pine bark [START_REF] Cutillas-Barreiro | Pine bark as bio-adsorbent for Cd, Cu, Ni, Pb and Zn: batch-type and stirred flow chamber experiments[END_REF], wheat straw [START_REF] Coelho | Competitive and non-competitive cadmium, copper and lead sorption/desorption on wheat straw affecting sustainability in vineyards[END_REF] that have been explored for their high capacity of removing heavy metals from contaminated aqueous solutions. The present work focused on the use of flax fibers as biosorbent, in order to remove zinc, copper and lead ions under competitive and non-competitive conditions. The choice of this plant material, is related to its low cost and high availability. The Normandy region is one of the most prolific producers of flax in France and in Europe [START_REF] Meirhaeghe | Évaluation de la disponibilité et de l'accessibilité des fibres végétales à usages matériaux en France[END_REF]. Depestele Group, for instance, has developed numerous applications of this natural material in civil engineering. Actually, flax has been extensively used in the manufacture of woven fabrics, insulation materials and non-woven geotextiles [START_REF] Ouagne | Mechanical characterisation of flax-based woven fabrics and in situ measurements of tow tensile strain during the shape forming[END_REF].

Results of a previous study realized by [START_REF] Abbar | Experimental investigation on removal of heavy metals (Cu2+, Pb2+, and Zn2+) from aqueous solution by flax fibres[END_REF] showed a good capacity of flax fibers to remove zinc, copper and lead ions from aqueous solutions in a ternary system at equal massic concentrations. However, numerous questions have been raised after that work, especially regarding concentration effect and adsorption behaviors in non-competitive form for each ion, in order to judge the competition effect which may occur when the three metals are added simultaneously. With regards to heavy metals competition, relatively few studies have considered both single and multi-element systems in biosorption studies. [START_REF] Park | Competitive adsorption of heavy metals onto sesame straw biochar in aqueous solutions[END_REF], for instance, evaluated adsorption behaviors of zinc, copper, cadmium, chromium and lead adsorption onto sesame straw biochar and found that multimetal and monometal adsorption behaviors were not similar. Similarly, [START_REF] Pejic | Biosorption of heavy metal ions from aqueous solutions by short hemp fibers: effect of chemical composition[END_REF] explored the removal of zinc, cadmium and lead ions from aqueous solutions by short hemp fibers and reported that the maximum total uptake capacity for each zinc and cadmium in monometal solutions (78 mmol.kg -1 ) were different from multimetal solution (35 mmol.kg -1 ), while for lead it was quite the same (78 mmol.kg -1 and 74 mmol.kg -1 respectively). However, as long as we know, no previous studies have dealt with zinc, copper and lead ions biosorption onto flax fibers in both single and multimetal ion trials. In view of that, a series of biosorption experiments of these three metals were performed in both single and ternary batch systems. The objectives were (i) to compare biosorption capacities of the three metals under competitive and noncompetitive forms, and (ii) to evaluate the competition effect on each metal biosorption onto flax fibers when the three metals are added simultaneously. Different conditions, resulting from changing parameters such as contact time, biosorbent concentration and pH, were investigated. Pseudo-first order and pseudo-second order kinetics and intraparticle diffusion models were tested for the adsorption kinetic studies when Langmuir and Freundlich models were used to fit the biosorption data. The goal of this study is to test afterwards a full-scale stormwater device as a biofiltration system, in order to purify industrial and road runoff.

Materials and methods

Preparation of biosorbent

Flax tows were obtained from Depestele group in Normandy. They were defibrated after retting and scutching processes of linen, which consist of separating the fibers from the straws. Hence, the retrieved biosorbent consisted of flax byproduct. Initially, the tows were rinsed with deionized water to remove dirt, and then dried in the oven for 24 h at 60 °C. The fibers were next cut into small pieces of 0,2 -2 mm. Scanning electron microscope images and FT-IR spectrum were performed with S-3000N scanning electron microscope and Perkin Elmer spectrum BX FT-IR instrument respectively. The spectrum of flax fibers was measured by a Fourier Transform Infrared Spectrometer within the range 600 -4000 cm -1 in order to determine the vibration frequency in the functional groups before and after adsorption.

Batch experiments

Adsorption experiments in both competitive and non-competitive forms have been conducted at a temperature of 18°C in order to determine adsorption characteristics of flax fibers for zinc, copper and lead ions. The biosorbent was added to 200 mL of the metallic solution initially prepared from zinc nitrates hexahydrated, (Zn(NO3).6H2O), copper nitrates trihydrate (Cu(NO3).3H2O) and lead nitrates (Pb(NO3)), all purchased from Fisher Scientific (France).

The pH was adjusted by adding 1M NaOH or 1M HNO3 solution, purchased from Sigma Aldrich (France).

All solutions were prepared with deionized water and all experiments were run in triplicate.

Kinetics trials in monometal and mulimetal solutions

Single and ternary metal ion experiments were conducted in continuously stirred beakers containing 200 mL of zinc, copper and lead ions solutions at a concentration respectively of 0.044 mmol.L -1 , 0.042 mmol.L -1 and 0.042 mmol.L -1 under the ternary form, and 0.045 mmol.L -1 , 0.045 mmol.L -1 and 0.046 mmol.L -1 under the single form. The pH was adjusted to 6.4 and the amount of biosorbent was 400 mg. The monometal and multimetal solutions were shaken for 5, 15, 30 and 60 min and 2, 4, 6, 8 and 10 h using magnetic agitators, then filtered through a 0.45 µm nylon membrane filter. Samples of 40 mL were retrieved from the filtrated solutions and acidified with 1% of nitric acid, then stored in polyethylene bottles at 4 °C for no more than three days. Zinc, copper and lead ions concentrations were determined in the liquid fractions by means of ICP-AES (Inductively coupled plasma, Atomic emission spectrometry) in COBRA laboratory at INSA-Rouen (France). The ICP-AES spectrometer (ICAP6300, ThermoFisher Scientific, USA) was regularly verified and was calibrated at each time of use.

The limits of detection were calculated as three times the standard deviation of the blank matrix and corresponded to 19.2 µg.L -1 (2.94 10 -4 mmol.L -1 ) for Zn, 0.9 µg.L -1 (1.42 10 -5 mmol.L -1 ) for Cu, and 2.1 µg.L -1 (1.01 10 -5 mmol.L -1 ) for Pb. The limits of quantification were calculated as ten times the standard deviation of the blank matrix and corresponded to 64.0 µg.L -1 (9.79 10 -4 mmol.L -1 ) for Zn, 3.0 µg.L -1 (4.72 10 -5 mmol.L -1 ) for Cu, and 7.0 µg.L -1 (3.38 10 -5 mmol.L -1 ) for Pb. The amount of each adsorbed metal (mmol.kg -1 ) per unit of weight of adsorbent, or adsorption capacity was calculated as the difference between the concentration added at the start, called also initial concentration (mmol.L -1 ), and the concentration of the remaining metal (mmol.L -1 ) in the dissolved fraction after any time t of adsorption, all over solid liquid ratio (kg.L -1 ) where m is the dried mass of adsorbent and the volume of the adsorbate solution:

(1)

The removal (%) of each heavy metal was calculated as the amount adsorbed (mmol.L -1 ) over the concentration added at the start:

.100

(2) Kinetic trials describe the biosorption behavior as a function of time, but also helps determine an equilibrium time that will be maintained for next adsorption experiments.

To better describe the kinetics of zinc, copper and lead biosorption onto flax fibers, the experimental data was applied to pseudo-first and pseudo-second order kinetic models and intraparticle diffusion model.

Introduced by [START_REF] Lagergren | Zur Theorie der sogenannten Adsorption gelöster Stoffe Kungliga Svenska Vetenskapsakad[END_REF], pseudo-first order kinetic model is obtained by integrating the following general equation

(3)

for n = 1: (4)

Eq (4) becomes as follows:

(5)

Where is the adsorption capacity at equilibrium and ( the rate constant of the pseudo-first order model.

The pseudo-second order kinetic model is obtained from equation (3) for n = 2 [START_REF] Ho | Pseudo-second order model for sorption processes[END_REF]:

(6)
Integrating for the boundary conditions = 0 at and at time , and rearranging the equation, we obtain:

(7)
where is the rate constant of pseudo-second order biosorption ( ).

According to the theory of [START_REF] Weber | Kinetics of adsorption on carbon from solution[END_REF], the intraparticle diffusion model is expressed by:

+ (8)
where is the intra-particle diffusion rate constant (mmol.kg -1 .min -1/2 ) and is a constant related to the thickness of the boundary layer. The higher is the value of , the greater is the effect of the boundary layer. The adsorption process is completely governed by intra-particle diffusion if the plot of versus gives a straight line passing through the origin.

The study of pH and biosorbent concentration effects

In order to examine the influence of pH on flax biosorption, 400 mg of flax fibers has been maintained in contact with 200 mL of zinc, copper and lead ions solutions, in both competitive and non-competitive forms under 60 min of magnetic agitation. The concentration of heavy metals in all experiments was about 0.045 mmol.L -1 , and the pH values tested were 1.8, 3.0, 4.0 and 6.4. pH was not raised upper to 7.0 in order to avoid the precipitation of the metal ions [START_REF] Abbar | Experimental investigation on removal of heavy metals (Cu2+, Pb2+, and Zn2+) from aqueous solution by flax fibres[END_REF].

The study of biosorbent concentration effect was also carried out at equal molar concentrations of 0.044 mmol.L -1 in both types of metal ion systems, and the tested concentrations were 0.5 g.L -1 , 1 g.L -1 , 2 g.L -1 and 5 g.L -1 .

Biosorption curves at equilibrium

For each batch test, a mass of 400 mg of flax fibers was added within 200 mL of zinc, copper and/or lead ions solutions, in monometal and ternary metal systems. The solutions were shaken for 60 min. Parameters values of the adsorption experiments are presented in Table 1.

Data obtained from adsorption experiments were adjusted to the Langmuir (Eq. ( 9)) and Freundlich (Eq. ( 10)) models.

(9) (10) Where (mmol.kg -1 ) is the maximum adsorption capacity, (L.mmol -1 ) the Langmuir constant related to the adsorption energy, and (L 1/n .mmol (1-1/n) .kg -1 ) and (dimensionless) are the Freundlich coefficients in Eq. ( 4).

The essential characteristic of Langmuir model, expressed in term of dimensionless separation factor was defined as:

(11)

This parameter may indicate the nature of adsorption to be either unfavorable ( > 1), linear ( , favorable , or irreversible (

We specify that in all the presented figures ahead, results related to zinc, copper and lead are referred to as Zn_tri, Cu_tri and Pb_tri respectively in the multimetal system, and Zn_mono, Cu_mono and Pb_mono in the monometal system.

Results and discussion

Characterization of biosorbent: SEM and FT-IR analysis

SEM images of dried flax fibers were performed, one of these is presented in Fig. S1 straw [START_REF] Coelho | Competitive and non-competitive cadmium, copper and lead sorption/desorption on wheat straw affecting sustainability in vineyards[END_REF] and Crambe abyssinica Hochst seeds [START_REF] Rubio | Removal of cadmium from water using by-product Crambe abyssinica Hochst seeds as biosorbent material[END_REF]. These functional groups present in lignin, pectin and hemicellulose are reported to be involved in the adsorption mechanism and responsible for metal binding [START_REF] Lee | Removal of Heavy Metal Ions from Aqueous Solutions Using Lignocellulosic Fibers[END_REF]. The FT-IR analysis of flax fibers after 60 min adsorption showed no specific difference at the use of 0.044 mmol.L -1 concentration of zinc, copper and lead in the ternary system (Fig. S2), same bands were observed, which may suggest an abundance of active sites and that only few of them were engaged in the biosorption process. At the use of higher concentrations, 0.6 mmol.L -1 and 1.2 mmol.L -1 , the peaks previously observed at 1638 and 1731 disappeared and the ones at 1368, 1426 were significantly dissipated (Fig. S2). This finding means that biosorption process involved chemical reactions between studied metals and existent functional groups. Also, the transmittance of the curves after adsorption was higher which confirms less present functional groups on the biosorbent after adsorption.

Biosorption kinetics

Each of zinc, copper and lead ions removal by flax fibers was plotted as a function of time and presented in Fig. 1, for both ternary and single metal ion solutions. Adsorption behavior was almost the same either in competitive or non-competitive form. Adsorption was very fast in the first 15 min then attained equilibrium within 60 min. This result has also been found by other authors, when using waste beer yeast [START_REF] Han | Biosorption of copper and lead ions by waste beer yeast[END_REF] and seashells powder [START_REF] Bozbaş | Low-cost biosorbent: Anadara inaequivalvis shells for removal of Pb (II) and Cu (II) from aqueous solution[END_REF], and was explained by a quick sites saturation. We also notice that for the three metals, the adsorption is a bit faster in the single metal ion solution (Fig. 1a, b, andc). As regards to the removal rate, it was higher in the single metal form for the three metals. Metal removal was significantly more important in the single ion solution for Zn (62.0% and 81.8% respectively in the ternary and the single metal ion solution), slightly more important for Cu (75.4% and 80.1% respectively in the ternary and single metal ion solutions), and quite the same for lead (94.4%) (Fig. 1a, b, andc). In the ternary metal ion solution (Fig. 1d), the adsorption selectivity was in the order Pb > Cu > Zn, which is a similar result as [START_REF] Krishnani | Biosorption mechanism of nine different heavy metals onto biomatrix from rice husk[END_REF], Pehlivan et al., (2009), [START_REF] Coelho | Competitive and non-competitive cadmium, copper and lead sorption/desorption on wheat straw affecting sustainability in vineyards[END_REF] and [START_REF] Cutillas-Barreiro | Pine bark as bio-adsorbent for Cd, Cu, Ni, Pb and Zn: batch-type and stirred flow chamber experiments[END_REF], found using different biosorbents mentioned in section 1. The difference in metals adsorption percentages as well as adsorption capacities at equilibrium between both types of solutions (Table S1 in SI) highlighted the competition taking place between metals when they are added simultaneously especially for zinc and copper. Besides, lead was still the most favorable removed heavy metal in both forms.

To describe zinc, copper and lead biosorption onto flax fibers in single and ternary metal ion solutions, the experimental data were applied to pseudo first order (Figure S3 in SI) and pseudo-second order (Figure S4 in SI) models. Adsorption capacities obtained from the pseudo-first order and pseudo-second order models were very close to the ones observed experimentally (Table S2 in SI). Lead data showed a better fit with the pseudo-second order with high correlation coefficients of 0.97 and 0.98 respectively in the monometal and ternary systems (Table S2 in SI), which assumes a chemisorption reaction due to valency forces through sharing or exchange of electrons between flax fibers and divalent metals, and thus a strong chemical bonding with functional groups. This model has been successfully applied to metal ions adsorption from aqueous solutions onto various materials, mainly vegetative fibers, activated carbons, bacteria and agricultural waste [START_REF] Ho | Review of second-order models for adsorption systems[END_REF]. Zinc data, on the contrary, exhibited a better adjustment with the pseudo-first order model in both systems (Table S2 in SI), which may indicate that zinc adsorption was controlled by weak interactions such as van der Waals forces between flax and zinc ions, generated by the surface charge density of the adsorbent, explaining thus the weak attachment of zinc to flax fibers compared to lead and copper. Similarly to lead, copper data showed a better fit with the pseudo-second order model in the non-competitive system with a correlation coefficient of 0.98, while in the noncompetitive system no specific difference could be observed between both models, which indicated that both physisorption and chemisorption may be involved in the adsorption mechanism when the three metals are simultaneously added.

In order to analyze the rate limiting step, the intra-particle diffusion model was also used. The data exhibited multilinear plots in this study for all the investigated metals (Fig. S5);

suggesting that more than one step influenced the process of biosorption and that other mechanisms may be involved besides intra-particle diffusion [START_REF] Duan | HF-free synthesis of nanoscale metal-organic framework NMIL-100 (Fe) as an efficient dye adsorbent[END_REF]. The values of and for all stages are listed in Table S3 in SI. It is reported that the first stage describes an instantaneous adsorption where the adsorbate migrates from the aqueous solution towards the external surface of the solid, representing a film diffusion. The second step concerns a gradual adsorption, where adsorbate migrates within the pores of adsorbent. The third and final equilibrium stage, also revealed very rapid, cannot be considered the rate controlling step. Hence, the overall rate is either governed by film diffusion or intra-particle diffusion or both. In addition, the obtained linear lines did not pass through the origin, this deviation can be related to the difference in the mass transfer rate in the initial and final stages of adsorption [START_REF] Tan | Adsorption isotherms, kinetics, thermodynamics and desorption studies of 2, 4, 6-trichlorophenol on oil palm empty fruit bunch-based activated carbon[END_REF].

Effect of pH

Solution pH is a very essential parameter in adsorption process. It has been very well documented in literature. The effect of pH variation on the removal of metal ions by flax fibers is shown in Fig. 2. Metal biosorption efficiency was very dependent on pH, it decreased along with pH decreasing for the three metals Zn, Cu and Pb. Similarly to [START_REF] Antunes | An evaluation of copper biosorption by a brown seaweed under optimized conditions[END_REF] and [START_REF] Dang | Equilibrium and kinetics of biosorption of cadmium (II) and copper (II) ions by wheat straw[END_REF] who investigated brown seaweed and wheat straw biosorption respectively, the results indicated that at lower pH, there might be more competition for the active sites between H + and metal ions, resulting in a very low biosorption of metals. This may also be explained by electrostatic repulsion forces occurring between the surface and the studied cations once active sites are occupied by the protons H + . In addition, it has been reported in literature that functional groups which act like active sites for metal binding, may react differently depending on pH. The same sorption selectivity of Pb > Cu > Zn, as in section 3.2, was observed at all pH values tested. Regarding competitive and non-competitive experiments for the three ions, it was apparent that each metal removal was higher in the noncompetitive form, at all pH values. Overall, biosorption efficiency greatly depended on solution pH and reached its optimal at 6.4 for the three metals in both mono and multimetal systems.

3.4-Effect of the amount of biosorbent

The effect of biosorbent concentration on zinc, copper and lead ions adsorption was studied in both single and multimetal forms at the concentration of 0.044 mmol.L -1 and pH around 6.4.

The removal of the three metals was found to increase rapidly with the increase in biosorbent concentration in all experiments (Fig. 3), showing higher availability of active sites. In multimetal solution, lead removal was observed to be the highest and zinc removal the least at all biosorbent concentrations. Thus the sequence was still Pb > Cu > Zn as previously shown in sections 3.2 and 3.3. However, zinc ion turned out to be well adsorbed when high amount of biosorbent was utilized, mainly at biosorbent concentration of 5 g.L -1 , where no difference was noticed between copper and zinc removal (Fig. 3a), owing to the abundance of exchangeable sites, as similarly found in previous studies as [START_REF] Han | Biosorption of copper and lead ions by waste beer yeast[END_REF], [START_REF] El-Ashtoukhy | Removal of lead (II) and copper (II) from aqueous solution using pomegranate peel as a new adsorbent[END_REF] and [START_REF] Abbar | Experimental investigation on removal of heavy metals (Cu2+, Pb2+, and Zn2+) from aqueous solution by flax fibres[END_REF]. Regarding the competition effect, it was very visible for zinc removal in the multimetal solution (Fig. 3b). In competitive experiments, Zn removal increased from 4.0% to 62.0% while in non-competitive experiments it increased from 30.1% to 81.8%, when biosorbent concentration increased from 0.25 g.L -1 to 2 g.L -1 . The difference of Zn removal between mono and multimetal solutions was though insignificant at the use of 5 g.L -1 of flax fibers. A similar behavior was shown in copper and lead removal as functions of biosorbent concentration (Fig. 3c, Fig. 3d), but only from 0.25 g.L -1 up to 1 g.L -1 . Beyond the concentration of 1 g.L -1 , a slight decrease of copper and lead removal was shown in the single metal ion solution, which can be explained by particle interaction such as aggregation of biosorbent that may lead to a decrease in total surface area (Shukla et al., 2002;[START_REF] Semerjian | Removal of heavy metals (Cu, Pb) from aqueous solutions using pine (Pinus halepensis) sawdust: Equilibrium, kinetic, and thermodynamic studies[END_REF]. Therefore, the biosorbent concentration of 2 g.L -1 was maintained in all subsequent experiments.

Effect of initial concentration

To study the effect of initial concentration of heavy metals, metal ion solutions of zinc, copper and lead were prepared in both single and multimetal forms, with initial concentrations ranging from 0.04 mmol.L -1 to 1.2 mmol.L -1 (See Table 1). For each adsorption experiment, 400 mg of flax fibers was added to 200 mL of metal ion solution. Figure 4a and Figure 4b show heavy metal removal and adsorption capacity, respectively, as functions of added concentrations for the three heavy metals studied, in both types of systems. When initial concentration increased, heavy metal removal generally decreased for the three metals in both single and ternary metal solutions (Fig. 4a) while adsorption capacity increased for all metals except for Zn in the ternary solution (Fig. 4b) where it decreased. In non-competitive experiments, the sorption sequence was in the order Pb >≈ Cu > Zn for trials with the lowest molar concentrations (0.04, 0.08, 0.16 and 0.36 mmol.L -1 ) with optimal adsorption capacity values of 108.76, 108.5 and 65.08 mmol.kg -1 respectively (Fig. 4b), corresponding to 58.0%, 57.5% and 36.2% of metal removal (Fig. 4a), obtained at the initial concentration of 0.36 mmol.l -1 . Whereas for the highest concentrations (0.67 and 1.2 mmol.L - 1 ), the selectivity sequence changed to Cu >≈ Pb > Zn with maximum adsorption capacity values of 119.2, 112.3 and 80.1 mmol.kg -1 respectively, corresponding to 32.3%, 31.3% and 24.1% of metal removal, obtained at the initial concentration of 0.67 mmol.L -1 . A slight decrease of sorption capacity was shown at 1.2 mmol.L -1 for the three ions.

On the other hand, the competitive experiments showed the selectivity sequence Pb > Cu >> Zn at all times, and also a considerable favorable retention for lead and copper comparing with zinc (Fig. 4a). Lead sorption capacities increased from 19.9 to 90.7 mmol.kg -1 , when the initial concentration increased from 0.04 to 1.2 mmol.L -1 , although its removal percentage decreased from 94.4% to 15.0%. Similarly, copper sorption capacities increased from 16.1 to 66.4 mmol.kg -1 with the increase of initial concentration, while its removal percentage decreased from 75.4% to 10.8%. For zinc, the adsorption behavior was different, sorption capacities were very low decreasing from 13.0 to 2.7 mmol.kg -1 , and its removal percentage also decreased from 62.0% to 0.9%, when initial concentrations increased from 0.04 to 1.2 mmol.L -1 . Zinc was thus the less adsorbed ion in all experiments. Comparing competitive and non-competitive experiments, zinc was also the most affected ion by the presence of other ions, its sorption was very considerably higher in the non-competitive trials (from 18.23 to 80.1 mmol.kg -1 ) than in the competitive trials (from 13.0 to 2.7 mmol.kg -1 ). For copper, the competition effect was also visible. Its sorption was higher in the non-competitive (from 17.9 to 119.2 mmol.kg -1 ) than in the competitive experiments (from 16.1 to 66.4 mmol.kg -1 ).

Concerning lead, the impact of competition was the less important but still quite visible between the non-competitive (21.9 to 112.3 mmol.kg -1 ) and competitive trials (19.9 to 90.7 mmol.kg -1 ).

Therefore, higher affinity with active sites was mainly observed for lead ions, as previously shown by Al-Asheh and Duvnjak, (1998) and [START_REF] Park | Competitive adsorption of heavy metals onto sesame straw biochar in aqueous solutions[END_REF] at the use of pine bark and sesame straw biochar respectively, which allows faster interaction with functional groups. As evidenced in FT-IR analysis in section 3.1, flax fibers contain negatively charged functional groups that are able to complex metals. The studies developed by [START_REF] Guo | Adsorption of metal ions on lignin[END_REF] and [START_REF] Lee | Removal of Heavy Metal Ions from Aqueous Solutions Using Lignocellulosic Fibers[END_REF] have demonstrated that the carboxylic and phenolic groups present in lignin play an important role in adsorption processes. They can induce complexes formation with heavy metals through the coordination bonds. In addition, and according to several researches, it has been suggested that ion exchange is one of the most responsible mechanisms in biosorption process. [START_REF] Chen | Removal of copper (II) ions by a biosorbent-Cinnamomum camphora leaves powder[END_REF] demonstrated that biosorption process using Cinnamomum camphora leaves powder was dominated by ion-exchange mechanism based on activation energies of the reactions. [START_REF] Božić | Adsorption of heavy metal ions by sawdust of deciduous trees[END_REF] showed that heavy metals and protons were substituted by the alkali-earth metals contained in the molecular structure of sawdust of deciduous trees, by investigating the release of Ca 2+ , Mg 2+ , Na + , K + and H + from the adsorbent.

It is also worthwhile to mention that the sorption capacities for the three metals in both systems followed the same order as their electronegativities Pb (2.33) > Cu (1.90) > Zn (1.65) [START_REF] Mcbride | Environmental Chemistry of Soils[END_REF], which is one of the justifications put forward to explain this selectivity order, also found in studies using chitosan and egg shell [START_REF] Shaheen | Heavy metals removal from aqueous solutions and wastewaters by using various byproducts[END_REF], pine bark [START_REF] Cutillas-Barreiro | Pine bark as bio-adsorbent for Cd, Cu, Ni, Pb and Zn: batch-type and stirred flow chamber experiments[END_REF], and sesame straw biochar [START_REF] Park | Competitive adsorption of heavy metals onto sesame straw biochar in aqueous solutions[END_REF]. This is in particular attributed to the fact that metal ions with higher electronegativities exhibit higher attractions for electrons [START_REF] Deng | Adsorption behaviour and mechanisms of cadmium and nickel on rice straw biochars in single-and binary-metal systems[END_REF]. Another explanation is that the hydrated radius of lead (4.01 Å) is smaller than that of copper (4.19 Å) and zinc (4.30 Å), which enables lead to form stronger bonds with most functional groups [START_REF] Park | Competitive adsorption of heavy metals onto sesame straw biochar in aqueous solutions[END_REF].

Regarding the ionic interaction effect between monometal and multimetal systems, one of the methods consists in evaluating the ratio R of the ion sorption capacity in the multimetal system Qe-mult to the ion sorption capacity in the monometal system Qe-mono [START_REF] Mohan | Single component and multi-component adsorption of metal ions by activated carbons[END_REF], in a way that for R > 1, the ion sorption is promoted in the multimetal system, and for R < 1 the ion sorption gets overcome by the presence of other ions, and for R = 1 no interaction occurs between ions. In this study, a destructive effect was mainly shown for zinc ions in the ternary metal system as zinc removal got promptly decreasing with the increase of metal initial concentrations. Its sorption capacities were also the lowest and quite constant, unlike zinc behavior in the single metal form, where sorption capacity joined the same behavior as copper and lead sorption capacities. In the ternary system, the ratios R(Zn) varied between 0.14 and 0.06 once beyond the initial concentration of 0.08 mmol.L -1 , while for copper it was ranged between 0.52 and 0.43, and for lead between 0.84 and 0.68. Other works have reported this destructive effect in multimetal aqueous solutions, such as the work developed by [START_REF] Reynel-Avila | A survey of multi-component sorption models for the competitive removal of heavy metal ions using bush mango and flamboyant biomasses[END_REF], which used flamboyant and bush mango biomasses in binary systems of Pb 2+ -Ni 2+ and Pb 2+ -Cd 2+ . Monometal experiments showed that cadmium and nickel were considerably affected by the presence of lead whose adsorption capacities in single and binary systems were almost identical (R(Pb) ≈ 1). A similar behavior was observed in a ternary metal system of Pb 2+ -Cu 2+ -Cd 2+ , using wheat straw as biosorbent [START_REF] Coelho | Competitive and non-competitive cadmium, copper and lead sorption/desorption on wheat straw affecting sustainability in vineyards[END_REF]. Cadmium ions were efficiently removed in the single metal ion experiments (metal removal ranging from 90.6% to 81.5%) but were overcome by the presence of lead and copper especially at the highest concentration of 6.0 mmol.L -1 where only 23.4% of cadmium was removed.

Biosorption curves in competitive and non-competitive experiments

Adsorption curves are essential to describe adsorption characteristics at a constant temperature. For that purpose, several adsorption models have been established in literature, the most common used are the Langmuir and Freundlich models. The first assumes a homogeneous surface and that all active sites have equal energies, while the second describes a heterogeneous surface and shows no limit saturation. To study biosorption curves of the three ions by flax fibers, experimental adsorption capacity at equilibrium was plotted against equilibrium concentration and compared with the Langmuir and Freundlich models for both monometal and multimetal solutions (Fig. 5), except for zinc in the ternary system as zinc adsorption capacity was significantly low and promptly decreased when added concentration increased. Hence, zinc adsorption curve in the ternary system couldn't fit the models and obtained values of KL and n were meaningless, which confirmed a very difficult adsorption of zinc on active sites. On the other hand, and based on the calculated values (Table 2), it can be inferred that the Langmuir model provides a slightly better fit for flax fibers suggesting thus a monolayer adsorption, besides for copper in the ternary system where the Freundlich showed a better adjustment, indicating a probable multilayer adsorption of copper when other ions are co-existent.

The monolayer adsorption capacities (mmol.kg -1 ) of flax fibers determined using the adsorption were only 2 and 1.4 times lower respectively. This clearly showed that zinc ions were very affected by competition and easily exchanged by other metals. In addition, the Langmuir constant KL, related to the affinity of the binding sites was higher in the noncompetitive experiments for copper and lead (Table 2), showing a stronger affinity with the adsorbent active sites. Furthermore, the parameter RL was represented in Fig. S6 in SI for all conducted experiments except for zinc in the multimetal solution. Fig. S6 (SI) indicated a very favorable adsorption for the studied metals in the single form where 0.02 < RL(Zn) < 0.41, 0.01 < RL(Cu) < 0.32 and 0.003 < RL(Pb) < 0.09. In the multimetal system, RL got higher but was still indicating a good adsorption for copper and lead as 0.02 < RL(Cu) < 0.39 and 0.01 < RL(Pb) < 0.22. The competition effect was thus very important for zinc, and less visible for copper and lead. Similarly, in a study conducted by Mahamadi and Nharringo, (2010) on Eichhornia crassipes, it has been shown that cadmium and zinc ions were overcome in the presence of lead. [START_REF] Park | Competitive adsorption of heavy metals onto sesame straw biochar in aqueous solutions[END_REF] have also found that cadmium and zinc adsorption were respectively 17.2 and 4.85 times lower in the multimetal system of (Cd, Zn, Cu, Cr, Pb) using sesame straw biochar. [START_REF] Liu | Non-competitive and competitive adsorption of Cd2+, Ni2+, and Cu2+ by biogenic vaterite[END_REF] have reported as well that the adsorption of cadmium and nickel ions on biogenic vaterite were reduced by 82.42% and 65.09% respectively in the presence of copper.

Compared to other lignocellulosic materials in the scientific literature, flax fibers adsorption capacity was very competitive (see Table S4 in SI). The maximum adsorption capacity of zinc using flax fibers (76.40 mmol.kg -1 ) was more important than that of jute fibers (54.30 mmol.kg -1 ) and hemp fibers (35.00 mmol.kg -1 ) [START_REF] Shukla | Adsorption of Cu (II), Ni (II) and Zn (II) on modified jute fibres[END_REF][START_REF] Pejic | Biosorption of heavy metal ions from aqueous solutions by short hemp fibers: effect of chemical composition[END_REF]. Also, the maximum adsorption capacity of copper (121.57 mmol.kg -1 ) was higher than those reported for bornean oil palm shell (27.63 mmol.kg -1 ), barley straw (73.01 mmol.kg -1 ), jute fibers (66.56 mmol.kg -1 ) and sawdust (28.16 mmol.kg -1 ) [START_REF] Chong | The adsorption of heavy metal by Bornean oil palm shell and its potential application as constructed wetland media[END_REF]Pehlivan et al., 2009a;[START_REF] Shukla | Adsorption of Cu (II), Ni (II) and Zn (II) on modified jute fibres[END_REF][START_REF] Yu | The removal of heavy metals from aqueous solutions by sawdust adsorption-removal of lead and comparison of its adsorption with copper[END_REF]. For lead, flax fibers biosorption (111.68 mmol.kg -1 ) was very high comparing with sisal fibers (6.47 mmol.kg -1 ), sawdust (15.40 mmol.kg -1 ), bornean oil palm shell (16.36 mmol.kg -1 ), chitosan (25.62 mmol.kg -1 ), rice husk (40.11 mmol.kg -1 ), almond shell (38.99 mmol.kg -1 ), and hemp fibers (74.00 mmol.kg -1 ) (Dos [START_REF] Santos | Biosorption of Pb (II) and Cd (II) ions by Agave sisalana (sisal fiber)[END_REF][START_REF] Yu | The removal of heavy metals from aqueous solutions by sawdust adsorption-removal of lead and comparison of its adsorption with copper[END_REF][START_REF] Chong | The adsorption of heavy metal by Bornean oil palm shell and its potential application as constructed wetland media[END_REF][START_REF] Zulkali | Comparative studies of Oryza sativa L. husk and chitosan as lead adsorbent[END_REF]Pehlivan et al., 2009b;[START_REF] Pejic | Biosorption of heavy metal ions from aqueous solutions by short hemp fibers: effect of chemical composition[END_REF], very close to that of barley straw (111.96 mmol.kg -1 ), and slightly lower than those of hazelnut shell (136.00 mmol.kg -1 ) and cashew nut shell (138.27 mmol.kg - 1 ) (Pehlivan et al., 2009b;[START_REF] Coelho | Removal of metal ions Cd (II), Pb (II), and Cr (III) from water by the cashew nut shell Anacardium occidentale L[END_REF]. Based on these results, it has been shown that flax fibers had a very good adsorption capacity and therefore it is a very efficient material for water treatment and heavy metals removal.

Using the Freundlich model, the Freundlich constant KF (L 1/n .mmol (1-1/n) .kg -1 ) related to the adsorption capacity was in the order Pb (92.66) > Cu (63.28) > Zn (8.03) in multimetal solution and Cu (128.52) > Pb (120.65) > Zn (80.10) in monometal solution (Table 2).

Comparing the ratio for each metal, it followed the order Zn (9.98) > Cu (2.02) > Pb

(1.30) indicating that zinc was very affected by competition while lead and copper were not very influenced, similarly as demonstrated above with the Langmuir model. Furthermore, the Freundlich constant n ranging between 2 and 10 (Table 2), attested that the biosorption was very favorable for all the ions, except again for zinc in the ternary metal system where n value was meaningless. Therefore, both adsorption models showed the same behavior of ions, even though a better adjustment was generally shown with the Langmuir rather than the Freundlich model.

Conclusion

Flax fibers were used to remove heavy metals from aqueous solutions. A series of batch experiments were performed adding lead, copper and zinc ions, either simultaneously or individually. The aim of the study was to evaluate and compare adsorption behavior of these metals in two conditions. The obtained results under competitive conditions were different from those obtained in non-competitive conditions. The adsorption efficiency of flax fibers related to each heavy metal was higher in the single metal ion solution, and the order of the adsorption selectivity was Pb ≈ Cu > Zn and changed to Pb > Cu >> Zn in the multimetal solution. Lead was the most retained metal in multimetal solution followed by copper, while zinc was very considerably affected by the presence of other ions. Electronegativity, hydrated radius, and ions interactions with functional groups were the main justifications of this finding, in addition to ion exchange which was reported to be the most dominant mechanism in biosorption process. On the other hand, the effectiveness of the ions removal was found to be very dependent on the aqueous solution pH, contact time, initial concentration of metals and biosorbent concentration.

Based on the overall results, flax fibers demonstrated a high biosorption capacity of heavy metals in both forms of solutions, except for zinc in the multimetal solution. It is thus an available and efficient low-cost material for water treatment, whose recycling should be promoted. Hence, in order to regenerate flax fibers and judge the reversibility of biosorption, further studies will be conducted to evaluate ions desorption and investigate conditions of ions release.

  (SI) with BSE mode and magnification of 500.The resulted FT-IR spectrum of unloaded flax fibers, presented in the Fig.S2in SI, indicated the presence of several functional groups for binding metal ions. The band at 3330 suggests the presence of phenolic, alcoholic or acidic hydroxyl groups (O-H), generally present in lignocellulosic materials (López[START_REF] Pasquali | Pyrolysis of lignin and IR analysis of residues[END_REF]. Bands at 1050, 1104 and 1156 cm -1 can be related to a C-O stretching of alcohols and carboxylic acids. The peaks at 1638 and 1731 can be assigned to the stretching vibrations of C=C, NH-group and C=O[START_REF] Monier | Preparation and characterization of chelating fibers based on natural wool for removal of Hg (II), Cu (II) and Co (II) metal ions from aqueous solutions[END_REF], which indicate the presence of carboxyl groups and amides. The bands at 2916, 1314, 1368 and 1426 can be attributed to the C-H group, also found in sisal fibers used for lead and cadmium biosorption[START_REF] Santos | Biosorption of Pb (II) and Cd (II) ions by Agave sisalana (sisal fiber)[END_REF], and other biosorbents such as wheat

Fig. 1 .

 1 Fig. 1. Effect of contact time on Zn, Cu and Pb biosorption in single and ternary metal systems at 0.044 mmol.L -1 metal concentration and biosorbent concentration of 2 g.L -1 and pH of 6.4.

Fig. 2 .

 2 Fig. 2. Effect of pH on Zn, Cu and Pb biosorption onto flax fibers in single and ternary metal systems at 0.044 mmol.L -1 metal concentration and biosorbent concentration of 2 g.L -1 .

  Fig. 3. a) Zn, Cu, and Pb removal in multimetal solution for different biosorbent concentrations (0.25, 0.5, 1, 2, 5 g.L -1 ). b), c), and d) Comparison of, respectively Zn, Cu, and Pb removal in competitive and non-competitive experiments for the same biosorbent concentrations at 0.044 mmol.L -1 metal concentration and pH of 6.4.

Fig. 4 .

 4 Fig. 4. a) The removal (%) and b) adsorption capacity (mmol.kg -1 ) of Zn, Cu and Pb by flax fibers for different initial concentrations (0.04, 0.08, 0.16, 0.36, 0.67 and 1.2 mmol.L -1 ) in competitive and non-competitive experiments, at biosorbent concentration of 2 g.L -1 and pH of 6.4.

  Langmuir model followed the order Pb (81.61) > Cu (57.48) > Zn (8.03) in the competitive experiments, and changed to Cu (121.57) > Pb (111.68) > Zn (76.40) in the non-competitive experiments. All adsorption capacities were lower in the multimetal solution, as shown in section 3.5. The ratio of each metal where and are the maximum sorption capacities in the mono and multimetal systems respectively, indicated that zinc adsorption was 10 times lower in the competitive experiments, while copper and lead

22Fig. 5 .

 5 Fig. 5. Experimental data and calculated values from the Langmuir and Freundlich models for a) zinc, b) copper and c) lead in both monometal and ternary metal solutions.

Ternary metal solutions Monometal solutions C(mmol.L -1 ) pH Zn Cu Pb Zn Cu Pb C(mmol.L - 1 ) pH C(mmol.L -1 ) pH C(mmol.L - 1 ) pH

	1.258	1.231	1.213	6.38	1.195	6.43	1.243	6.28	1.237	6.38
	0.677	0.681	0.713	6.26	0.664	6.45	0.739	6.35	0.719	6.30
	0.364	0.371	0.381	6.35	0.359	6.35	0.378	6.45	0.375	6.27
	0.167	0.175	0.191	6.46	0.178	6.50	0.176	6.45	0.187	6.48
	0.081	0.086	0.079	6.40	0.093	6.50	0.082	6.46	0.098	6.32
	0.044	0.042	0.042	6.45	0.045	6.45	0.045	6.40	0.046	6.47

Table 1 .

 1 Metal

concentration added C (mmol.L -1 ) and pH values of single and ternary metal batch experiments conducted onto flax fibers at a concentration of biosorbent of 2 g.L -1

Table 2 .

 2 ) .kg -1 ) Langmuir and Freundlich parameters values

	n	R²

Acknowledgments

This work was supported by French national education ministry and Depestele Group. We acknowledge Benoît Duchemin for his technical help in LOMC laboratory (UMR 6294 CNRS).

Heavy metals (Zn