Roger Roig Rodelas

Abhishek Chakraborty

Esperanza Perdrix

Emmanuel Tison

Véronique Riffault
email: veronique.riffault@imt-lille-douai.fr

Véronique

Real-time assessment of wintertime organic aerosol characteristics and sources at a suburban site in northern France

Keywords: AMS, organic aerosols, source apportionment, aqueous processing

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

Introduction

Atmospheric aerosols have gained attention worldwide due to their various impacts on human health [START_REF] Kelly | Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter[END_REF], climate, visibility and ecosystems [START_REF] Hallquist | The formation, properties and impact of secondary organic aerosol: current and emerging issues[END_REF]IPCC, 2013;[START_REF] Watson | Visibility: Science and regulation[END_REF]. In the year 2016 only, air pollution was responsible for 7 million deaths worldwide [START_REF] Who | Burden of disease from the joint effects of household and ambient Air pollution for 2016[END_REF], mostly due to inhalation of fine particulate matter (PM 2.5 ). In Europe, the premature mortality associated to air pollution is also alarmingly high, with estimations for the year 2016 ranging from 208,000 to 348,000 for high-income and low-to middle-income countries, respectively [START_REF] Who | Burden of disease from the joint effects of household and ambient Air pollution for 2016[END_REF]. The region of northern France is also affected by high levels of PM 2.5 , particularly during winter and spring (Atmo Nord-Pas- de-Calais, 2014). These high levels are mainly attributed to its location amidst various emission source areas including an extensive highway network, a high urban density, and the proximity to several European capitals (London, Paris, and Brussels) as well as a significant influence of industry and agriculture.

While the elemental and inorganic fractions of ambient aerosols are rather well understood, the characterization of the organic matter (OM) still remains a challenge due to their complex nature, associated to numerous emission sources and atmospheric transformations [START_REF] Hallquist | The formation, properties and impact of secondary organic aerosol: current and emerging issues[END_REF][START_REF] Jimenez | Evolution of Organic Aerosols in the Atmosphere[END_REF]. OM can represent from 20 to 90% of the total submicron aerosol (PM 1 ) mass [START_REF] Jimenez | Evolution of Organic Aerosols in the Atmosphere[END_REF], and hence understanding its sources and transformation processes is essential in order to develop effective mitigation policies. OM, also called organic aerosol (OA), is typically divided into primary (POA) and secondary organic aerosols (SOA). POA are directly emitted to the atmosphere by a variety of sources comprising anthropogenic ones such as traffic, industrial activities, and residential biomass combustion; and natural ones like sea spray, volcanic emissions, forest fires, etc. [START_REF] Hallquist | The formation, properties and impact of secondary organic aerosol: current and emerging issues[END_REF][START_REF] Mohr | Characterization of Primary Organic Aerosol Emissions from Meat Cooking, Trash Burning, and Motor Vehicles with High-Resolution Aerosol Mass Spectrometry and Comparison with Ambient and Chamber Observations[END_REF]. SOA are formed in the atmosphere through several physicochemical processes of gas phase VOCs (volatile organic compounds) or POA [START_REF] Kanakidou | Organic aerosol and global climate modelling: a review[END_REF].

Different techniques have been developed in the past decades to analyze the nature and composition of OA. Most of these techniques work in offline mode; post analysis of the collected samples. These techniques can provide more exhaustive information on the nature and characteristics of individual organic species, but they require large amounts of samples which generally result in a low time resolution (of several hours). Since most of the atmospheric reactions occur at a time scale of few tens of minutes, these offline techniques often fail to elucidate the underlying atmospheric processes [START_REF] Hallquist | The formation, properties and impact of secondary organic aerosol: current and emerging issues[END_REF]. On the contrary, online techniques provide less exhaustive information on individual chemical species but can characterize bulk OA with a very high time resolution (of seconds to minutes). Among the available online techniques, aerosol mass spectrometry (AMS) has become quite popular, since it allows the measurement of the chemical composition and mass loading as a function of the particle size in the submicron range [START_REF] Canagaratna | Chemical and microphysical characterization of ambient aerosols with the aerodyne aerosol mass spectrometer[END_REF].

Besides, the combination of AMS data with source apportionment techniques, mainly positive matrix factorization (PMF) and multi-linear engine (ME-2), has allowed for the study of the OA sources and characteristics [START_REF] Ulbrich | Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data[END_REF]. Several AMS-PMF studies have shown that OA can typically be separated into secondary or oxygenated OA (OOA), and several primary OA types like hydrocarbon-like OA (HOA), biomass burning OA (BBOA) and cooking OA (COA), depending on the site location and sampling season [START_REF] Bozzetti | Organic aerosol source apportionment by offline-AMS over a full year in Marseille[END_REF][START_REF] Crippa | Organic aerosol components derived from 25 AMS data sets across Europe using a consistent ME-2 based source apportionment approach[END_REF][START_REF] Florou | The contribution of wood burning and other pollution sources to wintertime organic aerosol levels in two Greek cities[END_REF][START_REF] Lanz | Characterization of aerosol chemical composition with aerosol mass spectrometry in Central Europe: an overview[END_REF][START_REF] Mohr | Identification and quantification of organic aerosol from cooking and other sources in Barcelona using aerosol mass spectrometer data[END_REF][START_REF] Poulain | Diurnal variations of ambient particulate wood burning emissions and their contribution to the concentration of Polycyclic Aromatic Hydrocarbons (PAHs) in Seiffen, Germany[END_REF][START_REF] Saarikoski | Chemical characterization of springtime submicrometer aerosol in Po Valley, Italy[END_REF][START_REF] Timonen | Characteristics, sources and water-solubility of ambient submicron organic aerosol in springtime in Helsinki, Finland[END_REF]. In France, a few studies have focused on the OA sources and characteristics using real-time measurements in Paris [START_REF] Crippa | Wintertime aerosol chemical composition and source apportionment of the organic fraction in the metropolitan area of Paris[END_REF], Marseille [START_REF] Bozzetti | Organic aerosol source apportionment by offline-AMS over a full year in Marseille[END_REF], and in the north of France in Douai and Dunkirk which showed that winter OA was moderately oxidized and mostly composed of OOA, while significant contributions of BBOA, HOA and sulfur-containing OA (SCOA) were also found (Crenn et al., 2017a(Crenn et al., , 2017b)).

In this context, this intensive campaign using a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and carried out in winter 2016 at a suburban site in Douai was deployed with the aim of complementing ongoing long-term measurements with a Monitor for Gases and AeRosols in ambient Air (MARGA) and other collocated instrumentation which focused on the source apportionment of the PM 2.5 inorganic aerosol with an hourly resolution (Roig Rodelas et al., in press). This work focuses on the characterization of the chemical composition of NR-PM 1 and of the sources of OA obtained by PMF analysis during winter in Douai. In addition, the influence of meteorological characteristics and long-range transport on the characteristics of NR-PM 1 is also evaluated.

Materials and methods

Measurement site

Observations of the chemical composition of atmospheric aerosols were carried out from 5 February to 16 March 2016 in Douai, northern France, at a suburban location outside the city center (Figure S1) which is considered as representative of the background pollution of the area (50°23'03''N, 3°05'08''E, and 20 m above sea level). The city of Douai is located in a fairly flat land and is close (about 25-30 km south) to the European Metropolis of Lille. Lille is the second most densely populated metropolis in France, after that of Paris, with about 1.2 million inhabitants and a population density of 1,832 inhab. km -2 . The climate in northern France is classified as temperate oceanic, characterized by low seasonal thermal amplitudes, and regular precipitations along the year, with no dry season.

Instrumentation

The high resolution-time of flight-aerosol mass spectrometer (HR-ToF-AMS) (Aerodyne Research, USA), hereafter named AMS, was deployed in order to measure in real-time the chemical composition and concentrations of the non-refractory PM 1 (NR-PM 1 ) [START_REF] Decarlo | Field-deployable, high-resolution, time-of-flight aerosol mass spectrometer[END_REF]. The major species composing NR-PM 1 include NO 3 , NH 4 , SO 4 , Cl and organic aerosols (OA). Due to the principle of AMS data analysis, the non-refractory species are vaporized and ionized by electron impact (70 eV). Hence, the names of these species as used throughout this article (i.e. without charges) correspond to the sum of all the m/z fragments related to one given species in the fragmentation table [START_REF] Allan | A generalised method for the extraction of chemically resolved mass spectra from Aerodyne aerosol mass spectrometer data[END_REF]. The AMS operates in two modes according to the trajectory of the ions: the low resolution but highly sensitive V mode and the high resolution but less sensitive W mode. In this study, the time resolution of the AMS was set to 5 minutes, with 3 minutes for mode V and 2 minutes for mode W. Only the results for the V mode are shown, since the lower sensitivity of the W mode delivered a too low signal-to-noise ratio during most of the campaign.

The mass concentration measurement accuracy of AMS depends on collection efficiency (CE) and ionization efficiency (IE) values. The CE considers the effects of incomplete focusing of the particle beam and bouncing of some particles from the vaporizer [START_REF] Drewnick | A New Time-of-Flight Aerosol Mass Spectrometer (TOF-AMS)-Instrument Description and First Field Deployment[END_REF].

Typically, a default CE value of 0.5 is used [START_REF] Middlebrook | Evaluation of Composition-Dependent Collection Efficiencies for the Aerodyne Aerosol Mass Spectrometer using Field Data[END_REF]. However, it has been shown that the CE is dependent on particle phase, which is influenced by the relative humidity in the sampling line above 80%, the acidity/neutralization of the sulfate, ammonium nitrate, and organic content [START_REF] Middlebrook | Evaluation of Composition-Dependent Collection Efficiencies for the Aerodyne Aerosol Mass Spectrometer using Field Data[END_REF]. In this work a Nafion dryer was used in order to reduce the relative humidity in the sampling line. In addition, a composition dependent CE (CDCE), which recalculates the concentrations of all the chemical species by taking into account the NO 3 fraction of the aerosol, was applied to the AMS data as proposed previously by [START_REF] Middlebrook | Evaluation of Composition-Dependent Collection Efficiencies for the Aerodyne Aerosol Mass Spectrometer using Field Data[END_REF].

The IE is defined as the ratio between the number of ions detected and the molecules of the parent species [START_REF] Jimenez | Ambient aerosol sampling using the Aerodyne Aerosol Mass Spectrometer[END_REF], and is species specific. Its value is determined through calibrations. However, since it is not practical to perform individual calibrations for all compounds in ambient aerosol, a reference calibration is typically performed for nitrate, and the relative IE (RIE) of every species is obtained relative to that of nitrate [START_REF] Alfarra | Characterization of urban and rural organic particulate in the Lower Fraser Valley using two Aerodyne Aerosol Mass Spectrometers[END_REF]. We carried out calibrations every two weeks in order to determine IE(NO 3 -) and RIE(NH 4 + ) using aqueous solutions of 10 -2 mol L -1 NH 4 NO 3 (Sigma Aldrich, 99.0%). NH 4 NO 3 particles were generated by an atomizer (TSI 3076) and then dried by passing through a silica gel drier (TSI 3062). A dilution system composed of a set of valves and a HEPA (High Efficiency Particle Air)

filter was used in order to vary the particle concentration. In addition, an electrostatic classifier (TSI 3080) and a differential mobility analyzer (TSI 3081) allowed selecting the particles with an electrical mobility diameter of 300 nm. Finally, the particles entered simultaneously a condensation particle counter (TSI, 3788) and the AMS. Additional calibrations were carried out using aqueous solutions of 10 -2 mol L -1 of (NH 4 ) for nitrogen oxides every 15 minutes and a BAM-1020 (Met One Instruments, USA) for the PM 2.5 total mass concentration every hour. The Delta-C variable was determined as the difference of light absorption between 370 and 880 nm, and represents the enhanced optical absorption of some specific OA compounds [START_REF] Allen | Evaluation of a New Approach for Real Time Assessment of Wood Smoke PM[END_REF]. It has previously been found to correlate with wood-burning markers such as levoglucosan and K + and therefore might be used as a tracer of wood combustion [START_REF] Allen | Evaluation of a New Approach for Real Time Assessment of Wood Smoke PM[END_REF][START_REF] Wang | Multiple-year black carbon measurements and source apportionment using Delta-C in Rochester, New York[END_REF]. Meteorological parameters including temperature, relative humidity (RH), and pressure were acquired from a BAM-1020 and a TEOM-FDMS. The wind speed and direction, and precipitation were monitored on site with an anemometer. The mixing layer height (MLH) was obtained from the GDAS meteorological data

(1 degree) on the NOAA website.

Source apportionment of OA

The Positive Matrix Factorization (PMF) method [START_REF] Paatero | Positive Matrix Factorization -A Nonnegative Factor Model with Optimal Utilization of Error Estimates of Data Values[END_REF] was applied

to the V mode HR OA mass spectra (m/z = 12 to m/z = 150) in order to investigate the different sources of OA using the PMF Evaluation Tool (PET) v. 3.00 integrated into Igor Pro [START_REF] Paatero | Positive Matrix Factorization -A Nonnegative Factor Model with Optimal Utilization of Error Estimates of Data Values[END_REF][START_REF] Ulbrich | Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data[END_REF]. PMF analysis was carried out as per the guidelines of [START_REF] Paatero | Positive Matrix Factorization -A Nonnegative Factor Model with Optimal Utilization of Error Estimates of Data Values[END_REF][START_REF] Paatero | Positive Matrix Factorization -A Nonnegative Factor Model with Optimal Utilization of Error Estimates of Data Values[END_REF][START_REF] Ulbrich | Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data[END_REF]. Weak species, with a signal-to-noise ratio (SNR) below 2 were down-weighted by a factor of 2. In addition, the errors of CO 2 + as well as the four ions associated with it in the fragment table (O + , HO + , H 2 O + , CO + ) were downweighted automatically using PET in order to prevent excessive weighting of the signal at m/z 44 (Allan et al., 2004;[START_REF] Ulbrich | Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data[END_REF]. No fragments (SNR < 0.2) were removed from the database. We examined PMF solutions between 2 and 8 factors. The selection of the most appropriate solution was made by taking into account several criteria such as the variation of the Q/Q exp ratio, the physical meaningfulness of the mass spectra representing each factor, the time series and daily cycles, and the relationship with external variables.

Geographical determination of sources

In order to get insight into the geographical origins of the sources of OA, we combined the data obtained by the PMF analysis with the wind speed and direction and the backtrajectories. The wind speed and wind direction were used by applying the non-parametric wind regression (NWR), a hybrid source-receptor model which locates and quantifies local sources of hourly averaged atmospheric concentrations of a pollutant based on wind speed (u) and direction (θ) [START_REF] Henry | Source region identification using kernel smoothing[END_REF] following equation 1:

( | , ) = ∑ • • ∑ • Eq. 1
where E is the concentration estimate at a wind direction θ and wind speed u; W i , U i and C i the wind direction, speed and atmospheric concentrations, respectively, measured at t i ; σ and h the smoothing factors (suggested by the software); and K 1 and K 2 are a Gaussian kernel function for wind direction θ and an Epanechnikov kernel function for wind speed u, respectively, used to get the smoothing [START_REF] Henry | Source region identification using kernel smoothing[END_REF].

The weighed concentrations obtained from Eq. 1 are then weighted by the wind frequency. Hence, an empirical joint probability density of wind speed and direction is calculated using the kernel density estimate, as shown in equation 2:

( , u) = !" • ∑ # $%& ! '( # ) *%+ " Eq. 2
where N is the total number of points.

The Potential Source Contribution Function (PSCF) is a statistical source-receptor model aiming at determining the geographical origins of high concentrations of air pollutants. It is based on the analysis of the residence times of air masses using air mass trajectories back in time. The main idea is that the longer an air mass stays over a pollution source, the higher the pollution brought by the air mass to the receptor site. Generally, the domain covered by the backtrajectories is meshed according to a regular grid. The PSCF model calculates the probability of having a pollutant source located inside each gridcell of the domain and responsible for pollutant concentrations measured at the receptor site above a given threshold, following equation 3:

,-. = / 0 1 0 Eq. 3
where m ij is the number of backtrajectory endpoints passing over the ij th gridcell at latitude i and longitude j and associated to concentrations measured at the receptor site exceeding a specific threshold (in this case the 75 th percentile was used); and n ij the total number of trajectory endpoints passing over the ij th gridcell.

A high value of the PSCF probability for the ij th gridcell indicates a high probability that this gridcell corresponds to a source location. The backtrajectories used for PSCF analysis were calculated with HYSPLIT 4 (HYbrid Single-Particle Lagrangian Integrated Trajectory) for an arrival height of half of the planetary boundary layer, at 3-h intervals (8 trajectories per day at 0, 3,6,9,12,15,18,21 UTC), and 72 hours back in time. Due to spatial resolution, PSCF was applied only to OA sources identified as regional by the NWR approach. In addition, a sigmoidal-type weighing function was applied when using PSCF in order to decrease the influence of grid cells with a few number of points.

The calculation of both NWR and PSCF was carried out using the Zefir v3.31 IGOR tool [START_REF] Petit | A user-friendly tool for comprehensive evaluation of the geographical origins of atmospheric pollution: Wind and trajectory analyses[END_REF].

Ventilation coefficient

Additionally, we calculated the ventilation coefficient (i.e. the product of the mixing layer height and the wind speed; in m 2 s -1 ) in order to evaluate whether the atmospheric conditions favor dispersion or accumulation of the pollutants at the sampling location [START_REF] Goyal | Assessment of atmospheric assimilation potential for industrial development in an urban environment: Kochi (India)[END_REF]. The dispersion condition in the site is considered as bad, moderate, good and excellent for ventilation coefficients between 0 and 2,000, 2,001 and 4,000, 4,001 and 6,000 and > 6,000 m² s -1 respectively, according to the criteria of [START_REF] Eagleman | Air pollution meteorology[END_REF].

Results and discussions

Overview of NR-PM 1

The time series of the meteorological parameters, concentrations of the main components of NR-PM 1 and OA elemental ratios are shown in Figure 1. The weather conditions during the campaign were mostly humid and cold, with average values for RH and T of 82 ± 12% and 5.7 ± 3.3°C, respectively. Relatively low wind speeds were observed during the campaign, with an average of 1.6 ± 1.2 m s -1 , with calm winds from north and northeast alternating with strong winds from the southwest.

Moderate to high levels of NR-PM 1 were observed during the measurement campaign, with concentrations ranging from 0.23 to 50.9 µg m -3 and a campaign average concentration of 11.1 ± 9.3 µg m -3 . The NR-PM 1 composition was dominated by OA (38.4%; 4.2 ± 3.1 µg m -3 ) and NO 3 (35.9%; 4.0 ± 4.3 µg m -3 ) followed by NH 4 (15.9%; 1.8 ± 1.8 µg m -3 ) and SO 4 (8.8%;

1.0 ± 0.9 µg m -3 ). The contribution of Cl was particularly low (1.0%, 0.23 µg m -3 ) throughout the campaign, and hence will not be further discussed. Previous studies in northern France carried out in winter also observed similar compositions for NR-PM 1 (Crenn et al., 2017b(Crenn et al., , 2017a));

Zhang et al., in prep.
). In addition, the major inorganic species in NR-PM 1 measured by the AMS (NO 3 , NH 4 and SO 4 ) are well correlated (r > 0.95) to those measured in PM 2.5 by a MARGA 1S (Roig Rodelas et al., in press). Some interesting changes were observed between the first part ( 5February -4 March 2016) and the second part (5-16 March 2016) of the campaign. During the second period, particularly high concentrations of NR-PM 1 (up to 50 µg m -3 ) were observed. This was attributed to an impact of air masses originating from continental Europe and rich in secondary aerosols, as will be discussed in detail in section 3.4.

The daily profiles of the average concentrations and fractional contributions for the main species of NR-PM 1 are shown in Figure S2. Concentrations of NO 3 and NH 4 are higher during the nighttime, in accordance with the lower temperatures and higher RH favoring the partitioning of ammonium nitrate to the aerosol phase. On the other hand, the concentrations of sulfate do not show noticeable differences throughout the day, suggesting that it might be a result of transformation of regionally emitted SO 2 . In addition, the daytime production of SO 4 might have been masked by the expansion of the boundary layer, and the nighttime lower boundary layer values might have caused higher SO 4 thus the similar levels between daytime and nighttime. The daily cycle of OA shows two maxima, one in the early morning which could be linked to the vehicular emissions during the traffic rush hours, and one in the evening, which could be attributed to emissions from biomass burning, as will be further discussed in section 3.3. The difference in composition of the NR-PM 1 between daytime and nighttime was small, although the contribution of OA was higher through the night (41% compared to 35% during daytime; Figure S2b), which could be attributed to a higher contribution of OA sources such as biomass burning during the nighttime, as will be discussed later.

The origin of the main components of NR-PM 1 was assessed with the use of polar plots, shown in Figure S3. We observed higher concentrations of NO 3 and NH 4 for low and moderate wind speeds from the NE sector, suggesting that a combination of local and regional sources could have contributed to the observed concentrations. High levels of SO 4 are observed for low and moderate wind speeds in the NE sector. However, high concentrations of SO 4 are also observed for other directions, implying that its origin could be rather regional, in agreement with its daily cycle. The polar plot for OA shows highest concentrations for very calm winds (<1m s -1 ), pointing out significant local contributions. However, high concentrations of OA are also observed for moderate wind speeds from the NE and SE sectors, suggesting that regional sources are also important.

The acidity of the NR-PM 1 aerosol was evaluated by using the neutralization ratio (NR) (Figure S4). This is defined as the ratio between the observed ammonium and that required for the full neutralization of nitrate and sulfate. NR was close to 1 during most of the campaign, implying that there was always enough NH 4 to neutralize NO 3 and SO 4 .

OA characteristics

Calculation of OA elemental ratios indicates that OA was moderately oxidized during the campaign. The elemental ratios were calculated based on the calibration factors from Aiken et al.

( 2008). The average values for the OM:OC, O:C and H:C ratios were 1.60 ± 0.15, 0.32 ± 0.11 and 1.55 ± 0.14, respectively. These values are consistent with previous wintertime observations at urban sites in France (Chakraborty et al., in prep.;Crenn et al., 2017b;[START_REF] Crippa | Wintertime aerosol chemical composition and source apportionment of the organic fraction in the metropolitan area of Paris[END_REF].

The daily profiles of the elemental ratios are shown in Figure 2. The OM:OC and O:C ratios are found to be slightly higher during nighttime than daytime hours, which could be attributed to aqueous processing during nighttime due to very high RH levels and occurrence of some fog events. Higher or comparable nighttime OM:OC and O:C values have also been observed in previous studies [START_REF] Brown | Variations in the OM/OC ratio of urban organic aerosol next to a major roadway[END_REF][START_REF] Florou | The contribution of wood burning and other pollution sources to wintertime organic aerosol levels in two Greek cities[END_REF][START_REF] Hayes | Organic aerosol composition and sources in Pasadena, California, during the 2010 CalNex campaign[END_REF]. High humidity and fog events can create a suitable environment for aqueous oxidation leading to higher O:C ratios. [START_REF] Eagleman | Air pollution meteorology[END_REF] (Figure S5b), may have also allowed more time for the processing of local air masses. For instance, aqueous oxidation of primary biomass burning aerosols has already been reported [START_REF] Gilardoni | Direct observation of aqueous secondary organic aerosol from biomass-burning emissions[END_REF]) and this will be discussed further in section 3.3. Relatively lower solar radiation (and thus less photochemical activity) may have led to the observed lower OA oxidation levels during daytime. The minima for OM:OC and O:C are found in the early morning (7-8 am UTC, that is to say 8-9 am local time) and afternoon (5-6 pm UTC), during the traffic rush hours. On the contrary, the H:C ratio presents higher values during daytime, with maximum values during the traffic rush hours. In the literature, H:C usually shows a sharp decrease after reaching its maximum in the morning [START_REF] Crippa | Wintertime aerosol chemical composition and source apportionment of the organic fraction in the metropolitan area of Paris[END_REF][START_REF] Docherty | The 2005 Study of Organic Aerosols at Riverside (SOAR-1): instrumental intercomparisons and fine particle composition[END_REF][START_REF] Florou | The contribution of wood burning and other pollution sources to wintertime organic aerosol levels in two Greek cities[END_REF][START_REF] Saarikoski | Chemical characterization of springtime submicrometer aerosol in Po Valley, Italy[END_REF]. However, in this study H:C shows only a minor dip after its morning maximum, and remains steady until the afternoon traffic rush hours.

This could be linked to the substantial contribution from some primary sources such as traffic outside rush hours (the road next to the sampling site is situated between an industrial zone and a commercial zone, implying that there is always some traffic of cars, trucks and buses).

The Van Krevelen plot (H:C vs O:C) can reveal some important information on the aging of the atmospheric OA [START_REF] Heald | A simplified description of the evolution of organic aerosol composition in the atmosphere[END_REF]. In Figure 3, OA elemental ratios are observed to move towards the lower right (higher O:C and lower H:C) as the campaign proceeds. Some inferences can be drawn on OA aging based on the slope of this change of OA elemental ratios. In the same plot, reactions involving the addition/subtraction of several functional groups are illustrated with straight lines of different slope values. For instance, a slope of -2 is obtained when an aliphatic carbon group (-CH 2 -) is replaced by a carbonyl group <(-C(=O)-), representing the loss of 2 hydrogens and the gain of 1 oxygen. The replacement of one hydrogen atom with an alcohol group (-OH-) results in a slope value of 0. Lastly, a slope of -1 is obtained by the simultaneous addition of the carbonyl and alcohol groups [START_REF] Heald | A simplified description of the evolution of organic aerosol composition in the atmosphere[END_REF]. The data plotted in Figure 3 presented an average slope of -1.05, which is in the range of values reported for other field campaigns (-0.8 to -1.1) [START_REF] Hayes | Organic aerosol composition and sources in Pasadena, California, during the 2010 CalNex campaign[END_REF][START_REF] Heald | A simplified description of the evolution of organic aerosol composition in the atmosphere[END_REF][START_REF] Timonen | Characteristics, sources and water-solubility of ambient submicron organic aerosol in springtime in Helsinki, Finland[END_REF]. It is also observed that the elemental ratios of OA tend to be confined within a narrow area towards the end of the campaign, when aged air masses were arriving from continental Europe, as will be discussed in detail in section 3.4. This observed tendency implies that with the atmospheric aging due to transport, the chemical characteristics of bulk OA tend to be homogenized, as reported in several previous studies [START_REF] Pan | Detection and characterization of biological and other organic-carbon aerosol particles in atmosphere using fluorescence[END_REF][START_REF] Williams | Chemical speciation of organic aerosol during the International Consortium for Atmospheric Research on Transport and Transformation 2004: Results from in situ measurements[END_REF].

Source apportionment of OA

The PMF analysis was applied to the high resolution mass spectra of OA by varying the number of factors from two to eight. The four factor solution was chosen based on the Q/Q exp ratio, the chemical signatures of the different mass spectra, the time series and daily cycles, and the correlation with tracers and external variables. Reasoning behind this choice is presented in Table S1. The PMF diagnostics for the chosen solution are shown in Figure S6. The four factors were identified as hydrocarbon-like OA (HOA), oxidized biomass burning OA (oBBOA), more oxygenated oxidized OA (MO-OOA) and less oxygenated oxidized OA (LO-OOA).

When the number of factors was increased up to five, a HOA-COA mixed factor was obtained, which entailed the decrease of residuals at m/z 55 among others. The mass spectra, time series and daily profiles are shown in the supplementary material (Figure S7). However, the Q/Q exp barely decreased with respect to the four factor solution, and hence the four factor solution was finally chosen.

The mass spectra profiles and time series for each factor are shown in Figure 4a and b, respectively. The higher contributions are observed for MO-OOA (33%) and oBBOA (28%), while LO-OOA (17%) and HOA (20%) constituted the remaining OA mass. The time series of each PMF factor and their correlation with tracer(s) are shown in Figure S8. In addition, in Table S2 the correlations between the PMF factors and the additionally available external variables are also presented.

As mentioned in section 2.4, linking the wind speed and direction data to the PMF factors can bring additional information on the sources of OA. Hence, in Figure 6 the NWR plots for each PMF factor are shown.

HOA

The HOA mass spectrum is dominated by the 1 2 )1% 3 and 1 2 )13 3 ion series (Figure 4a), which are characteristic of OA mass spectra from vehicular exhaust emissions (Canagaratna et al., 2004). Accordingly, the HOA factor has the highest H:C ratio (2.03), and lowest OM:OC

(1.36) and O:C (0.13) ratios among all the factors. The daily profile of HOA (Figure 5a) shows two prominent peaks in the morning and evening hours corresponding to higher traffic activities during the rush hours. HOA is observed to be strongly correlated with m/z 57 (r = 0.94; Figure S8a), which is typically used as an internal tracer for HOA. Good correlations are also observed with external traffic tracers like BC (r = 0.70) and NO x (r = 0.72) (Figure S8a and Table S2). The obtained HOA mass spectrum is also compared to reference ambient spectra [START_REF] Crippa | Wintertime aerosol chemical composition and source apportionment of the organic fraction in the metropolitan area of Paris[END_REF][START_REF] Docherty | The 2005 Study of Organic Aerosols at Riverside (SOAR-1): instrumental intercomparisons and fine particle composition[END_REF][START_REF] Mohr | Identification and quantification of organic aerosol from cooking and other sources in Barcelona using aerosol mass spectrometer data[END_REF][START_REF] Struckmeier | Atmospheric aerosols in Rome, Italy: sources, dynamics and spatial variations during two seasons[END_REF] obtained from the High Resolution AMS Spectral Database (http://cires1.colorado.edu/jimenez-group/HRAMSsd/) and excellent correlations are obtained (r = 0.89-0.98) (Figure S9). As shown in the NWR plot for HOA in Figure 6a, the highest concentrations are observed for very low wind speeds, suggesting that most of the HOA was locally emitted. In fact, a two-lane road is located adjacent to the sampling site and likely contributed notably to the observed HOA.

oBBOA

An oxidized BBOA factor was also retrieved by the PMF analysis. This factor is characterized by the presence of characteristic fragments of biomass burning at m/z 60 and 73, associated to C 2 H 4 O 2 + and C 3 H 5 O 2 + , respectively (Figure 4a) [START_REF] Alfarra | Identification of the mass spectral signature of organic aerosols from wood burning emissions[END_REF]. Accordingly, oBBOA is well correlated with its main tracer m/z 60 (r = 0.88; Figure S8b). In addition, good correlations are observed between oBBOA and Delta-C (r = 0.69; Figure S8c and Table S2) and water-soluble K + from MARGA measurements (r = 0.61) (Table S2). As explained in section 2.2, Delta-C can be used as a tracer of wood combustion.

oBBOA has a high degree of oxidation (OM:OC = 1.93 and O:C = 0.58), with notable peaks in its mass spectrum at m/z 28 (CO + ), 29 (CHO + ) and 44 (CO 2 + ). The observation of a unique oxidized BBOA factor in OA PMF analysis is not common. Instead, most studies typically report a primary BBOA factor or two separate primary and oxidized BBOA factors. However, a recent study conducted in Houston, USA [START_REF] Wallace | Source apportionment of particulate matter and trace gases near a major refinery near the Houston Ship Channel[END_REF], also reported the presence of a single oxidized BBOA factor (OM:OC and O:C of 2.03 and 0.65, respectively).

One previous field study has provided evidence of aqueous processing of primary BBOA [START_REF] Gilardoni | Direct observation of aqueous secondary organic aerosol from biomass-burning emissions[END_REF], mainly through its correlation with RH. [START_REF] Sun | Insights into secondary organic aerosol formed via aqueous-phase reactions of phenolic compounds based on high resolution mass spectrometry[END_REF] and [START_REF] Li | Aqueous-phase photochemical oxidation and direct photolysis of vanillin -a model compound of methoxy phenols from biomass burning[END_REF] observed the rapid formation of oxidized compounds and enhancement in O/C ratios via laboratory aqueous oxidation of phenolic compounds (phenol, syringol, guaiacol, etc.) originating from biomass burning activities, and identified similar signatures in ambient aerosols as well. So, both field and laboratory studies provide evidence that the aqueous oxidation of BBOA is possible under suitable atmospheric conditions. RH values during the present measurement campaign were particularly high, with an average of 82 ± 12%, which could promote aqueous processing of the OA. Indeed, when the correlation between the PMF factors and the RH (by bins) is evaluated (Figure 7), the concentration (and relative contribution) of oBBOA increases from 0.47 µg m -3 (12% of OA) for RH < 60% to 2.27 µg m -3 (31%) for RH > 95%. This positive trend is observed between oBBOA and RH, while it is not observed for the other PMF factors.

This, together with the NWR graph for oBBOA (Figure 6b) where higher concentrations are associated with low wind speeds, suggests that aqueous processing possibly led to the rapid oxidation of locally emitted biomass burning emissions forming oBBOA.

The daily profile of oBBOA (Figure 5) shows an increase of the concentration in the late afternoon and a maximum before midnight, after which the concentrations show a sharp decrease.

This profile is in agreement with the time of biomass burning activities (in the evening), then lower MLH and higher RH values during the night, promoting the aqueous processing of fresh BBOA emissions.

LO-OOA and MO-OOA

Two OOA factors were obtained and denoted as LO-OOA and MO-OOA (Figure 4a). Even though their mass spectra are similar (r = 0.83), they are being considered as two separate factors since their degree of oxidation and time series are different (r = 0.62; Figure 4b). The mass spectra of both factors are characterized by major peaks at m/z 28 and 44, attributed to CO + and CO 2 + , respectively. However, the MO-OOA also includes a peak at m/z 29 associated to CHO + , whereas the LO-OOA does not and instead presents small contributions from alkyl fragments at m/z 29 and 64, attributed to C 2 H 5 + and C 5 H 4 + , respectively. Therefore, the MO-OOA factor presents higher OM:OC and O:C ratios (2.41 and 0.95, respectively) in comparison to the LO-OOA factor (2.00 and 0.61). However, both factors have very similar H:C values (around 1.5)

suggesting there was no evolution of the LO-OOA towards the MO-OOA, but rather that each OOA factor has a different origin and/or has been processed over a different timescale. While both factors showed a high correlation with NO 3 (r = 0.79 and 0.86 for MO-OOA and LO-OOA, respectively), the LO-OOA factor correlated better with SO 4 (r = 0.84) than MO-OOA (r = 0.59).

This suggests that the LO-OOA could have a more regional origin. Even though the majority of AMS studies have reported correlations between sulfate and MO-OOA higher than with nitrate, there are a few exceptions. For instance, in China, Zhou et al. (2018) reported a poor correlation between MO-OOA and sulfate and concluded that both were being produced by aqueous oxidation. In addition, in more humid and colder conditions (similar to our sampling period), nitrate can stay longer in the particulate phase due to relatively lower evaporative loss, so it can be transported over longer distances just as sulfate [START_REF] Seinfeld | Wiley: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change[END_REF]Zhou et al., 2018).

Xu et al. ( 2016) reported higher MO-OOA vs. nitrate correlation than with sulfate, while LO-OOA correlated well with both sulfate and nitrate. They attributed this finding to the fact that the nitrate formation was primarily occurring via photochemistry owing to lower temperature and humid conditions. In this study, a similar phenomenon has been observed, so most likely MO-OOA and nitrate were both produced via photochemistry and showed good correlation, while LO-OOA, which has some significant regional contribution, correlates better with sulfate.

The time series of the MO-OOA factor shows similar concentrations throughout the whole campaign (Figure 4b). However, the LO-OOA factor presented low concentrations during most of the time except in the last period, where its contribution showed a remarkable increase (Figure 4b) due to the impact of air masses from continental Europe, as will be further discussed in section 3.4. The daily profiles of both factors showed a similar cycle, with smaller concentrations in the daytime (Figure 5a). For MO-OOA, the concentrations are observed to be steady during the night and early morning, but showed a decrease until the evening, and then started to increase again. For LO-OOA, the observed daily profile showed a similar but less pronounced trend.

Concentrations of oxygenated factors are expected to be usually observed in the daytime, particularly in the afternoon, due to higher solar radiation which promotes photochemistry leading to the formation of secondary organic aerosols. However, in this study, the concentrations of OOA factors have decreased over the day (Figure 5a). This could be attributed to the strong dilution effect of the mixing layer, the values of which are higher in the afternoon (Figure S5a).

Besides, although their concentrations are going down, their contributions to total OA are increasing (Figure 5b) indicating that OOA is produced but somewhat masked by the enhanced ML heights.

The NWR plots for the MO-OOA and LO-OOA factors show higher concentrations for lowto-moderate wind speeds from the NE sector (Figure 6c-d). This suggests that the origin of both factors is rather regional. The PSCF plots show that higher probabilities are observed for air masses from Belgium and Germany (Figure S10).

Impact of meteorological parameters and long range transport on NR-PM 1 characteristics

The entire campaign was further divided into two different periods based on mass loadings and meteorological conditions to gain further insights about the impact of meteorology and long range transport on aerosol characteristics. Period I (5 February to 4 March 2016) was characterized by moderate NR-PM 1 (average of 8.8 ± 7.3 µg m -3 ), ranging from 0.4 to 31.5 µg m - 3 . On the other hand, period II (5 to 16 March 2016) showed high concentrations of NR-PM 1 (average of 17.2 ± 10.9 µg m -3 ) with values up to 50 µg m -3 . The average temperatures and RH values were similar between both periods. However, period II was associated with anticyclonic conditions, with atmospheric pressure values higher than 1020 hPa, no precipitations (4 mm in comparison to 73 mm for period I) and calm winds from the N and NE. In addition, during period II significantly lower values were observed for the mixing layer height during the nighttime (Figure S5a), which could favor the accumulation of pollutants. The ventilation coefficient (Figure S5b) also shows a similar trend, with lower nighttime values in period II (< 500 m 2 s -1 ) which indicate adverse conditions for the dispersion of pollutants [START_REF] Eagleman | Air pollution meteorology[END_REF]. The backtrajectory density maps for each period (Figure S11) show that during period I higher probabilities of occurrence are observed for air masses from the north of France, suggesting that there is a higher influence from local areas. On the contrary, in period II high probabilities are observed for Belgium, Germany and Western Poland. Overall, this combination of factors contributed to the long-range transport of NR-PM 1 and its accumulation over the region of northern France.

The composition of the NR-PM 1 was also significantly different between both periods (Figure 8). In period I, the NR-PM 1 was dominated by OA, with an average of 43%, while the inorganic ions constituted the rest of the mass, with 33% NO 3 , 15% NH 4 , 8% SO 4 and 1% Cl. In period II the OA contribution decreased to 32%, and the percentage of the inorganic ions increases notably, particularly of NO 3 to 39%, but also of NH 4 (18%) and SO 4 (10%). This suggests that during period I, local sources contributed more significantly to OA, while during period II the long-range transported air masses led to an increase of the levels of the inorganic aerosols.

While the total OA mass in period II was not significantly higher in comparison to period I (as opposed to the inorganic part of the aerosol), we observed significant differences in its composition (Figure 8). On the one hand, in period I the OA was dominated by local sources (59%) with average contributions of 33% for oBBOA and 23% for HOA. Among the rather regional OA factors, MO-OOA clearly dominates (35%) over LO-OOA (9%) (Figure 8). This confirms that period I was mainly influenced by local sources.

In addition, what seems to be a nocturnal new particle formation ("NPF-like") event was distinguished in period I, and will be discussed in the next section. On the other hand, in period II the total contribution from local OA decreased significantly to 32%, with average contributions of 18% for oBBOA, and 14% for HOA. The lower contribution of oBBOA during period II (i.e.

under the influence of transported air masses from continental Europe) proves that primary biomass burning emissions are locally processed. The contribution of the regional factors increased up to 68% during period II. However, though the contribution of the MO-OOA factor is similar to that of period I (33%), the contribution of LO-OOA sharply increased to 34%.

Therefore, it seems that the increase in LO-OOA in period II could be attributed to the impact of aged air masses transported from continental Europe.

Despite a significantly different composition of OA between both periods, the elemental ratios were rather similar. This could be attributed to a compensation of these ratios between the different OA factors (i.e. lower oBBOA and higher LO-OOA during period II).

Nocturnal NPF-like event

During the period where the SMPS was deployed, several nocturnal NPF-like events were detected, presenting a characteristic banana shape [START_REF] Heintzenberg | How to find bananas in the atmospheric aerosol': new approach for analyzing atmospheric nucleation and growth events[END_REF]. This type of event corresponds to the growth of previously formed nanometer-sized particles. It may be characterized based on the apparent growth rate g(t, D p ) of particles calculated from Eq. 4:

4(5, 6 7 ) = ∆9 : ∆;
Eq. 4

where ∆D p is the geometric mean diameter of the particles (nm) between the beginning and the end of the event and ∆t is the duration of the event (h).

In total, four significant NPF-like events were identified, all of them during nighttime. A summary on the characteristics of each event is given in Table 1 together with information on other NPF or NPF-like events observed in urban environments. The initial growth rates of these events were similar (ranging between 5.1 and 6.4 nm h -1 ), and well within the range of 0.25-39 nm h -1 observed for other NPF events [START_REF] Cheung | Observation of new particle formation in subtropical urban environment[END_REF][START_REF] Fiedler | The contribution of sulphuric acid to atmospheric particle formation and growth: a comparison between boundary layers in Northern and Central Europe[END_REF][START_REF] Hamed | Nucleation and growth of new particles in Po Valley, Italy[END_REF][START_REF] Iida | Estimating nanoparticle growth rates from size-dependent charged fractions: Analysis of new particle formation events in Mexico City[END_REF][START_REF] Man | Comparison of Daytime and Nighttime New Particle Growth at the HKUST Supersite in Hong Kong[END_REF][START_REF] Salimi | Nocturnal new particle formation events in urban environments[END_REF][START_REF] Stolzenburg | Growth rates of freshly nucleated atmospheric particles in Atlanta[END_REF][START_REF] Wu | New particle formation in Beijing, China: Statistical analysis of a 1-year data set[END_REF].

In this article, the results for the most representative event, which occurred from 15 to 16 February 2016, are presented. The event took place under anticyclonic conditions, with clear sky, calm winds, low temperatures and high RH values. Additionally, the ambient concentrations of PM 2.5 in the previous hours of the NPF-like event were particularly low due to the occurrence of precipitations and marine air masses with little anthropogenic influence impacting the site. This low level of pre-existing particles favored the growth of newly-formed particles by limiting the sink linked to their collision with larger pre-existing ones, which generally hinders the observation of this kind of event at urban sites.

In Figure 9, the time series of the particle number size distribution (PNSD) and the geometric mean diameter of PM 1 obtained with a scanning mobility particle sizer (SMPS) are

shown together with the time series of the PMF factors and PM 2.5 total mass and main inorganic ions and black carbon (BC). The NPF-like event started at 6 pm and finished at 4 am (local time), with an average growth rate of 5.1 nm h -1 reaching a geometric mean diameter of 70 nm. Two phases of growth can be observed (Figure 9b): a first one with a steeper slope (g = 6.4 nm h -1 ), between 18:00 and 23:00, corresponding to a stronger increase of the aerosol size and a second one less pronounced (g = 2.8 nm h -1 ), between 23:00 and 04:00 the next day. During the growth phase, for a given diameter in the upper range of the nucleation mode (e.g. 20 nm) the particle number concentration decreased with time, whereas it increased in the Aitken mode (e.g. 60 nm).

At the same time, the concentrations of OA increased simultaneously up to 14 µg m -3 , with oBBOA clearly dominating with a contribution higher than 50% during most of the event. The concentrations of soluble K + (not shown in the Figure ) measured with the MARGA also showed the same increasing trend. This suggests that fast processing of organic matter from biomass burning emissions was strongly involved in this event. Simultaneously, increasing concentrations of NO 3 , SO 4 , and NH 4 were observed (Figure 9d). It is likely that NH 4 NO 3 played a major role in the event by condensing on the emitted fine particles from combustion processes, given the simultaneous increases -by a factor of 5.4 and 4, respectively -in the concentrations of NO 3 and NH 4 during the event. The observation of NH 4 NO 3 as one of the main components in particle growth is quite rare. Indeed, previous studies having reported measurements of the main species responsible for particle growth have shown that the main constituents at urban and regional sites are OA and SO 4 (Kerminen et al., 2018 and references therein). However, almost none of these studies have been conducted in North-Western Europe, where the contribution of NH 4 NO 3 is typically higher and as high as 30% [START_REF] Putaud | A European aerosol phenomenology-2: chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe[END_REF]. Only one study carried out in Melpitz, Germany, determined SO 4 , NH 4 and OA as the main components responsible for particle growth, although their fractional contributions were not estimated (Wu et al., 2015). In addition, most NPF events have occurred during daytime, and hence linked to a photochemical origin, in which OA and SO 4 are the main species involved [START_REF] Kulmala | On the formation and growth of atmospheric nanoparticles[END_REF]. Therefore, the nature of nighttime NPF events is likely to differ from daytime NPF events, where compounds that predominate during nighttime such as NH 4 NO 3 and the oBBOA fraction of OA can play a key role. However, up to date, fewer than 10 measurement sites worldwide have reported nighttime NPF observations, and their main components are still not well-known [START_REF] Kerminen | Atmospheric new particle formation and growth: Review of field observations[END_REF].

Among these, [START_REF] Man | Comparison of Daytime and Nighttime New Particle Growth at the HKUST Supersite in Hong Kong[END_REF] also related ammonium nitrate and OA to nocturnal particle growth in Hong Kong.

Conclusions

In this study, the characteristics and sources of submicron OA were investigated during an intensive winter (5 February to 16 March 2016) in northern France. Moderate concentrations of NR-PM 1 (11.1 ± 9.3 µg m -3 ) were observed, generally dominated by inorganic ions (62%).

Nitrate was the dominant inorganic ion (35.9%), followed by NH 4 (15.9%) and SO 4 (8.8%). In addition, the NR (neutralization ratio) of the aerosol was close to 1 during most of the campaign, implying that there was always enough NH 4 to neutralize NO 3 and SO 4 .

OA was found to be moderately oxidized These results indicate that during winter, aqueous processing of primary biomass burning emissions in North-Western Europe could be more important than what was previously thought, and even involved in NPF-like events during nighttime under specific meteorological conditions (clean and stagnant air masses, low temperatures, high relative humidity). In addition, air masses arriving from Eastern Europe have a considerable impact in the region of northern France, with higher concentrations of secondary inorganic and organic aerosols. Hence, the improvement of the mitigation policies in neighboring countries of France, mainly Belgium, Germany and Poland, should exert a significant impact on the air quality.

  (O:C = 0.32) and evolving along a slope of -1 in the Van Krevelen plot (H:C vs O:C) indicating that the simultaneous addition of carbonyl and alcohol groups could be predominant in the oxidation of OA. Application of the PMF analysis to the OA mass spectra revealed the presence of several types of OA at the sampling location.Identified OA were denoted as hydrocarbon-like (HOA), oxidized biomass burning (oBBOA), and two oxygenated factors classified into less oxidized (LO-OOA) and more oxidized (MO-OOA). The concentrations of the oBBOA factor were positively correlated with relative humidity, suggesting aqueous processing of primary biomass burning emissions took place. This was supported by high values of OM:OC and O:C during nighttime and by the NWR analysis which showed higher oBBOA concentrations for calm winds indicating local origins. On average, OA was dominated by MO-OOA (35%) and oBBOA (28%). In addition, oBBOA, water-soluble K + and NH 4 NO 3 were shown to be involved in a nighttime NPF-like event during period I, suggesting a condensation of SIA on freshly emitted fine particles. However, during the last part of the campaign (5 to 16 March 2016) the contribution of the LO-OOA factor increased up to 34%. During the first period of the campaign (5 February to 4 March 2016) the origin of the air masses alternated from oceanic and continental, which led to low to moderate concentrations of NR-PM 1 . However, during the second period of the campaign (5 to 16 March 2016) the sampling site was heavily impacted by air masses from Eastern Europe rich in aged aerosols, evidenced by the high contributions from secondary inorganic and organic aerosols. In combination with an anticyclonic situation and low ventilation coefficient values, this favored the presence of high NR-PM 1 concentrations in the north of France. Lastly, the OA confined into a narrower space in the VK diagram during the last period of the campaign, suggesting a homogenization of the different aerosol sources due to ageing of OA during transportation.

FIGURESFigure 1 .Figure 4 .Figure 5 .Figure 6 .Figure 7 .Figure 8 .Figure 9 .

 1456789 FIGURES

  Asia via AMS observations.[START_REF] Ge | Effect of aqueous-phase processing on aerosol chemistry and size distributions in Fresno, California, during wintertime[END_REF][START_REF] Dall'osto | Real-time secondary aerosol formation during a fog event in London[END_REF] reported formation of organosulfates during fog events from Fresno, California, and London using AMS.[START_REF] Sullivan | Evidence for ambient dark aqueous SOA formation in the Po Valley, Italy[END_REF] detected aqueous SOA formation even in dark under high RH conditions. So, under foggy and high RH conditions night-time oxidation can also produce highly oxidized SOA. Stagnant conditions during winter nights, suggested by ventilation coefficient values lower than 1000 m 2 s -1

[START_REF] Li | Evaluating the degree of oxygenation of organic aerosol during foggy and hazy days in Hong Kong using high-resolution time-of-flight aerosol mass spectrometry (HR-ToF-AMS)[END_REF]

and

[START_REF] Chakraborty | Real-time measurements of ambient aerosols in a polluted Indian city: Sources, characteristics, and processing of organic aerosols during foggy and nonfoggy periods[END_REF]

reported oxidation of OA and possible changes in oxidation mechanism (functionalization vs fragmentation) from two moderate to highly polluted locations of

Acknowledgments

IMT Lille Douai acknowledges financial support from the CaPPA project, which is funded by the French National Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir) under contract ANR-11-LABX-0005-01, the CLIMIBIO project, both financed by the Regional Council "Hauts-de-France" and the European Regional Development Fund (ERDF). This field campaign was carried out within the framework of the ISARD project funded by the AACT-AIR (ADEME) program (grant 1562C0011). R. Roig Rodelas thanks ARMINES for his PhD fellowship. The authors thank Atmo Hauts-de-France and Météo-France for providing air monitoring and meteorological data, respectively.


Nicolas Papon
email: nicolas.papon@univ-angers.fr

Brad M Binder

An Evolutionary Perspective on Ethylene Sensing in Microorganisms

Keywords: ethylene, receptors, phytohormones, bacteria, fungi, amoebae _______________________________________________________________________________

Ethylene is a gas and a plant hormone with wide ranging effects and a well-defined signaling pathway. The recent identification of ethylene receptors in various microorganisms provides new insights into the early propagation of the ethylene signaling pathway in the course of evolution.

______________________________________________________________ The roles of ethylene: in a historical way a plant story

Ethylene is a gas naturally produced by plants, algae, fungi, and bacteria [START_REF] North | Microbial pathway for anaerobic 5'-methylthioadenosine metabolism coupled to ethylene formation[END_REF]. The role of ethylene was first documented in plants where it acts primarily as a hormone that regulates many aspects of development such as seed germination, leaf senescence, and fruit ripening. It also orchestrates plant defenses against micro-organisms and animals, the establishment of symbiosis, and responses to abiotic stresses such as high salt, drought, flooding, and pollutants. The effects of ethylene on non-plant species is less studied and the data on this issue remains more fragmentary [START_REF] Abeles | Ethylene in plant biology[END_REF].

Ethylene signaling in plants and Synechocystis

Ethylene is produced by many microbes and is also abiotically generated from incomplete combustion and from light interacting with dissolved organics [START_REF] North | Microbial pathway for anaerobic 5'-methylthioadenosine metabolism coupled to ethylene formation[END_REF]. Thus, it is likely that ethylene was broadly present on ancient earth leading to the idea that this simple gas was co-opted as a signaling molecule by early organisms. However, most studies on ethylene signaling focus on plants and the first ethylene receptor (ETR) was discovered in the model plant Arabidopsis thaliana [START_REF] Chang | Arabidopsis ethylene-response gene ETR1: similarity of product to two-component regulators[END_REF]. ETRs of higher plants are related to the histidine kinase superfamily (Fig. 1A). Histidine kinases are relatively rare in eukaryotes and autophosphorylation of plant ETRs and phosphotransfer to other effectors are not critical for ethylene signaling. Upon binding to the dimerized ETR, ethylene negatively regulates a cell signaling pathway involving downstream mediators including the raf-like protein kinase Constitutive Triple Response 1 (CTR1), the endoplasmic reticulum-localized protein Ethylene Insensitive 2 (EIN2), and the EIN3 and EIN3-Like (EIL) transcription factors [START_REF] Schaller | Two-component systems and their co-option for eukaryotic signal transduction[END_REF]. This land plant-type ethylene signaling pathway likely occurs in the green lineage from unicellular algae since ETR homologs are detected in some chlorophyte genomes (e.g. Coelastrella and Coccomyxa sp.).

It has long been supposed that the above-described canonical ETR-mediated signaling pathways were restricted to terrestrial plants [START_REF] Ju | Conservation of ethylene as a plant hormone over 450 million years of evolution[END_REF], although, a putative ETR, slr1212, was identified almost 20 years ago in the cyanobacterium Synechocystis sp. [START_REF] Rodríguez | A copper cofactor for the ethylene receptor ETR1 from Arabidopsis[END_REF]. This led to the idea that plants acquired ETRs from the cyanobacterium that gave rise to chloroplasts and subsequent gene transfer to the host cell. However, it was only recently that this protein was shown to be a bona fide ETR, as well as photoreceptor, that governs various processes underlying cell motility [START_REF] Lacey | Ethylene regulates the physiology of the cyanobacterium Synechocystis sp. PCC 6803 via an ethylene receptor[END_REF][START_REF] Song | Near-UV cyanobacteriochrome signaling system elicits negative phototaxis in the cyanobacterium Synechocystis sp[END_REF] (Fig. 1B).

From a structural point of view, it is important to highlight ethylene sensing through ETRs occurs by the interaction of the gaseous molecule with the ethylene binding domain (EtBD) located at the N-terminus of the receptors. The EtBD consists of three hydrophobic transmembrane helices harboring seven conserved amino-acids required for ethylene binding [START_REF] Wang | Identification of important regions for ethylene binding and signaling in the transmembrane domain of the ETR1 ethylene receptor of Arabidopsis[END_REF] (Fig. 1C). This structural uniqueness has allowed for a more thorough search over the last few years for ETR homologs in additional prokaryotic and eukaryotic lineages.

Recent phylogenomic analyses revealed the presence of ethylene receptors in microorganisms

As mentioned above, ETRs were first identified in plants [START_REF] Chang | Arabidopsis ethylene-response gene ETR1: similarity of product to two-component regulators[END_REF]. Later, the sequencing of the Synechocystis genome revealed it also contained a putative ETR [START_REF] Rodríguez | A copper cofactor for the ethylene receptor ETR1 from Arabidopsis[END_REF]. More recent studies have identified plant ETR homologs in over 100 bacterial species consisting predominantly of proteobacteria and cyanobacteria [START_REF] Lacey | Ethylene regulates the physiology of the cyanobacterium Synechocystis sp. PCC 6803 via an ethylene receptor[END_REF][START_REF] Wang | Identification of important regions for ethylene binding and signaling in the transmembrane domain of the ETR1 ethylene receptor of Arabidopsis[END_REF] (Fig. 1C). Most of these prokaryotic ETRs display domain arrangements similar to those observed in plants (histidine kinase-like), but, interestingly, a few show atypical features with simplified or more complex structures and alternative output domains (Fig. 1C). This suggests that some bacterial ETRs (i) may integrate multiple input signals in addition to ethylene and (ii) have diverse biochemical outputs.

ETR homologs were also recently identified in various non-plant eukaryotic clades. Several fungal genomes were shown to encode proteins that strongly resemble the domain arrangement of higher plant ETRs [START_REF] Hérivaux | The identification of phytohormone receptor homologs in early diverging fungi suggests a role for plant sensing in land colonization by fungi[END_REF] (Fig. 1C). Specifically, these ETR homologs were found in "lower" fungi (so called Early Diverging Fungi") that are known to behave as plant symbionts, as well as in species that colonize decaying plant material. Emblematic of these are Glomeromycotina (i.e. Rhizophagus irregularis, Fig. 1C). These fungi are part of a symbiotic relationship (arbuscular mycorrhizae) along with plant roots, and ethylene has been demonstrated to be involved in this interaction. The presence of ETRs in this particular group of fungi deeply-associated to plants provides new insight into the hormonal communication that may operate in these symbioses. In addition, homologous sequences of ETRs are predicted to be present in several other early diverging fungi that colonize leaf litter, twigs, decaying fruit, and soil. It is also interesting to observe ETR homologs in the aquatic species Allomyces macrogynus (Chytridiomycota) and in its obligate parasite Rozella allomyces (Cryptomycota) (Fig. 1C). One could speculate that ethylene acts as a communication molecule between these two aquatic fungi. In sum, based on the distribution of ETR homologs in early diverging fungi, it is tempting to hypothesize that ethylene sensing has been co-opted by "lower" fungi for orchestrating interactions in several plant-fungi or fungi-fungi biotic systems.

A recent phylogenomic analysis revealed more ETR homologs in various additional lineages within the eukaryotic domain [START_REF] Kabbara | Diversity and evolution of sensor histidine kinases in eukaryotes[END_REF]. An ETR homolog was discovered by browsing the genome of Capsaspora owczarzaki (Fig. 1C). This species is representative of the Mesomycetozoa clade, the closest known unicellular group relative of animals. It is now accepted that animals do not possess archetypal histidine kinases, and by extension, lack ETRs. It is thus intriguing to observe that ancestors of animals have developed ethylene signaling pathways to regulate cellular processes, and this was dropped early during the evolution of animals towards multicellularity. Another striking fact is that ETRs were also found in free living amoebae (Acanthamoeba and Balamuthia sp.) (Fig. 1C), but not in obligate parasitic or social (i.e. multicellular) amoebae. As suggested for Mesomycetozoa, it is thus possible that ancestral amoebae developed ethylene sensing for cell-tocell communication, but lost this ETR-mediated transduction system during the path of evolution of these protists. Finally, it is highly likely that ETRs are even more widespread since further genome analysis revealed the presence of candidate genes in Stramenopiles, notably in the oleaginous diatom Fistulifera solaris (Fig. 1C), in the free-living pseudofungus Hyphochytrium catenoides, in many filamentous marine protists belonging to the Labyrinthulomycetes family, but also in two Alveolates associated to coral reefs, i.e. the zooxanthellae Symbiodinium microadriaticum (Fig. 1C) and the Chromerida Vitrella brassicaformis (Fig. 1C). Beyond all these recent observations, the strong structural diversity of EtBD-containing proteins in non-plant organisms, as observed in bacteria, teaches us that eukaryotic ETRs may have a large range of biochemical outputs for intracellular ethylene signaling.

Concluding remarks

In sum, these recent discoveries provide evidence that ethylene sensing, through receptors that are more or less structurally close to the originally described plant histidine kinase ETRs [START_REF] Chang | Arabidopsis ethylene-response gene ETR1: similarity of product to two-component regulators[END_REF], broadly occurs in various microbial lineages. Although these preliminary but noteworthy findings must be explored in more depth and must be strengthened by functional validation, a number of questions arise out of these considerations. One is to determine the role of ethylene and signaling pathways in microbes containing ETRs. It is likely that they are important for responses to environmental conditions or potentially in interspecies communication. A second is, with the exception of plants, why are ETRs predominantly found sporadically in microbial lineages? The appearance of ETRs in cyanobacteria and other bacteria suggests that ETRs were originally developed in ancestral prokaryotes, more than one billion years ago. ETRs were then acquired by several eukaryotic lineages likely via lateral gene transfer events including (i) the endosymbiosis of the cyanobacterium-like ancestor which resulted in plastid formation, (i) bacterial phagocytosis, (ii) and exchanges with large DNA viruses [START_REF] Schaller | Two-component systems and their co-option for eukaryotic signal transduction[END_REF][START_REF] Kabbara | Diversity and evolution of sensor histidine kinases in eukaryotes[END_REF]. Contrary to land plants which have retained and expanded histidine kinase-like ETRs during the evolution for their developmental programs, the other eukaryotic lineages have progressively lost these canonical ethylene-sensing proteins [START_REF] Kabbara | Diversity and evolution of sensor histidine kinases in eukaryotes[END_REF]. It is also curious that no typical ETR genes are detected in the genomes of some filamentous fungi (Ascomycota) or multicellular amoebae (Dictyostelids) that are known to sense ethylene for regulating various processes [START_REF] Wang | Identification of important regions for ethylene binding and signaling in the transmembrane domain of the ETR1 ethylene receptor of Arabidopsis[END_REF][START_REF] Amagai | Ethylene as a potent inducer of sexual development[END_REF]. This raises the exciting possibility that they possess atypical ETRs that remain hitherto unknown, and thus represents a new challenge to identify.

The recent description of ETR homologs in diverse prokaryotic and eukaryotic lineages sheds new light on the dissemination and evolution of the ethylene sensing pathway in microorganisms. However, tremendous effort is still needed to functionally characterize these putative ETRs and to decipher their physiological roles using complementary biochemical, genetic and modeling approaches.

Figure legend

Fig. 1 Ethylene signaling pathways in higher plants and cyanobacteria and identification of ethylene receptor (ETR) homologs in various lineages. (A) Ethylene signaling pathways in the higher plant Arabidopsis. ETR1 is related to the histidine kinase superfamily and is localized in the endoplasmic reticulum membrane. Upon binding to the ETR dimer, ethylene negatively regulates a cell signaling pathway involving downstream mediators including the CTR1 Raf-like kinase, the transmembrane protein EIN2, and the EIN3/EIL family of transcription factors. (B) Ethylene signaling in the cyanobacterium Synechocystis sp. Ethylene negatively regulates the ETR-like protein slr1212 which putatively signals to a downstream response regulator protein, slr1213. The GAF domain binds a chromophore and functions as a light receptor making this a bifunctional receptor. (C) Top: Identification of ETR homologs in various prokaryotic and eukaryotic lineages. The ethylene binding domain (EtBD) located at the N-terminus of the receptors consists of three hydrophobic transmembrane helices harboring seven conserved amino-acids required for ethylene signal perception and transduction. Bottom: An alignment of EtBD sequences from a series of representative ETRs from diverse organisms. The seven conserved amino acids involved in ethylene binding are in pink and marked with blue circles. Abbreviations: ER, endoplasmic reticulum; HK, histidine kinase A subdomain; HA, histidine kinase-like ATPase catalytic subdomain; GAF, cGMP-phosphodiesterase, Adenylyl cyclase, FhIA domain; P, PAS/PAC (Per-period circadian protein; Arnt-Ah receptor nuclear translocator protein; Sim-single minded protein/PAS C-terminus); R, Receiver domain; GGDEF, Diguanylate cyclase domain; EAL, c-di-GMP phosphodiesterase; STYKc, Protein kinase-unclassified specificity; DSRM, Double-stranded RNA binding motif; Athal, Arabidopsis thaliana; M. thiooxydans, Methylophaga thiooxydans; M. mobilis, Methylotenera mobilis; P. fluorescens, Pseudomonas fluorescens; A. cylindrical, Anabaena cylindrica; Rirre, Rhizophagus irregularis; Cowcz, Capsaspora owczarzaki; Acast, Acanthamoeba castellanii; Fsola, Fistulifera solaris.

Acknowledgments

Funding was provided by the Agence Nationale de la Recherche (program ANR-PRCE Mycormones) to Nicolas Papon and the US National Science Foundation (MCB-1517032) to Brad Binder.