Ambre Issart

Simon Godin

Hugues Preud'homme

Katarzyna Bierla

Ahmed Allal

Joanna Szpunar
email: joanna.szpunar@univ-pau.fr

Direct screening of food packaging materials for post-polymerization residues, degradation products and additives by liquid extraction surface analysis nanoelectrospray mass spectrometry (LESA-nESI-MS)

Keywords: liquid extraction surface analysis (LESA), nanoelectrospray mass spectrometry (ESI MS), food packaging, polymer additives, polymer degradation products

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

Introduction

Polymer packaging plays a fundamental role in food industry protecting products from the environment and enhancing their shelflife. Polymers, usually polyethylene terephthalates or polyolefins are thermoformed to minimize the amount of oxygen to extend the shelf life of food.

However, the high contact surface between the food and its package can provoke the contamination of the packed food in case of a low-quality plastic (migration phenomenon) [START_REF] Hernandez | Factors affecting permeation, sorption, and migration processes in package-product systems[END_REF] . The massive use of plastics for this purpose has been resulting in raising concerns about the associated health risks.

Plastics are mainly petroleum-based [START_REF] Standnes | Literature review of implemented polymer field projects[END_REF][START_REF] Breddermann | Printed pressure housings for underwater applications[END_REF][START_REF] Smith | Thermochemical and physical properties of printed circuit board laminates and other polymers used in the electronics industry[END_REF][START_REF] Erica | Kirchain Randolph: Strategic materials selection in the automobile body: Economic opportunities for polymer composite design[END_REF] and most of them contain additives which not only provide good mechanical properties and attractive look, but also improve their life time and lower processing costs. These additives as well as post-polymerisation residues or degradation products formed during recycling can migrate into food. The migration of constituents of polymer packaging into food has therefore two aspects. The additives, post-polymerization or degradation residues, and nonintentionally added species (NIAS) are a risk for the consumers but, at the same time, "intelligent" packaging releasing natural antioxidants improve food shelf life [START_REF] Dobrucka | Active And Intelligent Packaging Food -Research And Development -A Review[END_REF] .

In order to evaluate the safety and functionalization of polymers in direct contact with food, they should be monitored for the presence of species able to migrate into food. The analysis and the characterisation of the mobile polymer fraction is necessary to evaluate the risk they can represent for food consumers.

Different methods for the mapping of polymer additives have been described, usually requiring extraction followed by chromatographic analysis coupled with mass spectrometry [7][START_REF] Shahidi | Novel antioxidants in food quality preservation and health promotion[END_REF][START_REF] Moreta | Determination of plastic additives in packaging by liquid chromatography coupled to high resolution mass spectrometry[END_REF][START_REF] Lin | Determination of silver in nano-plastic food packaging by microwave digestion coupled with inductively coupled plasma atomic emission spectrometry or inductively coupled plasma mass spectrometry[END_REF][START_REF] Coulier | Identification and quantification of (polymeric) hindered-amine light stabilizers in polymers using pyrolysis-gas chromatographymass spectrometry and liquid chromatography-ultraviolet absorbance detection-evaporative light scattering detection[END_REF] . The preparation step can degrade the sample itself; for example, solvents used to extract specific species could also remove polymer chains, additives or other untargeted molecules, thus degrading the sample and increasing the complexity and noise in the analysis. Also, batch extraction is solvent and time consuming. Its tediousness stimulated interest in direct methods such as matrix assisted laser desorption analysis (MALDI), desorption electrospray ionisation (DESI) or direct analysis in real time (DART) mass spectrometry (MS) [START_REF] Fouyer | Direct Monitoring of the Role Played by a Stabilizer in a Solid Sample of Polymer Using Direct Analysis in Real Time Mass Spectrometry: The Case of Irgafos 168 in Polyethylene[END_REF][START_REF] Gross | Direct analysis in real time--a critical review on DART-MS[END_REF][START_REF] Haunschmidt | Rapid identification of stabilisers in polypropylene using time-of-flight mass spectrometry and DART as ion source[END_REF][START_REF] Hintersteiner | Determination of stabilisers in polymeric materials used as encapsulants in photovoltaic modules[END_REF][START_REF] Ackerman | Assessing direct analysis in real-time-mass spectrometry (DART-MS) for the rapid identification of additives in food packaging[END_REF] . However, MALDI and DESI require a matrix addition or a sample swelling before the analysis whereas DART uses relatively harsh desorption conditions negatively affecting the method's selectivity (compounds other than those being able to migrate into food can be ionized).

Recently, liquid extraction surface analysis (LESA) 17,[START_REF] Walworth | Direct sampling and analysis from solid-phase extraction cards using an automated liquid extraction surface analysis nanoelectrospray mass spectrometry system[END_REF] was introduced for microvolume metabolomics, lipid analysis, protein analysis, carbohydrate analysis and enzyme monitoring [START_REF] Eikel | Liquid extraction surface analysis mass spectrometry (LESA-MS) as a novel profiling tool for drug distribution and metabolism analysis: the terfenadine example[END_REF][START_REF] Menezes | Monitoring metabolites from Schizophyllum commune interacting with Hypholoma fasciculare combining LESA-HR mass spectrometry and Raman microscopy[END_REF][START_REF] Parson | Analysis of chloroquine and metabolites directly from whole-body animal tissue sections by liquid extraction surface analysis (LESA) and tandem mass spectrometry[END_REF][START_REF] Sarsby | Top-Down and Bottom-Up Identification of Proteins by Liquid Extraction Surface Analysis Mass Spectrometry of Healthy and Diseased Human Liver Tissue[END_REF] . Also, its potential for the detection of pesticides in sliced fruit and vegetables leaves was tested. LESA requires a minimum amount of solvent applied directly to the sample surface and is rapid and eco-friendly. The choice of extraction conditions can be optimized to focus on species able to migrate into food which results in simpler mass spectra [START_REF] Martin | Direct detection of additives and degradation products from polymers by liquid extraction surface analysis employing chip-based nanospray mass spectrometry[END_REF] . LESA works well on homogeneous hydrophobic surfaces making it highly compatible for many synthetic polymers used for food packaging [START_REF] Martin | Ambient ionisation mass spectrometry for the characterisation of polymers and polymer additives: A review[END_REF] . The applications of LESA in polymer science are scarce 17,[START_REF] Martin | Direct detection of additives and degradation products from polymers by liquid extraction surface analysis employing chip-based nanospray mass spectrometry[END_REF][25][START_REF] Paine | Characterising in situ activation and degradation of hindered amine light stabilisers using liquid extraction surface analysis-mass spectrometry[END_REF][START_REF] Eikel | Liquid extraction surface analysis (LESA) of food surfaces employing chip-based nano-electrospray mass spectrometry[END_REF] ; to date, it has never been used for the quality control of food contact materials.

The objective of this work was the development of a method based on LESA for automated extraction of polymer additives and contaminants able to migrate into food and to combine it with nanoESI-MS analysis for the extracted compounds identification and quantification. We demonstrate the optimisation of LESA-MS parameters for the analysis of food contact polymer films for rapid screening for the presence of post-polymerization residues, oligomers resulting from polymer recycling, products of polymer oxidative degradation and polymer additives. We validate the method by the classical in-batch extraction protocol. We also show that, when suitable solid phase standards with guaranteed additive's concentration are available, LESA-MS can be used for quantitative analysis.

Experimental

2.1.Materials

Reagents:

Methanol and acetonitrile (Chromasolv, Honeywell), chloroform and propanol (Chromasolv Plus, Honeywell) and formic acid were LC-MS grade (Optima, Fischer Chemicals). Alpha-tocopherol, linseed oil and ascorbic acid, used as additives in polymer films, were both HPLC grade (Sigma Aldrich, France).

Samples:

PolyEthylene Terephtalates (PET) samples come from a commercial packaging purchased in a local supermarket. Polyethylene and polypropylene films made of additive-free Low-Density PolyEthylene (LDPE) and isotactic PP (iPP) were provided as pellets by Repsol (Repsol, Madrid, Spain). The polymers blends containing different contents of additives were realized thanks to a double-screwed extruder (LabTech, Samutprakarn, Thailand) at 160 °C. The obtained blends were then shaped as films using a hydraulic heated press.

Procedures

LESA of the polymer constituents able to migrate into food. The principle of the LESA is outlined in Fig. 1. The extraction solvent (methanol/chloroform 1:1 v/v containing 0.1 % of formic acid) drop is applied to analysed plastic material. A microjunction is created between the solvent drop and the plastic resulting in the diffusion of plastic impurities into the drop. Then, the drop is sprayed using a nanoESI silicon-based chip. The polymer samples were analysed directly after having been cut to the appropriate size and put into the instrument holder. The robotic system allowed a nanoESI infusion of the extract. The measurements were done in the positive ionization mode with a scan range from m/z 100 to 2000 at a resolution of 500,000 (FWHM at m/z 200). The automatic gain control (AGC) was set to 1.0e05 ions and a maximum injection time of 100 ms. Care was taken not to touch and contaminate the surface to be analysed.

Validation by the batch extraction method.

The extraction consisted of 8 different steps: first about 0.25 g of the film was cut in small pieces, the sample was extracted 3 times with 2 mL of hexane, the latter extraction being carried out in the presence of 0.5 mL of 10% NaCl. Then, pooled extracts were salted out with NaCl. The organic layer was taken and dried. After drying, 1 mL of acetonitrile was added and the solution was warmed up to 37° and centrifuged. The supernatant was separated and dried again. Finally, the obtained residue was re-dissolved in 0.1 mL of acetonitrile and analysed by

ESI-MS.

Comparison with DART MS. Helium was used as the reagent gas at flow rate of 3 mL min -1 , between 200 and 300°C. Positive ion mode was used for a mass range between 150 and 1000 m/z with helium for the analysis and nitrogen gas in standby mode. The grid electrode voltage used value was 350 V in both positive and negative mode. The detector voltage was 1.8 kV with a maximum value of 2.3 kV.

The mass spectra were recorded every second with a resolution of 60,000 at m/z 400.

MS instrumentation

LESA MS spectra were acquired using a TriVersa NanoMate robotic system (Advion, Ithaca, NY) coupled to an Orbitrap Fusion LUMOS mass spectrometer (Thermo Scientific, Waltham, MA) with Thermo Scientific Xcalibur software. The instrument was calibrated daily and optimized for sensitivity by infusing the manufacturer's standard calibration solution employing TriVersa NanoMate chipbased nanoESI ion source. DART MS analyses were performed using a VELOS Orbitrap (Thermo) Electrospray Ionization (ESI) mass spectrometer equipped with an IonSense DART source (Danvers, MA).

Results and discussion

Method optimization

The optimization was carried out using a commercial PET plastic film known to contain oligomeric degradation products. The method was validated by a comparison to classical in-bulk extraction mass spectrometry.

Extraction solvent. Solvent mixtures reported in the literature: 0.1% formic acid in (i) (4:1) methanolwater [START_REF] Eikel | Liquid extraction surface analysis (LESA) of food surfaces employing chip-based nano-electrospray mass spectrometry[END_REF] , (ii) (2:1) methanol -chloroform 26 , (iii) (4:2:1) propanol -methanol -chloroform 25 were tested and compared with (iv) 0.1% formic acid in (3:1) acetonitrile -water. The results show the evolution of the intensities of the families of peaks as a function of the solvent used (Fig. 1 SI). The highest sensitivity was obtained for 0.1% formic acid in (2:1) methanol -chloroform for all the series and this mixture was used throughout the study.

Instrumental parameters. The dispension and aspiration height depends on the hardness and composition of the surface and the micro junction creation depends on the surface wettability. The number of repetitions and the time of the extraction were optimized accordingly with this concentration phenomenon explained above. The spray voltage and the pressure where chosen based on the supplier guidelines for individual solvents. Table 1 below summarized the optimum parameters.

Consecutive extractions. A concentration effect was checked by carrying out LESA-MS analysis five times on the exact same point and under the exact same conditions (Table 1). The aim was to evaluate if there is any concentration change in the extracted species after several extractions. It was done twice for reproducibility reasons; the clear trend of decreasing of the peak intensity in the second and third extraction and then the stabilization of the signal was observed. In terms of repeatability, the spectra obtained for consecutive extractions contained 89, 86, 82 and 74 peaks common with the first spectrum for the 2 nd , 3 rd , 4 th and 5 th extractions, respectively. In this situation it was decided to avoid repetitions and the time was optimized and set at 8 s (as a compromise between acceptable extraction and a stable micro junction).

Homogeneity. The first step in the study was to evaluate the homogeneity of the plastic sample (transparent part of a commercial food packaging). This information was considered to be essential as the size of the drop is tiny in comparison with the size of plastic samples. To do so, five points were chosen all over the plastic sample and analysed by LESA-MS under the conditions given in Table 1. The evaluation of the spectra obtained consisted of comparing the number of common peaks and their intensities (and relative intensity). The statistical analysis proved that 78.4% peaks are common between the five analyses with a slight deviation in terms of peaks intensities. These results allow one to consider the plastic sample as homogeneous and the results obtained as representative for the whole film surface.

Identification of polymer species able to migrate into food from polyethylene terephthalate (PET)

Polyethylene terephthalate (PET) is a common thermoplastic polymer of the polyester family used in containers for liquids and foods. Fig. 2a shows a LESA -nanoESI mass spectrum showing a series of small (M <700) molecules which are likely to migrate from the polymer into food. These molecules have molecular masses separated by a regular interval of 44 Da (Table SI 1) which corresponds to polyethylene oxide (PEO, aka polyethylene glycol, PEG). PEO is used for PET synthesis; its presence can thus originate from polymerization residues or polymer degradation residues. Also, PEO is also often used as an additive for its plasticizing properties [START_REF] Rezaei Kolahchi | Enhancing hydrophilicity of polyethylene terephthalate surface through melt blending[END_REF] . The presence of PEO on the surface of the polymer creates a risk of its migration into food. Validation by the batch method. A mass spectrum (Fig. 2b) obtained using batch extraction from bulk (0.25 g) of the material using a multistep procedure outlined above is shown in Fig. 2b. In spite of the differences in the extraction processes, the obtained mass spectrum shows striking similarities in the m/z > 300 range; the same series of species can be observed. This type of mass spectrum can be used as a fingerprint of PET migration into food. Note that even if the mass spectrum of the batch extract shows a higher signal-to noise ratio, the same species are detected which validates the LESA procedure which is much shorter (minutes instead of several hours) and consumes much less solvent.

Comparison with DART-MS. The method was compared with another ambient ionisation method -DART-MS. The obtained mass spectrum is more complex as it represents the PET polymer itself and not the migrating PEO. Actually, the use of a heated gas stream and no solvent provokes degradation of the plastic thus making it impossible to see residual PEO. The latter brings about the risk of the occurrence of false positives suggesting the migration of other molecules that would have migrated during the contact with food. LESA-MS offers a soft extraction method and thus is more adapted for studies of migration from the polymer surface than DART-MS. However, DART-MS offers other possibilities for polymer analysis allowing access to different data sets as compared to LESA-MS.

Identification of polymer species able to migrate into food from low-density polyethylene (LDPE)

Low-density polyethylene (LDPE) is a thermoplastic produced by polymerisation of ethylene. Its most common use is in plastic bags and films. Being a polyolefin, it readily undergoes oxidation (Fig. 3a).

The mass spectrum shows different degradation products including alcohols and esters but the formation of a carboxylic group on the polymer chain is the most systematic. For example, the major peak (m/z 554.5506 Da) corresponds to the oxidised molecule shown in the inset (Fig. 3a). Note that different adducts can also be identified.

This oxidation can be prevented by the addition of an antioxidant such as Vitamin E (Fig. 3b). The mass spectrum shows the absence of the degradation product and a predominant peak of vitamin E (m/z 429.37272)

Monitoring of the formation of species able to migrate into food during recycling of polyethylene and polypropylene

As recycling requires the polymer treatment at high temperature, the polymer is prone to oxidation.

The method developed allows the monitoring of the formation of degradation products during polymer recycling (Fig. 4). The mass spectra are composed of peaks separated by 14 Da, corresponding to the methylene group. The intensity of the peaks from clusters of oligomers increases with the number of recycling cycles. Fig. 4 clearly shows polymer degradation as a result of the extrusion and the creation of small molecules potentially able to migrate into food. The risk of the formation of undesirable molecules able to migrate into food increases with the increasing number of the recycling cycles. This phenomenon is even more visible when analysing polypropylene as it is extruded at higher temperatures (180 °C instead of 150 °C for PE) and degrades by chainscissions (whereas polyethylene suffers both from chains reticulation and chain scissions), creating clusters of oligomers. Nevertheless, both polymers show similar spectra throughout the recycling cycles.

Assessment of the efficiency of antioxidants used to stabilize polymers

As mentioned above, antioxidants (such as e.g. vitamin E) are used to stabilize the polymers. Fig. 5 demonstrated that LESA MS allows a fast assessment of the efficiency of antioxidants used to stabilize polymers. Two samples of polypropylene modified by the addition of the same amount of natural antioxidants: vitamin E and linseed oil were compared. The result shows a weak efficiency (although higher than in the case of linseed oil) of vitamin E in protecting the polymer from the degradation. The peak intensity was normalized to the signal m/z 338.341 present in all the samples. Note that in the sample of polypropylene stabilized with vitamin E, a clear signal of this species was detected (at m/z 429.373) documenting its potential for migration from the polymer which is considered beneficial as means of the protection of food.

Linearity of the LESA -MS signal in polymer films as a function of concentration

In addition to its potential for qualitative analysis, the developed LESA -MS procedure can be useful for the quantitative determination of vitamin E in the polymers and, potentially, in food. A series of polyethylene doped with different amounts of vitamin E were produced to construct a calibration curve showing a linear response of the m/z 429.373 peak (vitamin E) intensity in LESA-MS as a function of the concentration of the antioxidant in the polymer. Note that the additive's concentration in the standard produced by the extrusion process may be different from that in the initial mixture. Therefore, in order to assure the accuracy of the quantitative method, the composition of the standards used for quantification has to be confirmed by another method. This observation demonstrates the possibility of direct quantitative analysis of polymer films by LESA -nanoESI MS provided that standards with the guaranteed additive's concentration are available.

Conclusions

LESA combined with nanoESI ionisation -MS was demonstrated to be a convenient method for the direct analysis of polymer packing materials. It was optimized to monitor (i) the release of residual polymerisation or polymer degradation residues, (ii) the degradation of additive-free low-density polyethylene during recycling, and (iii) the efficiency of antioxidants additives to stabilize the polymer. Moreover, the release of additives, such as vitamin E, could be quantified using external calibration with solid state standards. The method was validated by the currently used method based on the extraction from the bulk followed by ESI-MS. LESA-nanoSI MS does not require sample preparation, is fast, eco-friendly and highly reproducible. It also compares favourably with ambient ionisation DART-MS in terms of selectivity producing simpler spectra limited to the mobile compounds of interest. It has to be mentioned that the sensitivity of LESA MS is inversely size dependent due to higher solubility and thus easier extraction from the surface of smaller species. At the same time, the potential migration into food is also facilitated in the case of small molecules.

  Note that the complexity of the mass spectra results from the fact that the [M n +H + ] series is accompanied by adducts including: [M n +Na + ], [M n +NH 4 + ] and [M n +K + ] and by the presence of isotopic isomers of the detected compounds (for the sake of the clarity of the figure, only some peaks have been labelled). The actual distribution of the intensity of the individual degradation products can vary with the extraction solvent (Fig. SI 1).

Figure 1 .Figure 1 .

 11 Figure 1. Identification of different series of peak.

Figure 2 .

 2 Figure 2. Mass spectra obtained for the analysis of a candidate packing materials (a) by LESA MS accompanied by the identification of the signals and (b) after a classical batch sample preparation method. For the sake of clarity only 13C peaks were labelled.

Figure 3 .

 3 Figure 3. LESA-MS spectrum of (a) an oxidized LDPE and (b) LPDE stabilized with Vitamin E.

Figure 4 .

 4 Figure 4. LESA-MS analysis on recycled bare polyethylene: (a) one cycle, (b) 9 cycles.

Figure 5 .Figure 6 .

 56 Figure 5. LESA-MS analysis of polypropylene stabilized with different antioxidants: (a) Vitamin E, (b) linseed oil Figure 6. LESA MS of a polyethylene stabilized with vitamin E (m/z 429.373)

Figure SI 1 .

 1 Figure SI 1. Example of the evolution of the intensity of the signals in the function of the solvent used for the analysis of a PET commercial sample.

Figure 1 .

 1 Figure 1. LESA-MS principle.

Figure 2 .

 2 Figure 2. Mass spectra obtained for the analysis of a candidate packing materials (a) by LESA MS accompanied by the identification of the signals and (b) after a classical batch sample preparation method. Note: for the clarity of presentation only some species have been labelled, examples of the identification of M n species (where n denotes the number of monomer units) are given inTable 1 SI.

Figure 4 .

 4 Figure 4. LESA-MS analysis on recycled bare polyethylene: (a) one cycle, (b) 9 cycles, (c) detail spectrum of the first oligomers' cluster of figure 4(b).

22 Figure 5 .Figure 6 .Figure SI 1 .

 22561 Figure 5. LESA-MS analysis of polypropylene stabilized with different antioxidants: (a) Vitamin E, (b) linseed oil

Table 1 . LESA-MS parameters used

 1 Example of the identification of the species in LESA MS spectrum of a PET commercial sample.

	Solvent	Solvent aspired, µL	Air aspired, µL	Solvent dispensed, µL	Dispense height, mm	Aspiration height, mm	Time, s	Spray Voltage, kV	Pressure, psi
	2:1 MeOH -CHCl 3 + 0,1% FA	2	0.8	1.8	3	2.8	8	1.4	0.35

Table SI 1

 SI . Example of the identification of the species in LESA MS spectrum of a PET commercial sample.

Acknowledgement: the support of the European Regional Development Fund (FEDER) in the frame of the Interreg POCTEFA 2014-2020 Program Foodyplast (EFA099/15) and the EQUIPEX MARSS project (11-EQPX-0027) is acknowledged. AI acknowledges the co-financing of the Communauté de Communes de Lacq-Orthez (CCLO).

Supplementary information:


Elise Potier

Antoine Waked

A Bourin

Fanny Minvielle

Jean-Christophe C Péré

Esperanza Perdrix

Vincent Michoud

V Riffault

L Y Alleman

S Sauvage

Characterizing the regional contribution to PM10 pollution over northern France using two complementary approaches: Chemistry transport and trajectory-based receptor models

Keywords: CTM, Chemistry MFE, Mean Fractional Error, MLA, Monthly Limit Alt itude, OC, Organic Carbon, PBL, Planetary Boundary Layer, PM, Particu late Matter, PPM, Primary Particu late Matter (anhropogenic primary species), PSCF, Potential Source Contribution Function, RMS E, Root Mean Squared Error, SOA, Secondary Organic Aerosols, ST, Short-range Trajectory, TRM, Trajectory-based statistical Receptor Model, WF, Weighting Function CHIMERE, receptor model, PSCF, Concentration Field, PM 10 sources

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

1.

Introduction

At the global scale, aerosols play an important role on climate by scattering and absorbing solar and terrestrial radiations, which contribute to both cooling and warming of the atmosphere [START_REF] Boucher | Climate Change 2013: The Physical Science Basis[END_REF]. At the regional scale, particles are known to have adverse health effects, especially small particles with diameters lower than 10  m and 2.5  m (PM 10 and PM 2.5 respectively) [START_REF] Who | Health aspects of air pollution with particulate matter, ozone and nitrogen dioxide: report on a WHO working group[END_REF]. Exposure to particulate pollution is notably more significant in urbanized areas where denser emissions are released due to the concentration of humans activities.

In Europe, the Air Quality Directives limit values and the WHO Air Quality thresholds are often exceeded according to the data of the European air quality database (Air Quality e-reporting database, [START_REF] Eea | Air Quality e-Reporting Database[END_REF]).

The occurrence of these particulate pollution episodes is the result of complex processes involving direct primary emissions, physico-chemical transformations, transport and deposition, depending on meteorological factors. Among these processes, the variety of regionally spread sources and the atmospheric lifetime of compounds make the long-range transport of pollutants an important factor influencing the pollution over a given region. The Hauts-de-France region (HdF thereafter), located in the heart of a dense urbanized and industrialized area of northwestern Europe, is regularly impacted by PM 10 pollution episodes of both local and regional origins [START_REF] Favez | Main properties and origins of winter PM10 pollution events in France[END_REF][START_REF] Bessagnet | Origin of particulate matter pollution episodes in wintertime over the Paris basin[END_REF]. Large scale particulate pollution episodes observed in the region were shown to be predominated by secondary aerosols (e.g. ammonium nitrate and sulfate; secondary organic aerosols [START_REF] Beekmann | In situ, satellite measurement and model evidence on the dominant regional contribution to fine particulate matter levels in the Paris megacity[END_REF]). Consequently, understanding the role of regional sources is needed to improve our comprehension of the occurrence of particulate pollution episodes in this specific region. For such an analysis, different modelling approaches such as deterministic eulerian Chemistry Transport Models (CTMs) or Trajectory-based Receptor-oriented statistical Models (TRMs) based on ambient air observations at sampling sites can be used. The originality of this study is to combine both approaches to accurately estimate the geographical origin of pollutant sources impacting HdF.

Several CTMs are currently developed in Europe for both research and forecast purposes, like those in the ENSEMBLE system [START_REF] Cams | Regional Production, Description of the operational models and of the ENSEMBLE system[END_REF]: CHIMERE (INERIS, CNRS, France, [START_REF] Menut | Chimere 2013: a model for regional atmospheric composition modelling[END_REF]), EMEP (MET Norway, Norway, [START_REF] Simpson | The EMEP MSC-W chemical transport model -technical description[END_REF]), EURAD (RIUUK, Germany, [START_REF] Memmesheimer | Long-term simulations of particulate matter in Europe on different scales using sequential nesting of a regional model[END_REF]), LOTOS-EUROS (KNMI, TNO, The Netherlands, [START_REF] Schaap | The Lotos-Euros Model: Description, Validation and Latest Developments[END_REF]), MATCH (SMHI, Sweden, [START_REF] Andersson | Coupling aerosol optics to the MATCH (v5.5.0) chemical transport model and the SALSA (v1) aerosol microphysics module[END_REF]), MOCAGE (Météo-France, France [START_REF] Sic | Modelling of primary aerosols in the chemical transport model MOCAGE: development and evaluation of aerosol physical parameterizations[END_REF]) and SILAM (FMI, Finland, [START_REF] Sofiev | Construction of the SILAM eulerian atmospheric dispersion model based on the advection algorithm of Michael Galperin[END_REF]). These models explicitly simulate the emission, physico-chemical transformations, transport and deposition of pollutants based on detailed emission inventories as well as empirical assumptions.

Hence, they are well adapted to analyze the relative contributions of the variety of emission sources over a specific area. In this study numerical simulations were performed with the CHIMERE regional CTM using an approach similiar to [START_REF] Rea | Source contributions to 2012 summertime aerosols in the Euro-Mediterranean region[END_REF] who studied the contribution of different types of PM sources in the Euro-Mediterranean region, or [START_REF] Bessagnet | Origin of particulate matter pollution episodes in wintertime over the Paris basin[END_REF] who performed sensitivity analyses on emission inventories to study the origin of PM pollution events over the Paris region in 2003.

A complementary Trajectory-based statistical approach for the determination of the geographical origins of sources using the combination of air mass back trajectories (BTs) calculations with pollutants concentrations observations at multiple receptor sites has been widely used in many studies (Petit et al., 2017a;[START_REF] Waked | Source apportionment of PM10 in a north-western Europe regional urban background site (Lens, france) using positive matrix factorization and including primary biogenic emissions[END_REF][START_REF] Xie | The use of conditional probability functions and potential source contribution functions to identify source regions and advection pathways of hydrocarbon emissions in Houston, Texas[END_REF][START_REF] Polissar | Source regions for atmospheric aerosol measured at Barrow, Alaska[END_REF]. Trajectory frequencies and residence time of air masses enable to identify air parcels responsible of an exposure to high levels of PM 10 concentrations observed at the receptor sites.

Among TRMs, the Concentration Field (CF) and the Potential Source Contribution Function (PSCF) models are used to characterize the transport of pollution over geographical areas. In the CF approach, the geographical origins of the sources are provided for all the range of concentration levels whereas with the PSCF method, a threshold of high pollutant concentrations is selected in order to identify only geographical origins during the most severe pollution episodes.

The main objectives of this work are to (1) compare two independant approaches, the CF/PSCF and the CTM for identifying the regional and continental sources of pollution and (2) quantify their contributions to the pollution over HdF. CF/PSCF and CTM methodologies are presented in Section 2. In Section 3 results for the entire year 2010 are analyzed, with a focus on pollution events. Section 4 discusses uncertainties relative to both approaches as well as their synergies and includes concluding remarks.

Methodology

The study area

The study area is HdF in Northern France (Fig. 1a, yellow highlights). It covers an area of 31,813• km 2 and is influenced by many anthropogenic (e.g. residential and industrial sectors, agriculture as well as the transport sector) and biogenic emissions. The region encompasses many petrochemical, metallurgic and non-metallurgical industries. The coastal zone located near the strait of Dover and the English Channel is impacted by oceanic sea spray and shipping emissions.

The region can also be influenced by long-range trans-boundary air pollution from Central Europe.

PM 10 annual and daily EU limit values are exceeded in many areas of HdF (Atmo-HdF, 2017).

The climate is temperate with an oceanic influence characterized by mild temperatures (average annual values in the range of 6-8 o C) and regular precipitations amount (600-800 mm yr 1  ).

Winds are prevailing mostly from the South-West as well as from the North and North-East directions whereas insolation ranges from 1600-1700 hours on a yearly basis.

Trajectory-based receptor models 2.2.1. Input data

Atmospheric concentrations of PM 10 were collected on an hourly basis for the year 2010 from 01/01 to 31/12 (http://www.atmo-hdf.fr/) from the regional Air Quality Monitoring Network Atmo Hauts-de-France. A total of twelve urban, sub-urban and rural sampling sites (Fig. 1b) covering the whole region were selected in a way that excluded an influence from temporary or specific local sources not representing the general characteristics of the studied region (e.g. sites influenced by temporary road works or by local industrial emissions). The PM 10 atmospheric concentrations were measured by equivalent methods (e.g. TEOM-FDMS and Beta-Gauge) depending on the sampling sites. The equivalence to a reference method for sampling and measurement of PM 10 concentrations had been demonstrated for all the used instruments [START_REF] Verlac | Suivi d'équivalence des analyseurs automatiques de PM10 et PM2[END_REF][START_REF] Bihan | Demonstration of equivalence of PM automated monitoring methods: results of the 1st measurement campaign on a parisian urban background air quality station[END_REF]. Missing values were in the range of 2-10 %.

BTs were calculated every 3 h for each day (8 trajectories per day) by means of the Linux-based version of the NOAA's Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT). Each trajectory went back 72 h with a temporal resolution of 1 h (72 endpoints per trajectory) and an ending latitude-longitude coordinates corresponding to one of the twelve receptor sites chosen for the study. The input meteorological data for the HYSPLIT model [START_REF] Stein | Noaas hysplit atmospheric transport and dispersion modeling system[END_REF] were retrieved from weekly NCEP's Global Data Assimilation (GDAS) System with a 1.0 o x 1.0 o spatial resolution. The ending altitude of the BTs was 500 m above the ground level in order to reduce the effect of the surface friction and to represent winds in the boundary layer. It was found to be satisfactory and was used in many studies in the literature [START_REF] Begum | Evaluation of the potential source contribution function using the 2002 Quebec forest fire episode[END_REF][START_REF] Hafner | Potential sources of pesticides, pcbs, and pahs to the atmosphere of the great lakes[END_REF][START_REF] Gao | Potential source contribution function analysis and source apportionment of sulfur species measured at Rubidoux, CA during the southern California air quality study, 1987[END_REF]. Input data (8 tri-hourly averaged PM 10 concentrations matching 8 air mass BTs per day) were then used in the Zefir Igor package (Petit et al., 2017b) in order to plot geographical origin maps using CF and PSCF models. In both PSCF and CF methodologies, a regular 0.5 o x 0.5 o grid was superimposed to the region encompassing the considered receptor sites and the computed air mass BTs. The 0.5 o x 0.5 o grid latitudes ranged from 37 o N to 69 o N and the longitudes from 26 o W to 31 o E. PSCF and CF methodologies are briefly described in the following sub-sections and in further details in [START_REF] Waked | Investigation of the geographical origins of pm10 based on long, medium and short-range air mass back-trajectories impacting northern france during the period 20092013[END_REF].

CF method

The CF method (Petit et al., 2017b;[START_REF] Charron | Receptor modelling of secondary and carbonaceous particulate matter at a southern UK site[END_REF][START_REF] Polissar | Source regions for atmospheric aerosol measured at Barrow, Alaska[END_REF][START_REF] Seibert | Trajectory Analysis of High-Alpine Air Pollution Data[END_REF] uses the residence time of air masses in each grid cell to calculate a residence time-weighted PM 10 concentration using the following equation:

,, =1 , ,, =1 = M i j l l l ij M i j l l n logC logC n    (1)
where C , ij is the residence time-weighted concentration of the ij th grid cell, C l the concentration measured at the receptor site on the arrival of a trajectory l, M the total number of trajectories and n ,, i j l the residence time of an air mass trajectory l on a grid cell ij. A geometric mean is used as the distribution of PM 10 concentrations is log-normal and provides an estimation not skewed by extreme values.

The underlying assumption is that the longer an air mass stays in a grid cell, the more it may get polluted by the sources located in this grid cell. The residence time-weighted concentrations associated to each grid cell form a "concentration field". In the obtained CF map, a grid cell showing a high residence time-weighted concentration is interpreted as the possible location of a source area which impacts the considered receptor site. In multi-site CF (or PSCF), multiple receptor sites are considered simultaneously, thus increasing the spatial representativeness and the statistical power of the results. This was the case in this work where 12 receptor sites were considered (Fig. 1b).

PSCF method

The PSCF method [START_REF] Pongkiatkul | Assessment of potential long-range transport of particulate air pollution using trajectory modeling and monitoring data[END_REF][START_REF] Zeng | A study of the sources of acid precipiation in Ontario, Canada[END_REF] provides a probability field which can be used to determine the areas potentially responsible of high levels of PM 10 concentrations above a given threshold measured at the receptor sampling sites [START_REF] Li | Analysis of the transport pathways and potential sources of PM10 in Shanghai based on three methods[END_REF][START_REF] Cheng | Qualitative determination of source regions of aerosol in Canad ian high Arctic[END_REF] as follows:

, , , = ij ij ij m PSCF n (2)
where n , ij is the total number of endpoints of BTs passing over a grid cell ij and m , ij is the number of endpoints of BTs passing over the grid cell ij and having a PM 10 concentration (a tri-hourly average in this study) measured at the receptor site higher than the chosen specific threshold (40  g m 3  during this study which corresponds to the EU annual limit value for PM 10

) in order to perceive the geographical origin of PM 10 during the occurrence of pollution episodes.

Sensitivity analysis

PM 10 depletion processes during air mass transport: In both CF and PSCF methods, the atmospheric lifetime of PM 10 is taken into account through the given backtime of air mass trajectories (here 72 h). In both methods, the influence of permanent sinks such as dry deposition and photochemical ageing is assumed to be comparable for all trajectories. Howewer this assumption does not apply in the case of temporary sinks such as rain events which only affect given trajectories at given locations and times. Therefore the effect of temporary sinks such as rain occuring during the transport of an air mass is generally neglected. In order to get a more realistic maps, precipitation data provided by HYSPLIT along the BTs were used to remove all the endpoints preceding a precipitation event with an intensity stronger than 1 mm h 1  [START_REF] Kim | Effect of precipitation on air pollutant concentration in Seoul, Korea[END_REF] due to the fact that heavy precipitations are believed to strongly decrease PM 10 concentrations. In addition, another phenomenon which may reduce the PM 10 concentration of an air parcel is its dilution in the free troposphere. Therefore, endpoints for which altitude was above the Planetary Boundary Layer (PBL) height were removed along with all the preceding endpoints.

To do so, monthly limit altitudes (MLA) established as representative of the separation between the PBL and the free troposphere (Table 1) were used. These MLA were measured at noon at a comparable site in term of latitude in Germany from 2000 to 2009 by von [START_REF] Von Engeln | A planetary boundary layer height climatology derived from ECMWF reanalysis data[END_REF]. The use of constant MLA from measurements instead of using the variable PBL heights simulated in GDAS fields was preferred as it enables to minimise the excessive removal of nighttime endpoints due to the high uncertainty in the simulatio n of the nocturnal residual boundary layer [START_REF] Waked | Investigation of the geographical origins of pm10 based on long, medium and short-range air mass back-trajectories impacting northern france during the period 20092013[END_REF]. It should be noted that when endpoints were removed for precipitations and altitude, the air mass age of BTs was reduced and became lower than 72h.

Spatial density of the BTs:

A weighting function W , ij included within the Zefir package (Petit et al., 2017b) was used for both the CF and PSCF methods in order to down-weight grid cells presenting a low number of endpoints as follows:

, , ,

                    ,, 1) < 0.35 [ ( 1)] j i j max log n           (3)
where n , ij is the total number of endpoints in the ij th grid cell.

When using the weighting function (WF), concentrations corresponding to frequent air mass trajectories are given more weight and consequently the highlighted potential source areas are supposed to concur frequently to the observed pollution. In addition, as the spatial density of back trajectory endpoints is higher when getting closer to a receptor site, the most distant endpoints are less frequently observed. Therefore the application of the WF gives maps corresponding to medium-range BTs, hereafter named as "mediumrange maps". Conversely without the weighting function, the potential source areas found may correspond to exceptional situations not so frequently observed and to sources located at a long distance from the receptor site. These maps are called "long-range" maps.

Short-range back-trajectories: Lastly, in order to perceive the impact of the local and nearest regional sources on the concentrations measured at the receptor sites, the following equation was used to calculate the great distance circle between a BT endpoint and the receptor site: BTs for all the twelve sampling sites were kept in comparison with the 35000 long and medium range BTs. Nevertheless, this number remains sufficiently high to be considered statistically representative, therefore enabling the calculation of the CF and PSCF maps.

Uncertainty evaluation

In this study, a qualitative uncertainty assessment was performed for the output CF/PSCF maps taking into account the precision of the input data. The uncertainty of the measured tri-hourly concentrations of PM 10 was estimated to be less than 50 % [START_REF] Kiss | Bias caused by water adsorption in hourly pm measurements[END_REF]. However, because the deterministic CTM approach considers measured PM 10 concentrations as "true" values, here the uncertainty of the measured values will not be considered. The uncertainty related to the meteorological data was evaluated by comparing the CF/PSCF maps obtained using 1.0 o x 1.0 o GDAS meteorological data with the use of GDAS data with a higher spatial resolution of 0.5 o x 0.5 o for the period from 05/08/10 to 31/12/10 (i.e. the common period of availability of GDAS data with both spatial resolutions). The obtained maps showed non-significant differences with correlation coefficients of around 0.9 and average absolute relative deviations of about 2 % for both the CF and PSCF. These results compare well with the position error of 20 % found by [START_REF] Kiss | Bias caused by water adsorption in hourly pm measurements[END_REF] for trajectories computed from analyzed wind fields. Lastly, the influence of the spatial resolution of the gridded domain was also tested within the Zefir package considering grid cells of 1.0 o x 1.0 o , 0.5 o x 0.5 o or 0.1 o x 0.1 o in size and was considered low with a maximum average absolute relative deviations of 24 % since non-significant differences in geographical source area maps were observed. It should be noted also that the maps obtained by constraining the BTs (cut-off for rain and altitude, short-range trajectories) should present less spatial uncertainty than the non-constrained maps because of the shortening of constrained BTs. It is in fact well-established that shorter BTs are more accurate than longer ones [START_REF] Charron | Intercomparison between three receptor-oriented models applied to acidic species in precipitation[END_REF] leading to less uncertainty of the cell estimate for these CF and PSCF maps. Therefore, the global qualitative uncertainty in the geographical origin source maps (CF and PSCF) was considered as low and would be in the range of 20-30 %.

Chemistry-transport model approach 2.3.1. Description of CHIMERE

The model version used in this study was CHIMERE2016a. All simulations were performed for the year 2010 over a domain represented on Fig. 1 and with a model configuration summarized in Table 2. The studied domain was HdF in a larger simulation domain that covered a large part of Europe, to obtain a detailed representation of the variety of regional pollutant sources.

The horizontal simulation mesh grid was 0.2 o in latitude and 0.2 o in longitude and the vertical grid was composed of 15 levels from the surface (top of the first layer 997 hPa) to 500 hPa.

CHIMERE simulates gas-phase chemistry, aerosol formation, transport and deposition at urban and regional scales by using information on gaseous and particulate emissions. Anthropogenic emissions came from the EDGAR-HTAP-V2 European inventory at a resolution of 0.1 o x 0.1 o for the year 2010 [START_REF] Janssens-Maenhout | EDGAR-HTAP: a harmonized gridded air pollution emission dataset based on national inventories[END_REF]. The available emitted masses for methane (CH (SO 2 ), nitrogen oxides (NO x ), ammonia (NH 3 ), PM 10 , PM 2.5 , black carbon (BC) and organic carbon (OC) were disaggregated into model species and mapped onto the chosen simulation grid with a specific CHIMERE preprocessing. Biogenic emissions were calculated on-line using the global Model of Emissions of Gases and Aerosols from Nature (MEGAN; [START_REF] Guenther | Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature)[END_REF]).

It should be noted that mineral dust emissions were not activated in this study as we focused primarily on the impact of anthropogenic pollution. Furthermore in-situ measurements in North

Europe showed that the contribution of dust in the aerosol composition is small (5-12 %, Putaud et al. ( 2010)). Initial and boundary conditions for chemistry came from monthly climatology (5-years averaged) of the LMDZ-INCA model [START_REF] Hauglustaine | A global model simulation of present and future nitrate aerosols and their direct radiative forcing of climate[END_REF]. CHIMERE also uses as inputs meteorological informations (such as temperature, wind, humidity,...) provided by the Weather Research and Forecasting model at a one hour resolution (WRF; [START_REF] Skamarock | A description of the advanced research WRF version 3[END_REF]).

In this study, several simulation tests were performed with modifications on these anthropogenic emissions detailed in section 2.3.3 and reported in Table 2.

Evaluation

The Hourly PM 10 concentrations from the model in the surface layer were daily averaged and then statistics were used to compare measured (C obs ) and modeled (C mod ) compound concentrations. Spatio-temporal average (  ) and standard deviation (  ) were calculated on the whole PM 10 concentration dataset at the sites and their corresponding mesh, with N the number of data. N = number of daily data  number of numerical mesh (or number of sites):

1 = N i i C N    (5) 2 =1 () = N i i C N     (6)
=1 () = N mod obs ii i CC RMSE N   (7)
Statistical indicators such as the mean fractional error (MFE) and the mean fractional bias (MFB) between observations and modeled surface concentrations, were also used as recommended in air quality simulation performance evaluation [START_REF] Boylan | Pm and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models[END_REF].

=1 1 = ( ) / 2 mod obs N ii mod obs i ii CC MFB N C C    (8) =1 || 1 = ( ) / 2 mod obs N ii mod obs i ii CC MFE N C C    (9)
MFB indicates systematic errors which lead to always underestimate or overestimate the measured values. It can vary between -200 % (extreme under-estimation) to +200 % (extreme over-estimation). MFE avoids compensations between over-and under-estimations. It varies between 0 % to +200 %. Values of zero for both MFB and MFE indicate an ideal model. As defined by [START_REF] Boylan | Pm and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models[END_REF], we considered that if both || MFB and MFE were lower or equal to 30 % and 50 % respectively, the performance goals were achieved and if they were lower or equal to 60 % and 75 % respectively, the model met the performance criteria.

Area contribution calculations

A recent study carried out by research scientists and the HdF air quality monitoring network [START_REF] Atmo-Hdf | Programme de surveillance de la qualité de l'air en Nord-Pas de Calais[END_REF]Petit et al., 2017a;[START_REF] Waked | Investigation of the geographical origins of pm10 based on long, medium and short-range air mass back-trajectories impacting northern france during the period 20092013[END_REF] PM 10 aerosols are composed of different chemical species. The main species simulated by CHIMERE are: nitrate, sulfate, ammonium, black carbon, organic carbon, anthropogenic primary species (PPM: primary particulate matter, e.g. from anthropogenic erosion processes) and secondary organic aerosols (SOA). The influence of the three areas (BI, Near-East, Far-East) on the aerosol chemical composition modeled over HdF will be also analyzed.

Results

CF/PSCF potential source areas maps

Maps of the geographical origin of potential source areas impacting HdF in 2010 optimized by taking into account the cut-offs for precipitations and altitude (CRA) are plotted in Fig. 3 for long trajectories (without WF), medium-range trajectories (applying the WF) and short-range trajectories (selecting trajectories with 75 % of endpoints within 500 km around the receptor sites) using both the CF (considering all days) and the PSCF models (only for pollution episodes up to 40

 g m 3 
).

The long-range CF results used for the investigation of the geographical origin of the PM 10 sources with all the measured data showed highest contributing areas mainly from central Europe (e.g. north of Italy, Germany, Austria, Czech Republic, Hungary, Slovakia, south of Poland) as well as from the west of Ukraine and of Belarus (Fig. 3a, CF+CRA). The application of a WF for the CF maps in order to investigate medium-range trajectories frequently impacting HdF showed an important influence from nearer areas such as the Benelux, west of Germany as well as HdF itself (Fig. 3c, CF+CRA+WF). Investigating the impact of nearest regional sources with the selection of short trajectories highlighted a significant influence of the Benelux area as well as the west of Germany (e.g. the Ruhr region) with high geometric averaged concentrations ranging from 40-60  g m 3  (Fig. 3e, CF+ST+CRA). This regional map showed a significant influence of short-distant sources in HdF when a weighting function was further app lied, in particular from

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Therefore it may be concluded that the particulate pollution observed in HdF is for a small part related to long-range trans-boundary pollution which is unfrequent but may contribute to high PM 10 concentrations (in the range of 40-60  g m 3  ), for a large part to frequent medium-range transboundary pollution from neighbouring countries (mainly Belgium and Germany) giving moderate PM 10 concentrations (up to 30  g m 3  ), and that another significant part is due to nearest contribution from the French territory giving also moderate PM 10 concentrations.

Using the PSCF method to identify the geographical origins of potential PM 10 sources associated with the highest concentrations 40  g m 3  observed in HdF (i.e. during pollution episodes), it was found that the region of Central Europe, in particular, Austria, Hungary, west of Ukraine and Belarus, had high probability levels (contributions more than 80 %) during episodes of particulate pollution (Fig. 3b, PSCF+CRA). For medium and short-range trajectories, geographical origin maps during episodes showed comparable results to that of the CF method where the region of the Benelux area and Germany were highlighted whereas on a more regional scale, HdF (e.g. Lille metropolitan area; Belgian borders near the Lille area) and to a lesser extent the Paris area are identified with conditions probabilities ranging from 30 to 50 % (Fig. 3, PSCF+CRA+WF(d) and PSCF+CRA+WF+ST(h)).

On a seasonal scale, long-range BTs investigated using both CF and PSCF methods showed the highest concentrations of the contributing areas in winter and spring and the lowest in summer (Fig. 4, CF+CRA(a-d) and PSCF+CRA(i-j)). While during the winter season, potential source areas tend to be closer in term of distance to HdF (e.g. Germany, Austria and west of Slovakia), during the spring season, more distant potential source areas were identified in the west of Poland and Belarus, especially during pollution episodes (Fig. 4 PSCF+CRA(i-j) seasonal maps). These high concentrations of PM 10 for longer BTs could be related to their enrichment with secondary pollutants (e.g. ammonium sulfate) or gaseous precursors of these pollutants as already found in the literature [START_REF] Waked | Source apportionment of PM10 in a north-western Europe regional urban background site (Lens, france) using positive matrix factorization and including primary biogenic emissions[END_REF][START_REF] Pay | Assessing sensitivity regimes of secondary inorganic aerosol formation in Europe with the CALIOPE-EU modeling system[END_REF]Petit et al., 2017a). For instance trans-boundary pollution related to more distant sources showed a contribution around 50 % during and outside episodes. This is due to the fact that, when WF was used, the condit ional probabilities as well as the concentrations of PM 10 for the nearest sources were reduced by a factor of two on average. The use of a WF showed also that when back trajectories frequently impacting HdF are selected, the identified potential geographical areas are the west of Germany, Benelux and the region of northern France and Paris. These areas appear with the highest concentrations during winter due to probably more intense sources (e.g. residential wood burning) and to lower PBL height which hinders dilution of pollutants. In the spring and fall seasons, pollution episodes impacting HdF (i.e. PSCF results), showed higher contributions from more distant geographical areas such as Germany, west of Poland and Austria highlighting the potential role of transboundary transport.

Seasonal variations of short-range trajectories CF maps showed the highest concentrations during the winter season and the lowest concentrations during summer (Fig. 5). Identified potential areas are located in Belgium, in the west of Germany in the Ruhr region (e.g. coal combustion power plants and industries), in the region near the Strait of Dover (e.g. mouth of the Rhine river (Petit et al., 2017a)) where major French harbors, industrial areas as well as intense shipping activities are located [START_REF] Bressi | Sources and geographical origins of fine aerosols in Paris (France)[END_REF] and in highly populated cities in France (e.g. Lille metropolitan area) and Belgium (e.g. Brussels, Ghent and Antwerp). The use of the WF for short trajectories showed clearly the influence of the Paris area for all seasons with the highest concentrations reported in winter (Fig. 5, CF+CRA+WF(e-h) seasonal maps). Using the PSCF model for the investigation of pollution episodes, a significant influence is shown in winter and fall from the eastern part of HdF, namely the Avesnois rural area where biomass burning activities are intense, as well as from the cities of Maubeuge, Paris and Metz. In contrast, during the warmest seasons (spring and summer), there is no remarkable influence from these areas highlighting the likely impact of residential heating as an important source during winter/fall seasons. Indeed, according to [START_REF] Waked | Source apportionment of PM10 in a north-western Europe regional urban background site (Lens, france) using positive matrix factorization and including primary biogenic emissions[END_REF] during a study conducted in Lens in northern France, the contribution of biomass burning activities to total PM 10 is higher during winter by a factor of 4 on average in comparison to the spring season.

CTM approach 3.2.1. Evaluation of the main meteorological and chemical parameters

The formation, transport and thus concentration of air pollutants can be sensitive to meteorological parameters such as wind, air temperature or precipitation. As a first step, it is thus important to evaluate the capability of the meteorological WRF model to reproduce these main meteorological parameters for the year 2010. The input surface temperature, zonal and meridional wind speeds and precipitation simulated by WRF for the year 2010 were compared to available European meteorological measurements. The results, displayed in Table 3, show good agreements.

The modeled and measured meteorological parameters were strongly to very strongly correlated with r in the range 0.70-0.82 for the both zonal and meridional wind speeds and 0.83-0.91 for the temperature. For each variable, RMSE was lower than or in the order of magnitude of its standard deviation. Simulated precipitation amounts were moderately correlated to measured ones (r = 0.31-0.56), indicating that WRF is able to reproduce the main precipitation eve nts over the year.

These scores are similar to the ones obtained by [START_REF] Zhang | Application of WRF/Chem-MADRID and WRF-Polyphemus in Europe -part 1: Model description, evaluation of meteorological predictions, and aerosol-meteorology interactions[END_REF] who performed an extensive validation of the WRF meteorological model over Western Europe for the year 2001 (r in the range 0.4-0.5 for precipitations, 0.5-0.9 for wind and 0.9 for temperature).

In a second step, the surface PM 10 concentration modeled by CHIMERE were evaluated with measurements from the AirBase database. CHIMERE simulation for the reference scenario Table 4 presents statistical scores for surface PM 10 concentrations simulated by CHIMERE. Comparisons between simulations and measurements indicated that spatio-temporal correlations were lower compared to the meteorological variables (r = 0.17-0.49, depending on seasons). However, they remained similar to current performances of state-of-the-art air quality models used in the Air Quality Model Evaluation International Initiative (AQMEII) project [START_REF] Im | Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. part 2: Particulate matter[END_REF]. RMSE was in the range 10-20  g m 3  and MFB was lower than 60 % for all seasons (19-58 %) suggesting that the model performance criteria was met. Although some model biases were present, CHIMERE was able to reproduce the development and transport of the main PM 10 plumes over Europe for the year 2010.

Contributions of areas to PM 10 concentrations over HdF

In this part, the potential contribution of the 3 areas (BI, Near-East and Far-East) to PM 10 over HdF is assessed using simulation results from the CHIMERE model and using the methodology described in section 2.3.3.

On an annual mean, Table 5 shows that contributions of BI and Near-East to PM 10 concentrations over HdF were very similar. Results of simulations indicated that 17  23 % of PM 10 modeled come from each of these two areas. Also, 5.8 to 6.9 % of the PM 10 spatial variability simulated over HdF was due to these 2 areas. The influence of the Far-East was minor in our simulations with a contribution of 2  5 % over the year.

On a seasonal basis, the 3 studied areas contributed individually within 0-25 % to PM 10 concentrations in average over HdF (Table 6), which corresponded to an additional concentration of 0 to 4  g m 3  (Fig. 7). Contrarily to annual average, seasonal averages revealed differences between BI and Near-East contributions. It appeared that the largest contributions of PM 10 came from the Near-East, in particular, during winter and spring seasons (18.8 % and 25.2 % respectively, Table 6). The influence of BI was also important in summer, around 23 %, but corresponded to lower absolute values (Fig. 7, around 2  g m 3  in average over HdF) when

Near-East contribution in winter and spring reached 3.7  g m 3  in average over HdF. The very low seasonal influence of the Far-East was confirmed, with a contribution lower than 2.6  5 %, corresponding to additional concentrations lower than 1  g m 3  as highlighted in Table 6 and Fig. 7.

The influence of the 3 studied areas on hourly PM 10 concentrations, modeled over HdF, during pollution events (  25  g m 3  ), was also evaluated. Table 5 shows that the Near-East contribution prevailed and accounted for 43 % in HdF PM 10 concentrations while BI and Far-East contributed only to 7.7 % and 2.7 % respectively. We also observed that the Near-East contribution increased with increasing threshold to define polluted events. Polluted days influenced by Near-East occurred mainly in spring and winter, as highlighted on Fig. 8. The occurrence of polluted particulate events are more frequent during these seasons [START_REF] Guerreiro | Air quality status and trends in Europe[END_REF].

In terms of spatial variability, no clear differences could be observed between the northern and the southern part of HdF (Fig. 8). The Near-East, Far-East and BI contributed to PM 10 pollution events over the entire HdF, except during summer where the southern part of HdF seemed barely impacted by the three potential source areas. Largest contributions from Near-East appeared in the eastern part of HdF in spring while they are larger in the western part of HdF in fall. It should be noted that the southern part of HdF was likely impacted by pollutants coming from the Paris region.

Impact of BI and Near-East emissions on the aerosol chemical composition over HdF

In this section, the mean aerosol chemical composition modeled by CHIMERE over HdF for the reference scenario (including all emitting areas) is presented. Also, we discuss how the contribution of the studied areas (BI and Near-East) can modify this chemical speciation. Results

for Far-East will not be presented as the influence of this region was found to be very low.

The reference simulation showed that HdF was mainly influenced by inorganic species for all seasons (Fig. 9a). In yearly mean, aerosols were dominated by nitrate (40.2 %), followed by sulfate (19.2 %) and ammonium (18.1 %). This result is consistent with observational studies highlighting that an important fraction of aerosols over northern France is constituted by inorganics [START_REF] Putaud | A european aerosol phenomenology-2: chemical characteristics of particulate matter at kerbside, urban, rural and background sites in Europe[END_REF][START_REF] Waked | Source apportionment of PM10 in a north-western Europe regional urban background site (Lens, france) using positive matrix factorization and including primary biogenic emissions[END_REF][START_REF] Oliveira | Identification of the main sources and geographical origins of PM10 in the northern part of France[END_REF]. The fraction of nitrates was lower in summer (30.4 %) than in winter (45.3 %). Inversely, the fraction of sulfates increased from winter (14.6 %) to summer (27.4 %) while the proportion of ammonium remained constant over 2010 (17.2-19.1 %). Primary organic matter (PPM+OC, see Fig. 9) was also an important constituent of aerosols throughout the year (17-19 %).

When BI and Near-East emissions were removed, the modeled proportion of nitrates was decreased by about 3-7 % with the largest impact in spring and summer. This suggests that both areas act as emitters of nitrate gaseous precursors for HdF (Fig. 9 b-c). On the contrary, sulfates and ammonium proportions were very little impacted by these emissions. As a consequence of a higher summer proportion of nitrate over HdF due to BI and Near-East emissions, the proportion of primary organic matter was shown to decrease during this season (2-5 %) when emissions of these two areas were removed in the CHIMERE simulation.

Discussions and concluding remarks

In this study the CHIMERE CTM deterministic model used for Europe, as well as the CF/PSCF trajectory-based statistical receptor models run on the Zefir Igor package using HYSPLIT BTs, were used for the first time to the best of our knowledge as complementary approaches, in order to identify the possible geographical origins of PM 10 sources impacting HdF during the year 2010. Regarding uncertainty assessment of both methodologies, the results obtained from the CF/PSCF approaches are estimated to be within 30 % (e.g. spatial resolution as well as uncertainties in the GDAS input data) whereas uncertainties related to CTM outputs are evaluated against statistical indicators and are within the range of 20-60 %. The results obtained concerning these potential pollution source areas affecting HdF could be used in order to develop action plans for reducing air pollution. For instance, 20 exceedance days (above 25  g m 3  threshold) were counted for 2010 on a daily observation basis averaged over the 12 sampling sites used in the CF/PSCF approach. These exceedance days occurred mostly in winter (65 %), spring (23 %) and fall (12 %) consistent with the fact that France as well as Western Europe is frequently subject to large-scale PM pollution episodes, notably during winter and spring [START_REF] Vieno | The UK particulate matter air pollution episode of March-April 2014: More than Saharan dust[END_REF]Petit et al., 2017a;[START_REF] Favez | Main properties and origins of winter PM10 pollution events in France[END_REF][START_REF] Bessagnet | Aerosol modeling with chimere-preliminary evaluation at the continental scale[END_REF]. Comparison of the results between the two complementary approaches (CF/PSCF and CTM) revealed an important trans-boundary contribution to PM 10 concentrations in HdF originating from the Near-East "Benelux, Germany and west of Czech Republic" area. The CF and PSCF results suggested also a possible impact from the Far-East "Poland, Slovakia and East of the Czech Republic " region; however, air masses coming from this area do not frequently impact HdF. Furthermore, the impact of the BI region was

shown to be less significant in comparison to that of the Near-East region during pollution episodes observed in HdF for 2010.

According to the CTM model, the chemical speciation of background PM 10 concentrations showed a predominance of secondary inorganic aerosols with nitrate, ammonium and sulfate accounting for 77 % of the average mass. Highest recorded contributions occured in spring (79 %) and fall (78.3 %) and lowest recorded contributions in summer (75 %). This shows a possible effect of more aged air-masses for spring and fall seasons where contributions of secondary PM 10 are therefore slightly increased. Indeed, according to CF and PSCF results, a higher contribution of more distant areas is more perceived during the spring season. Comparison to measurement data reported for HdF [START_REF] Waked | Source apportionment of PM10 in a north-western Europe regional urban background site (Lens, france) using positive matrix factorization and including primary biogenic emissions[END_REF] showed, for 2011, a contribution from secondary inorganic compounds in the range of 36-49 %, respectively, lower than that of the CTM models. It is mostly related to the fact that CTM models are known to underestimate the organic fraction of PM 10 , in particular Secondary Organic Aerosols (SOA) by an order of magnitude [START_REF] Bessagnet | Regional modeling of carbonaceous aerosols over Europe-focus on secondary organic aerosols[END_REF], therefore mass closure of PM 10 could be significantly affected.

Nevertheless, when dealing with ambient concentrations, modeled secondary inorganic aerosols of 9.4  g m 3



for the winter of 2010 are comparable to the measured values of 8.2  g m 3  for the winter of 2011 in Lens [START_REF] Waked | Source apportionment of PM10 in a north-western Europe regional urban background site (Lens, france) using positive matrix factorization and including primary biogenic emissions[END_REF].

Regarding the results obtained, both approaches, the receptor-based models (CF and PSCF) as well as the deterministic CTM, exhibited similar conclusions for the geographical so urce areas of PM 10 concentrations impacting HdF. On one hand, the receptor-based models are based on simplified hypotheses that do not take into account chemical reactivity and dry deposition during transport, but have the advantage of using real measurement data. This approach gives valuable information on the localization of potential impacting source areas with a qualitative estimation of their contribution, but no chemical speciation was available during this study. On the other hand, the deterministic CTM provides a quantitative estimation of the contribution of the different emission regions as well as chemical speciation. However, it depends strongly on the quality of the input data, in particular, the comprehensiveness and acc uracy of the emission inventory which could be associated to significant uncertainties for some sources as well as the performance of the physico-chemical mechanisms. The good agreement obtained by both independent approaches increases the level of confide nce to the findings of this study. Also, we demonstrated the synergy of both strategies. Indeed, the trajectory-based receptor study gives at first useful qualitative information about the localization of potential source areas that help to set-up, in a second step, efficient CTM quantitative modeling studies.

The results obtained from this work showed also that background pollution levels were not explained only by the three regions studied using the CTM approach. Thus, it will be necessary to also estimate the contributions of other less distant anthropogenic emission areas, such as Paris or HdF itself in order to highlight a possible influence of nearest emission sources. Indeed, the results of CF and PSCF applied to short-range BTs over a distance of 500 km from the receptor sites highlighted the existence of impacting sub-areas such as Paris, cities characterized with high population densities in northern France and Southern Belgium as well as marked seasonal influence for some regions which could be linked to specific activities such as biomass burning, road traffic as well as agricultural activities. From the CTM approach, an influence of the Paris area is shown to affect the south of HdF whereas an influence from the Lille metropolitan area is shown also to have an impact on the north of HdF in agreement with the results obtained for the CF/PSCF modeling results. Consequently, it will be interesting to detail these sub-areas by CTM to better evaluate their contributions. Therefore, this work shows how r eceptor-based models can effectively help to orientate further CTM studies.  Two independent approaches, statistical sources-receptor and deterministic chemistry-transport models are used.

5.

 Both approaches showed consistency, highlighting the influence of several areas on the northern France pollution.

  the earth radius), A the receptor site and B the considered endpoint of the BT, S AB  the distance in km from the trajectory endpoint to the receptor site, A  and B  , the latitudes in radians of respectively A and B, and d  , the difference between the longitudes in radians of B and A. The trajectories that contained 75 % or more of their 72 endpoints within a distance of 500 km (i.e. at least 75 % of endpoints with S AB   500 km) from the receptor site were kept. The spatial grid resolution for the trajectory output maps (called "short-range maps") was increased to 0.1 o x 0.1 o . The dimensions of the short-range maps are in latitude 48 o N to 52.6 o N and in longitude from 5 o W to 8.25 o E. It should be noted that after the selection of short trajectories, around 3500

  Statistical indicators were used to evaluate the agreement between observations and modeled surface concentrations: the Pearson correlation coefficient (r) is calculated by dividing the covariance of the two variables (C obs and C mod ) by the product of their standard deviations. It reflects the linear relationship between the two variables. The data are considered moderatly correlated when r reaches 0.5 and strongly correlated when r gets close to 1. The root mean squared error (RMSE) is also calculated. It can be interpreted as the standard deviation of the unexplained variance. The lower the RMSE, the better the model fits the observations.

  2



  highlights that air masses coming from three different areas mainly impact HdF: the South-South-West, the North-West, and the North-East. These areas can then be seen as potential sources of pollution affecting the air quality over HdF. More specifically, two directions were explored in this work: the North-West and the North-East with two distance levels from HdF for this last direction, defining three areas displayed on Fig.1. The first one corresponds to the area including the British Isles (BI thereafter), the second one called the Near-East covers Benelux/Germany and the western part of the Czech Republic and the third one, called Far-East, covers Poland/Slovakia and the eastern part of the Czech-Republic. The study of this last area was prioritized instead of Paris region in accordance to results of the CF/PSCF approach (Sect. 3.1). To study the potential transport of pollutants from these 3 areas over HdF, 3 tests were performed in which all anthropogenic emissions over the specific area have been set to zero.Each of the three areas contributions (  C area ) was calculated as the difference between the concentration of PM 10 in the reference simulation (including all emitting areas), C ref , and the concentration of PM 10 in the simulation without anthropogenic emission in the selected area C to mesh in HdF and h to hourly time step. Maps of the contributions averaged on the full year or per season were generated. Averaged standard deviations were also spatial mean on the M meshes in HdF.Distinctions have also been made between contributions to background levels and contributions to pollution events. For this purpose, a threshold on PM 10 concentrations in the reference simulation was fixed to 25  g.m 3  . We reduced the official threshold of 50  g m 3  on daily concentrations since the modeled PM 10 concentrations are representative of the model meshgrid scale and may correspond to higher local concentrations. Sensitivity tests were performed: contributions of the 3 areas were calculated when modeled hourly PM 10 concentrations were above 10, 20, 30, 40, and 50  g m 3  in the reference simulation (Results not shown). Then the threshold was refined between 20 and 30  g m 3  and we found that 25  g m 3 seemed a good compromise. It was high enough to be representative of model pollution level and it gave a sufficient number of model exceedances regarding measurements. With a threshold of 25  g m 3  , around 5 % of the dataset was regarded as polluted.

  cities densely populated such as the Lille Metropolitan area as well as the capital city of Paris in accordance with emission inventory maps for Europe(Pay et al., 2012) (Fig. 3g, CF+CRA+WF+ST). It is worth noting that the averaged PM 10 concentrations associated with short-range BTs highlighting Lille and Paris as potential origins sources do not exceed 30  g m 3  (i.e. are rather limited) and represent solely around 50 % of the maxima observed for the long-range map. Also the short-range BT maps represent only a limited period of the year (around 10 %, as explained in section 2.2.4) when anticyclonic conditions are centered over the HdF region. Therefore the rather higher PM 10 averages obtained in the HdF region for ST maps in comparison to long and medium-range trajectory maps under similar constraints (e.g. when comparing Fig.3awith Fig.3eunder CRA constraint, or Fig.3bwith Fig.3funder CRA+WF constraints) are explained by these HdF-centered low-wind anticyclonic conditions which favour the accumulation of pollution over the HdF region and highlight the influence of local sources.

(

  including all emitting areas) indicated surface PM 10 hourly concentrations over HdF between 5 and 100  g m 3  for 2010. Figure 6 indicates that the annual means of PM 10 concentrations were in the range 5-14  g m 3  . These values at the model meshgrid scale are typically lower than measurements that represent local scale and are mostly near urbanized areas. The north of HdF, corresponding to the Lille Metropole, and the south border, near the Paris region, presented the highest annual concentrations (10-12  g m 3 ) but also the highest variability (  7  g m 3  ).

Figure 6

 6 Figure 6 also shows that the maximum concentrations around 100  g m 3  were reached near the

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: a) Map of the domain considered for the CHIMERE CTM simulations with colored areas

Figure 4 :

 4 Figure 4: Seasonal maps of the geographical location of potential source areas of PM 10 impacting

Figure 5 :Figure 6 :

 56 Figure 5: Seasonal maps of the geographical location of potential source areas of PM 10 impacting

Figure 7 :

 7 Figure 7: Map of seasonal averaged contributions of the 3 selected areas (BI, Near-East, Far-East)

Figure 8 :Figure 9 :

 89 Figure 8: Map of seasonal averaged contributions of the 3 tested areas (BI, Near-East, Far-East) to

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8

 12345678 Figure1

Figure 9

 9 Figure9

  meteorological fields used to drive the CHIMERE model were compared to European meteorological measurements available in 2010. Surface temperature, zonal and meridional wind speeds were evaluated with measurements from 43 French, English, Belgian and German airports gathered in the meteorological database of the University of Wyo ming (http://weather.uwyo.edu). AirBase, https://www.eea.europa.eu). Localizations of the air quality and meteorological measurement sites used in this study are displayed on Fig.2.

	Precipitations were evaluated against daily cumulated precipitations measured at 12 French and

German EMEP sites (http://ebas.nilu.no). In order to evaluate PM 10 modeled concentrations, measurements from 123 French, English, Belgian, Dutch and German air quality monitoring sites were used (

Table 1 :

 1 Monthly Limit Altitudes (MLA) above which backward-trajectories endpoints were removed (from von Engeln and Teixeira (2013)).

	Month	Altitude (m)
	January	1000
	February	1100
	March	1300
	April	1600
	May	1600
	June	2000
	July	1800
	August	1800
	September	1500
	October	1000
	November	800
	December	800

Table 2 :

 2 Specific characteristics of the CHIMERE simulation

		Area where
		anthropogenic
		emissions were not		Anthropogenic
	Simulations	taken into account	Domain area	emissions
		-	14 o W-26 o E 35 o N-59	EDGAR-HTAPV2
			o N at 0.2 o resolution	inventory at 0.1 o
	Reference			resolution
	BI	British Isles
		Belgium, Netherlands,
		Germany, Luxembourg,
		western part of Czech
	Near-East	Republic
		Poland, Slovakia,
		eastern part of Czech
	Far-East	Republic

Table 3 :

 3 WRF validation with statistics,on modeled and observed temperature (T), zonal (U) and meridional (V) wind speeds and precipitation (P). Modeled and observed means (  ), standard deviation ( ), correlation coefficient (r) and root mean square error (RMSE).

			mod			mod		obs			obs	r	RMSE
	T( o C)	-0.23  4.72 -0.46  4.41	0.83	2.75
	U(m/s)	0.49  5.11 0.02  3.33	0.78	3.30
	Winter
	V(m/s)	0.16  4.52 0.14  3.15	0.78	2.89
	P(mm/day) 3.63  5.93 2.81  5.38	0.56	5.36
	T( o C)	9.39  5.23 8.86  5.63	0.91	2.36
	U(m/s)	1.20  4.52 0.68  3.24	0.79	2.83
	Spring
	V(m/s)	-0.73  4.46 -0.44  3.10	0.79	2.81
	P(mm/day) 1.88  4.12 2.32  4.98	0.31	5.40
	T( o C)	17.3  6.18 18.2  5.07	0.87	3.14
	U(m/s)	1.81  3.77 1.20  2.73	0.72	2.72
	Summer
	V(m/s)	-0.58  3.57 -0.10  2.65	0.70	2.64
	P(mm/day) 1.29  4.42 2.85  7.14	0.35	7.14
	T( o C)	9.17  4.76 10.1  5.06	0.89	2.44
	U(m/s)	1.38  4.69 0.64  3.34	0.82	2.82
	Fall
	V(m/s)	0.95  4.15 0.79  2.91	0.76	2.72
	P(mm/day) 3.05  6.07 2.95  6.28	0.54	5.90

Table 4 :

 4 CHIMERE validation with statistics on modeled and observed PM 10 concentrations.Modeled and observed seasonal means (  ), standard deviation (  ), correlation coefficient (r), root mean square error (RMSE), mean fractional error (MFE) and mean fractional bias (MFB).

	Season		mod			obs		r	RMSE ( 	MFE (%) MFB (%) Perf. Goal Perf Crit.
			( 	 ( 		g/m 3 )
		mod		obs
		g/m 3 )	g/m 3 )
	Winter 14.1 	24.5 	0.49	19.6	58.0	-35.7	no	yes
			7.8		19.1
	Spring	10.7 	18.5 	0.44	12.3	57.4	-43.6	no	yes
			4.6		10.6
	Summer 8.0  3.2 15.6 	0.17	10.6	66.9	-58.1	no	yes
						7.2
	Fall	11.8 	15.7 	0.35	10.3	53.0	-19.5	no	yes
			6.2			9.7

Table 5 :

 5 Contributions of each selected area to modeled PM 10 surface concentrations by CTM (   ), over HdF, on annual basis and during pollution events (  25  g/m 3 ) ; corresponding temporal standard deviations ( ); annual mean of the spatial standard deviation over HdF (

	 )
	s

area C t 

Table 6 :

 6 Contributions of each selected area to modeled PM 10 surface concentrations by CTM ( The continental and regional sources of pollutants impacting northern France are identified and quantified.

		C 	area	 ), over HdF, on a seasonal basis.
					C 	area	
	Area	Winter (%)	Spring (%)	Summer (%)	Fall (%)
	BI	13.0		16.6	23.4	15.2
	Near-East	18.8		25.2	11.1	14.9
	Far-East	2.6		1.4	0.3	1.0

Acknowledgements

The authors thank the CPER Climibio (Contrat plan État-Région), funded by the Région Hauts-de-France, the DREAL Hauts-de-France and the Ministère de l'Enseignement Supérieur et de la Recherche; The CaPPA project (Chemical and Physical Properties of the Atmosphere) funded by the French National Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir) under contract ANR-11-LABX-0005-01 and by the Regional Council, Hauts-de-France, and the European Funds for Regional Economic Development (FEDER); the Institut Pierre-Simon Laplace, Ecole Polytechnique, INERIS, CNRS, for developing the CHIMERE Chemistry Transport Model; Tiphaine Delaunay and Sandra Vermeesch of Atmo Hauts-de-France for providing pollutants data; the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model; the European Environment Agency, the University of Wyoming and the European Monitoring and Evaluation Program (EMEP) for meteorological and air quality data available in their databases and the research institutes which provide these data.