# Investigation of the geographical origins of PM10 based on long, medium and short-range air mass back-trajectories impacting Northern France during the period 2009–2013

Antoine Waked, Aude Bourin, Vincent Michoud, Esperanza Perdrix, Laurent Alleman, Stéphane Sauvage, Tiphaine Delaunay, Sandra Vermeesch, Jean-Eudes Petit, Véronique Riffault

► To cite this version:

Antoine Waked, Aude Bourin, Vincent Michoud, Esperanza Perdrix, Laurent Alleman, et al. Investigation of the geographical origins of PM10 based on long, medium and short-range air mass back-trajectories impacting Northern France during the period 2009–2013. Atmospheric Environment, 2018, 193, pp.143-152. 10.1016/j.atmosenv.2018.08.015. hal-02552756

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Investigation of the geographical origins of PM₁₀ based on long, medium and short-range air mass back-trajectories impacting Northern France during the period 2009-2013.

Antoine Waked¹, Aude Bourin¹, Vincent Michoud¹*, Esperanza Perdrix¹‡, Laurent Y. Alleman¹,
Stéphane Sauvage¹, Tiphaine Delaunay², Sandra Vermeech², Jean-Eudes Petit³**, and
Véronique Riffault¹

1. IMT Lille Douai, Univ. Lille, SAGE - Département Sciences de l'Atmosphère et Génie de l'Environnement, 59000 Lille, France

2. Atmo Hauts-de-France, 55 Place Rihour, 59044 Lille Cedex, France

3. Atmo Grand-Est, 5 rue de Madrid, 67300 Schiltigheim, France

*Now at Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA), UMR CNRS 7583, Université Paris-Est Créteil and Université Paris-Diderot, Institut Pierre-Simon Laplace (IPSL), Créteil, France

**Now at Laboratoire des Sciences du Climat et de l'Environnement, UMR CNRS 8212, CEA/Orme des Merisiers, 91191 Gif-sur-Yvette, France.

‡Corresponding author:

E. Perdrix: esperanza.perdrix@imt-lille-douai.fr
1 Abstract

Trajectory-based statistical models (TMs) are widely used to locate source areas responsible for atmospheric pollution at receptor sites. In order to study the influence of long, medium and short-range back-trajectories (BTs) on geographical sources location, an original approach was applied to daily PM₁₀ concentrations measured for 5 years (2009-2013) at 12 receptor sites in the Hauts-de-France (HdF) region bounded on the south by the Paris megacity and on the north by the Benelux countries. The methodology used was based on two complementary TMs, Concentration Field (CF) and Potential Source Contribution Function (PSCF), and included several meaningful constraints in order to improve the spatial representativeness and the statistical significance of the potential source maps. The selection of BTs depending on the occurrence of scavenging precipitations (> 1 mm h⁻¹) during the transport of air masses and altitude limitations with respect to the planetary boundary layer height led to more realistic maps. The variability of the density of BT endpoints was accounted for by a weighting function which highlighted medium-distant yet recurring (e.g. Benelux and West of Germany) potential sources of PM₁₀. Investigating the effect of short-range BTs (75% of endpoints within a radius of 500 km from the receptor sites) pointed out at nearby large conurbations (Lille and Paris areas). Comparing our potential source maps with European PM₁₀ emission inventory maps as well as PM₁₀ measurements at European background monitoring stations for similar periods confirmed our results for Central Europe, the Benelux and the East of Germany. This study showed how the combination of several receptor sites enables the determination of potential source areas impacting a whole region. It pointed out also that the fine-tuned parametrization of the CF and PSCF TMs may considerably help improving the knowledge on geographical location of sources related to long, medium and short-range BTs and could be easily implemented for other regions of interest.

Keywords: Concentration Field, Potential Source Contribution Function, Europe, trajectory analysis, HYSPLIT, particulate matter, back-trajectories.

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# 1 Introduction

Particulate matter (PM) has been the subject of many studies in the last decades due to its impact on climate (e.g. radiative forcing and cloud condensation nuclei), visibility and human health (Pope et al., 2009; Hallquist et al., 2009; Jimenez et al., 2009). For instance, PM₁₀ (PM with an aerodynamic diameter less than 10 µm) is small enough to penetrate the thoracic region of the respiratory system and can be responsible for severe health impacts such as respiratory and cardiovascular morbidity and mortality (Pope et al., 2002). According to the World Health Organization (WHO), a reduction of annual PM₁₀ concentrations from 70 to 20 µg m⁻³ would result in a reduction in mortality due to air pollution by 15% (WHO, 2015). PM₁₀ is made of primary and secondary pollutants, which for the latter sources are formed within the atmosphere through chemical and photochemical reactions from gaseous precursors (Claeys et al., 2004). These secondary pollutants can be transported over long distances (El-Haddad et al., 2013). For instance, source apportionment studies of PM₁₀ conducted by means of Positive Matrix Factorization (PMF) for North-Western Europe showed an important contribution (30-50 %) from secondary sources (e.g. Waked et al., 2014; Belis et al., 2013). Overall, the measured values of PM₁₀ at any receptor site are due to both local and distant sources. In order to effectively tackle particulate pollution, identifying and apportioning the geographical origins of the emission sources are recognized as approaches complementary to source apportionment studies. The geographical origin of sources can be assessed by means of either statistical trajectory-based methodologies (TMs) (Polissar et al., 2001b) or deterministic chemistry-transport models (CTMs) (Seigneur, 2001). CTMs assimilate both atmospheric reactivity and deposition and use source emission factors as input data in order to model air pollution, but depend strongly on the quality of these input data. TMs such as Potential Source Contribution Function (PSCF; Hopke et al., 1995) and Concentration Field (CF; Seibert et al., 1994) build on the ability of air pollutants to be transported over large geographical scales (Polissar et al., 2001b) and rely on simplified hypotheses that do not take into account their chemical reactivity and deposition during transport. However they have the advantage of using real measurement data (Brioude et al., 2013) which combine pollutant atmospheric concentrations with air mass back-trajectories (BTs) and can take into account the residence time of air masses within each cell of a gridded territory. Potential source areas can be inferred from further analysis of the spatial distribution of the residence time of air masses associated with high pollutant concentrations measured at the receptor sites (Fleming et al., 2012; Petit et al., 2017). These methodologies have been largely used worldwide for locating sources impacting a single receptor site (e.g. Waked et al., 2014; Bressi et al., 2014; Li et al., 2012) or impacting a whole region with multiple receptor sites (Xie and Berkowitz, 2007; Hsu et al., 2003; Zheng and Hopke 1989). This latter approach will be referred hereafter as "multisite".

Frequent PM₁₀ pollution episodes have been recorded in North-Western Europe over the years (EEA, 2012) notably in the Hauts-de-France (HdF) region located in Northern France. In its ambient air quality directive (2008/EC/50), the European Union (EU) has set an annual
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limit value for PM₁₀ of 40 µg m⁻³ and a daily limit value of 50 µg m⁻³ which must not be exceeded more than 35 days per year. In case of pollution events particularly, it is of tremendous importance for policy-makers that short, medium and long-range transport contributions to the observed concentrations can be assessed in order to take suitable and more efficient measures to improve air quality.

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To address this issue, the geographical origins of sources impacting the daily PM₁₀ concentrations in Northern France were assessed over a 5-year-period (2009-2013) by using PM₁₀ daily concentrations measured by the regional air quality monitoring network at Hauts-de-France at 12 sampling sites covering the whole area of the HdF region. Multi-site CF and PSCF analyses were performed on both annual and seasonal bases applying different constraints to investigate the influence of precipitation, air mass dilution and to identify the transport range of PM₁₀. Furthermore, the obtained PM₁₀ potential source maps were compared to European emission inventory maps and observations from background European monitoring sites to highlight similarities as well as discrepancies.

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# 2 Methodology

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## 2.1 Description of the sites

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The studied HdF region is located between the 49th and 51th northern parallels and covers a relatively flat area of 31 813 km² (equivalent to the size of Belgium) in the North of France (Figure 1). With a relatively high population density of 189 inhabitants per km², the region is influenced by many anthropogenic sources (e.g. road traffic, residential sector, agriculture and industries). For instance, many petrochemical, metallurgical and non-metallurgical industries are located along the HdF coast and distributed across the region (Waked et al., 2014; Alleman et al., 2010; Roukos et al., 2009; Crenn et al., 2017; Xiang et al., 2012). Boarding the Strait of Dover, this region can be influenced by maritime transport as well as natural oceanic sea spray episodes (Kfoury et al., 2016).
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<figure><img src="image_2.png" /><figcaption>**Figure 1.** Location of the Hauts-de-France region (left) and the 12 receptor sites (right) represented by circles on the map.</figcaption></figure>

Sampling sites abbreviated as: AR2: Saint-Laurent-Blangy; ARR: Arrest; BE2: Béthune Stade; CA8: Calais Berthelot; CRE: Creil; DO1: Douai Theuriet; MN3: Tourcoing; NOG: Nogent-sur-Oise; PHI: Saint-Quentin Stade; RIE: Rieux; SAL: Salouël; VA9: Valenciennes Acacias. Additional emission hot spots mentioned in the text are indicated by blue stars, namely Lille, Ghent, Brussels, Antwerp, the Avesnois countryside, Dunkirk, Paris, Le Havre, the Rhine-Delta and the Ruhr area.

The region is under the influence of a temperate oceanic climate with an average annual temperature of around 10°C and precipitations in the range of 600-800 mm yr⁻¹. The insolation ranges from 1600 to 1700 hours per year whereas winds are prevailing mostly from the south-west and north-east quadrants. The locations of the 12 receptor sites where the daily PM₁₀ concentrations used in this study were measured are presented in Figure 1. They were selected to ensure a good coverage of the studied area, and present various typologies (urban, suburban, and rural) to span a large set of potential sources without focusing on specific local emissions (e.g. traffic and specific industrial sources). Detailed geographical coordinates of the 12 sampling sites are summarized in Table S1 in the supplementary material.

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## 2.2 PM₁₀ measurements

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Daily PM₁₀ mass concentrations have been routinely measured by the regional French air quality monitoring network atmo Hauts-de-France from 2009 to 2013 for all the sampling sites by means of various automated equivalent methods depending on the location of the sampling sites (e.g. TEOM at 50°C to which a correction for the semi-volatile fraction was applied; TEOM-FDMS and Beta-Gauge with an RST system to take into account the semi-volatile fraction). The uncertainty in these measurement techniques ranges between 8 and 20% (Verhlac, 2017; Chueinta and Hopke, 2001). Quality control procedures led to missing
1 values for all the sampling sites ranging from 2 to 11%, except for the Rieux (RIE) sampling
2 site for which missing values reached 22% due to interrupted measurements during 2010.

## 2.3 Computed BTs

72-hour BTs were calculated every 6 hours by means of the HYSPLIT v4.1 model (Stein et al., 2015) from January 1st 2009 to December 31st 2013. The 5-year period (2009-2013) enabled us to include an inter-annual variability in terms of meteorology which is important when using parameters such as BTs, precipitations as well as Planetary Boundary Layer Height (PBLH). Weekly Global Data Assimilation System (GDAS) files with a 1 degree resolution were used. An ending altitude of 500 m above ground level was chosen to reduce the effect of surface friction and to represent winds in the boundary layer. This ending altitude was satisfactorily used in many sources studies (Ara Begum et al., 2005; Gao et al., 1993; Hafner and Hites, 2003). Using the input data (i.e. daily PM₁₀ mass concentrations and 72-hour computed BTs), statistical CF and PSCF calculations were then performed with ZeFir, a software package written for Igor Pro (Wavemetrics, USA) for the evaluation of the geographical origins of atmospheric pollutants (Petit et al., 2017b), with a temporal resolution of 6 hours and a spatial resolution of 0.5°.

## 2.4 CF approach

The CF approach relies on the analysis of the air mass residence times in order to identify source areas that could contribute to PM₁₀ concentrations measured at receptor sites. The underlying assumption is that the more an air mass remains above an emitting area, the more pollutants it transports (theoretically without any loss) to the receptor sites. The CF model therefore combines air mass BTs with PM₁₀ concentrations by calculating a geometric mean weighted by the residence time of air masses (Eq. 1):

$$ \log \bar{C}_{ij} = \frac{\sum_{l=1}^{M} n_{ijl} \times \log C_l}{\sum_{l=1}^{M} n_{ijl}} \quad (\text{Eq. 1}) $$

where $\bar{C}_{ij}$ is the estimated mean concentration of PM₁₀ in each ijth grid cell of longitude i and latitude j, $C_l$ the concentration of PM₁₀ measured at the receptor site on the arrival of a given BT l, M the total number of BTs and $n_{ijl}$ the residence time of air mass BT l in the grid cell ij.

The outcome is a map (field) showing mean PM₁₀ concentrations ($\bar{C}_{ij}$) expressed in µg m⁻³. A high (respectively low) value of $\bar{C}_{ij}$ indicates that on average air masses passing over the ijth grid cell lead to high (respectively low) PM₁₀ concentration measured at the receptor sites, suggesting the possible presence of a strongly (respectively weakly) impacting source at position (i, j).

## 2.5 PSCF approach
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PSCF calculates conditional probabilities (scaled from 0 to 1, Eq. 2) describing the probable geographical source locations where the measured daily PM₁₀ concentrations at receptor sites exceed a specific threshold concentration:

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$$PSCF_{ij} = \frac{m_{ij}}{n_{ij}} \quad (\text{Eq. 2})$$

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where nᵢⱼ is the total number of BT endpoints passing over a grid cell ij, and mᵢⱼ is the number of BT endpoints passing over the same grid cell and having an associated measured pollutant concentration at the receptor site higher than a specific threshold. It was used here for high particulate pollution events therefore a threshold of 40 µg m⁻³ was chosen (Cheng et al., 1993; Li et al., 2012, Pongkiatkul and Kim Oanh, 2007; Zeng and Hopke, 1989). The outcome is a map of probabilities. A high (respectively low) probability suggests the existence (respectively absence) of a source area leading to the highest PM₁₀ concentrations measured at the receptor site.

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## 2.6 Constrained analyses

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### 2.6.1 Accounting for depletion processes

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In both CF and PSCF approaches, the effect of depletion processes, such as wet deposition by heavy precipitations or dilution in the free troposphere for air mass altitudes above the PBLH, is generally neglected although they could hamper the ability of models to highlight properly some source areas. In this study, wet deposition was taken into account using precipitation data from the BT files (calculated with the HYSPLIT model). Similarly to Kim et al. (2014), back-trajectories were cut for endpoints preceding rainfalls with a rate higher than 1 mm h⁻¹. To minimize the effect of dilution, endpoints for which altitude was in the free troposphere were also removed along with all the preceding endpoints. For this purpose, we used monthly averages for the limit altitude that separates the free troposphere from the boundary layer. Note that the use of monthly averages for the limit altitude was chosen instead of the tri-hourly mixing layer heights calculated by the HYSPLIT model, because the latter values would have led to an excessive removal of endpoints at nights. Indeed, a nocturnal residual boundary layer possibly loaded with nighttime pollution generally forms at nights between the boundary and the free layers (Michoud et al., 2015). Since PBLH measurements were not available for the HdF region, we used monthly limit altitudes (MLA) from radiometric and virtual temperature-based measurements obtained by Von Engeln and Teixeira (2013) at a comparable site in term of latitude between 2000 and 2009 in Germany (Table S2). These MLA were provided at local noon time when the PBLH was at its maximum. Thus all endpoints above these MLA are believed to be in the free troposphere regardless of the time of the day. The comparison between our set of MLA values (retrieved from Von Engeln and Teixeira, 2013) and the monthly PBLH averages measured by Pal and Haeffelin (2015) in Paris (around 200 km south from the center of the HdF region at a comparable longitude) from 2008 to 2013 shows that both are in good agreement with average relative differences inferior than 10%. Therefore, the use of these MLA values is
believed to be appropriate for this study. In the following sections, the results obtained without constraints are referred as “Base Run” (BR) whereas those for which both constraints were applied are noted “Cut for Rain and Altitude” (CRA).

## 2.6.2 Accounting for the spatial density of BT endpoints

The higher the number of endpoints in a grid cell, the better the statistical significance of the model. Therefore a weighting function (WF) which downweights grid cells with less endpoints was applied (Eq. 3):

$$8 \quad \begin{cases} 1 \times C_{ij} \text{ if } \log(n_{ij} + 1) \ge 0.85 \max \log(n_{ij} + 1) \\ 0.725 \times C_{ij} \text{ if } 0.6 \max \log(n_{ij} + 1) \le \log(n_{ij} + 1) < 0.85 \max \log(n_{ij} + 1) \\ 0.475 \times C_{ij} \text{ if } 0.35 \max \log(n_{ij} + 1) \le \log(n_{ij} + 1) < 0.6 \max \log(n_{ij} + 1) \\ 0.175 \times C_{ij} \text{ if } \log(n_{ij} + 1) < 0.35 \max \log(n_{ij} + 1) \end{cases} \quad (\text{Eq. 3})$$

where the different WF(nij) values are based on previous studies (Petit et al., 2017b; Waked et al., 2014; Polissar et al., 2001; Bressi et al., 2014). The resulting maps are referred as (CRA+WF) as they combine both constraints.

## 2.6.3 Restriction to short-range BTs

To perceive more clearly the impact of regional sources and their geographical origins, only BTs containing at least 75% of their 72 endpoints (BTs temporal resolution of 72 hours) within a distance of 500 km from the receptor site (corresponding to twice the maximum distance within the region) were considered, in a second series of model runs, using Eq. 4:

$$18 \quad S_{A-B} = 6378 \times \cos^{-1}(\sin \alpha_A \sin \alpha_B + \cos \alpha_A \cos \alpha_B \cos d\beta) \quad (\text{Eq. 4})$$

with A the receptor site and B a given BT endpoint; S<sub>A-B</sub> the distance in km from the BT endpoint to the receptor site; 6378 the Earth radius in km; α<sub>A</sub> and α<sub>B</sub>, the latitudes (in radians) of points A and B, respectively, and dβ the difference between the longitudes of B and A (in radians). BTs for which S<sub>A-B</sub> ≤ 500 for at least 75% of endpoints were kept. These maps are referred as ST (short-range). Accordingly, the spatial resolution of the geographical sources maps was increased to 0.1 degree.

## 2.7 Uncertainty evaluation

A qualitative uncertainty assessment was performed for the output maps based on sensitivity tests taking into account the precision of the input data. Uncertainty in measured daily values of PM<sub>10</sub> was evaluated on the order of 8-20 % (Verlhac, 2017; Chuenta and Hopke, 2001). Uncertainty in meteorological GDAS data used for computing BTs was evaluated by comparing those with spatial resolutions of 1° and 0.5° for a few months in 2010 (August to December). An absolute percentage difference of 2% was found. In addition, the influence of the spatial resolution of the gridded domain for both CF and PSCF maps was
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also tested considering grid cells of 1.0°, 0.5° and 0.1° in size. It led to a maximum absolute percentage difference of 24%. Overall, the combined uncertainty, calculated as the root sum of squares, would be in the range of 20-30%.

## 2.8 Limitation of the CF/PSCF approach

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Contrary to a source apportionment approach, this methodology does not give any indication regarding the specific sources of a pollutant but only on their possible location. Furthermore, even though this kind of approach is more frequently applied to remote sites to highlight long and medium-range transport, it was used in many studies for non-remote sites (e.g. urban, suburban and rural; Waked et al., 2014; Petit et al., 2017; Riuttanen et al., 2013; Oliveira 2017). In this study, it is applied to urban, suburban and rural sites where the average contribution of local sources to daily PM₁₀ concentrations was estimated to be in the range of 30-40% (Oliveira, 2017). Consequently, the information on source location provided by the CF/PSCF approach is likely associated to 60 to 70% of the PM₁₀ concentrations. In the context of our study, the term “short-range” defined in section 2.6.3 from the dimension of the HdF region corresponds to a few hundreds of kilometers (up to 500 km), thus being more regional compared to local emission sources (more highlighted with traffic sampling sites) which cannot be very well characterized using this approach. Similarly, the term “medium-range” corresponds to twice the distance of the short-range (roughly between 500 to 1000 km) being more interregional and the term “long-range” to twice the distance of the medium-range (roughly between one to a few thousand kilometers) being more continental. These spatial scales are thus adapted to the case of the HdF region but could be modified in other cases.

# 3 Results and discussion

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## 3.1 PM₁₀ levels in the HdF region

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Measured daily PM₁₀ values averaged over the 2009-2013 period ranged from 24.7 ± 14.9 to 29.7 ± 16.2 µg m⁻³ for suburban and urban sampling sites, respectively. The lowest mean concentrations were found at the rural site of Arrest (21.5 ± 13.1 µg m⁻³). The urban sites under industrial influence of Rieux (RIE) and Valenciennes (VA9), presented mean concentrations of 26.5 ± 14.8 µg m⁻³ and 27.5 ± 14.8 µg m⁻³, respectively, within the range of values recorded for urban/suburban sampling sites. The PM₁₀ average annual values recorded for all the sampling stations from 2009 to 2013, ranging from 24.8 ±14.1 µg m⁻³ to 29.2 ± 14.8 µg m⁻³, exceeded the WHO annual guideline value (WHO, 2015) but were below the EU annual limit value (Air quality directive 2008/EC/50). However, 8 out of the 12 sampling sites showed exceedances of the EU daily limit value of 50 µg m⁻³ for more than 35 days per year (Figure 2a) during the studied period (2009-2013).
<figure><img src="image_3.png" /></figure>

**Figure 2.** (a) Number of days for which PM₁₀ EU daily limit value is exceeded for each sampling site from 2009 to 2013 (the red horizontal line refers to the limit of 35 days per year). (b) Average PM₁₀ value in µg m⁻³ for all the sampling sites for the period 2009-2013 on a seasonal basis.

A median value of 24 µg m⁻³ was reported for North Western Europe for the 1997-2007 period (Putaud et al., 2010). At other urban/suburban sites in Europe, mean levels of 13 µg m⁻³ in Toulouse, France (Calvo et al., 2008), 19 µg m⁻³ in Erfurt, Germany (Yue et al., 2008) and 22 µg m⁻³ in Vienna, Austria (Astel, 2010) were significantly lower than the mean value of 26.5 ± 14.7 µg m⁻³ reported for the 12 sites, highlighting that the HdF region is subjected to high concentrations of PM₁₀.

Seasonal variations of PM₁₀ (Figure 2b) averaged over the studied period showed, as expected, the highest values across all sampling sites in spring (27-36 µg m⁻³) and winter (23-33 µg m⁻³) and the lowest ones during summer (18-24 µg m⁻³), confirming results found in previous studies conducted in the same region (e.g. Waked et al., 2014; Oliveira, 2017). This seasonal pattern is mostly related to the intense activities of sources such as residential heating in winter (Favez et al., 2012), photochemical episodes and increased agricultural activities during the spring season (Dall'Osto et al., 2009) and more efficient photochemical processes as well as a higher PBL height in summer.

## 3.2 Non-constrained multi-site CF and PSCF results

In this section, potential source maps for the whole region (Figure 3a and Figure 3b) were created by applying both CF and PSCF multi-site models to the 12 receptor sites, without any constraint (referred as base run, BR). Multi-site mapping with a higher number of sites will improve the statistical robustness of the results thanks to the increased number of BTs (Xie and Berkowitz, 2007). The results obtained for CF multi-site maps (Figure 3a) showed Central Europe (e.g. Hungary, Romania, Bosnia and Herzegovina, Serbia) as the main potential source areas impacting the HdF region with significant contributions for the measured PM₁₀ concentrations higher than 40 µg m⁻³. Central Europe has been identified as a potential high emitting area in many studies conducted previously (e.g. Riuttanen et al., 2013; Salvador et al., 2007; Petit et al., 2017a) due to the presence of intense industrial and residential activities (Crippa et al., 2016). Potential source areas with the lowest concentrations are associated with western flows where air masses passed over the Atlantic Ocean and only a few urban areas before arriving to the sampling sites. To identify the
geographical origins linked to high PM₁₀ concentrations, the PSCF approach was applied for the HdF region for the period 2009-2013 using a threshold of 40 µg m⁻³ for PM₁₀ data. The results obtained showed high probability (Figure 3b) for air masses coming from regions located in Poland, Belarus, Slovakia, Hungary and the Czech Republic in accordance with PM₁₀ measured concentrations showing these areas as potential hotspots (EMEP, 2017). For instance, it was shown that countries such as Poland, which is characterized by intense emissions from the industrial and residential sectors, play an important role as potential source areas during high pollution episodes (PM₁₀ > 40 µg m⁻³). However, when all PM₁₀ concentrations are considered (Figure 3a), these countries are not the highest potential source contributors due to the fact that BTs frequently impacting the HdF region do not come from these regions. On the other hand, a significant influence was also reported from the Adriatic region as well as Serbia and Croatia (Karaca et al., 2009). It could be related to primary emission sources (e.g. maritime transport and urbanized areas) as well as secondary sources due to long-range transport. However, these regions are associated with low trajectory densities (Figure S1) and are not frequently impacting the HdF region. At the regional scale, the highest potential source areas are located in the North-Eastern part where air masses crossing over Belgium, the Netherlands and the North Sea are associated with intense anthropogenic activities (e.g. industries and shipping emissions; Pay et al., 2012).

## 3.3 Constrained approach for the optimization of geographical origins of PM₁₀

This section presents the additional constraints applied to provide more realistic outputs (as described in section 2.6).

### 3.3.1 Effect of precipitation events and altitude

The constraints added for precipitation and altitude (thresholds for rain events above 1 mm hour⁻¹ and altitude above the MLA) resulted in the shortening of the average length of BTs, from 72 to 45 hours (38 % decrease in duration). Constrained maps (CRA; Figure 3c and 3d) show consistent results when compared to BR ones (Figure 3a and 3b) with a Pearson correlation of r = 0.8 between both. For instance, the same potential sources areas for CF and PSCF (e.g. Central Europe) are highlighted for BR and CRA. Nevertheless, the CRA spatial coverage is smaller due to the reduction of the BT lengths, as can be seen for the Po Valley and its surrounding region in Northern Italy where lower contributions are related to the influence of the Alps barrier (Figure 3c) (Isotta et al., 2014). Furthermore, for PSCF including CRA (Figure 3d), BTs show higher conditional probability contributions from Germany, Poland and Lithuania and lower ones from Belarus, underlining the significant influence of precipitations and/or mountain barriers (e.g. the Carpathians Mountains). Consequently, the results obtained with CRA constraints seem more realistic concerning potential sources impacting the HdF region.

### 3.3.2 Effect of the weighting function
To perceive the impact of BTs reaching the HdF region with more occurrences, the WF constraint (as described in section 2.6) was applied in addition to the previous constraints (CRA+WF). At the European scale, CRA+WF results point out at major potential source areas frequently impacting the HdF region at a medium-range scale, mostly coming from the Benelux countries and Germany, with moderate contributions to PM₁₀ at the receptor sites in the range of 25-35 µg m⁻³ (Figure 3e). This area as well as the Ruhr region in western Germany are known as hot spot emission sources, in particular near the Rhine and Ruhr rivers (Petit et al., 2017a) where many industrial and shipping activities are located (Gladtke, 1998). PSCF including CRA+WF maps (Figure 3f) exhibit a significant probability of 30-35% from Germany, the Czech-Republic and western Poland as potential source locations of high levels of PM₁₀. This result is in agreement with European emissions maps (Pay et al., 2012) which show high levels for particulate pollutants and precursors (e.g. NH₃, SO₄²⁻, PM₁₀). Therefore, applying the WF constraint allowed for locating potential source areas frequently impacting the HdF region at a medium-range scale.
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Multi-site potential source maps of measured PM₁₀ in the HdF region for 2009-2013 including (from top to bottom) base run maps (BR); constrained maps with cut-offs for BTs when rain events exceeded 1 mm h⁻¹ or when air mass altitude was above the monthly average of the PBLH (CRA); constrained maps with the use of an additional weighting function (CRA+WF); constrained maps for short-trajectories selected within a radius of 500 km from the receptor sites without (ST(CRA)) and with the use of a weighting function (ST(CRA+WF)). Different scales are used for the CF model (left; contribution in µg m⁻³) and the PSCF one (right; conditional probabilities scale from 0 to 100%). It should be noted that the upper bound of the scales is always lowered when using the weighting function (WF) in order to better perceive the impact of BTs frequently reaching the HdF region.</figcaption></figure>
### 1.3.3 Effect of using short-range BTs

Finally, only short BTs (ST) within a radius of 500 km from the receptor sites were considered (as described in section 2.6) to investigate the impact of potential sources related to short-range BTs. In addition, a finer spatial resolution of 0.1° was used for CF and PSCF models. The maps obtained for a long period (2009-2013) and 12 receptor sites were plotted with more than 8000 BTs (corresponding to about 10% of the total BTs used for the base run).

Both CF and PSCF maps obtained for the HdF sub-domain (Figure 3g and 3h) showed that when WF is not used, the highest potential source areas ($PM_{10}$ contributions around $40 \cdot 50 \ \mu g \ m^{-3}$ and conditional probabilities higher than 80%) point out at western Germany and the Ruhr valley. The region of the Rhine delta, located near the Strait of Dover and the coast of the Netherlands, and characterized by intense shipping activities, was also found to be a potential source area with $PM_{10}$ contributions in the range of $40-50 \ \mu g \ m^{-3}$ for CF and conditional probabilities higher than 50% for PSCF. On an even more regional scale, a significant contribution is also found from Northern France itself, although not related to $PM_{10}$ concentrations above $40 \ \mu g \ m^{-3}$ (conditional probabilities lower than 30%). When WF is used (Figure 3i and 3j), the contributions of local sources are even more emphasized. For instance, the Lille area and its surroundings on the French-Belgian border including the cities of Brussels, Ghent and Antwerp (Figure 1) up to the Rhine delta showed the highest contributions and conditional probabilities in the range of 40-50% (Figure 3j). Furthermore, an influence from the Paris region in the south is clearly evidenced with conditional probabilities in the range of 20-30% and recorded $PM_{10}$ contributions above $25 \ \mu g \ m^{-3}$. Potential sources were at their maximum for areas characterized by high population densities in the range of 1500-7000 inhab. $km^{-2}$ (e.g. the Lille metropolitan area), suggesting an important contribution from residential, traffic and industrial sectors.

### 3.4 Seasonal variations of the constrained maps

Constrained maps for the different seasons (winter: December, January and February; spring: March, April and May; summer: June, July and August; fall: September, October and November) are presented in Figure 4 for both multi-site CF and PSCF for the period 2009-2013. Results are presented for spatial resolutions of 0.5° (respectively 0.1°) for the investigation of long and medium-range (resp. short-range) BTs. CF results for long and medium-range seasonal variation displayed the highest concentrations in winter and spring and the lowest concentrations in summer. The potential source areas showed as expected Central Europe (e.g. Austria, Hungary, Romania, Croatia and Slovenia) as the highest contributing areas especially during the spring and winter seasons. When using the PSCF model, conditional probabilities higher than 80% originate also from Central Europe, Germany, the Czech Republic, Poland and Ukraine for the spring season. As reported by Waked et al (2014), secondary sources of $PM_{10}$ such as nitrate-rich and sulfate-rich sources are at their maximum during the spring season. This observed seasonal pattern reaching
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maxima in the spring season is a consequence of both a low photochemical activity and a higher occurrence of North-Easterly winds in the North of France. In winter, potential source areas tend to be closer to the HdF region with Germany and Belgium as the main potential source areas. In full agreement with European emission maps (ECCAD, 2017, http://eccad.aeris-data.fr/), combustion sources which are at their maximum during the winter season show maximum source areas in Benelux and eastern Poland.

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Applying WF on the same domain showed potential source maps from the Benelux and Germany in particular for winter and spring with contributions exceeding 30 µg m⁻³. The main potential source areas in spring were located slightly further away than in winter, indicating more aged air masses and a higher influence of long-range transport. Results of the PSCF analysis showed also Benelux and Germany as the highest potential source areas with conditional probabilities of more than 50%. On a more regional scale, the importance of the HdF region itself as well as the region of Paris, in particular during winter is also pointed out. For PSCF maps, the HdF region during winter could contribute to more than 50% indicating that one in every two BTs passing over emission areas of the HdF region contributes to high concentrations above 40 µg m⁻³. However, its contribution in spring does not exceed 20% in terms of conditional probabilities. The result highlights the importance of primary sources in the HdF region in winter where combustion processes for residential heating are at their maximum. The results are in accordance with previous studies conducted in Northern France where the contribution of biomass burning to total PM₁₀ in winter increased by at least a factor of 2 in comparison to the annual average value (Waked et al., 2014).

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Investigating short trajectories (ST(CRA) maps) allowed identifying several highest potential source areas such as the Rhine-Ruhr region during winter, spring and fall for both CF and PSCF maps. During winter and spring, an impact from the industrial harbors of Dunkirk near the Strait of Dover (Bressi et al., 2014) and Le Havre (Figure 1) cannot be ruled out. Additionally a significant PM₁₀ contribution is also shown from the Channel during winter possibly related to marine aerosols in the range of 15-30% to total PM₁₀ as already mentioned in the literature due to winter storms (Bressi et al., 2014; (Waked et al., 2014)). On a more regional scale, and applying WF (ST(CRA+WF) maps), major potential source areas appear located near the cities of Lille and Paris for almost all the seasons as well as areas near large cities in Belgium such as Brussels, Ghent and Antwerp with the highest PM₁₀ contributions and conditional probabilities for winter and spring. On the other hand, the Avesnois countryside (Figure 1) is shown as a hot spot in winter with conditional probabilities higher than 50%, likely resulting from biomass burning used for residential heating (DREAL, 2014). These results are in line with the regional emission inventory of the HdF region showing the highest values of PM₁₀ emissions near the cities of Lille (LIL in Figure1), Valenciennes (VA9) and Dunkirk on the west coast (Atmo HDF, 2017).

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<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

**Figure 4.** Seasonal multi-site potential source maps for the HdF region of 2009-2013 for long, medium and short-range BTs using both CF and PSCF methodologies.

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1 **4 Comparison to emission inventories and measurements from monitoring stations**

Potential source maps for CF and PSCF were compared to the regional emission inventory for Europe (ECCAD, 2017; 2005-2010 period) (Figure 5, left). Emission inventories are used to assess the effectiveness of reduction policies and strategies as well as input data in CTMs applied for forecasting. These inventories could be associated to significant uncertainties and need to be compared against direct atmospheric measurements (Figure 5, right) to pinpoint possible missing or misestimated source emissions that could impact a given area. CF and PSCF models which use atmospheric measurements as input data and assimilated meteorological parameters represent useful tools that make possible the identification of hotspot areas possibly contributing to high concentration levels of pollutants observed at receptor/sampling sites. It is worth noting the non-linearity between sources and receptor sites since PM₁₀ are partly secondary and thus the comparison cannot be quantitative. Nevertheless the comparison could outline some agreements or discrepancies for some hot spot areas between CF/PSCF maps and emission inventories.

The EMEP emission map (Figure 5, left) showed higher PM₁₀ released from urbanized areas of North-Western and Central Europe (e.g. Benelux, Poland, western Germany, Hungary, the Balkans and Paris). The outputs from CF and PSCF for long, medium and short-range BTs highlighted similar areas for PM₁₀ emissions impacting the HdF region with the exception of Croatia and the Adriatic Sea which do not correspond to identified emitting areas but are appearing on the CF/PSCF maps, in particular during spring as already found in some previous studies (e.g. Karaca et al., 2009). Conversely, emissions from Southern Europe (e.g. from the Lisbon area, Portugal), Ukraine, Romania and Bulgaria are more visible in the EMEP emission map than in the CF/PSCF ones, suggesting that these known source areas do not impact the HdF region. These discrepancies could be related to transformation processes, natural obstacles (e.g. the Carpathian Mountains), spatial uncertainties for BTs or/and unfavorable wind regimes. Furthermore, the EMEP PM₁₀ emission map does not include emissions from long-range transport and secondary sources which could be significant, especially during the spring season when transboundary pollution is observed in France (Petit et al., 2017a). Finally, emission inventories face high uncertainties related to data collection and emission factors and to the low spatial and temporal resolutions for the sources taken into account (Reis et al., 2009; Ni et al., 2014).

A comparison of the CF/PSCF results to the European map of annual mean concentrations of PM₁₀ in µg m⁻³ for the base year 2010 (EEA, 2017; http://www.eea.europa.eu/themes/air/interactive/pm10) is presented in Figure 5 (right). Such a comparison, in addition to the emission map could highlight the influence of dynamic processes such as deposition, or physico-chemical reactions in the atmosphere. Similar geographical origins were identified stressing out the region of the Benelux area, East-Germany and Central Europe with the highest annual PM₁₀ concentrations exceeding 40 µg m⁻³ in agreement with this study. On a more regional scale, Northern France and the
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Paris region were associated with the highest PM₁₀ concentrations, also in accordance with medium and short-trajectory CF and PSCF maps, which underlines the interest of the constrained approach to identify otherwise unseen nearest source areas. On the other hand, and as mentioned previously, some heavily polluted areas in Southern Europe and the Mediterranean basin were not identified as potential source areas in CF/PSCF maps, because they were probably not covered by the 3-day BTs or impeded by natural obstacles. Nevertheless, when considering regions covered by this set of BTs, our results globally agree with emission inventories and measurements at monitoring sites where many potential source areas are identified in all approaches.

<figure><img src="image_7.png" /><figcaption>**Figure 5.** Anthropogenic emissions of PM₁₀ in kg s⁻¹ for the 2005-2010 period in Europe from the EMEP emission inventory (ECCAD, 2017; left) and PM₁₀ annual average measurements for 2010 from background monitoring stations (EEA, 2017; right).</figcaption></figure>

# 5 Conclusion

In this work, an original methodology to locate pollution sources was applied to PM₁₀ concentrations measured for 5 years (2009-2013) at 12 receptor sites in a region submitted to multiple sources (the HdF border region in Northern France). Two complementary trajectory-based statistical models, CF and PSCF, were applied using various constraints to distinguish between sources inferred from long, medium and short-range BTs. First, these models were applied using a multi-site approach for a better coverage of the studied area. Second, applying several additional constraints linked to (i) the occurrence of scavenging precipitations during the transport of air masses and (ii) the altitude with respect to the PBL height led to more realistic maps by taking into account the sinks of PM₁₀ between the sources and the receptor sites. Furthermore, taking into account the density of BT endpoints allowed the distinction between long-distant but infrequent (e.g. Central Europe) and medium-distant but common (e.g. Benelux and West of Germany) emission source areas.
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Third, a criterion to select short BTs was used to find out the nearest source areas, highlighting the impact of the region itself (e.g. the city of Lille) but also neighboring territories (e.g. the megacity of Paris).

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Thanks to the multi-site approach which resulted in the use of a high total number of BTs, an investigation of the seasonal variability of the potential source maps was performed. It showed a stronger contribution from the nearest sources during winter and fall where the thickness of the PBL was at its minimum and primary local sources such as domestic heating and traffic were at their maximum. A higher impact of long-range sources was observed during spring when secondary aerosols (e.g. nitrate and sulfate-rich aerosols) are believed to be predominant in the North of France (Waked et al., 2014; Oliveira, 2017). Comparison of CF and PSCF potential source maps to emission inventories and measurements at background monitoring sites showed a good agreement for many potential source areas (e.g. Benelux and Germany, Central Europe, the HdF region and Paris). Discrepancies were observed for Southern Europe and the Mediterranean Basin likely due to natural barriers and to the low occurrence of winds coming from these directions. Nevertheless, this study has identified for the first time PM₁₀ potential source areas from the regional to continental scale for Northern France. It also confirmed the usefulness of this approach which could be easily implemented in other regions of interest with available historical PM₁₀ dataset to help public authorities and decision makers in taking actions to improve air quality at the regional and continental scales.

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## Acknowledgments

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This work was achieved in the frame of Labex CaPPA project (Chemical and Physical Properties of the Atmosphere) is funded by the French National Research Agency (ANR) through the PIA (Programme d'Investissement d'Avenir) under contract ANR-10-LABX-005.

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The authors thank the Région Hauts-de-France, and the Ministère de l'Enseignement Supérieur et de la Recherche (CPER Climibio), and the European Fund for Regional Economic Development for their financial support; Atmo Hauts-de-France for providing PM₁₀ data; the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and the ECCAD (Emissions of atmospheric Compounds & Compilation of Ancillary Data) community for providing data on emission inventories.

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