Dimitri Besset

Brahim Selmaoui

Pierre Tourneux

André Leke

Stephane Delanaud

René De Seze

Dr Erwan Stephan-Blanchard

Environmental radiofrequency electromagnetic field levels in a department of pediatrics

Keywords: preterm infant, radiofrequency electromagnetic fields, neonatal intensive care unit

Mean radiofrequency levels measured in a neonatal intensive care unit were low, relative to current guidelines.

• Peak values in mobile phone frequencies were detected.

• Radiofrequency emissions by medical devices were very low.

Introduction

The World Health Organization defines prematurity as a live birth before 37 weeks of gestation [START_REF] Althabe | Born too soon: the global action report on preterm birth[END_REF]. The prevalence of preterm infants is increasing as a result of a greater incidence of preterm births and higher survival rates [START_REF] Msall | Measuring functional outcomes after prematurity: developmental impact of very low birth weight and extremely low birth weight status on childhood disability[END_REF]. Preterm infants require respiratory, hemodynamic, and nutritional support with optimal growth [START_REF] Glendinning | Technology-dependent children in the community: definitions, numbers and costs[END_REF], together with parental commitment to developmental care [START_REF] O'brien | A pilot cohort analytic study of Family Integrated Care in a Canadian neonatal intensive care unit[END_REF].

Hi-tech devices are increasingly used in medical care. Although these devices improve the quality of care, they can potentially expose patients and healthcare professionals to electromagnetic fields (EMF). This is especially true in neonatal intensive care units (NICU), where many sources of radiofrequency electromagnetic fields (RF-EMF) are present: mobile phones that use the Global System for Mobile Communications (GSM) and Universal Mobile Telecommunications System (UMTS) standard, cordless phones that use the Digital Enhanced Cordless Telecommunications (DECT), a specific frequency for emergency calls [START_REF] Calvente | Radiofrequency exposure in the Neonatal Medium Care Unit[END_REF], and new-generation monitoring devices that connect to Wireless Fidelity (WiFi) networks, for example. However, very few studies have analyzed the extent of EMF exposure in preterm neonates. Most of the research to date has focused on extremely low frequency EMF (ELF-EMF; 0-100 kHz). It has been reported that exposure to ELF-EMF was associated with changes in a neonate's autonomic nervous activity and a transient increase in melatonin production [START_REF] Bellieni | Is newborn melatonin production influenced by magnetic fields produced by incubators?[END_REF]. These results raise questions about the potential influence of RF-EMF exposure on outcomes in preterm neonates.

Preterm neonates may be particularly vulnerable to RF-EMF exposure, as a result of their physiological immaturity [START_REF] Paliwoda | Physiological vital sign ranges in newborns from 34 weeks gestation: A systematic review[END_REF] -especially with regard to the central nervous system (CNS) [START_REF] Modrzejewska | Changes in the central nervous system of prematurely born children[END_REF] -and high sensitivity to the NICU environment [START_REF] Maki | O efeito da manipulação sobre o sono do recém-nascido prematuro[END_REF]. Specifically the proliferation, differentiation and connections of neurons in the CNS are vulnerable to environmental factors (Blackburn, 2009a(Blackburn, , 2009b)). Furthermore, the lower skull bone density and higher water content in a child's brain (relative to an adult's) enables RF-EMF energy to penetrate more deeply into tissue [START_REF] Christ | Age-dependent tissuespecific exposure of cell phone users[END_REF]. Moreover, some very-low-birth-weight preterm neonates will have to stay in the NICU for a prolonged period -perhaps up to 100 days [START_REF] Ancel | Survival and Morbidity of Preterm Children Born at 22 Through 34 Weeks' Gestation in France in 2011: Results of the EPIPAGE-2 Cohort Study[END_REF] -and so will potentially be exposed to high EMF levels during their growth [START_REF] Lai | Iatrogenic Environmental Hazards in the Neonatal Intensive Care Unit[END_REF]. [START_REF] Li | Generation of infant anatomical models for evaluating electromagnetic field exposures: Infant Models for EMF Evaluation[END_REF] considered that the safety limits prescribed by the International Commission on Non-Ionizing Radiation Protection (ICNIRP, 1998) may not be suitable for the exposure of preterm neonates to RF-EMF [START_REF] Li | Generation of infant anatomical models for evaluating electromagnetic field exposures: Infant Models for EMF Evaluation[END_REF]. Furthermore, current EMF safety levels are based on literature data on the short-term risk, whereas a large number of diseases may have a long latency period [START_REF] Calvente | Outdoor characterization of radio frequency electromagnetic fields in a Spanish birth cohort[END_REF].

A few studies have shown that neonates are exposed to ELF-EMF inside incubators, with peak levels of magnetic flux density ranging from 8.84 μT [START_REF] Bellieni | Is newborn melatonin production influenced by magnetic fields produced by incubators?[END_REF] to 74.56 μT. ELF-EMF levels in the NICU's general environment were found to be lower, with values ranging from 0.1 to 0.2 μT [START_REF] Aasen | Fifty-Hertz magnetic field exposures of premature infants in a neonatal intensive care unit[END_REF][START_REF] Riminesi | ELF magnetic field exposure in a neonatal intensive care unit[END_REF]. One recent study reported on levels of RF-EMF in the frequency band between 87.5 MHz and 5.8 GHz, although it focused on measurements in a small neonatal medium care unit [START_REF] Calvente | Radiofrequency exposure in the Neonatal Medium Care Unit[END_REF]. To the best of our knowledge, data on RF-EMF levels to which preterm neonates may be exposed during their stay in a NICU are not available in the literature.

The objective of the present study was thus to quantify the levels of RF-EMF levels in a NICU by mapping the RF-EMF distribution in the staff area and in children's rooms and by measuring spot emissions from various medical devices switched on. We expected that this would enable us to (i) better characterize the sources and conditions of exposure, and (ii) provide new data about the actual levels to which preterm neonates might be exposed during their stay in the NICU.

Materials and Methods

1. Study area RF-EMF levels were measured in the Department of Pediatrics at Amiens University Medical Center (Amiens, France) between March 26 th , 2018, and October 18 th , 2018 (Appendix 1). The department was inaugurated in September 2014, and comprises two different units: a 18-bed neonatal critical care unit (NCCU) for children born before 32 weeks of gestation or after 32 weeks of gestation but requiring hemodynamic and/or invasive ventilatory support, and a 16-bed neonatal intensive care unit (NICU). Measurements were made in the department's staff area and in the preterm children's rooms. No changes in the service's activities (either the medical staff or visitors) were implemented .

Equipment

Four dosimeters (EME Spy 200, Microwave Vision Group, Paris, France) were used to measure the RF-EMF in 20 different frequency bands from 88 MHz to 5850 MHz (Table 1) and thus calculate the total RF-EMF exposure (i.e. the quadratic sum of the RF values in the different frequency bands). The EME Spy 200 dosimeter senses EMF via three orthogonally arranged antennae, with a sensitivity ranging from 0.01 V/m to 6 V/m (FM, TV3, TETRA, TV4&5 and WiFi 5G) and from 0.005 V/m to 6 V/m (LTE 800, GSM, DCS, DECT, UMTS, WiFi 2G, LTE 2600, and WiMax). Measurements were recorded every 20 s for 24 hours, representing 4231 points per day. The dosimeters were moved every 24 hours. For each measurement point, the values recorded over 24 hours were averaged (63 in the NCCU's staff area, and 56 in the NCCU's staff area).

In the preterm infants' rooms, measurements over 24 hours were made by placing three dosimeters at various points around the incubator. The three measurements were averaged over 24 hours to give a single value per room and per day (13 in the NCCU's children's rooms, and 12 in the NCCU's children's rooms).

Measurements of medical devices used in the department

In a second set of experiments, we used a Faraday chamber to determine whether various medical devices frequently used in the children's rooms (e.g. incubators and monitoring devices) were potential sources of exposure to RF-EMF. The levels emitted by the operating devices (i.e. in the "on" position) were measured one by one every 4 second for 3 minutes in the Faraday chamber. For measurements inside incubators, three dosimeters were placed at the usual position of the feet, abdomen and head. These three values were averaged to provide a single value per incubator. The dosimeter was placed as close as possible to the other devices investigated (transport monitors, phototherapy devices, warmers, respirators, syringe drivers, and milk syringe drivers).

Statistics

For each of the 144 measurement points, the maximum value and the mean value over 24 hours were calculated. The maximum values were averaged over all the measurement points. The single greatest value was also extracted. Furthermore, we calculated the time during which the RF-EMF level at each measurement point was more than two standard deviations (SDs) above its mean.

All statistical analyses were performed with Statview software (SAS Institute, Inc., Cary, NC). The threshold for statistical significance was set to p<0.05. The normality of the data distribution was checked by applying the Kolmogorov Smirnov test. Differences (between the NCCU and the NICU, between staff areas and children's rooms, and between daytime and night-time) were probed using a t-test or (for non-normally distributed data) the Mann-Whitney test.

Results

The 24-hour RF-EMF levels in the staff area vs. the children's rooms in the NCCU and in the NICU are shown in Figure 1. In the NCCU, the mean ± SD RF-EMF levels were significantly higher in the staff area (0.05 ± 0.02 V/m; n=63 measurement points) than in the children's rooms (0.03 ± 0.01 V/m, n=13; p <0.0011). In the NICU, there was no difference between the staff area (0.03 ± 0.01 V/m, n=56) and the children's rooms (0.03 ± 0.01 V/m, n=12). The values for the staff areas and the children's rooms

were pooled for the NCCU and the NICU. The mean ± SD RF-EMF levels were significantly higher in the NCCU (0.04 ± 0.02 V/m; n=76 measurement points) than in the NICU (0.03 ± 0.01 V/m, n=68; p <0.0001). The time during which the RF-EMF level was more than 2 SDs above the 24-h mean for the NICU and the NCCU (for the 20 frequency bands, and in total) is shown in Figure 2. Overall, the mean ± SD values did not differ significantly when comparing the NCCU and the NICU (48 ± 15 min, n=76, and 49 ± 14 min, n=68, respectively; p=0.81). However, there significant differences between the two units for the TETRA I, TETRA III, LTE 800 (UL), GSM + 900 (UL), DECT, WiFi 2G, LTE 2600 (UL), and WiFi 5G bands. The NCCU and NICU did not differ significantly with regard to the average maximum values for the whole set of measurement points (1.08 ± 0.87 vs. 0.93 ± 0.68 V/m, respectively; p=0.52). The single greatest value recorded in the NCCU saturated the dosimeter (6.0 V/m for the GMS + UMTS 900 (UL) frequency bands in the staff area) and was notably greater than the single greatest value recorded in the NICU (3.7 V/m for the UMTS 2100 (UL) frequency band in the children's rooms).

In the NCCU, there was a significant difference between RF-EMF levels during the night (0.04 ± 0.04 V/m) and during the day (0.05 ± 0.05 V/m; p<0.001) (Figure 3). The same was true for the NICU, with a value of 0.03 ± 0.04 V/m during the night and 0.04 ± 0.05 V/m during the day (p<0.0001). It is noteworthy that the single greatest values were recorded during the night for both the NCCU and NICU. The single greatest values recorded during the day was 3.0 V/m (for the UMTS 2100 (UL) frequency band in the staff area) in the NCCU and 1.9 V/m (for the GMS + UMTS 900 (UL) frequency band in the staff area) in the NICU. In the second set of experiments (performed in a Faraday chamber), the only device to exceed the limit of detection (0.005 V/m) was the Mediprema Satis + incubator (Figure 4), for which we noted oscillation in the TV3 band (explaining why a SD is indicated in the Figure 4). Discussion:

Technological developments have resulted in the extensive generation of man-made EMF. The impact of exposure to these EMF on human health is receiving a great deal of attention from scientists and the general public -especially with regard to the exposure of sensitive populations like preterm neonates. A precise evaluation of the type and level of exposure is always an essential preliminary step towards understanding clinical outcomes in detail.

To the best of our knowledge, the present study is the first to have measured RF-EMF levels in a department of pediatrics, including an NCCU and a NICU. Mean RF-EMF levels were significantly higher in the staff area than in the children's rooms for the NCCU (p <0.0011). Overall, were also significantly higher in the NCCU than in the NICU RF-EMF (p< 0.0001). Although the two units did not differ significantly with regard to the average maximum values, the single greatest value recorded in the NCCU (6 V/m for the GSM and UMTS 900 frequency bands in the staff area) was above twice that recorded in the NICU (3.70 V/m for the UMTS 2100 (UL) frequency band in the care area). The NCCU and NICU did not differ significantly with regard to the time during which the RF-EMF level was more than two SDs above the mean. Lastly, the RF-EMF level was significantly higher during the day than during the night (p<0.001).

The RF-EMF levels in the present study were lower (0.04 ± 0.02 V/m) than those measured by by [START_REF] Calvente | Radiofrequency exposure in the Neonatal Medium Care Unit[END_REF] may not accurately reflect the RF-EMF levels related to care activities inside NICU.

In our study, the frequency bands with the highest EMF levels were GSM + UMTS 900 (UL) (0.02 ± 0.009 V/m) and DECT (0.02 ± 0.014 V/m) in the NICU, and GSM + UMTS 900 (UL) (0.014 ± 0.007 V/m) for the NCCU. There are several possible explanations for the low overall RF-EMF levels measured here. Firstly, our hospital was built recently (in 2014). Secondly, and although the department of pediatrics is equipped with modern devices that can communicate via WiFi networks, the hospital decided to used wired communication exclusively. Lastly, the hospital is located on the outskirts of the city of Amiens (i.e. close to the countryside) and it is not close to any telecommunications masts.

We therefore hypothesize that RF-EMF levels in hospitals are site-dependent in terms of the sources (modern vs. older buildings) and prevention policy (e.g. wired vs. wireless)

Our study did not include an analysis of levels of activity in the units: this prevented us from precisely identifying the sources of the RF-EMF. Nevertheless, our measurements in a Faraday chamber showed that none of the tested medical devices emitted RF-EMF levels. One can therefore conclude that the RF-EMF measured in the children's rooms were generated elsewhere (i.e. in staff areas, corridors, etc.). It is possible to determine the emission sources indirectly via an assessment of the frequency bands during the day (when most care activities take place) and the night. The RF-EMF level was significantly higher during the day than during the night. We hypothesize that this difference was mainly due to use of the parents' and carers' mobile and cordless phones during the day because the preponderant frequency bands were in the GSM + UMTS 900 (UL) and DECT bands.

In contrast, the average maximum values (in the GSM + UMTS 900 (UL) and UMTS 2100 (UL) bands)

were greater at night, and were perhaps related to phone use; given the lower levels of care activity at night, the parents and present may have had more time to consult their phone -as already suggested by [START_REF] Calvente | Radiofrequency exposure in the Neonatal Medium Care Unit[END_REF]. Our present data consolidate the hypothesis whereby the RF-EMF levels detected within the department of pediatrics were mainly due to wireless communication systems used during routine care activities. A subsequent experiment in the Faraday chamber (data not shown) showed that a DECT phone used in the department was associated with a mean field of 0.743 ± 0.192 V/m over an hour. Under these conditions, the device was constantly searching for a signal and so this operation might also account for the high values that we recorded.

Overall, the measured RF-EMF levels were very low (<0.1 V/m); the average in the NCCU (0.04 V/m) was around 1250 times lower than the recommended limit of 50 V/m. Even though the latter value was calculated (ICNIRP, 1998) to take account of the population's heterogeneity and frailty, it was not specifically designed for preterm neonates. Moreover, we also observed a single transient greatest RF value of 6 V/m, corresponding to the dosimeter's saturation level. Hence, we cannot rule out the possibility that even higher RF-EMF levels were produced at some point.

The presence of these high peak values raises the question of their potential impact on the neurodevelopmental outcome of preterm infants. At present, there are no literature data on putative effects of RF peaks. However, children (and especially preterm neonates) may be particularly vulnerable to RF exposure. The most frequently suggested reasons are the vulnerability of the developing CNS (Blackburn, 2009a(Blackburn, , 2009b)), the greater absorption of RF energy (relative to adults) [START_REF] Fernández | Absorption of wireless radiation in the child versus adult brain and eye from cell phone conversation or virtual reality[END_REF], and the longer period of lifetime exposure (i.e. starting at birth or even before). The greater absorption of RF-EMF energy in children is attributed to a higher tissue water content (vs. adults) and thus differences in dielectric values (conductivity and permittivity) [START_REF] Christ | Age-dependent tissuespecific exposure of cell phone users[END_REF]. In a study of a porcine model, the dielectric properties of brain tissues were found to be significantly greater in young animals than in adult animals (up to 43%, for white matter) [START_REF] Peyman | Dielectric properties of tissues; variation with age and their relevance in exposure of children to electromagnetic fields; state of knowledge[END_REF]. At a given frequency, the radiation penetrates more deeply and spreads more widely within a child's brain than within an adult's brain [START_REF] Fernández | Absorption of wireless radiation in the child versus adult brain and eye from cell phone conversation or virtual reality[END_REF].

Conclusions

The present study is the first to have measured environmental RF-EMF levels in a department of pediatrics' NCCU and NICU. The levels were low, relative to current guidelines. However, a combination of low-level, chronic exposure with transient, elevated peak values in a vulnerable population of preterm neonates may be of particular concern. In a department of pediatrics, decreasing preterm neonates' exposure to RF-EMF should primarily involve a limitation on the use of wireless communication devices by staff members and parents. Even though some earlier research [START_REF] Bellieni | Is newborn melatonin production influenced by magnetic fields produced by incubators?[END_REF][START_REF] Bellieni | Electromagnetic fields produced by incubators influence heart rate variability in newborns[END_REF]) has focused on the potential danger of low-frequency EMF, the impact of chronic RF-EMF exposure on the physiological and/or developmental trajectory in preterm neonates now warrants investigation.

FM

  Measurements in staff areas and children's roomsIn a first set of experiments, we measured RF-EMF levels in the NCCU and NICU's staff areas. The department of pediatrics was mapped in order to get the best possible representation of the distribution of the electromagnetic fields. The measurement sites (n=194) were defined on a standard map (1x1m) and adjusted according to the configuration of the premises. The dosimeters were placed throughout the department in a pseudo-random manner while avoiding the possibility of simultaneous side-by-side measurements. The dosimeter was set vertically on a tripod at a height of 110 cm from the floor (corresponding to the height of a preterm infant in an incubator).

Figure 1 :

 1 Figure 1: 24-hour RF-EMF levels in the staff area and children's rooms for the NCCU and the NICU. The cross indicates the mean value. Staff area NCCU Children's rooms NCCU Staff area NICU Children's rooms NICU

Figure 2 :

 2 Figure 2: Time during which the RF-EMF level was more than 2 SDs above the 24-h mean for the NICU and the NCCU.

Figure 3 :

 3 Figure 3: RF-EMF levels by period of the day (night-time: 6 pm to 8 am; daytime: 8 am to 6 pm). The cross indicates the mean.

Figure 4 :

 4 Figure 4: RF-EMF levels emitted by various medical devices in a Faraday chamber

  Calvente et al. (2017) (0.81 ±. 0.07 V/m). The high values in Calvente et al.'s study were mainly due to the FM frequency band (0.79 V/m). Given that in-hospital medical devices do not emit in the FM band, we conclude that Calvente et al.'s RF-EMF came from outside. Accordingly, the values reported

Table 1 :

 1 Bands in the RF spectrum

Acknowledgments

The authors thank the staff of the Neonatology Department for their assistance with the experiments. They also wish to thank David Fraser (Biotech Communication SARL, Ploudalmézeau, France) for copy-editing assistance.

Funding

This work was funded by the French National Research Program for Environmental and Occupational Health of ANSES (EST/2017/2 RF/03).

Appendix 1: Floorplan of the NCCU and NICU in the Department of Pediatrics at Amiens University Medical Center (Amiens, France).


Alexandre Albinet
email: alexandre.albinet@gmail.com

Grazia Maria Lanzafame

Deepchandra Srivastava

Nicolas Bonnaire

Frederica Nalin

Stephen A Wise

+3334556485

Analysis and determination of secondary organic aerosol (SOA) tracers (markers) in particulate matter standard reference material (SRM

Keywords: Aerosol, SOA, Tracers, QuEChERS, Analysis

Introduction and objectives

Organic matter (OM) accounts for a large fraction of the fine atmospheric particulate matter (PM, aerosol) (about 20 to 90 %) [START_REF] Zhang | Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes[END_REF]. Carbonaceous species that constitute the organic aerosol fraction (OA) are classified following their origins. Primary organic compounds directly emitted into the atmosphere belongs to the primary OA fraction (POA) while secondary organic particles, formed in the atmosphere by homogenous or heterogenous (photo)-chemical reactions of volatile or semivolatile organic compounds (COVs) followed by gas-particle conversion processes such as nucleation and/or condensation, are defined as secondary OA (SOA) [START_REF] Ziemann | Kinetics, products, and mechanisms of secondary organic aerosol formation[END_REF][START_REF] Carlton | A review of Secondary Organic Aerosol (SOA) formation from isoprene[END_REF][START_REF] Hallquist | The formation, properties and impact of secondary organic aerosol: current and emerging issues[END_REF][START_REF] Kroll | Chemistry of secondary organic aerosol: Formation and evolution of low-volatility organics in the atmosphere[END_REF]. This latter fraction accounts for up to 80 to 90 % of total OA under certain conditions [START_REF] Zhang | Ubiquity and dominance of oxygenated species in organic aerosols in anthropogenically-influenced Northern Hemisphere midlatitudes[END_REF][START_REF] Zhang | Understanding atmospheric organic aerosols via factor analysis of aerosol mass spectrometry: a review[END_REF][START_REF] Srivastava | Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies[END_REF] making its characterization and apportionment essential in terms of air quality or climate impacts.

Different methodologies have been developed and reported in the literature to apportion SOA (or secondary organic carbon, SOC) [START_REF] Srivastava | Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies[END_REF]. Several studies have identified key characteristic molecular compounds of the formation of SOA from specific precursors or sources and commonly defined as molecular SOA markers (or tracers if assuming that they are chemically stable) [START_REF] Srivastava | Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies[END_REF][START_REF] Nozière | The Molecular Identification of Organic Compounds in the Atmosphere: State of the Art and Challenges[END_REF] (Table 1). These compounds are used to apportion SOA in the so-called SOA tracer method [START_REF] Srivastava | Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies[END_REF][START_REF] Kleindienst | Estimates of the contributions of biogenic and anthropogenic hydrocarbons to secondary organic aerosol at a southeastern US location[END_REF] or in source-receptor models, such as chemical mass balance (CMB) or positive matrix factorization (PMF) [START_REF] Srivastava | Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies[END_REF][START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 2: Intensive short-term campaign in the Paris area (France)[END_REF][START_REF] Srivastava | Speciation of organic fraction does matter for source apportionment. Part 1: A one-year campaign in Grenoble (France)[END_REF][START_REF] Hettiyadura | Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols[END_REF][START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: combining off-line and online measurements[END_REF]. The quantification of these SOA markers is critical because inaccurate values could lead to significant bias in the results obtained [START_REF] Srivastava | Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies[END_REF]. In most cases, no authentic standards are commercially available for these compounds and proxy compounds are generally used for their quantification. In addition, in terms of validation of analytical procedure, quality assurance and control, the analysis of reference material is useful, but no SOA marker concentration values have been reported yet in any particulate matter reference material.

Thus, the objectives of this work were to evaluate the presence and to determine the concentration values of some typical molecular SOA markers in the commonly used National Institute of Standards and Technology's (NIST) urban dust standard reference material (NIST SRM 1649b). This material has been largely characterized in the past years [START_REF] Wise | Standard reference materials (SRMs) for determination of organic contaminants in environmental samples[END_REF][START_REF]Standard reference material 1649b, Urban dust[END_REF][START_REF] Albinet | Simultaneous analysis of oxygenated and nitrated polycyclic aromatic hydrocarbons on standard reference material 1649a (urban dust) and on natural ambient air samples by gas chromatography-mass spectrometry with negative ion chemical ionisation[END_REF][START_REF] Albinet | A simple QuEChERS-like extraction approach for molecular chemical characterization of organic aerosols: application to nitrated and oxygenated PAH derivatives (NPAH and OPAH) quantified by GC-NICIMS[END_REF][START_REF] Larsen | Determination of Levoglucosan in Particulate Matter Reference Materials[END_REF] but to our knowledge, no concentration values for such compounds have been reported so far in the literature. Our objectives also included the comparison of the extraction efficiencies, for the analysis of molecular SOA markers in PM, of two different extraction procedures namely, sonication and QuEChERS-like (Quick Easy Cheap Effective Rugged and Safe). Sonication is commonly used for the analysis of SOA marker compounds in aerosol samples [7-9, 16, 27, 33-40] while QuEChERS-like procedure has been developed and successfully applied for the molecular chemical characterization of OA including polycyclic aromatic compounds (PAHs), nitro-PAHs, oxy-PAHs [START_REF] Albinet | A simple QuEChERS-like extraction approach for molecular chemical characterization of organic aerosols: application to nitrated and oxygenated PAH derivatives (NPAH and OPAH) quantified by GC-NICIMS[END_REF][START_REF] Albinet | A really quick easy cheap effective rugged and safe (QuEChERS) extraction procedure for the analysis of particle-bound PAHs in ambient air and emission samples[END_REF][START_REF] Tomaz | Sources and atmospheric chemistry of oxy-and nitro-PAHs in the ambient air of Grenoble (France)[END_REF][START_REF] Tomaz | One-year study of polycyclic aromatic compounds at an urban site in Grenoble (France): Seasonal variations, gas/particle partitioning and cancer risk estimation[END_REF] and also SOA markers [START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 2: Intensive short-term campaign in the Paris area (France)[END_REF][START_REF] Srivastava | Speciation of organic fraction does matter for source apportionment. Part 1: A one-year campaign in Grenoble (France)[END_REF][START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 3: combining off-line and online measurements[END_REF][START_REF] Mutzel | An improved method for the quantification of SOA bound peroxides[END_REF]. In addition, this latter technique should minimize the degradation of some fragile SOA marker and preserve the integrity of the sample as it is done at room temperature [START_REF] Albinet | A simple QuEChERS-like extraction approach for molecular chemical characterization of organic aerosols: application to nitrated and oxygenated PAH derivatives (NPAH and OPAH) quantified by GC-NICIMS[END_REF][START_REF] Mutzel | An improved method for the quantification of SOA bound peroxides[END_REF]. Finally, three research laboratories/institutes (INERIS, NIST and LSCE) using two extraction procedures and two analytical techniques (gas chromatography coupled to mass spectrometry (GC-MS) and high-pressure liquid chromatography/tandem mass spectrometry (HPLC/MS-MS)) performed the analysis of 12 key SOA markers in the SRM 1649b (Table 1, except 2-methylthreitol) and their results were compared.

Experimental

Chemical, gases and solvents

Information about the chemicals, gases and solvents used, their degrees of purity, and the supplier data are reported in the Supplementary Information (SI). Pure and authentic SOA marker compounds (liquids or solids) were used in this study. They have been purchased or synthetized on purpose from/by different suppliers (Table S1).

Standard reference material (SRM) 1649b (urban dust)

During the mid to late 1970s, NIST collected urban PM using a baghouse specially designed for the purpose from the Washington, DC area. PM were collected over a period greater than 12 months and represents an atmospheric particulate matrix to validate analytical methods. SRM 1649b (urban dust) has been prepared from the bulk material (SRM 1649) collected at this time but it has been sieved to a smaller particle size with a mean diameter of 105 µm [START_REF]Standard reference material 1649b, Urban dust[END_REF].

For both extraction procedures tested, 5 to 13 (in total 5 for NIST, 10 for LSCE and 23 for INERIS) samples of SRM 1649b of about 50 mg (balance precision = 0.01 mg) were analyzed. Triplicate analyses (3 injections of the sample extracts) were performed, except for 5 samples for each extraction procedure for INERIS, injected only once. Moisture content was determined using a Karl Fischer method (2.94 % moisture, analyzer HR 73, Mettler Toledo). SOA marker concentrations were corrected and are reported here on dry-mass basis. (ratio sample extract to derivatizing reagent of 1:1). For LC/MS-MS (LSCE) analyses, the supernatants were directly analyzed without any other sample preparation.

Extraction and derivatization procedures

GC/MS analysis

The analysis of 11 to 12 SOA markers was achieved using GC/MS (Agilent 7890A GC coupled Analyses were performed in selected ion monitoring mode (SIM). Autotune parameters were adopted for the electron multiplier conditions with a gain factor of 10. Monitored ions and dwell times are shown in Table 2. The quantification of the SOA markers was based on 8 to 10-points calibration curves (gravimetrically diluted stock standard solutions (prepared in acetonitrile (ACN) or MeOH) from 5 (or 20) to 5000 pg µl -1 in MeOH; 0.99 > r² > 0.90 for all compounds). All SOA marker compounds were quantified using authentic standards and labelled surrogate standards by internal calibration except for both methylnitrocatechols. Preliminary recoveries tests performed on precleaned SRM 1619a powder (twice, with DCM using pressurized liquid extraction and MeOH using sonication) spiked with known amount (500 ng) of SOA marker compounds, showed significant differences in the results between internal and external calibration for the quantification of these compounds highlighting that the initial deuterated surrogate used (4-methylcatechol-d3) to quantify them was not suitable for the GC/MS analyses (Figure S1). As no labelled methylnitrocatechol standards exists, both compounds were finally quantified by external calibration. In addition, the extraction recoveries obtained for DHOPA and 2-methylerythritol were quite low. The solvent of extraction used (MeOH) was probably not the best for the specific extraction of these compounds.

However, it was the best compromise for the analysis of all the SOA markers targeted in this work.

The labelled surrogates used (no labelled DHOPA and 2-methylerythritol are commercially available)

to quantify these both compounds were not able to fully correct the sample preparation losses but at least, they did not induce any analytical drift like in the case of methylnitrocatechols.

Using these conditions, the instrumental limits of quantification (LOQ), defined as the lowest concentration of the compound than can be determined with a signal to noise ratio of 10 (calculated using the chromatograms obtained from the calibration solution with the lowest concentration level), were within the range 0.6-14.3 pg injected.

The list of all SOA markers quantified, labelled surrogates used, retention times, monitored ions and limits of quantification are given in Table 2 for both, NIST and INERIS GC/MS analytical procedures.

HPLC/MS-MS analysis

The quantification of SOA markers was achieved by HPLC/MS-MS (HPLC Dionex U3000 coupled to a triple quadrupole MS AB Sciex 3200 QTRAP). Two different columns, Thermo porous graphic carbon (PGC) Hypercarb column (2.1 mm × 150 mm × 3 μm) and Waters Acquity C18

HSST3 column (2.1 mm × 100 mm × 1.8 μm), were employed for the chromatographic separation.

For the HSST3 column, the mobile phases consisted of a 0.1% formic acid in Milli-Q water (A) and ACN (B). The following program was applied during the LC analysis: start with eluent B at 5% with a gradient to 60% in 10 min followed by a second one to 100% in 2 min kept at 100% for 4 min, decreased back to 5% in 1 min, and conditioned at 5% for 10 min for the following analysis. For the Hypercarb column, the mobile phases consisted of a 0.1% NH3 in Milli-Q water (A) and ACN (B).

The elution program used was the following: start with eluent B at 4% kept for 2.5 min followed by a with a gradient to 100% in 9.5 min kept at 100% for 4 min, decreased back to 4% in 1 min kept for 5 min for conditioning the following analysis. Samples were injected with an injection volume of 10 μL and the analysis was carried out using a flow rate of 0.2 (Hypercarb) and 0.3 (HSST3) mL min -1 , respectively.

The targeted compounds were ionized using electrospray ionization in the negative mode. The mass spectrometer, with unit mass resolution, was operated under the following conditions: source voltage at -4.5 kV; desolvation temperature at 650 °C, desolvation gas pressure (air) of 60 psi, nebulizer gas pressure (air) of 40 psi, collisional activated dissociation (CAD) set on medium with nitrogen used as collision gas, compound-selective scheduled multiple reaction monitoring (MRM) mode with 40 or 140 s detection windows (HSST3 and Hypercarb, respectively), target scan time of 0.5 s, MR pause of 5 ms.

The quantification of the compounds was based on 6-points calibration curves from 1 to 32 pg µl -1 in MeOH (0.99 > r² > 0.90 for all compounds). Again, all SOA marker compounds were quantified by internal calibration, using authentic standards and labelled surrogate standards, except for 2-methylerythritol which was quantified by external calibration cause, using this method, the labelled meso-erythritol-1,1,2,4,4-d6 surrogate standard was not well detected. Using these parameters, the instrumental LOQ were within the range 20-40 pg injected.

The compounds quantified, labelled surrogates used, retention times, parameters for each MRM transition and limits of quantification are given in Table 3 for the HPLC/MS-MS analytical procedure.

Results and discussion

QuEChERS-like extraction vs sonication and GC/MS vs HPLC/MS-MS

The comparison of the results obtained according to the different extraction procedures and analytical techniques used is shown on Figure 1 (see Table S2 for details). Results are also presented by laboratories/institutes.

Overall, whatever the analytical protocol used, all the results were in quite good agreement, except for 2-methylerythritol, and to a lesser extent MBTCA and 3-hydroxyglutaric acid, which showed large discrepancies. First, this study highlighted that the SRM 1649b contains quantifiable and quite homogeneous amounts of SOA markers. Note that, β-caryophyllinic acid has been reported by NIST as below LOQ (Table S2). This SOA marker is usually observed at low concentration levels in urban atmosphere [START_REF] Srivastava | Comparison of Measurement-Based Methodologies to Apportion Secondary Organic Carbon (SOC) in PM2.5: A Review of Recent Studies[END_REF][START_REF] Srivastava | Speciation of organic fractions does matter for aerosol source apportionment. Part 2: Intensive short-term campaign in the Paris area (France)[END_REF][START_REF] Srivastava | Speciation of organic fraction does matter for source apportionment. Part 1: A one-year campaign in Grenoble (France)[END_REF][START_REF] Fu | Molecular Markers of Secondary Organic Aerosol in Mumbai, India[END_REF][START_REF] Kleindienst | Contribution of primary and secondary sources to organic aerosol and PM2.5 at SEARCH network sites[END_REF]. Second, these results also showed that the extraction efficiencies of sonication and QuEChERS-like were similar demonstrating the validity of the QuEChERS-like extraction procedure for the analysis of SOA markers in PM. Except 2-methylerythritol, the results obtained showed also a good agreement between both analytical techniques used, GC/MS and HPLC/MS-MS. The differences observed were mainly due the discrepancies obtained between INERIS and NIST, both using GC/MS. In that case, it would difficult to conclude on any influence of the derivatizing agent used (BSTFA and MSTFA). Finally, further tests showed that both 2methyltetrol isomers, namely 2-methylthreitol and 2-methylerythritol, were not separated with the column and elution program used here for the analyses performed by HPLC/MS-MS (the exact same transitions were also observed). Thus, the concentration values reported on Figure 1 for HPLC/MS-MS are the sum of both compounds. Based on the literature data about these compounds, 2methylerythritol accounts generally for about 2/3 of the sum of 2-methyltetrols [9, 35-37, 39, 45-49].

However, this only explained in part the differences observed. In addition, the difference in the results reported by NIST and INERIS were still significant. The analysis of 2-methyltetrols seems still challenging and the results obtained here may also indicate that the homogeneity in the SRM 1649b for this compound is not optimal. Further tests must be done to support our observations.

SOA marker concentrations in SRM 1649b (urban dust)

The final SOA marker concentration values determined in this study are presented on Table 4. A total of 28 to 38 full replicates (extraction + analysis, triplicate injections), depending on the compound considered, has been considered. Note that, the concentrations of 2-methylerythritol obtained by HPLC/MS-MS have been corrected by a factor 2/3 to consider that the sum of 2methytetrols was quantified in this case. The value reported here for this compound is given only for information.

Large standard deviations were obtained for some compounds such as DHOPA, pinonic acid, methylnitrocatechols, MBTCA and 2-methylerythritol with coefficients of variation (standard deviation/mean value, CV) larger than 70%. A detailed study of the different analytical steps showed that the injection repeatability (CV ranging from 2 to 15%) accounted for a low part in the variations observed (Table S3). Only DHOPA for LSCE (37%) and 2-methylerythritol (111%) for NIST, showed large injection repeatability variations. This latter result highlighted again the challenge in the analysis of 2-methylerythritol. Finally, the large standard deviations obtained for these SOA markers were due to the different analytical procedures used and the influence of the experimenter/laboratory/institute (variations in terms of reproducibility).

Conclusions

The analysis of molecular SOA markers (tracers) is useful to apportion SOA mass in the atmosphere. The different SOA markers studied here were all present in the NIST SRM 1649b (urban dust). Sonication and QuEChERS-like procedures gave comparable results for the extraction of the SOA markers. As this study provides the first indicative values in a reference material for typical SOA markers, the analysis of SRM 1649b (urban dust) should be used for quality control/assurance purposes when such compounds are used to apportion the SOA fraction in ambient air.

  SRM extraction was done by both, sonication and QuEChERS-like procedures using methanol (MeOH) as solvent. SRM 1649b samples were placed in centrifuge glass tubes (∅ = 16 mm, L=100mm, screw cap with PTFE septum face; Duran), spiked with known amounts of 3 to 5 labelled surrogate standards (200 to 800 ng added, depending on the final volume of the extract after evaporation), and 7 mL of solvent was added for the extraction. Sonication was done for 15-20 min while for QuEChERS-like extraction, the tubes were shaken by vortex mixing with a multiposition vortex mixer for 1.5 min (DVX-2500 Multi-Tube Vortexer; VWR). After extraction, samples were centrifuged at room temperature for 5 min at 4500 rpm or 30 min at 3800 rpm, for INERIS/LSCE and NIST, respectively (Sigma 3-16 PK and Beckman GS-6R centrifuge). For GC/MS analyses, supernatant extracts (4.5 or 5.5 ml) were collected and reduced to dryness under a gentle nitrogen stream to remove any trace of water or MeOH and avoid additional consumption of BSTFA or MSTFA. Extracts were dissolved into 50, 100 or 200 μL of acetonitrile and subjected to derivatization (silylation) for 30 minutes at 60°C after addition of 50 or 100 µl of N-methyl-N-)trifluoroacetamide (BSTFA) (NIST), both with 1% trimethylchlorosilane (TMCS)

  to 5975C MS at INERIS and Agilent 6890 GC coupled to 5973 MS at NIST) in electron ionization mode (EI, 70 eV). 1 or 2 μL of the extracts were injected in the splitless mode at 250 °C using, for INERIS, a programmed temperature vaporizer injector system (CIS-4 Gerstel, with a Restek Sky single baffle liner with wool, 3 × 2 × 71 mm, OD × ID × L) and for NIST, a splitless injector (with a Restek Sky low pressure drop precision inlet liner w/wool, 6.3 × 4 × 78.5 mm). Compounds were separated using capillary columns with equivalent 5% phenyl-methylpolysiloxane phase 60 m × 0.25 mm × 0.25 μm (Agilent J&W DB5-MS with 10 m guard column for INERIS or Restek Rtx-5MS for NIST), using the following temperature programs: INERISstart at 70 °C for 1 min, then ramped to 260 °C at 5 °C min -1 , followed by a ramp to 290 °C at 20 °C min -1 , further followed by a ramp to 300 °C at 5 °C min -1 , then ramped at 10 °C min -1 to 320°C held for 10 min; NISTstart at 70 °C for 1 min, increased at a rate of 5°C min -1 to 300 °C held for 1 min. The carrier gas (He) flow was set to 1.2 (NIST) or 1.5 (INERIS) mL min -1 throughout the analysis and transfer line heated at 310 °C. The ion source and quadrupole temperatures were 230 and 150 °C, respectively.

Fig. 1 .

 1 Fig. 1. Comparison of the average SOA marker concentrations (mg kg -1 ) (dry-mass basis) in the SRM 1649b

Table 2 .

 2 GC/MS conditions used for SOA marker analysis and typical instrumental limits of quantification (LOQ)

	Compounds	Monitored ions (m/z)	Retention times d (min)	Dwell times (s)	LOQ (pg injected)
	Succinic acid-2,3,3,3-d4	147, 251	19.54	0.06/0.035	-
	Succinic acid a	129, 247	19.61	0.035	1.0-1.3
	α-Methylglyceric acid	219, 306	19.74	0.06/0.035	1.0 -2.3
	2,3-Dihydroxy-4-oxopentanoic acid (DHOPA)	218, 189, 350	24.71	0.04/0.05	3.7-11.0
	cis-Pinonic acid	171, 125	25.27	0.04/0.05	2.2-7.5
	3-Hydroxyglutaric acid	185, 349	26.26	0.08/0.05	1.3-7.0
	Pinic acid	129, 171	28.50	0.05/0.05	6.3-7.6
	Phthalic acid b	221, 295	29.04	0.05/0.05	0.9-8.4
	Meso-erythritol-1,1,2,4,4-d6	208, 220	24.50	0.04/0.1	-
	2-Methylerythritol	116, 117, 219	25.74	0.05/0.05	1.1-4.2
	1,9-Nonanedioic acid-d14	213, 331	31.10	0.06/0.035	-
	3-Methylbutane-1,2,3-tricarboxylic acid (MBTCA)	204, 245, 405	30.34	0.08/0.05	0.6-14.5
	β-Caryophyllinic acid	117, 200	36.08	0.08/0.05	8.9-14.3
	4-Methyl-5-nitrocatechol c	296, 313	31.06	0.06/0.035	1.5-10.3
	3-Methyl-5-nitrocatechol c	298, 313	32.28	0.10/0.10	1.4-6.1

a Not analyzed by NIST. b For NIST, quantified using 1,9-nonanedioic acid-d14 as surrogate. c Quantified by external calibration. d For INERIS method. Underlined: NIST parameters. Bold: Quantification ions.

Table 3 .

 3 UPLC/MS-MS conditions used for SOA marker analysis and instrumental limits of quantification (LOQ)

	Compounds	Transitions (m/z)	DP c	EP d	CEP e CE f CXP g	Retention time (min)	Column	LOQ (pg injected)
	Succinic acid-2,3,3,3-d4	121→77	30	4	10	16	0	1.73	Hypercarb	-
	3-Methylbutane-1,2,3-tricarboxylic acid (MBTCA)	203→185	30	5.5	12	16	4	1.61	Hypercarb	40
	Succinic acid	117→73	25	8	10	14	0	1.82	Hypercarb	40
	α-Methylglyceric acid	119→73	35	2	36	16	0	1.92	Hypercarb	80
	2,3-Dihydroxy-4-oxopentanoic acid (DHOPA)	147→73	15	5	10	18	0	2.16	Hypercarb	40
	3-Hydroxyglutaric acid a	-	-	-	-	-		-	-	-
	Phthalic-3,4,5,6-d4 acid	169→125	25	2.5	12	14	0	1.78	Hypercarb
	Phthalic acid	165→121	30	5	12	12	0	2.27	Hypercarb	20
	1,9-Nonanedioic acid-d14	201→138	35	9.5	30	18	2	2.29	HSS T3	-
	Pinic acid	185→141	35	9	12	18	0	5.97	HSS T3	40
	cis-Pinonic acid	183→141	30	11.5	12	16	0	7.45	HSS T3	40
	β-Caryophyllinic acid	253→209	40	6	10	14	2	10.64	HSS T3	40
	4-Methylcatechol-d3	126→108	60	3	10	22	0	6.65	HSS T3	-
	4-Methyl-5-nitrocatechol c	168→138	35	9	12	14	2	7.83	HSS T3	80
	3-Methyl-5-nitrocatechol c	168→138	45	5.5	12	22	2	8.66	HSS T3	160
	2-Methylerythritol b	135→85	30	10.5	12	22	0	2.64	Hypercarb	320

a Not analyzed by UPLC/MS-MS b Quantified by external calibration c Declustering potential (V) d Entrance potential (V) e Collision cell entrance potential (V) f Collision energy (eV) g Collision cell exit potential (V)

Table 4 .

 4 SOA marker concentrations (mg kg -1 ) (dry-mass basis) in SRM 1649b (urban dust) UPLC/MS-MS results were corrected by a factor 2/3 to consider that the sum of 2-methytetrols was quantified in this case

	Compound	Mean a	Standard deviation a
	Succinic acid	104.7	29.6
	α-Methylglyceric acid	3.1	1.3
	DHOPA	1.1	0.7
	cis-Pinonic acid	7.3	5.1
	3-Hydroxyglutaric acid	8.2	2.9
	Pinic acid	11.1	4.4
	Phthalic acid	83.1	25.5
	4-Methyl-5-nitrocatechol	2.8	2.7
	3-Methyl-5-nitrocatechol	2.5	2.2
	2-Methylerythritol b	20.6	26.6
	MBTCA	2.2	2.1
	β-Caryophyllinic acid	0.4	0.3
	a n=45 to 55

b

Acknowledgements

The authors wish to thank the French Ministry of Environment for their financial support. They also thank Siham Ayachi, Xavier Pollion (LSCE) and Jérôme Beaumont (INERIS), for their technical assistance and the chemical analyses performed.

Supplementary information

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/XXXXXXXXX Conflict of interest S. Wise and F. Nalin worked at the NIST which also provides the standard reference materials (SRM). The other authors declare that they have no conflict of interest.

Organic markers

Predominant origin Precursors/Sources