Florine Dellapiazza

Lee Audras-Torrent

Cécile Michelon

Amaria Baghdadli

Clinical characteristics of children with ASD and comorbid ADHD: Association with social impairment and externalizing and internalizing behaviours

Keywords: autism spectrum disorders, attention deficit disorder, comorbidities, children, social impairments

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

Introduction

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are frequently occurring comorbid conditions, but their association has only recently been recognized in medical classification through the DSM-5 (American Psychiatric Association, 2013). The prevalence rates of comorbid ADHD symptoms in ASD range from 37 to 85%, suggesting that ADHD is the most frequent comorbid condition in ASD [START_REF] Lecavalier | An exploration of concomitant psychiatric disorders in children with autism spectrum disorder[END_REF][START_REF] Murray | Attention-deficit/Hyperactivity Disorder in the Context of Autism Spectrum Disorders[END_REF][START_REF] Simonoff | Psychiatric disorders in children with autism spectrum disorders : Prevalence, comorbidity, and associated factors in a population-derived sample[END_REF][START_REF] Van Der Meer | Are autism spectrum disorder and attentiondeficit/hyperactivity disorder different manifestations of one overarching disorder? Cognitive and symptom evidence from a clinical and population-based sample[END_REF]. ASD with comorbid ADHD is more frequently found in males, with the sex ratio being 3:1 in single ADHD and 4:1 in single ASD [START_REF] Fombonne | Epidemiology of Pervasive Developmental Disorders[END_REF][START_REF] Kern | Are ASD and ADHD a Continuum? A Comparison of Pathophysiological Similarities Between the Disorders[END_REF]. Moreover, it has been reported that individuals with ASD and comorbid ADHD (ASD+ADHD) have more severe ASD symptoms, lower intellectual functioning, and more psychiatric comorbidities than individuals with single ASD alone [START_REF] Craig | Overlap Between Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder : Searching for Distinctive/Common Clinical Features[END_REF][START_REF] Jang | Rates of comorbid symptoms in children with ASD, ADHD, and comorbid ASD and ADHD[END_REF][START_REF] Yamawaki | Clinical characteristics of boys with comorbid autism spectrum disorder and attention deficit/hyperactivity disorder[END_REF]. Thus, the association of ASD and ADHD could worsen the developmental course of such children, making it important to investigate their clinical characteristics and behaviours.

Social communication deficits in ASD cover deficits in socio-emotional reciprocity and nonverbal communication used for social interaction and developing, maintaining, and understanding relationships (American Psychiatric Association, 2013). Such deficits, generally typical of ASD, are also observed in ADHD, in which individuals are faced with social problems as they encounter global interpersonal difficulties with peers, parents, and siblings [START_REF] Greene | Social impairment in girls with ADHD : Patterns, gender comparisons, and correlates[END_REF]. Although studies concerning social impairment in ADHD are more limited than those on ASD, several have reported comparable social deficits in the two conditions. [START_REF] Rommelse | Differentiating between ADHD and ASD in childhood : Some directions for practitioners[END_REF] emphasized that social difficulties are common in both conditions from the early stages of development and that this clinical dimension cannot allow a differential diagnosis. Individuals with ASD and those with ADHD share the same social deficits and both have difficulties in social interactions with peers [START_REF] Kern | Are ASD and ADHD a Continuum? A Comparison of Pathophysiological Similarities Between the Disorders[END_REF] and poor socialisation skills [START_REF] Avni | The Presence of Comorbid ADHD and Anxiety Symptoms in Autism Spectrum Disorder : Clinical Presentation and Predictors[END_REF]. Studies comparing individuals with ASD+ADHD to those with a single diagnosis have suggested greater impairment in their social adaptation [START_REF] Avni | The Presence of Comorbid ADHD and Anxiety Symptoms in Autism Spectrum Disorder : Clinical Presentation and Predictors[END_REF].

Several studies found specific social impairment in children with ASD+ADHD, including greater social communication deficits, problems with social awareness [START_REF] Factor | Does the Presence of Anxiety and ADHD Symptoms Add to Social Impairment in Children with Autism Spectrum Disorder[END_REF], and social behavioural challenges [START_REF] Mcvey | Social difficulties in youth with autism with and without anxiety and ADHD symptoms[END_REF]. In her review, [START_REF] Leitner | The Co-Occurrence of Autism and Attention Deficit Hyperactivity Disorder in Children -What Do We Know[END_REF] highlighted that psychosocial problems are more prevalent in children with ASD+ADHD, regardless of the behavioural dimensions that were examined.

However, these findings are difficult to generalize because there was significant disparity in the assessment of social impairments across the reviewed studies. Although there is evidence that children with ASD+ADHD show greater social impairment than children with a single-diagnosis, there is no consensus concerning the determinants of such results. One hypothesis is that social impairment, generally attributed to ASD, may be greater in cases of comorbid ADHD due to attention deficit-related symptoms and low verbal intellectual quotient [START_REF] Sokolova | A Causal and Mediation Analysis of the Comorbidity Between Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD)[END_REF], as well as by shared theory of mind's deficits [START_REF] Kern | Are ASD and ADHD a Continuum? A Comparison of Pathophysiological Similarities Between the Disorders[END_REF][START_REF] Leitner | The Co-Occurrence of Autism and Attention Deficit Hyperactivity Disorder in Children -What Do We Know[END_REF] and associated anxiety [START_REF] Mcvey | Social difficulties in youth with autism with and without anxiety and ADHD symptoms[END_REF]. In parallel, studies focusing on social difficulties in children with ADHD have emphasized that they may be due to ASD-related symptoms and not only ADHD symptoms [START_REF] Grzadzinski | Examining autistic traits in children with ADHD : Does the autism spectrum extend to ADHD[END_REF]. One limitation of these studies is that children with ASD had comorbid ADHD traits and no ascertained diagnosis of ADHD.

The high prevalence of externalizing behaviours in children with ADHD is broadly reported in the literature [START_REF] Carta | Characterization of Clinical Manifestations in the Co-occurring Phenotype of Attention Deficit/Hyperactivity Disorder and Autism Spectrum Disorder[END_REF][START_REF] Mcrae | Predicting Child Behavior : A Comparative Analysis between Autism Spectrum Disorder and Attention Deficit/Hyperactivity Disorder[END_REF][START_REF] Noordermeer | Risk factors for comorbid oppositional defiant disorder in attention-deficit/hyperactivity disorder[END_REF]. Relative to children with ASD alone, those with ASD+ADHD appear to display more externalizing problems, such as aggressive and oppositional behaviours [START_REF] Berenguer | Children with autism and attention deficit hyperactivity disorder. Relationships between symptoms and executive function, theory of mind, and behavioral problems[END_REF][START_REF] Craig | Overlap Between Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder : Searching for Distinctive/Common Clinical Features[END_REF][START_REF] Matsushima | Evaluation of attention-deficit/hyperactivity disorder symptoms in male children with high-functioning pervasive developmental disorders[END_REF]. Similarly, [START_REF] Lecavalier | An exploration of concomitant psychiatric disorders in children with autism spectrum disorder[END_REF] showed an elevated prevalence of a range of behavioural problems in a large sample of 658 children with ASD. Indeed, 81% of the children with ASD had ADHD, and within this group of children, 46% also had oppositional disorders and 12% conduct disorders. Van Stneesel et al. (2013) also reported that children with ASD+ADHD have a higher level of oppositional behaviours than those with ASD or ADHD alone. Moreover, a study that compared externalizing behavioural problems in children with ASD (n = 255), ADHD (n = 40), or ASD+ADHD (n = 47) found that the rate of tantrum behaviours was higher in the ASD+ADHD group (Goldin et al., 2013). For some authors, the occurrence of externalizing behavioural problems in children with ASD+ADHD may be due to the clinical severity of the ASD [START_REF] Berenguer | Children with autism and attention deficit hyperactivity disorder. Relationships between symptoms and executive function, theory of mind, and behavioral problems[END_REF][START_REF] Green | Autism spectrum disorder symptoms in children with ADHD : A community-based study[END_REF], especially high levels of restricted and repetitive behaviours [START_REF] Avni | The Presence of Comorbid ADHD and Anxiety Symptoms in Autism Spectrum Disorder : Clinical Presentation and Predictors[END_REF]. Conversely, other studies have supported that externalizing behaviours in children with ASD+ADHD are related to the clinical severity of ADHD [START_REF] Craig | Overlap Between Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder : Searching for Distinctive/Common Clinical Features[END_REF][START_REF] Yerys | Attention deficit/hyperactivity disorder symptoms moderate cognition and behavior in children with autism spectrum disorders[END_REF]. Finally, inconsistencies in the existing literature concerning the factors related to externalizing behaviours in children with ASD+ADHD are obstacles to the individualization of interventions, and further studies are required.

In the literature, internalizing behaviours are reported to be a frequent condition in children with ASD [START_REF] Gjevik | Exploring the agreement between questionnaire information and DSM-IV diagnoses of comorbid psychopathology in children with autism spectrum disorders[END_REF][START_REF] Lecavalier | An exploration of concomitant psychiatric disorders in children with autism spectrum disorder[END_REF][START_REF] Van Steensel | Psychiatric Comorbidity in Children with Autism Spectrum Disorders : A Comparison with Children with ADHD[END_REF]. [START_REF] Shephard | Early developmental pathways to childhood symptoms of attention-deficit hyperactivity disorder, anxiety and autism spectrum disorder[END_REF] found similar clinical patterns in children with ASD and those with anxiety, creating difficulties in the differential diagnosis, especially in early stages of life. Anxiety disorders have been reported to co-occur in 42% of children with ASD and mood disorders in 8% [START_REF] Lecavalier | An exploration of concomitant psychiatric disorders in children with autism spectrum disorder[END_REF]. Relative to children with ASD or ADHD alone, children with ASD+ADHD have been reported to have higher rates of anxiety symptoms [START_REF] Avni | The Presence of Comorbid ADHD and Anxiety Symptoms in Autism Spectrum Disorder : Clinical Presentation and Predictors[END_REF][START_REF] Lecavalier | An exploration of concomitant psychiatric disorders in children with autism spectrum disorder[END_REF] and more severe depressive symptoms [START_REF] Jang | Rates of comorbid symptoms in children with ASD, ADHD, and comorbid ASD and ADHD[END_REF]. [START_REF] Rommelse | Differentiating between ADHD and ASD in childhood : Some directions for practitioners[END_REF] emphasized that selfregulation problems are common in both ASD and ADHD from the early stages of development and that they do not allow a differential diagnosis. Conversely, other studies found that children with a ADHD alone had similar emotional problems [START_REF] Craig | Overlap Between Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder : Searching for Distinctive/Common Clinical Features[END_REF] and anxiety symptoms [START_REF] Grzadzinski | Examining autistic traits in children with ADHD : Does the autism spectrum extend to ADHD[END_REF] as children with ASD+ADHD. One study separately considering each diagnosis in children (single ASD or ADHD) found that the occurrence of routines was predictive for internalizing behaviours [START_REF] Mcrae | Predicting Child Behavior : A Comparative Analysis between Autism Spectrum Disorder and Attention Deficit/Hyperactivity Disorder[END_REF]. Such internalizing behaviours can also manifest as psychosomatic disorders, such as sleep disturbance and anxiety, which are frequently reported for both clinical conditions [START_REF] Kern | Are ASD and ADHD a Continuum? A Comparison of Pathophysiological Similarities Between the Disorders[END_REF]. Both children with single ASD and single ADHD exhibit internalizing behaviours, but they appear to be more severe in ASD+ADHD. However, [START_REF] Carta | Characterization of Clinical Manifestations in the Co-occurring Phenotype of Attention Deficit/Hyperactivity Disorder and Autism Spectrum Disorder[END_REF] found similar level of internalizing behaviours in the three clinical groups. Currently, there is no consensus concerning the factors that increase internalizing behaviours in ASD+ADHD. [START_REF] Avni | The Presence of Comorbid ADHD and Anxiety Symptoms in Autism Spectrum Disorder : Clinical Presentation and Predictors[END_REF] attributed their increase to ADHD symptoms, whereas Lecavalier et al. [START_REF] Lecavalier | An exploration of concomitant psychiatric disorders in children with autism spectrum disorder[END_REF] attributed them to cognitive deficits. Consequently, another challenge is to determine the factors related to internalizing behaviours in children with ASD+ADHD to propose adapted personalized intervention [START_REF] Jang | Rates of comorbid symptoms in children with ASD, ADHD, and comorbid ASD and ADHD[END_REF].

Research on children with ASD+ADHD has increased since the DSM-5 acknowledged its nosological validity (American Psychiatric Association, 2013). However, data remain scarce and they are conflicting. Further studies are thus required to investigate whether certain components of their clinical presentation are more severe than in single ASD or single ADHD. The comorbidity between ASD and ADHD raises many issues in professional routines in terms of awareness of the children's specific needs and individualization of their interventions. This requires well-designed comparative clinical studies between children with ASD+ADHD, those with ASD or ADHD alone, and typically developing children (TD).

Here, our first aim was to compare the level of social impairment and externalizing/internalizing behavioural problems across four groups of children according to their diagnosis: ASD+ADHD, ASD alone, ADHD alone, and TD. Based on previous studies, we hypothesized that: 1) The group with ASD+ADHD would have more severe social impairment and externalizing/internalizing behavioural problems than the three others, 2) the groups with ASD+ADHD and ADHD alone would show more externalizing behavioural problems than those with ASD alone or TD, and 3) the groups with ASD+ADHD and ASD or ADHD alone would show more severe social impairment and internalizing behavioural problems than the TD group. Our second aim was to examine the bidirectional relationship between ASD and ADHD symptoms within the ASD+ADHD group and clinical characteristics, such as social impairment and externalizing/internalizing behavioural problems. We expected that a higher level of ASD and ADHD symptoms in the group with ASD+ADHD would be associated with greater severity in social impairment and externalizing/internalizing behavioural problems.

Methods

Participants

This study extended our previous findings to examine sensory and attention processing in four subgroups of children: single ASD, single ADHD, ASD+ADHD, and TD [START_REF] Dellapiazza | Sensory processing related to attention in children with ASD, ADHD, or typical development : Results from the ELENA cohort[END_REF]. We collected more data to increase the number of participants with ASD and comorbid ADHD. Our sample included 186 participants from 6 to 12 years of age, divided into the four groups described below.

The ASD+ADHD group (N = 29) consisted of children recruited from the ELENA cohort, a large multicentric, longitudinal, prospective cohort of children diagnosed with ASD for which the protocol has already been extensively described [START_REF] Baghdadli | Investigating the natural history and prognostic factors of ASD in children : The multicEntric Longitudinal study of childrEN with ASD -the ELENA study protocol[END_REF]. The ASD diagnosis was confirmed by a multidisciplinary team using a standardized clinical approach. This standard process relied on DSM-5 (American Psychiatric Association, 2013) criteria and consisted of the Autism Diagnostic Observation Schedule 2 (ADOS 2 ; [START_REF] Lord | Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) Manual (Part I) : Modules 1-4[END_REF], the Autism Diagnostic Interview-Revised (ADI-R ; Le [START_REF] Le Couteur | Autism Diagnostic Interview, Revised (ADI-R)[END_REF], the Vineland second version (VABS-II ; [START_REF] Sparrow | Vineland Adaptive Behavior Scales, Second Edition[END_REF] and the direct psychological assessment of intellectual functioning.

For this study, we used a subsample of 29 participants recruited from the ELENA cohort for which the inclusion criteria were being from 6 to 12 years of age with comorbid ADHD clinically ascertained by experienced psychiatrists on the basis of DSM-5 criteria (American Psychiatric Association, 2013) . We excluded participants with genetic syndromes or a best-estimate intelligence quotient (IQ) < 80.

The single ASD group (n = 96) was also a subsample recruited from the ELENA cohort. We excluded children who had an ADHD diagnosis according to DSM-5 criteria (American Psychiatric Association, 2013), a genetic syndrome, or a best-estimate IQ < 80.

The single ADHD group (n = 28) was recruited from the Department of Child and Adolescent Psychiatry of the University Hospital of Montpellier during consultations. Experienced child psychiatrists used DSM-5 criteria and a medical report to diagnose ADHD and exclude ASD.

The TD group (n = 31) consisted of children in mainstream school and without any medical history of neurodevelopmental disorders matched for age with the other clinical groups. These children were selected after parental agreement to participate. The absence of history of neurodevelopmental disorders was confirmed by asking parents if they have ever been concerned about their child's development and whether their child has ever been diagnosed with a neurodevelopmental disorder. In addition, SRS-2 and CBCL scales were used to systematically screen for ASD and ADHD, respectively. None of the children in this group, met the clinical threshold on these scales.

Exclusion criteria for each group were having sensory deficits (blindness and auditory deficit) and to have received a medication for attention deficit.

Measures

Social impairment was assessed using the SRS-2, a 65-item questionnaire measuring ASD trait severity [START_REF] Constantino | The Social Responsiveness Scale TM , Second Edition (SRS TM -2)[END_REF]. For the present study, we utilized the school-age form (from 2.5 to 18 years), which assesses social impairment on five subscales: Social Awareness (ability to pick up on social cues), Social Cognition (ability to interpret social cues), Social Communication (includes expressive social communication), Social Motivation (to engage in social communication), and Restricted Interests and Repetitive Behaviours (RIRB; includes stereotypical behaviour). Each question was rated from 1 (Not True) to 4 (Almost Always True). The Total composite score (T) (M = 50, SD = 10) was used to assess the severity of symptoms. T-scores ≥ 90 indicate extremely severe social impairment. Total scores of ≤ 59 correspond to the average range, 60 to 65 a mild degree of impairment, 66 to 75 a moderate degree of impairment, and 76 or higher, a severe degree of impairment. The internal consistency was 0.95.

Externalizing and internalizing behavioural problems were assessed using the Child Behaviour Checklist [START_REF] Achenbach | Manual for the ASEBA school-aged forms and profiles[END_REF], a standardized caregiver-report exploring emotional and behavioural problems in children and adolescents aged from 6 to 18 years. The CBCL provides two scales of child behaviour:

internalizing and externalizing problems. The internalizing scale consists of five subscales: anxious/depressed, withdrawn/depressed, somatic complaints, social problems and thoughts problems. The externalizing scale consists of three subscales: rule-breaking behaviour, attention problems and aggressive behaviour. The CBCL also provides a score for ADHD, called the DSM-oriented scale. T-scores based on age and sex were obtained; T-scores ≥70 are considered clinically significant and T-scores from 65 to 69 borderline clinically significant.

Internal consistency ranged from 0.72 to 0.91, the inter-rater reliability from 0.63 to 0.88, and the test-reliability was 0.90.

The best-estimate IQ was assessed from psychometric scales, depending on the age of each participant. The nonverbal cognitive level was estimated from the "fluid reasoning" dimension of the WISC-V (Wechsler, 2014a) and the WPPSI-IV [START_REF] Wechsler | WPPSI-IV, échelle d'intelligence de Wechsler pour enfants[END_REF], "perceptual reasoning" dimension of the WISC-IV [START_REF] Wechsler | WPPSI-IV, échelle d'intelligence de Wechsler pour enfants[END_REF], and the "performance IQ" of the WPPSI-III [START_REF] Wechsler | Wechsler Preschool and Primary Scale of Intelligence TM -Third Edition (WPPSI TM -III)[END_REF] or "simultaneous process" of the K-ABC II [START_REF] Kaufman | Kaufman assessment battery for children : Technical manual[END_REF].

A standardized parental questionnaire was used to collect the following information about the children: birth date, other neurodevelopmental disorders, sensory deficits, and medication used for ADHD.

Procedure

Parents of the children recruited from the ELENA cohort were asked to complete the CBCL and SRS-2 questionnaires electronically on a web database. Parents of the children in the ADHD and TD groups were asked to complete the CBCL and SRS-2 questionnaires in print format and to return them to the research team. The intellectual level was assessed during diagnosis for the children recruited from the ELENA cohort (with single ASD or ASD+ADHD) and those with ADHD alone. The intellectual level of the children with TD was not assessed and considered to be normal when the grade level matched the chronological age. Informed consent forms were completed by all parents and the data were anonymized.

Statistical analyses

Descriptive statistics were analysed to characterize each group (ASD, ASD+ADHD, ADHD, and TD). Bivariate analyses, consisting of Chi-square tests of associations for categorical variables and one-way ANOVA or the Kruskal-Wallis non-parametric test for continuous variables, were conducted to assess intergroup comparisons. For significant differences, a Bonferroni correction was applied for 2-by-2 comparisons. Association between the SRS-2 score and externalising and internalising behaviours scores were then analysed in the ASD and ASD+ADHD groups using Pearson's or Spearman's correlation coefficient, as were associations between the attention deficit score (DSM-5 scale) and social impairment and externalising and internalising behaviours scores. Statistical analyses were performed using SAS 9.3 software. A two-sided p < 0.05 was considered to be statistically significant.

Results

The sample included 186 children from 6 to 12 years and 11 months of age (mean age 9.1 ± 1.8 years). They had a diagnosis of ASD (n = 98), ADHD (n = 28), or comorbid ASD+ADHD (n = 29) or had TD (n = 31). Intergroup comparisons showed no differences in age (ASD: mean age 9.1 ± 1.9, ADHD: mean age 9.2 ± 1.8, ASD+ADHD: mean age 8.5 ± 1.6, TD: mean age 9.3 ± 1.6) or gender. There were 81.6% males in the single ASD group, 82.1% in the single ADHD group, 75.9% in the ASD+ADHD group, and 61.3% in the TD group.

No differences were found in nonverbal cognitive level (all p > 0.05). No significant difference were presented between ASD and ASD+ADHD groups on clinical characteristics (ADOS severity score, VABS-II and ADI) described in Table 1.

[Insert Table 1]

Intergroup comparisons showed significant differences in SRS-2 and CBCL scores (Table 2). Post hoc analysis showed higher scores for SRS-2 and CBCL in the three groups with neurodevelopmental disorder (NDD) than in the TD group, except for social motivation, for which the ADHD group had scores comparable to those of the TD group. As expected, the ASD+ADHD and ADHD groups had higher CBCL attention problems scores than the single ASD group (p < 0.05). The ASD and ASD+ADHD groups had higher SRS-2 scores (for each dimension) than the single ADHD group (p < 0.05), except for social awareness, for which the ADHD group had lower scores than the TD, ASD, and ASD+ADHD groups.

[Insert Table 2]

Correlations between the SRS-2 and CBCL scores performed by group are presented in Table 3.

For the ASD group, all the SRS-2 scores correlated from moderate to high with the CBCL internalizing behaviours, thoughts problems and attention problems scores. In this group, the SRS-2 total score correlated with all CBCL internalizing scores, CBCL externalizing behaviours, attention problems and aggressive behaviours.

RIRB score and social motivation correlated from moderate to high with all CBCL internalizing and attention problems. Overall, for the ASD group, as the severity of ASD increased, all internalizing behaviours and CBCL externalizing behaviours also increased (except rule breaking behaviours).

For the ASD+ADHD group, the SRS-2 total score and social communication score highly correlated with the CBCL internalizing behaviours score, withdraw depressed, somatic complaints. The CBCL ADHD DSMoriented score highly correlated with the CBCL attention problems and aggressive behaviours. However, there is no significant correlation between CBCL ADHD DSM-oriented score and the SRS-2 score (all p > 0.05).

Overall, for the ASD+ADHD group, as the severity of ASD increased, internalizing behaviours, withdraw depressed and somatic complaints increased and as the severity of ADHD increased, attention problems and aggressive behaviours also increased.

For the ADHD group, the CBCL ADHD DSM-oriented score highly correlated with the CBCL internalizing behaviours (anxious depressed, withdraw depressed, social problems, thoughts problems), externalizing behaviours (rule breaking behaviours, aggressive behaviours) and social impairment (SRS-2 total score, r = 0.52, p < 0.01; social cognition, r = 0.39, p < 0.05; social communication, r = 0.46, p < 0.05; RIRB, r = 0.60, p < 0.001). Overall for the ADHD group, when ADHD severity increased, internalizing, externalizing and social impairments increased too.

[Insert Table 3]

Discussion

Overall, our results show that children with NDDs have more severe social impairment and externalizing/internalizing behaviours than children with TD. As expected, social impairment was more likely to occur in children with ASD alone or ASD+ADHD than in those with ADHD alone. In addition, attention problems were more prevalent for children with ADHD alone or ASD+ADHD than in those with ASD alone.

The prevalence of externalizing/internalizing behaviours was high in the three groups with NDD. Thus, the ASD+ADHD group showed scores similar to those of the ASD and ADHD groups. Thus, we did not confirm this part of our hypothesis. However, the correlation analysis revealed that internalizing behaviours are more highly related to ASD severity and that externalizing behaviours are more often associated with ADHD severity.

In contrast to our hypothesis, the severity of social impairment was comparable between the ASD+ADHD and ASD groups. Social impairment is generally considered to be typical of ASD [START_REF] Grzadzinski | Examining autistic traits in children with ADHD : Does the autism spectrum extend to ADHD[END_REF]. However, our results reinforce those of previous studies [START_REF] Rommelse | Differentiating between ADHD and ASD in childhood : Some directions for practitioners[END_REF]) that found significant social difficulties in children with ADHD, suggesting that this cannot be the only dimension used to confirm a diagnosis of ASD. Although significant social problems were found in the ADHD group, they were greater in both the ASD and ASD+ADHD groups, as illustrated by higher SRS-2 scores, also supporting the relevance of the SRS scale in the differential diagnosis.

We hypothesized that the single ADHD and ASD+ADHD groups would exhibit more externalizing behaviours than the single ASD and TD groups. We found the externalizing scores to be comparable between the three groups with NDDs relative to the TD group, except for the attention score, for which the ASD+ADHD and single ADHD groups had higher scores than the single ASD group. Moreover, the percentage of externalizing behaviours was higher in the single ADHD group (75%) than in the single ASD (45%) and ASD+ADHD (38%) groups. Our results agree with those of the literature, which highlight a high percentage of externalizing behaviours in ADHD [START_REF] Carta | Characterization of Clinical Manifestations in the Co-occurring Phenotype of Attention Deficit/Hyperactivity Disorder and Autism Spectrum Disorder[END_REF][START_REF] Mcrae | Predicting Child Behavior : A Comparative Analysis between Autism Spectrum Disorder and Attention Deficit/Hyperactivity Disorder[END_REF][START_REF] Noordermeer | Risk factors for comorbid oppositional defiant disorder in attention-deficit/hyperactivity disorder[END_REF]. However, our results differ from those of past studies, which showed more externalizing behaviour in ASD+ADHD than ASD [START_REF] Berenguer | Children with autism and attention deficit hyperactivity disorder. Relationships between symptoms and executive function, theory of mind, and behavioral problems[END_REF][START_REF] Craig | Overlap Between Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder : Searching for Distinctive/Common Clinical Features[END_REF][START_REF] Matsushima | Evaluation of attention-deficit/hyperactivity disorder symptoms in male children with high-functioning pervasive developmental disorders[END_REF] or ADHD [START_REF] Goldin | A comparison of tantrum behavior profiles in children with ASD, ADHD and comorbid ASD and ADHD[END_REF][START_REF] Van Steensel | Psychiatric Comorbidity in Children with Autism Spectrum Disorders : A Comparison with Children with ADHD[END_REF] alone. In addition, our results reinforce the evidence that the percentage of externalizing behavioural problems is related to the degree of ADHD symptom severity in the ASD+ADHD and ADHD groups [START_REF] Craig | Overlap Between Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder : Searching for Distinctive/Common Clinical Features[END_REF][START_REF] Yerys | Attention deficit/hyperactivity disorder symptoms moderate cognition and behavior in children with autism spectrum disorders[END_REF].

Similarly, our results confirm that the three groups with NDD present more internalizing behavioural problems than children with TD. This result highlights that internalizing behaviours, commonly found in ASD [START_REF] Gjevik | Exploring the agreement between questionnaire information and DSM-IV diagnoses of comorbid psychopathology in children with autism spectrum disorders[END_REF][START_REF] Lecavalier | An exploration of concomitant psychiatric disorders in children with autism spectrum disorder[END_REF][START_REF] Van Steensel | Psychiatric Comorbidity in Children with Autism Spectrum Disorders : A Comparison with Children with ADHD[END_REF], are also found in ADHD. This result is concordant with those of past studies, which showed that children with a single diagnosis of ASD or ADHD presented similar emotional problems and anxiety symptoms as children with ASD+ADHD [START_REF] Carta | Characterization of Clinical Manifestations in the Co-occurring Phenotype of Attention Deficit/Hyperactivity Disorder and Autism Spectrum Disorder[END_REF][START_REF] Craig | Overlap Between Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder : Searching for Distinctive/Common Clinical Features[END_REF][START_REF] Grzadzinski | Examining autistic traits in children with ADHD : Does the autism spectrum extend to ADHD[END_REF]. Internalizing behaviours in the ASD+ADHD group were associated with ASD severity and the association was particularly strong for the domain of restricted, stereotyped, and repetitive behaviours. It was previously suggested that such restricted and repetitive behaviours help individuals with ASD to cope with their anxiety [START_REF] Mcrae | Predicting Child Behavior : A Comparative Analysis between Autism Spectrum Disorder and Attention Deficit/Hyperactivity Disorder[END_REF][START_REF] White | Anxiety in children and adolescents with autism spectrum disorders[END_REF]. We also observed that ADHD severity was highly associated with internalizing behaviours, which highlight the relevance of taking into account these difficulties in this population, which are generally less studied than externalizing behaviours [START_REF] Mcrae | Predicting Child Behavior : A Comparative Analysis between Autism Spectrum Disorder and Attention Deficit/Hyperactivity Disorder[END_REF][START_REF] Noordermeer | Risk factors for comorbid oppositional defiant disorder in attention-deficit/hyperactivity disorder[END_REF].

One strength of our study was the comparison of three clinical groups to a TD group matched for age. In addition, we recruited the children with ASD (single or with comorbid ADHD) from the ELENA cohort, for which they were rigorously diagnosed. The children with single ADHD were also thoroughly diagnosed. In addition, we studied comorbidity using the SRS-2 and CBCL, which were standardized and validated questionnaires for clinical research and practice.

However, our study also had several limitations. Although the diagnoses were performed in the context of a clinical examination by experienced clinicians, the information on comorbidities was mainly collected by parental questionnaires. Indeed, as reported by [START_REF] Moens | In the Eye of the Beholder? Parent-Observer Discrepancies in Parenting and Child Disruptive Behavior Assessments[END_REF] there is negative bias concerning child behaviours in parents reports. We haven't collected parent's socioeconomic status, which may influence questionnaires responses validity. In addition, there was a risk of selection bias related to the recruitment of participants from different sources (Child and Adolescent Psychiatry Department for the ADHD group and Elena cohort for the ASD group), but this risk was limited by the standardization of the diagnostic process for the two groups. Furthermore, the children with NDD had an IQ > 80 to allow comparison with the TD group and, thus, we cannot generalize our findings to lower-functioning children. The differences in recruitment sources also explain differences in groups' sample size and the size of the groups ASD+ADHD and single ADHD was limited. Larger studies will thus be necessary to investigate the clinical characteristics of the ASD+ADHD group.

This study has several clinical implications. First, our results reveal that children with NDD exhibit both greater social impairment and more severe externalizing/internalizing behaviours than TD children. These results stress the importance of checking for comorbidities during the diagnosis of ASD and to take into account such comorbidities in the intervention plan. We found that ADHD symptom severity in the ADHD group was strongly associated with internalizing behaviours, implying the importance of professionals to search for anxiety issues, which are less systematically investigated in individuals with ADHD than externalizing behaviours.

Internalizing behaviours in the ASD group were strongly associated with restricted and repetitive behaviours, suggesting that they also need to be managed in individuals with ASD, such as the current practice for anxiety. Further studies will be necessary to better understand the specific clinical characteristics of ADHD in ASD on a larger sample. It will be important to prospectively examine adaptive trajectories in samples with ASD and ADHD to describe clinical changes and their determinants.

Conclusion

In conclusion, this study focused on comorbidities across groups of children with ASD+ADHD, ASD or ADHD alone, or TD. Groups with NDD presented a higher level of social impairment and externalizing/internalizing behaviours than TD children. In particular, internalizing behaviours were more often associated with ASD

Informed Consent

Signed informed consent is obtained from all participating families.

List of Abbreviations

  severity and externalizing behaviours with ADHD severity. This underlies the necessity of searching for the presence of comorbidities during the diagnosis of NDD. The ASD+ADHD association has only been recently recognized in medical classification and future studies are needed to prospectively follow and describe clinical changes and their determinants in this population. The study and informed consent procedure have been approved by the Ethics Committee on the Research of Human Subjects at Marseille Mediterranean and the National Commission for Computing and Liberties (CNIL. number DR-2015-393).

Table 1 .

 1 Clinical characteristics for ASD and ASD+ADHD groups and intergroup comparisons. ASD, autism spectrum disorder; ASD+ADHD, comorbidity form; SD, standard deviation; ADOS CSS, autism diagnostic observation schedule calibrate severity scale; VABS-II, Vineland second version; ADI-R, autism diagnostic interviewrevised; RIRB, restrictive interest and repetitive behaviours.

		ASD	ASD+ADHD	p-value
		n = 98	n = 29	intergroup comparison
		Mean (SD)	Mean (SD)
	ADOS CSS	6.4 (2.2)	6.5 (2.2)	0.9
	VABS-II
	Communication	82.5 (13.6)	81.8 (10.8)	0.7
	Socialization	74.1 (9.6)	71.9 (6.8)	0.2
	Daily living skills	78.8 (10.5)	78.9 (9.9)	0.9
	ADI-R
	Communication	12.4 (5.8)	11.1 (3.9)	0.3
	Social interaction	15.5 (6.3)	14.8 (5.4)	0.6
	RIRB	5.5 (2.5)	5.4 (2.2)	0.7

Table 2 .

 2 SRS-2 and CBCL scores for each group and intergroup comparisons. ADHD ASD, autism spectrum disorder; ADHD, attention deficit hyperactivity disorder; TD, typical development; ASD+ADHD, comorbidity form; ALL, all other groups.

		ASD		ASD+ADHD		ADHD		TD		p-value	Post hoc test a
		n = 98		n = 29		n = 28		n = 31		intergroup comparison
		Mean (SD)	% clinical range	Mean (SD)	% clinical range	Mean (SD)	% clinical range	Mean (SD)	% clinical range
	TOTAL SRS-2	78.5 (10.5)		79.0 (7.10)		69.5 (7.5)		59.1 (2.5)		0.0001	TD<ADHD<ASD+ADHD,ASD
	Social awareness	71.4 (11.5)		73.3 (10.8)		63.5 (6.7)		70.3 (5.4)		0.0001	ADHD<TD, ASD+ADHD,ASD
	Social cognition	75.5 (11.1)		77.2 (8.8)		73.1 (7.0)		63.9 (4.7)		0.0001	TD<ADHD<ASD+ADHD,ASD
	Social communication	77.6 (9.3)		78.7 (7.5)		68.4 (8.3)		59.1 (3.0)		0.0001	TD<ADHD<ASD+ADHD,ASD
	Social motivation	71.2 (12.5)		69.0 (10.4)		61.8 (7.8)		56.7 (3.9)		0.0001	TD, ADHD<ASD+ADHD, ASD
	RIRB	78.4 (10.3)		77.7 (8.7)		66.5 (12.5)		45.2 (4.7)		0.0001	TD<ADHD<ASD+ADHD,ASD
	CBCL Internalizing	18.6 (9.8)	72%	20.3 (10.2)	80%	15.8 (7.7)	64%	5.7 (5.1)	13%	0.0001	TD<ALL
	Anxious depressed	9.8 (5.6)	43%	10.2 (4.4)	42%	8.5 (4.0)	32%	3.1 (3.2)	3%	0.0001	TD<ALL
	Withdrawn depressed	5.1 (3.5)	39%	5.2 (3.3)	38%	3.3 (2.7)	10%	1.5 (1.5)	3%	0.0001	TD<ALL
	Somatic complaints	3.7 (3.2)	17%	4.9 (4.0)	27%	4.1 (3.4)	25%	1.1 (1.4)	0%	0.0001	TD<ALL
	Social problems	8.4 (4.3)	35%	9.7 (3.5)	58%	7.9 (3.1)	29%	1.4 (1.7)	0%	0.0001	TD<ALL
	Thought problems	7.9 (4.5)	50%	7.8 (4.1)	54%	6.2 (5.2)	39%	1.1 (1.5)	0%	0.0001	TD<ALL
	CBCL Externalizing	15.6 (9.2)	45%	14.4 (8.2)	38%	20.4 (9.6)	75%	4.3 (4.1)	3%	0.0001	TD<ALL
	Attention problems	9.7 (4.1)	32%	12.1 (3.6)	58%	12.3 (3.8)	50%	1.7 (2.1)	0%	0.0001	TD<ASD<ASD+ADHD, ADHD
	Rule breaking behaviors	3.8 (3.1)	14%	3.4 (2.8)	7%	4.9 (3.1)	25%	1.2 (1.2)	0%	0.0001	TD<ALL
	Aggressive behavior	11.8 (6.7)	28%	11.0 (6.3)	19%	15.5 (7.2)	43%	3.1 (3.3)	0%	0.0001	TD<ALL
	ADHD DSM-oriented	7.7 (3.1)	23%	9.4 (2.3)	39%	10.4 (2.9)	63 %	1.6 (1.7)	0%	0.0001	TD<ASD<ASD+ADHD,

a Bonferroni correction.

Acknowledgment

We thank the contributing families, the ELENA cohort staff (Ela Miniarikova, Flore Couty, Mathilde Bérard, Myriam Soussana, Julie Loubersac, Laetitia Ferrando, and Christelle Vernhet), and the clinical team of Montpellier Hospital. We express gratitude to the CNSA and DGOS for funding to conduct this research and prepare the results for publication.

Acknowledgment

We thank the contributing families, the ELENA cohort staff (Ela Miniarikova, Flore Couty, Mathilde Bérard, Myriam Soussana, Julie Loubersac, Laetitia Ferrando, and Christelle Vernhet), and the clinical team of Montpellier Hospital. We express gratitude to the CNSA and DGOS for funding to conduct this research and prepare the results for publication.

The study and informed consent procedure have been approved by the Ethics Committee on the Research of Human Subjects at Marseille Mediterranean and the National Commission for Computing and Liberties (CNIL.

Funding

This research received support from the French Health Ministry (DGOS) and CNSA. Additional support was provided by the CHU of Montpellier (AOI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Funding

This research received support from the French Health Ministry (DGOS) and CNSA. Additional support was provided by the CHU of Montpellier (AOI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author Contributions

FD and LAT conceived the study, contributed to the collection, analysis, and interpretation of the data, and drafted the manuscript. AB is the PI of the ELENA cohort and also participated in the design of the current study, drafted the work, and critically revised it for the principal intellectual content. CM analysed and interpreted the data. All authors read and approved the final version to be published.

Declarations

Conflict of Interest

The authors have no conflict of interest to declare.

Author Contributions

FD and LAT conceived the study, contributed to the collection, analysis, and interpretation of the data, and drafted the manuscript. AB is the PI of the ELENA cohort and also participated in the design of the current study, drafted the work, and critically revised it for the principal intellectual content. CM analysed and interpreted the data. All authors read and approved the final version to be published.

Declarations

Conflict of Interest

The authors have no conflict of interest to declare.

Ethical


Dumitru Duca

Mostafiz Rahman

Yvain Carpentier

Claire Pirim

Adam Boies

Cristian Focsa
email: cristian.focsa@univ-lille.fr

Chemical characterization of size-selected nanoparticles emitted by a gasoline direct injection engine: Impact of a catalytic stripper

Keywords: Nanoparticles, Carbonaceous aerosols, Size-selective chemical characterization, Internal combustion engine, Catalytic stripper

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

Introduction

Internal combustion engine (ICE) powered vehicles constitute a major source of airborne particulate matter (PM), especially in urban areas [START_REF] Guerreiro | Air quality 2018 -EEA report 12 2018[END_REF][START_REF] Karagulian | Contributions to cities' ambient particulate matter (pm): A systematic review of local source contributions at global level[END_REF]. Particles emitted from vehicle engines are complex mixtures, mainly consisting of a carbonaceous core with a multitude of adsorbed compounds such as unburnt and partially oxygenated hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), sulfates, and metal oxides [START_REF] Johnson | A review of diesel particulate control technology and emissions effects[END_REF][START_REF] Stanmore | The oxidation of soot: a review of experiments, mechanisms and models[END_REF]. The particle's outer organic layer (the so-called surface organic fraction -SOF) exerts a considerable influence on the properties of particulate emissions (i.e. reactivity, toxicity, nucleation properties) and is considered a major contributor to particle-associated health hazards [START_REF] Ristovski | Respiratory health effects of diesel particulate matter[END_REF]. In this context, special attention is drawn by the ultra-fine particles (size <100 nm) due to their higher deposition fraction, deeper penetration, and higher retention rate in the lungs [START_REF] Kwon | Ultrafine particles: unique physicochemical properties relevant to health and disease[END_REF][START_REF] Li | Association of long-term near-highway exposure to ultrafine particles with cardiovascular diseases, diabetes and hypertension[END_REF]. The inhalation of these smallest particles can result in health problems beyond the lungs: the presence of combustion derived nanoparticles has been detected in the frontal cortex of autopsy brain samples [START_REF] Maher | Magnetite pollution nanoparticles in the human brain[END_REF], urine of healthy children [START_REF] Saenen | Children's urinary environmental carbon load. a novel marker reflecting residential ambient air pollution exposure?[END_REF], and even in the fetal side of the placenta [START_REF] Bové | Ambient black carbon particles reach the fetal side of human placenta[END_REF]. If transported to the fetus, these particles -as carriers for potentially toxic chemical species -could significantly affect fetal health and development [START_REF] Bové | Ambient black carbon particles reach the fetal side of human placenta[END_REF]. Most recently, a strong association between increases in PM concentration and mortality rates due to COVID-19 was evidenced [START_REF] Setti | Searching for SARS-COV-2 on particulate matter: A possible early indicator of COVID-19 epidemic recurrence[END_REF][START_REF] Contini | Does air pollution influence COVID-19 outbreaks?[END_REF].

The automotive industry has made significant efforts to reduce the amount and impact of internal combustion engine emissions [START_REF] Frey | Trends in onroad transportation energy and emissions[END_REF][START_REF] Bielaczyc | Trends in automotive emission legislation: impact on ld engine development, fuels, lubricants and test methods: a global view, with a focus on wltp and rde regulations[END_REF][START_REF] Giechaskiel | European regulatory framework and particulate matter emissions of gasoline light-duty vehicles: A review[END_REF]. For this purpose, various after-treatment systems based on conversion, adsorption and trapping technologies, such as three-way catalysts, diesel oxidation catalysts, selective catalytic reduction systems and particulate matter filters have been implemented in the exhaust track of both spark and compression ignition engines [START_REF] Koltsakis | Catalytic automotive exhaust aftertreatment[END_REF][START_REF] Meloni | Most recent advances in diesel engine catalytic soot abatement: Structured catalysts and alternative approaches[END_REF][START_REF] Mamakos | Effect of selective catalytic reduction on exhaust nonvolatile particle emissions of euro vi heavy-duty compression ignition vehicles[END_REF]. Devices providing chemical sites for oxidation and reduction reactions (i.e. catalytic strippers/converters) are particularly useful as they can convert toxic by-products present in the exhaust into less hazardous substances such as carbon dioxide, water vapor, and nitrogen gas. In the meantime, recent improvements in engine technology resulted in a significant decrease in the total number and mass of PM emitted by on-road vehicles. This, however, also led to a shift in the particle diameter toward smaller sizes (lower than 100 nm [START_REF] Karjalainen | Exhaust particles of modern gasoline vehicles: A laboratory and an on-road study[END_REF]).

Although not a major contributor to the emitted particle mass, these ultra-fine sizes represent an important share in the total particle number (PN), which is regulated by 17 out of the 20 countries of the G-20 group (accounting for 90% of the global vehicle sales [START_REF] Williams | A technical summary of euro 6/vi vehicle emission standards. icct (international council on clean transportation) briefing[END_REF]). Current European Union (EU) regulations limit PN emissions for sizes above 23 nm. However, sub-23nm particles are produced in large concentrations by both Diesel and gasoline direct injection (GDI) engines [START_REF] Giechaskiel | Engine exhaust solid sub-23 nm particles: I. literature survey[END_REF] and can sometimes reach 30-40% of the total PN for vehicles equipped with a GDI [START_REF] Giechaskiel | Investigation of vehicle exhaust sub-23 nm particle emissions[END_REF]. Based on these findings, the PMP (particle measurement programme) group of the working party on pollution and energy (GRPE) of the United Nations Economic Commission for Europe (UNECE) has worked on a protocol to lower the PM 23 nm cut-off point to 10 nm [START_REF] Martini | PMP IWG Progress Report; 81st UNECE GRPE session[END_REF], the PM referring only to solid (non-volatile) particles [START_REF] Giechaskiel | Overview of soot emission measurements instrumentation: From smoke and filter mass to particle number[END_REF], i.e. those which do not evaporate below 350°C.

Lowering the cut-off size of measured PM might be quite challenging, as it requires more efficient aerosol conditioning technologies to remove volatile and semi-volatile compounds with minimal size-dependent particle losses and to avoid the creation of artifacts (i.e. particles generated in the sampling / conditioning system). Three EU Horizon 2020 projects [START_REF]PEMs4Nano. European Union's Horizon 2020 research and innovation programme under grant agreement Nr. 724145[END_REF][START_REF] Downtoten | European Union's Horizon 2020 research and innovation progamme under grant agreement[END_REF][START_REF]European Union's Horizon 2020[END_REF] have worked in parallel over the past few years to propose a robust methodology and associated instrumentation (portable emissions measurement systems -PEMS) for PN measurements down to 10 nm. Recent assessment campaigns [START_REF] Giechaskiel | Evaluation of a 10 nm particle number portable emissions measurement system[END_REF][START_REF] Giechaskiel | Solid particle number (SPN) portable emissions measurement systems (PEMS) in the European legislation: A review[END_REF][START_REF] Giechaskiel | Assessment of 10-nm Particle Number (PN) Portable Emissions Measurement Systems (PEMS) for Future Regulations[END_REF] confirmed that the developed PEMS prototypes are ready to be introduced in the future regulations. A major change proposed in the PMP draft recommendations [START_REF] Martini | PMP IWG Progress Report; 81st UNECE GRPE session[END_REF] is to impose the use of a catalytic stripper (CS) as volatile particle remover (VPR) in the future 10-nm PEMS, which is considered a safer option [START_REF] Giechaskiel | Non-Volatile Particle Number Emission Measurements with Catalytic Strippers: A Review[END_REF] than the evaporation tube (ET) or the thermodenuder (TD). The ET, currently used in automotive particle measurement systems, is a simple and robust method when measuring particles larger than 23 nm as it is able to completely evaporate most hydrocarbons. However, this technology might lead to re-nucleation of semivolatile species at high hydrocarbon particle concentrations and thus higher primary dilution ratios are required to reliably measure solid particles smaller than 23 nm [START_REF] Giechaskiel | Theoretical investigation of volatile removal efficiency of particle number measurement systems[END_REF][START_REF] Yamada | Application of the PMP methodology to the measurement of sub-23 nm solid particles: calibration procedures, experimental uncertainties, and data correction methods[END_REF]. Therefore, for measuring particles in the sub-23 nm range, removing the semi-volatile aerosol fraction using a CS optimized for small losses of ultra-fine particles can prove more efficient [START_REF] Melas | Development and evaluation of a catalytic stripper for the measurement of solid ultrafine particle emissions from internal combustion engines[END_REF][START_REF] Amanatidis | Evaluation of an oxidation catalyst ("catalytic stripper") in eliminating volatile material from combustion aerosol[END_REF][START_REF] Ntziachristos | Use of a catalytic stripper as an alternative to the original PMP measurement protocol[END_REF].

The use of CSs in emission measurement systems has been reviewed by Giechaskiel et al. [31]. Up to now, the impact of CSs on the resulting emissions has mostly been characterized physically, with comparisons between stripped and unstripped particle mass, number or size distribution. What comes into view is the current lack of information regarding its impact on the chemical composition (at the molecular level) of the emitted aerosols (with the notable exception of one study addressing marine exhaust aerosols [START_REF] Amanatidis | Comparative performance of a thermal denuder and a catalytic stripper in sampling laboratory and marine exhaust aerosols[END_REF]). As the chemical composition (and thus the degree of volatility) making up the SOF layer can significantly vary with engine regimes or with PM size, an extensive investigation of the physico-chemical properties of the measured objects should be undertaken to ensure the development of a PEMS instrument capable of maintaining its accuracy and reliability for a wide range of engine operating conditions (i.e. in real driving conditions).

In the frame of the EU Horizon 2020 PEMS4Nano project [START_REF]PEMs4Nano. European Union's Horizon 2020 research and innovation programme under grant agreement Nr. 724145[END_REF] we conducted extensive measurement campaigns to thoroughly analyze the physico-chemical properties of particles emitted by a single-cylinder engine in a bottom-up approach combining experimental [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF] and theoretical studies [START_REF] Lee | Model Guided Application for Investigating Particle Number (PN) Emissions in GDI Spark Ignition Engines[END_REF] for the development of a PEMS prototype. The action of a home-made (University of Cambridge) CS on the measured aerosol (polydisperse and size-selected particles, gas phase) was investigated both on-line (physical characterization by an original tandem arrangement of aerodynamic aerosol classifier, differential mobility analyzer, and centrifugal particle mass analyzer [START_REF] Kazemimanesh | Morphology and volatility of particulate emissions from gasoline direct injection engine using aerodynamic diameter, mobility diameter, and mass measurements in tandem[END_REF]) and off-line (laser mass spectrometry chemical characterization of filter-collected samples). As on-line chemical characterization by aerosol mass spectrometers is limited to particles larger than ∼50nm [START_REF] Amanatidis | Comparative performance of a thermal denuder and a catalytic stripper in sampling laboratory and marine exhaust aerosols[END_REF], the off-line approach adopted here is the only possible solution when focusing on smaller nanoparticles (10-32 nm in the present study) of actual interest for the 10-nm PEMS development.

Materials and methods

Sampling

A generic single-cylinder gasoline direct injection engine was used on a test bench to generate particles with various properties for building an extensive database through multi-technique physico-chemical characterization [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF]. The engine was described in detail in a previous publication [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF], only the main characteristics are briefly reminded in Table 1, Supporting Information. For the present study, two set-points were used, called in the following low speed (LS, 1200 rpm) and high speed (HS, 2000 rpm). The IMEP (indicated mean effective pressure) was kept constant at 10 bar, λ at 1.01, and the injection at 270°bTDC (before top-dead center). The engine was operating on Euro Stage V E5 Gasoline (CEC-RF-02-08 E5) with the ignition timing set for the maximum brake torque (MBT). The temperature of both coolant and oil (Agip SIGMA, 10W-40) was kept at 80°C. To ensure the cleanliness of the combustion chamber, the engine was conditioned with methane (CH 4 ) before changing the operating point. A custom sampling line (Figure 1) was used for the simultaneous collection of engine exhaust for offline characterization and online volatile mass fraction measurements [START_REF] Kazemimanesh | Morphology and volatility of particulate emissions from gasoline direct injection engine using aerodynamic diameter, mobility diameter, and mass measurements in tandem[END_REF]. Raw engine exhaust was sampled from the exhaust pipe 10 cm downstream of the manifold. Sampled flow was then diluted using a Dekati FPS 4000 (1:30 dilution ratio) to prevent the condensation of volatile species as well as their aggregation. The main objective of this study was to assess the impact of the catalytic stripping on the gas phase, polydisperse, and size-selected particles. Two CSs were used: CS1 (1.5 l/min flow) for on-line measurements (Catalytic Instruments CS-015) and CS2 (presented in detail in reference [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF]), which was manufactured identical to CS1 by the University of Cambridge with the exception that it was adapted to accept higher flows (10 l/min) to increase the exhaust collection efficiency needed for the subsequent offline analysis. Both

CSs performances in terms of hydrocarbon removal were tested to be >99% for 30 nm tetracontane (C 40 H 82 ) particles at a concentration of >10 4 cm -3 . As this work focuses on the offline analysis, only the corresponding experimental details are given below, while information about the online measurement can be found in reference [START_REF] Kazemimanesh | Morphology and volatility of particulate emissions from gasoline direct injection engine using aerodynamic diameter, mobility diameter, and mass measurements in tandem[END_REF]. The upstream (unstripped) flows were analyzed/sampled using bypasses (red dashed lines in Figure 1), while the downstream (stripped) flows were collected after passing through the CS. The sampling of size-selected particles was performed with a NanoMOUDI II cascade impactor (TSI, model 125R) able to separate particles into 13 different size-bins, with nominal cut sizes of 10000, 5600, 3200, 1800, 1000, 560, 320, 180, 100, 56, 32, 18, 10 nm.

The size-selected particles were deposited on aluminum foils that were cleaned and thermally treated (at 300°C) prior to the sampling to remove all possible surface contaminants. The sampling time varied between 6h and 12h. An alternative (separate) sampling of polydisperse particles and exhaust gas phase was performed with an original double-filter system recently developed in our laboratory [START_REF] Ngo | Chemical discrimination of the particulate and gas phases of miniCAST exhausts using a two-filter collection method[END_REF]. This device is made of two quartz fiber filters (QFF) placed in series:

the front filter (FF) retains the particulate matter while letting through the gas/volatile phase, which is then adsorbed by a layer of activated carbon placed on the back filter (BF). This sampling system was proven to be very efficient in separating the particulate (non-volatile) and the gas phases in combustion emissions [START_REF] Ngo | Chemical discrimination of the particulate and gas phases of miniCAST exhausts using a two-filter collection method[END_REF]. Both front and back filters were thermally conditioned before the sampling in order to remove any possible contaminants. The sampling time was adjusted (60-80 min) in accordance with the particle concentration and size distribution in the exhaust to obtain a homogeneous layer of particles on the FF.

A total of 44 samples were collected in the two engine regimes for subsequent offline analysis. A sample labeling scheme is used in the following, indicating the engine set-point (HS or LS), the downstream (CS) or upstream sampling point, the size-bin (for size-selected particles) or the collection QFF in the doublefilter device (FF for polydisperse particles, BF for gas phase). For instance, (CPMA, Cambustion Ltd.) followed by a condensation particle counter (CPC, 3776, TSI Inc.) [START_REF] Kazemimanesh | Morphology and volatility of particulate emissions from gasoline direct injection engine using aerodynamic diameter, mobility diameter, and mass measurements in tandem[END_REF].

HS CS F F indicates polydisperse particles collected downstream the CS in HS engine regime, while LS 10-18 labels 10-18 nm size-selected particles collected upstream the CS in the LS engine regime. In addition, a set of blank samples (aluminum foil, a neat QFF, and an activated carbon-covered QFF) were prepared, stored (at 4°C) and managed in a similar way than the collected samples.

Chemical analysis

The chemical characterization of collected particles was performed using a two-step (desorption/ionization) laser mass spectrometry technique (L2MS) described in detail elsewhere [START_REF] Faccinetto | Highsensitivity detection of polycyclic aromatic hydrocarbons adsorbed onto soot particles using laser desorption/laser ionization/time-of-flight mass spectrometry: An approach to studying the soot inception process in low-pressure flames[END_REF][START_REF] Faccinetto | Progress toward the Quantitative Analysis of PAHs Adsorbed on Soot by Laser Desorption/Laser Ionization/Time-of-Flight Mass Spectrometry[END_REF]. The mass spectrometer used in this study Since mass spectra of the analyzed samples contain a large number of peaks its interpretation can be challenging. To optimally exploit this large amount of information, the MS data treatment follows a dedicated methodology developed in our group [START_REF] Irimiea | A comprehensive protocol for chemical analysis of flame combustion emissions by secondary ion mass spectrometry[END_REF][START_REF] Irimiea | Unveiling trends in soot nucleation and growth: When secondary ion mass spectrometry meets statistical analysis[END_REF][START_REF] Duca | On the benefits of using multivariate analysis in mass spectrometric studies of combustion-generated aerosols[END_REF], which includes mass defect and multivariate analysis approaches. In this framework, a variety of advanced statistical techniques such as Principal Component Analysis (PCA) [START_REF] Adam | Characterization of puff-by-puff resolved cigarette mainstream smoke by single photon ionization-time-offlight mass spectrometry and principal component analysis[END_REF], Hierarchical Clustering on Principal Components (HCPC), and volcano plots [START_REF] Li | Volcano plots in analyzing differential expressions with mRNA microarrays[END_REF][START_REF] Shi | Cross-platform comparability of microarray technology: Intra-platform consistency and appropriate data analysis procedures are essential[END_REF] can help uncover "hidden" patterns in complex datasets, group samples based on their similarities and identify the most significant mass peaks contributing to sample differentiation.

Results and discussion

Polydisperse particles and gas phase

The impact of the catalytic stripper on polydisperse PM and the gas phase was assessed by comparing the chemical composition of samples collected with and without the CS. Examples of mass spectra recorded for polydisperse particles (FF) and gas phase (BF) samples collected (in the high-speed engine regime) upstream and downstream the CS are displayed in Figure 2. The spectra contain predominantly polycyclic aromatic species and their fragments; their partial ion count (PIC) was shown to be representative of the organic carbon content of the sample [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF][START_REF] Duca | On the benefits of using multivariate analysis in mass spectrometric studies of combustion-generated aerosols[END_REF][START_REF] Delhaye | The MERMOSE project: Characterization of particulate matter emissions of a commercial aircraft engine[END_REF]. When comparing downstream (HS CS F F ) and upstream (HS F F ) particles, a 7-fold reduction of aromatic PIC is observed, which clearly demonstrates the efficiency of the CS in stripping the surface organic layer. Although a direct quantitative comparison is not possible (different experimental configurations), we note that this efficiency is in line with previous measurements performed with a soot particle aerosol mass spectrometer (SP-AMS) on marine engine exhaust, where organic species reductions in the range 94-97%

were observed upon stripping [START_REF] Amanatidis | Comparative performance of a thermal denuder and a catalytic stripper in sampling laboratory and marine exhaust aerosols[END_REF]. The relatively small difference between the results presented here and the ones reported in Amanatidis et al. [START_REF] Amanatidis | Comparative performance of a thermal denuder and a catalytic stripper in sampling laboratory and marine exhaust aerosols[END_REF] might be explained by the fact that the SP-AMS instrument is only able to measure particles larger than ∼50 nm and the smallest particles below this threshold are expected to carry a higher SOF. A detailed discussion on this will be provided in the section 3.2, which focuses on size-selected particles (especially the 10-32 nm range). The efficiency of the CS in removing organics is even higher on the gas phase, as a 20-fold reduction in aromatic PIC was recorded downstream (HS CS BF sample) with respect to upstream (HS BF ). Indeed, the mass spectrum of the HS CS BF sample contains mostly carbon clusters (C + n , representative of the elemental carbon content [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF][START_REF] Duca | On the benefits of using multivariate analysis in mass spectrometric studies of combustion-generated aerosols[END_REF]) and is almost identical to the blank BF spectrum. We emphasize that the presence of carbon clusters on back filters, sampled both with and without the CS, is determined by the layer of activated carbon intended to trap the gas phase [START_REF] Ngo | Chemical discrimination of the particulate and gas phases of miniCAST exhausts using a two-filter collection method[END_REF] and cannot be associated with the exhaust gas phase combustion by-products.

To properly interpret mass spectrometric data, mass defect analysis [START_REF] Irimiea | A comprehensive protocol for chemical analysis of flame combustion emissions by secondary ion mass spectrometry[END_REF][START_REF] Irimiea | Unveiling trends in soot nucleation and growth: When secondary ion mass spectrometry meets statistical analysis[END_REF][START_REF] Duca | On the benefits of using multivariate analysis in mass spectrometric studies of combustion-generated aerosols[END_REF] was used to assign chemical formulas to the most intense peaks. The full list of assigned peaks is provided in Table S1. A volcano plot [START_REF] Li | Volcano plots in analyzing differential expressions with mRNA microarrays[END_REF][START_REF] Shi | Cross-platform comparability of microarray technology: Intra-platform consistency and appropriate data analysis procedures are essential[END_REF] was used to highlight the CS-induced changes in the chemical composition of both particulate and gas phases. The two phases carry chemical species of different volatility and mass, and thus the separate study of these phases allows to better evaluate the efficiency of the CS. The differences between the chemical composition of the particulate and gas phases can clearly be seen when the front (particulate phase) and the back filter (gas phase) sampled without the CS are compared, Figure S1. The front filter receives a high contribution from higher-mass aromatic species, with more than 4 aromatic rings. According to Bari et al. [START_REF] Bari | Particle-phase concentrations of polycyclic aromatic hydrocarbons in ambient air of rural residential areas in southern Germany, Air Quality[END_REF],

the volatility of aromatic compounds can be inferred from the total number of aromatic rings: compounds consisting of only two aromatic rings are considered volatile, three to four -semi-volatile, and those with more than four aromatic rings -non-volatile. It should be noted that all the peaks corresponding to carbon clusters (C + n ) have been excluded from this analysis since, in this case, they do not originate from the same source (soot particles for the front filters and the pre-applied black carbon layer for the back filter). However, when comparing only front filter samples (Figure 3a), carbon clusters provide important information about the variation of the OC/EC ratio (organic carbon to elemental carbon content) of sampled particles as they are commonly considered as markers of EC [START_REF] Duca | On the benefits of using multivariate analysis in mass spectrometric studies of combustion-generated aerosols[END_REF][START_REF] Ferge | Fast determination of the relative elemental and organic carbon content of aerosol samples by on-line single-particle aerosol time-of-flight mass spectrometry[END_REF][START_REF] Pagels | Fine-particle emissions from solid biofuel combustion studied with single-particle mass spectrometry: Identification of markers for organics, soot, and ash components[END_REF][START_REF] Popovicheva | Chemical composition of diesel/biodiesel particulate exhaust by FTIR spectroscopy and mass spectrometry: Impact of fuel and driving cycle[END_REF]. Once the particles pass through the CS the contribution of aromatic compounds, considered as good indicators of the organic carbon content [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF][START_REF] Delhaye | The MERMOSE project: Characterization of particulate matter emissions of a commercial aircraft engine[END_REF], is significantly reduced, effectively decreasing the OC/EC ratio.

Moreover, stripped particles feature a higher contribution from oxygenated and nitrogenated species which can be linked to the oxidation processes occurring in the CS.

A similar picture can be seen when the two samples corresponding to the gas phase (HS BF and HS CS BF ) are compared, Figure 3b. The gas phase sampled without the CS presents a high content of aromatic compounds (mostly low-mass, up to 4 aromatic rings), while the one collected downstream the stripper is characterized only by carbon clusters (coming from the underlying black carbon layer). The fact that no organic species contribute in a significant way to the HS CS BF mass spectrum implies that the CS removed the majority of combustion-generated compounds from the gas phase.

A principal component analysis of all FF and BF samples collected (with and without the CS) in both engine regimes (HS and LS) was performed. This approach allows the differentiation between samples and highlights (through the analysis of the loading plots, Figure S2) the contribution of individual compounds or groups of highly correlated species to this differentiation. The PCA was able to separate all the samples with only two principal components accounting for ∼72% of the total variance. The first principal component (∼63.9%) separates the samples based on the OC/EC ratio (a high positive PC1 score reflects a low OC/EC ratio). The volatility of the samples can be inferred from their PC2 (∼8.2% of explained variance) scores. A positive PC2 score is associated with the presence of non-volatile aromatic species (more than 4 aromatic rings) with m/z ≤ 363, while a negative PC2 score is linked to the presence of volatile and semi-volatile compounds, as well as some high-mass aromatic species (m/z > 363). Three major regions can be seen on the score plot presented in Figure 4: i) particulate phase sampled without the CS (HS F F and LS F F , dark blue),

ii) particulate phase sampled with the CS along with the gas phase sampled without it (HS CS F F , LS CS F F -dark red, HS BF , and LS BF -light blue), and iii) the gas phase collected after the CS (HS CS BF and LS CS BF -light red).

It is worth noting that even though particulate phases sampled without the stripper are located fairly close to each other and can be grouped together, their chemical composition is different. The PC1 score for the front filter (par- A volcano plot (Figure S3) was employed to compare the particulate phase produced in the HS and LS engine regimes. When comparing unstripped particles, Figure S3a, we can see that while both samples contain a large amount of aromatic compounds, the one obtained at lower speed (LS) contains more high-mass species (i.e. non-volatile compounds [START_REF] Ngo | Chemical discrimination of the particulate and gas phases of miniCAST exhausts using a two-filter collection method[END_REF][START_REF] Bari | Particle-phase concentrations of polycyclic aromatic hydrocarbons in ambient air of rural residential areas in southern Germany, Air Quality[END_REF]). Unstripped particles collected in different regimes have a noteworthy difference in the chemical composition, demonstrated by the fact that 55 compounds contribute in a statistically significant way to their separation. In contrast, stripped particles appear to be very similar, Figure S3b. Only a few species contribute to the separation between the mass spectra of stripped particles generated in different engine regimes, and these mass spectra are mostly constituted of carbon clusters (representative of EC). This is an important result, showing that the CS is able to treat particles exhibiting quite different initial chemical composition, effectively stripping their (different) surface organic layers and leaving them as solid non-volatile PM.

Size-selected particles

Size-selected particles produced in the two engine regimes, and sampled with or without the CS were chemically characterized. For the first engine regime (HS) the stages of the cascade impactor that collected sufficient material (with and without the CS) covered a quite extended size-range: from 10 nm up to 560 nm (HS 10-18 -HS 320-560 samples). Mass spectra of these samples are presented in Figure 5. One can see that mass spectra of particles from different size-bins are very different, indicating that the chemical composition of emitted particles significantly changes with the size. Mass spectra of particles collected without the CS show a high contribution from heavy-mass PAHs that can be associated with the remnants of the fuel or lubricating oil [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF]. The majority of these compounds are successfully removed by the CS, which is illustrated by the decrease in the absolute signal intensity. At the same time the contribution of carbon clusters (C + n ) increases, thus indicating a much lower OC/EC ratio for the stripped particles. The relative contribution of low-mass PAHs and their fragments increases toward smaller particle sizes which suggests an increase in the overall volatility of the surface organic fraction [START_REF] Kazemimanesh | Morphology and volatility of particulate emissions from gasoline direct injection engine using aerodynamic diameter, mobility diameter, and mass measurements in tandem[END_REF].

PCA was applied to the mass spectrometric data to study the impact of CS on the size dependent chemical composition. The first two principal components The second principal component is related to the presence of PAHs (i.e.

stabilomers [START_REF] Stein | High-temperature stabilities of hydrocarbons[END_REF], negative PC2 score). Smaller particles (<100 nm) show a high contribution from fragments and PAH derivatives (positive PC2) while bigger particles (100-560 nm) contain a larger number of stabilomer PAHs that are removed by the CS (PC2 score changes from being negative to positive or almost zero). It should be noted that the spread between data points across the second dimension (PC2) is much smaller for particles collected with the CS (HS CS), indicating that, when it comes to the organic fraction, the chemical composition of stripped size-selected particles is not very different.

HCPC was performed on the first five principal components, accounting for more than 82% of the variance within the data set, to identify the clusters that form in the principal component space and discriminate between particles, Figure 6b. Three separate clusters can be identified: small particles collected with the CS (HS CS 10-18 and HS CS 18-32 ) -black markers, small particles collected without the CS (HS 10-18 , HS 18-32 , and HS 32-56 ) -green markers, and bigger particles collected in both regimes -red markers. This shows that the chemical composition of the smallest particles was significantly changed by the CS, thus supporting our previous conclusion that small particles contain a high surface organic fraction which makes them more susceptible to a catalytic treatment. This conclusion is also supported by online aerodynamic-mass-mobility measurements that show that the volatile mass fraction increases for smaller particles, Figure S8.

The compounds that are efficiently removed from the smallest particles (10-18 nm) by the stripper were identified with a volcano plot, Figure S5. The CS removes the organic fraction from these particles, leading to the decrease of the OC/EC ratio. It is worth noting that, for this particular size-bin, the CS effectively removes PAHs from the entire mass-range and particles end up showing only a negligible contribution from organic species.

As the chemical composition of the emitted particles depends on the engine set-point, the CS may impact differently particles belonging to the same size-bin but generated in different engine regimes (LS or HS). To identify the (possible) change in CS efficiency, the differences between the initial (unstripped) chemical compositions of size-selected particles produced in the two engine regimes must be first identified with PCA (Figure 7a). The first two principal components account for 61% of the variation in the data set and will be used to explain the differences in the chemical composition. From the loadings plot (Figure S6a), PC1 can be linked to the contribution of PAHs (positive value), fragments, and carbon clusters (negative PC1), while PC2 can be used to separate samples based on the OC/EC ratio. For instance, samples with a low OC/EC ratio have a negative PC2 score, while the ones with a high amount of organic species exhibit a positive PC2 value. On the score plot (Figure 7a) the data points corresponding to the two engine regimes are well separated by PC1. Therefore, it is possible to distinguish between particles produced in these two regimes based only on the contribution of PAHs, fragments, and carbon clusters. PM collected in the LS engine regime features a much higher contribution from PAHs, with particles from all size-bins having a negative PC1 score (except for LS 10-18 ). In contrast, particles emitted in the HS engine regime exhibit a higher signal coming from fragments and carbon clusters. Note that the fact that HS samples show a positive PC1 score cannot be linked to a missing organic fraction and should only be seen as a lower relative contribution of the peaks attributed to organic species compared to that observed for LS samples. PC2 shows that in the LS regime, the smallest particles yield the highest OC/EC ratio, also higher than that of the corresponding HS sample. The biggest particles (56-560 nm) produced in the HS regime show the highest contribution from carbon clusters, Once the differences between the chemical compositions of unstripped particles produced in the two engine regimes have been identified, the effect of the CS on size-selected PM with different chemical composition can be determined by PCA, as shown in Figure 7b. The first two principal components explain 69% of the observed variance and are able to distinguish between stripped particles generated in the two engine regimes based on the few chemical species left on the surface. The first principal component (61%) groups samples based on their OC/EC ratio, determined from the corresponding loadings plot (Figure S6b). It should be noted that PCA is emphasizing the variance in the dataset, and thus the separation between particles observed on the score plot (Figure 7b) cannot be used to quantify, for instance, the difference in the organic content. The majority of organic species present on raw particles are successfully stripped by the CS, making their chemical composition comparable, similar to what was shown before for polydisperse particles. However, the high sensitivity of the analytical technique [START_REF] Faccinetto | Progress toward the Quantitative Analysis of PAHs Adsorbed on Soot by Laser Desorption/Laser Ionization/Time-of-Flight Mass Spectrometry[END_REF] used here reveals distinct features in size-selected stripped particles collected in different engine regimes. For instance, when comparing the smallest treated particles (10-18 nm) collected in different engine regimes with a volcano plot (Figure S7) we can see that only a few species contribute to the separation between the samples. Due to their small mass, the chemical information related to these particles is usually lost when polydisperse PM is collected, thus illustrating the importance of size-selective analysis. The spread between data points on the PC1 axis is rather small, suggesting that stripped particles of different sizes have a comparable amount of OC.

The second principal component is linked to the amount of aromatic species (positive PC2 score), oxygenated, and hopanoid compounds (negative PC2 score). The latter group of compounds is often used as marker species for remnants of lubricating oil [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF]. The CS successfully removes these compounds from particles smaller than 100 nm, however, bigger LS CS particles (100-560 nm) still contain residues of these compounds. Even though particles in three size-bins sampled in the HS CS engine regime (HS CS 320-560 , HS CS 18-32 , HS CS 10-18 ) also show a negative PC2 score, they do not exhibit a high hopanoid content but are instead characterized by a higher content of oxygenated species (identified from volcano plots). The HS CS cluster is located very close to the PC2 axis, suggesting, once again, their lower OC/EC ratio compared to LS CS particles.

Conclusions

To A specificity of our work is the study of the chemical composition of particles selected by size. Indeed, the composition of engine exhaust PM can significantly change not only with the engine set-point but also with the particle size [START_REF] Focsa | Multi-technique physico-chemical characterization of particles generated by a gasoline engine: towards measuring tailpipe emissions below 23 nm[END_REF].

When polydisperse PM is studied, a weighted average composition of particles of various sizes is obtained, with individual weights related to the initial particle size distribution which can significantly change depending on the engine set-point. This is clearly illustrated here by analyzing PM emitted in two engine operation regimes. The fine statistical analysis of the CS action on size-selected particles revealed (despite the high removal efficiency) the "memory" of the initial (unstripped) composition of the particles, which was not possible with the polydisperse PM. This further stresses how important it is to sample and characterize size-selected particulate matter, especially since the chemical composition of the smallest particles (with low contribution to the total mass in a polydisperse sample) cannot be otherwise inferred. Deposition on filters/substrates and offline analysis seems to be the only option here, as aerosol mass spectrometers are typically limited to sizes above ∼50 nm.

This work also showed that the smallest particles (10-32 nm) are the most affected by the CS, indicating that particles in this size range carry a larger volatile fraction. This conclusion concurs with the results of online aerodynamic-massmobility tandem measurements [START_REF] Kazemimanesh | Morphology and volatility of particulate emissions from gasoline direct injection engine using aerodynamic diameter, mobility diameter, and mass measurements in tandem[END_REF] performed in parallel with the collection of samples described here: on-line (physical) and off-line (chemical) investigations are in excellent agreement. The online measurements revealed an increasing contribution of the particle-bound volatile mass fraction toward smaller particles sizes, as illustrated in Figure S8. The excellent agreement between these two completely independent (chemical and physical) characterization methods demonstrates the reliability of the used experimental approaches and validates the main conclusions of the study, which is also supported by theoretical investigations [START_REF] Lee | Model Guided Application for Investigating Particle Number (PN) Emissions in GDI Spark Ignition Engines[END_REF] conducted in the PEMS4Nano project and showing the same trend of the SOF with size. Moreover, the obtained trend also matches previous dynamometer and on-road testing showing that semi-volatile particles represent a significant fraction of the smallest nanoparticles [START_REF] Ko | Characteristics of onroad particle number (PN) emissions from a GDI vehicle depending on a catalytic stripper (CS) and a metal-foam gasoline particulate filter[END_REF][START_REF] Momenimovahed | Particle number emission factors and volatile fraction of particles emitted from onroad gasoline direct injection passenger vehicles[END_REF].

We demonstrated that a fine molecular-level characterization of the exhaust PM is necessary to precisely evaluate the effect of a CS, especially for the smallest ultra-fine particles. As the particles' outer organic layer consists of a multitude of chemical species (e.g. PAHs) and that the smallest particles tend to exhibit a larger volatile fraction, the ultra-fine PM could present a double riskdue to their small size they penetrate deeper in the respiratory system while also carrying a larger amount of potentially toxic compounds. This not only shows how important is the addition of oxidation catalysts in after-treatment systems of modern vehicles to remove the (potentially toxic) organic fraction from the engine exhaust, but also how crucial is the regulation of the small sub-23 nm particles.

Figure 1 :

 1 Figure 1: Schematic representation of the experimental setup. Sampling of the exhaust was conducted with a NanoMoudi II cascade impactor to size-select particles and a two-filter collection device [41] to separate the PM (polydisperse particles) and the gas phase. The collection was performed with and without the CS. The online SOF mass fraction measurements were performed with a combination of Aerodynamic Aerosol Classifier (AAC, Cambustion Ltd.), differential mobility analyzer (3080 DMA, TSI Inc.), and centrifugal particle mass analyzer

(

  Fasmatech S&T) combines ion cooling, Radio Frequency (RF) guiding and Time-of-Flight (ToF) analyzer to reach a mass resolution of ∼15000. The sample, placed under vacuum (10 -8 mbar residual pressure), is irradiated at 30°angle of incidence by a frequency doubled Nd:YAG laser beam (Quantel Brilliant, λ d =532 nm, 4 ns pulse duration, 0.10-0.22 J cm -2 fluence, 10 Hz repetition rate) focused to a 0.07 mm 2 spot on the surface. The desorbed compounds form a gas plume that expands in the vacuum normally to the sample surface, and are ionized by an orthogonal UV laser beam (Quantel Brilliant, λ i = 266 nm, 4 ns pulse duration, 10 Hz repetition rate, ∼0.3 J cm -2 fluence). The generated ions are then RF-guided to a He collision cell for thermalization and subsequently mass analyzed in a time-of-flight mass spectrometer equipped with a reflectron (ToF-MS). The desorption and ionization fluences were adjusted for each sample to obtain the maximum signal intensity while minimizing fragmentation in the mass spectra. Each mass spectrum was obtained by averaging the signal from 200 laser shots applied on a small (∼2 mm 2 ) zone of the sample. To check the homogeneity of the sample surface, the analysis was performed on four different zones of each sample and all the results were used in the subsequent statistical data treatment.

Figure 2 :

 2 Figure 2: Mass spectra of HS F F/BF and HS CS F F/BF samples (particulate and gas phase collected with and without the CS) obtained with L2MS.

Figure 3 :

 3 Figure 3: Volcano plots showing the "differential expression" of detected chemical species: a) comparison between the chemical composition of the particulate phase sampled with and without the CS, and b) influence of the CS on the chemical composition of the gas phase.

Figure 4 :

 4 Figure 4: Score plot of the first two principal components for the particulate and gas phase samples collected with and without the CS in two different engine regimes (HS and LS). The arrows represent the meaning of the first principal component derived from the corresponding loadings plot, Figure S2.

Figure 5 :

 5 Figure 5: Mass spectra of size-selected particles produced in the HS engine regime and sampled with (red line -HS CS) and without (blue line -HS) the catalytic stripper. The labels indicate the corresponding NanoMOUDI size-bin.

Figure 6 :

 6 Figure 6: Impact of the CS on size-dependent PM chemical composition: a) Score plot of the first two principal components obtained from mass spectra of size-selected particles collected with (red symbols) and without (blue symbols) the CS in the HS engine regime; the arrows show the meaning of the principal components obtained from their loadings, and b) HCPC performed on the first five principal components (explaining more than 82% of the variance); three biggest clusters correspond to: small particles collected with the CS (HS CS 10-18 and HS CS 18-32 ) -black markers, small particles collected without the CS (HS 10-18 , HS 18-32 , and HS 32-56 ) -green markers, and bigger particles collected in both regimes (HS and LS)purple markers.

  i.e. the lowest OC/EC. In addition, smaller particles from the HS regime (10-100 nm) show the highest partition of organic species. Particles in the size range of 100-320 nm produced in both regimes display a similar PC2 score, implying that they are the least affected by the change in engine operating conditions.

Figure 7 :

 7 Figure 7: Score plot of the first two principal components obtained from the mass spectra of size-selected particles collected in the LS and HS engine regimes without (a) and with (b) the catalytic stripper. The arrows indicate the meaning of principal components obtained from their corresponding loadings.

Table 1 :

 1 Engine specifications (b/aTDC -before/after Top Dead Center)

	Specification	Value
	Cylinder head	Pentroof type
	Compression ratio	12.5:1
	Bore	82 mm
	Stroke	85 mm
	Displacement	449 cm 3
	Fuel direct injection system	Central mounted generic six-hole injector
	Injection pressure	150 bars
	Spark plug location	Exhaust side
	Open 334 deg. bTDC
	Intake valve timing:
	Close	166 deg. bTDC
	Open 154 deg. aTDC
	Exhaust valve timing:
	Close	330 deg. aTDC

  the best of our knowledge, this is the first study to tackle a detailed, molecular-level characterization of the impact of a CS (in a PEMS context) on the chemical composition of an ICE exhaust. Most of the studies test the CS removal efficiency on tetracontane (C 40 H 82 ) particles, following the recommendations of the PMP protocol[START_REF] Williams | A technical summary of euro 6/vi vehicle emission standards. icct (international council on clean transportation) briefing[END_REF]. Amanatidis et al.[START_REF] Amanatidis | Evaluation of an oxidation catalyst ("catalytic stripper") in eliminating volatile material from combustion aerosol[END_REF] used liquid decane (C 10 H 22 ) and toluene (C 7 H 8 ) injected in a 10% v/v O 2 in N 2 gas mixture to measure efficiencies >90% for the removal of these hydrocarbons by a CS, while preliminary tests with a heavier (C 16 ) species led to artifacts and inconclusive results. In another study, Amanatidis et al.[START_REF] Amanatidis | Comparative performance of a thermal denuder and a catalytic stripper in sampling laboratory and marine exhaust aerosols[END_REF] measured stripping efficiencies of organics in the range 94-97% from particles (>50 nm) emitted by a marine engine. The results of the present study on polydisperse particles and gas phase emitted by a single-cylinder GDI are well in line with those previous findings and foster the use of a CS-based VPR system in future PEMS.

Acknowledgments

This study has received funding as a part of the PEMS4Nano project from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 724145. Additionally, this work was supported by the French National Research Agency (ANR) under contract ANR-18-CE22-0019 (UNREAL) and through the PIA (Programme d'Investissement d'Avenir) under contract ANR-10-LABX-005 (LABEX CaPPA -Chemical and Physical Properties of the Atmosphere) and by the UK EPSRC Centre for Sustainable Road Freight (EP/R035199/1) and NERC Integrated Research Observation System for Clean Air (NE/T001909/1).