Conclusion

IV.9.

General Introduction

Atmospheric pollution is considered to have one of the most dangerous impacts on human health, especially in large cities. In addition to gaseous compounds (volatile organic compounds, sulfur dioxide, nitrogen oxides and ozone), suspended particles constitute the most important atmospheric pollutants. This particulate matter, consistently present in the atmosphere, can originate from natural sources (marine aerosols, soil erosion, volcanic eruptions, biogenic particles…etc.) as well as from anthropogenic ones (road traffic, industrial emissions…etc.) which are very significant in heavily populated regions.

Atmospheric suspended particles are suspected to have an impact on human health on one hand and to influence the chemistry of the troposphere on the other. The human health risks can be explained by the fact that the micrometer diameter of the particles, and submicrometer in a lot of cases, cause irritations of the respiratory tract and organs after deep penetration through inhalation. In addition, if the considered aerosol contains toxic components (transition metals, metalloids, and polycyclic aromatic hydrocarbons), other dangerous consequences will emerge in case these toxic species interact with respiratory organs tissues.

Globally, the air quality was enhanced during the past decades in most industrialized countries. The European directive 1999/30/CE of April 22 nd 1999 defines the annual average limit values for PM 10 in ambient air, which has been 40 µg/m 3 since January 1 st 2005.

However, this directive indicated that all European states should take measures in order to identify the principal sources of particles in the regions that exhibit above limit values, as well as to take sufficient measures to decrease the particle diffusion in an attempt to secure a cleaner ambient air, thus limiting health problems. Despite these efforts, many European Union states like Italy, Austria, Greece, Spain, Belgium, France…etc, have exhibited episodes of above limit concentrations of atmospheric particles since a couple of years ago.

The Nord-Pas-de-Calais region is included in the French zones exhibiting high PM 10 levels frequently registered by official air quality monitoring stations of the "Réseau agréé de Surveillance de Qualité de l'Air", in particular during the winter season and the beginning of spring, which is also reproduced in other French regions as well (Ile de France, Rhône Alpes).

For these reasons, at the beginning of 2011, the European Committee launched a litigation procedure against France for the disrespect of the legislation. On the national level, there is an urgent need to understand the reasons behind these high particulate levels as well as the need to gather as much scientific data as possible in order to provide answers needed on the national and European level.

In parallel, the legislation on PM 2.5 limit values issued under directive 2008/50/EC have evolved since 2010, and is defined to an annual limit value of 25 µg/m 3 starting the 1 st of January 2015. In addition, we can also remind that a long term quality objective equals to 10 µg/m 3 (annual average) is defined for a sufficient human health and environmental protection all together.

Nowadays, few studies have been conducted on the exposure levels to fine particles in cities of the Nord-Pas-de-Calais region. Other than the concentration measurements realized by official monitoring networks, there are no additional knowledge on the chemical composition of PM 2.5 in the region, despite the fact that the exposition to road transport, domestic activities and/or industrial activities emissions is between the highest.

Within the scope of this thesis, we propose the study of the chemical composition and origin of PM 2.5 in moderately sized cities, located near the littoral facade of the Nord-Pas-de-Calais region (Dunkerque, Boulogne-sur-Mer and Saint-Omer). In fact, few studies have been conducted in the urban-industrial zone of Dunkerque, and no study has revealed the chemical composition of PM 2.5 in other cities like Boulogne-sur-Mer (coastal city) and Saint-Omer (inland: 40 km distance from the coastline).

The main objectives of this work aims at: i. Acquiring a better knowledge on the chemical composition of PM 2.5 (ionic species, metallic major and trace elements, and total carbon).

ii. Identifying the different sources of PM 2.5 emission.

iii. Conducting a geographical and chronological record of the composition of particles, and compare the exposure level from one site to the other. iv. Evaluating the contributions of natural and anthropogenic sources separately to the PM2.5 concentration.

Therefore, this thesis report is divided into five chapters.

The first chapter presents the generalities on atmospheric particles. It particularly aims at exposing different aerosols sources and their physicochemical characteristics. The chapter also focuses on the emissions of particles into the atmosphere on a global, European and French scale, while highlighting the small size fractions of these particles. In addition, a review on the international and French atmospheric particles limit values is realized, followed by the analysis of the evolution of this science with emphasis on the chemical composition and anthropogenic sources of particles including the most important industrial emissions composition which can be found in the literature. Afterwards, a section on air quality in the Nord-Pas-de-Calais region and the exposition of the main objectives of this study will conclude this chapter.

The second chapter describes in details the methodology that is used in order to achieve the objectives mentioned at the end of the first chapter. It begins with a detailed description of the sampling sites, which includes the geographical locations as well as the most important nearby influences including natural and anthropogenic sources of emissions. Furthermore, the equipments used in this study are also detailed in this chapter. It encloses the sampling materials (filters and samplers) in addition to the analytical techniques used to determine the chemical species concentrations in the atmosphere. These techniques are ICP-MS, ICP-AES and CHNS-O micro analyzer for an important list of elements and total carbon analysis, and ion chromatography for water soluble ions quantification. Finally, the chapter ends with a meticulous description of the customized sampling protocol used for the collect of the particles.

The third chapter begins with a description on the recorded meteorology during the sampling campaigns, including temperatures, wind activities and precipitations. This step is followed by the first presentation of the chemical analysis results. This latter is depicted in a first step in a comparison of average concentrations of major elements, ions and total carbon. These average concentrations are compared between the sampling sites as well as to other studies in Europe. In addition, the chronological evolution of the different chemical species was conducted, and included the same groups mentioned earlier. Another important section of this chapter follows and revolves around the identification and detailed analysis of above limit values episodes of PM 2.5 . However, the chapter content at this stage includes an emphasis on the evolution of each major chemical species by wind sector in a concentration rose analysis section, which allowed the analysis of the concentration variation by wind sector and concluded the chapter.

Following the third chapter, a deeper investigation on the trace elements chemical trends constitutes the subject of chapter four. In this latter, the trace metals concentrations are analyzed in the form of averages and compared between the sites for each campaign, but also to other average concentrations found in European studies. However, in order to understand the impact of anthropogenic sources, an industrial impact and crustal enrichment factor analysis followed the comparison section. To this section is added another type of analysis that includes a correlation study between some elements, which points at some possible anthropogenic sources of emissions. The correlation analysis is followed by a study of concentrations evolution by wind sector, which included two important subsections. The first is a concentration rose analysis by campaign, which showed the principal sectors under which we observed high concentrations. However, the second part included another correlation study, using some selected samples that are under a specific anthropogenic influence. At the end of this last section, elemental ratios are proposed to trace the influence of some industrial PM 2.5 sources.

Finally, the collected information on the chemical composition of the PM 2.5 samples are modeled using a non-negative matrix factorization based method detailed in the fifth chapter.

A description of the modeling techniques begins the chapter, which is then followed by a comparison between the modeling methods in order to attest our final choice. The latter relied on a weighted non-negative matrix factorization (NMF) method developed with the collaboration of the "Laboratoire d'Informatique, Signal et Image de la Côte d'Opale" (LISIC). The developed NMF is fed by the chemical data provided by the chemical analysis in order to uncover the main emission profiles and their respective contributions in each collected sample. The chapter contains details about the operating conditions of NMF modeling used in this study as well as a detailed verification of the output reliability.

Afterwards, the calculated profiles and contributions are discussed extensively to uncover [START_REF] Whitby | Fine particles[END_REF] 2: Flow chart of possible formation reactions of different nitrates and sulfates chemical species; taken from [START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF] who reproduced it from [START_REF] Matsumoto | Formation and dissociation of atmospheric particulate nitrate and chloride: an approach based on phase equilibrium[END_REF], [START_REF] Cited Alastuey | Monitoring of atmospheric particulate matter around sources of secondary inorganic aerosol[END_REF], [START_REF] Pakkanen | Study of formation of coarse particle nitrate aerosol[END_REF], and [START_REF] Hewitt | Handbook of Atmospheric Science: Principles and Application[END_REF] which represent (a), (b), (c), and (d) respectively. ........ Figure 3: Anthropogenic emissions trends of TSP, PM 10 , and PM 2. Table 1: Particles flux estimation in the atmosphere [START_REF] Delmas | Physique et chimie de l'atmosphère[END_REF]

I.1. Introduction

In this chapter, we will restate the terminology used in the science of atmospheric particles, starting by the basic definitions of particulate matter (PM). The origins, physical and chemical characteristics, and the evolution of PM in the atmosphere will be discussed as well, with emphasis on the fine particle fraction under study (diameter < 2.5 µm). In addition,

European and French emissions of PM, the latest standards for particulate matter (PM) pollution released by the European Parliament as well as those recommended by the World Health Organization (WHO) and the United States Environmental Protection Agency (USEPA) will be reviewed. On the other hand, emphasis will be laid upon the chemical composition of PM and the link between this latter and the sources of emissions that was found in some of the oldest scientific publications that we were able to acquire until the latest in 2012. The importance of tracking PM sources, origins and chemical composition resides within the health effects of PM inhalation, especially on the respiratory tract and organs; therefore, this subject will be included in this section as well. Finally, the situation in the region of the study will be investigated.

I.2. Atmospheric Particulate Matter -Aerosol

By definition, an aerosol is a general term that technically refers to a suspension of fine solid particles or liquid droplets in a gas. This term refers to atmospheric particles and their gaseous environment. It is commonly used by researchers who work in the fields of atmospheric dynamics and climate. However, researchers who study atmospheric pollution levels and their respective health impacts use the term "particulates" instead. This explains the fact that the air associated to the particles when talking about aerosols is nothing but a transport vector of these particles in the air and into the respiratory tract [START_REF] Villenave | Air pollution by particulate matter: connecting stakeholders and scientific advances[END_REF].

These particles can be directly emitted into the atmosphere by natural and anthropogenic sources, and are called "primary aerosols". On the other hand, some particles can be generated by gas-to-particle conversion processes, thus they are called "secondary aerosols". More definitions will come along as we move into subdivisions that can be related to the size and to the chemical composition of atmospheric PM.

I.2.1. Size classification of PM

The interest in PM is majorly related to its effect on the respiratory tract and organs, and therefore is linked to the penetration capacity when inhaling. Consequently, smaller particle diameter means deeper penetration. The importance of PM size doesn't stop at health effects only, but it also stretches to environmental pollution, effects on climate and visibility.

Therefore, studying the characteristics of the different size fractions of PM comes in first, in order to understand the sources of emissions and level of impacts.

Before boarding the subject of size distribution, we should specify that the diameter of the particles discussed herein will be the "equivalent aerodynamic diameter" (D ae ) instead of Stokes or volume equivalent diameters. The reason behind this differentiation is the facts that we consider that atmospheric particles do not necessarily have well-defined spherical, cubic, elliptical, or other shapes. Therefore we define the diameter of an atmospheric particle (regardless of its shape) in the atmosphere as equal to the diameter of a spherical one that has the same terminal velocity considering a density of 1 g/cm 3 , and hence is called "equivalent aerodynamic diameter". For example, an atmospheric particle with D ae = 2.5 µm is defined as a particle that have the same terminal velocity of a spherical shaped particle (density = 1 g/cm 3 ) with a 2.5 µm diameter. Generally, the size of atmospheric PM is strongly related to their generation mode and atmospheric conditions. For that reason, the D ae value range can stretch from a few angstroms (Å) up to a hundred micrometers.

Between inhalable PM, the size ranges that are mostly studied are the ones discussed hereafter. Total suspended particles (or TSP) represent all atmospheric suspended particles enclosing a D ae range from few nano-meters to more than a hundred micrometers.

Within the TSP class, there are many subclasses of PM used in the literature. In general, recent classification of particles is based on their D ae only, when other methods link D ae with formation mechanisms [START_REF] Whitby | Fine particles[END_REF].

The first classification method, based on the size, divides atmospheric PM into three subclasses:

• "Coarse" fraction with a diameter that exceeds 2.5 µm. The inhalable part of this fraction includes a D ae range between 2.5 and 10 µm (PM 10-2.5 ).

• "Fine" fraction particles with an aerodynamic diameter between 0.1 and 2.5 µm (PM 2.5-0.1 ). Human activities are known to contribute in the largest part of this fraction of PM that is characterized by its deep penetrating capacity when inhaled.

• "Nano" fraction is the one having a D ae that ranges between 0.03 and less than 0.1 µm. It is either generated by anthropogenic activities (mostly combustion) or by natural events (oxidation of natural terpenes emitting secondary organic aerosol). This fraction is important from a public health point of view since it gets into alveolar tissue and can diffuse rapidly in the blood stream.

The second classification method takes in consideration the formation mechanisms, and also shows other ranges of PM that are illustrated in Figure 1. We notice that this distribution is consisted from three ranges starting from:

1) The Nucleation range or Aitken nuclei range that regroups all PM with an aerodynamic diameter less or equal to approximately 0.1 µm. This class represents particles formed by gas-to-particle conversion mechanisms or coagulation of primary particles.

2) The Accumulation range that includes PM with a D ae between 0.1 and 2.5 µm.

Particles of this class are formed in the beginning by vapor condensation on Aitken particles, or a coagulation of this latter to form particles with greater diameter.

3) The Coarse range regroups particles with an aerodynamic diameter exceeding 2.5 µm. It regroups natural but also anthropogenic direct particles emissions. The Aitken nuclei range particles has a relatively short suspension time in the atmosphere due to their rapid transformation to give larger particles. However, the Accumulation range is characterized by the longest suspension time in the atmosphere when compared to Aitken and Coarse, and it accounts for most of the aerosols surface area and constitutes an important part of the aerosol mass. The last range is characterized by a rapid sedimentation of particles due to its large size. In this project, the particle size under study will include the Accumulation range and a part of the Aitken nuclei: D ae between 0.03 and 2.5 µm (PM 2.5-0.03 ).

I.2.2. Origins of PM

Generally atmospheric PM can be generated from natural sources (terrestrial dust, volcanic action, sea spray, reactions between natural gaseous emissions), but also from human activities: industrial processes, fuel combustion, transportation and non-industrial fugitive sources (roadway dust, construction, etc).

I.2.2.1. Naturally Emitted Aerosols

I. 2.2.1.1. Mineral dust Terrestrial dust (also known as soil dust) is naturally generated by Aeolian erosion of soil surfaces, which generates and suspends dust, when wind speed exceeds 3 m/sec [START_REF] Masclet | Atmospheric particles: Physicochemical characteristics[END_REF]. The main species found in soil dust are quartz, calcite, gypsum, aluminosilicates and some iron oxides. The proportions of these components vary from one study site to another, due to the difference in the soil chemical composition around the world [START_REF] Wedepohl | The composition of the continental crust[END_REF]. Gravitational settling and wet deposition are the major removal processes for mineral dust from the atmosphere (Seinfeld & Pandis, 2006).

I.2.2.1.2. Sea salts

Sea salts are produced by oceanic spray [START_REF] Blanchard | Bubble formation and modification in the sea and its meteorological significance[END_REF]. Waves movement generates salt water droplets that are emitted into the atmosphere by the "bubbling" phenomenon. These droplets are dried up in the atmosphere to give solid salts, which chemical composition is very close to that of sea water, and is listed hereafter by order of decreasing weight percentage (wt %): chlorine, sodium, sea salt-sulfates (ss-SO 4 2-

), magnesium, calcium and potassium. Sea salt-sulfates represent the sulfate load in marine aerosols. Sulfur is found majorly in the form of dimethylsulphide (DMS = (CH 3 ) 2 S) in sea water and sulfates are generated by the oxidation of the latter which is emitted from algae. It is important to distinguish between these sulfates and the ones emitted from different anthropogenic sources in the atmosphere called non sea salt-sulfates (nss-SO 4

2-

).

I.2.2.1.3. Volcanic ash

Volcanic ash is emitted into the atmosphere occasionally with the occurrence of a volcanic eruption [START_REF] Hidy | Aerosols, an industrial and environmental science[END_REF] and [START_REF] Andrea | Climatic effects of changing atmospheric aerosol levels[END_REF], like the eruption of Eyjafjöl (Iceland) in 2010. This type of PM can be found loaded with silicon, metals, and sulfates resulting from gas to particle conversion mechanisms.

I. 2.2.1.4. Biogenic aerosols Biogenic organic aerosols can be directly emitted into the atmosphere as pollen, spores, and plant fragments. These constituents are usually found concentrated in the coarse fraction of PM (Seinfeld & Pandis, 2006). It can also be generated by atmospheric oxidation or condensation of gaseous hydrocarbons emitted from plants such as terpenoïds, like isoprene and other species [START_REF] Masclet | Atmospheric particles: Physicochemical characteristics[END_REF]. In this case, these particles are included within the secondary organic aerosol (SOA) group.

I.2.2.2. Anthropogenic Aerosols

Anthropogenic aerosols can be encountered as primary aerosols or/and secondary aerosols, and are related to human activities (industrial and domestic). The chemical composition includes a large variety of elements, like carbon in the form of "soot" and/or debris from wheel abrasion, lead, iron, zinc, copper, vanadium, etc. In addition, these aerosols can contain toxic and carcinogenic substances such as PAHs in small but significant quantities.

I.2.2.2.1. Secondary anthropogenic aerosol

Secondary anthropogenic aerosols are produced with the condensation of vapors emitted into the atmosphere on the surface of the particles coupled usually to a photochemical oxidation. For that reason they are called "conversion aerosols", and are characterized by their relatively small size. Its chemical composition varies depending on the origins of emissions and on the climatic conditions. However, these particles contain a substantial load of sulfates, nitrates, organic matter, and a variety of metal oxides [START_REF] Masclet | Atmospheric particles: Physicochemical characteristics[END_REF].

I.2.2.2.2. Carbonaceous particles

One of the most important primary anthropogenic aerosols is carbonaceous particles.

These particles contain black carbon "BC" (also known as elemental carbon "EC"), and organic materials (Seinfeld & Pandis, 2006). Elemental carbon can be produced only by combustion processes, and is solely primary, whereas organic compounds can be emitted directly with the carbon rich particle in a primary fashion, or are adsorbed on the surface of carbon particles, or transformed to particles in the atmosphere to form secondary particles.

The carbon content of both elemental carbon fraction and organic is called the "total carbon".

I.2.2.2.3. Biomass burning

Another type of anthropogenic aerosols is the one produced by biomass burning, which is defined here as the intentional burning of land, for example: tropical vegetation burning, savanna burning in the dry season, etc. The quantity and type of emissions from this kind of combustion is related to vegetation types, moisture content, ambient temperature, humidity, and wind speed. However, CO 2 , CO, NO x , CH 4 , organic PM, are the most important chemical substances present in this type of emissions (Seinfeld & Pandis, 2006). I. 2.2.2.4. Secondary Inorganic Aerosols (SIA) This atmospheric PM profile is specifically important since it allows tracing local and distant human activities. Figure 2 summarizes the chemical reactions that leads to the chemical species encountered in SIA. These latter are majorly composed of ions, namely:

nitrates, sulfates, and ammonium; but still contains other chemical species including metals (Figure 2). The origin of this type of aerosols is anthropogenic through sulfur and nitrogen oxides emissions, interacting with natural emissions of ammonia and metals in a relatively high humidity medium. Recent studies show that the industrial and land transportation sectors are the main sources of SO 2 and NO x emissions respectively, whereas ammonia emission in Europe is essentially linked to agricultural activities. On a global scale, emissions for both primary and secondary particles can be estimated in the atmosphere depending on their anthropogenic or natural sources. By looking at Table 1, global natural emissions of PM account for about 90% of the total flux, whereas anthropogenic PM account for the remaining 10% of the PM mass emitted yearly in the atmosphere of the planet.

Table 1: Particles flux estimation in the atmosphere [START_REF] Delmas | Physique et chimie de l'atmosphère[END_REF] This difference between natural and human induced emissions is essentially caused by the natural emissions of sea salts and mineral dust. However, the relatively small participation of human activities in global PM emissions (~10%) should not be taken lightly, since it is mainly in the fine particles range. It is persistent in the atmosphere of heavily populated areas from which it was mostly emitted in the first place. In the case of large cities, the proportions between natural and anthropogenic particles are strongly different, leading in some cases to an inversion of the tendencies. Furthermore, its chemical composition can cause adverse health effects due to the organic component as well as the transition metals that can lay down many risks on human health.

This discussion about the emission inventories for global PM emissions aimed to grasp a general idea about the amount of PM emitted into the atmosphere. However, detailed study of the emission trends of specific PM sizes such as PM 10 and PM 2.5 , as well as metals emissions in Europe and in France will follow in the forthcoming part.

I.3.2. European anthropogenic emissions

The data used in this section is extracted from the European Environment Agency (EEA(a), 2012). It will be used to discuss emission trends, from 1990 to 2010, around Europe for TSP, PM 10 , PM 2.5 and some metals. This first analysis on global European emissions will be followed by a detailed analysis on some chosen sectors emissions, all anthropogenic, for the same period. The chosen sectors are: energy production and distribution, energy use in industry, road transport, non-road transport, commercial/institutional and household, industrial processes, agriculture, waste, and solvent/product use.

I.3.2.1. TSP, PM 10 , and PM 2.5 emissions

Figure 3 shows the evolution of anthropogenic emissions in Europe for the major three size classes of particles: TSP, PM 10 , and PM 2.5 . A reduction in the emissions can be observed for the three classes of PM from 1990 till 2010. However, TSP emissions decreased significantly between the 90's and 2010 value when compared to PM 10 and PM 2.5 who conserved a moderate decrease for that same period. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in Kt (=1000 tonnes) TSP PM10 PM2.5

We can also notice that in all cases, PM 2.5 accounted for more than 66% of PM 10 which proves the extent of human activities contribution in the load of these specific fine particles on the European level.

I.3.2.2. Metals emissions

We emphasize on the importance of tracing these emissions specifically for As, Cd, Cr, Ni, and Pb which all have limit values defined by the World Health Organization (WHO), United States Environmental Protection Agency (US EPA), and the European Union (EU).

Metallic emissions in Europe were retrieved from EEA (EEA(a), 2012) for the following metals: As, Cd, Cr, Se, Ni, Cu, Pb, and Zn. The trends are illustrated in Figure 4 and Figure 5. As, Cd, Ni and Cr levels have decreased in that same time period (Figure 5). Se was adopting almost the same pattern as Cu, with some recent values slightly exceeding older levels for this metal. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in Kt (=1000 tonnes) Cu Pb Zn Global anthropogenic emissions trends in Europe evolved with a descending pattern for most of the studied metals. However, a closer look is essential to uncover which sector had a high participation in the emissions of PM and metals, which will be discussed hereafter.

I.3.2.3. Anthropogenic emissions by sector

In this section, we will be analyzing the emissions trend from 1990 to 2010 for PM and metals by activity sector. The sectors under study will be: energy production and distribution, energy use in industry, road transport, non-road transport, commercial/institutional and household, industrial processes, agriculture, waste, and solvent/product use. In each part of the following analysis will include all sectors except the "agriculture" and "Non road transportation" sectors which will be detailed separately due to the difference in the scale of emissions.

I.3.2.3.1. TSP emissions by sector

Emissions of TSP are found different from one sector to another. TSP emissions had decreasing trends for all the studied sectors except "commercial / institutional and household"

and "solvent and product use" (Figure 6 and Figure 7). Furthermore, a remarkable decrease was found in the case of "energy production and distribution" when it reached a value of 237.1 Kt in 2010, which is almost 1/8 th the value of 1990.

As for the "waste" sector, the figures conserved certain stability in the emission loads of TSP until 2005. After which, it was followed by a significant decrease that remained constant till 2010. 3 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in Kt (=1000 tonnes)

As Cd Cr Se Ni In this part, PM 10 anthropogenic emissions data by sector were illustrated in Figure 8 and Figure 9.

The emissions in this case showed decreasing trends except in the case of "commercialinstitutional and household" sector only. In this latter, a recent increase in the emissions can be observed between 2007 and 2010. PM 10 emission trends are found having a similar pattern with those for TSP in the case of the "waste" sector, whereas "solvent and product use" 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 TSP emissions in tonnes (=1000 kg)

Waste

Solvent and product use I. 3.2.3.3. PM 2.5 emissions by sector Data on the emissions of PM 2.5 by sector are illustrated in Figure 10 and Figure 11. The trends show a similar patter than the one observed in the case of PM in all the above discussed sectors. This shows that these two size fractions are highly related to human activities, as well as related to each other. By comparing PM 10 to PM 2.5 data, especially for the "road transport" sector, we can notice that PM 2.5 anthropogenic emissions constitute a significant part of PM 10 emissions, which can be related to combustion emissions of fine particles. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2007 2008 2009 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2007 2008 2009 2010 PM10 emissions in tonnes (=1000 kg)

Waste

Solvent and product use I. 3.2.3.4. Anthropogenic emissions for "non-road transport" and "agriculture" sectors TSP, PM 10 , and PM 2.5 anthropogenic emissions show similar patterns when studying "non-road transport" emissions, with a relative decrease between 2008 and 2010 (Figure 12).

Furthermore, PM 2.5 contributes significantly to PM 10 (about 90%) and TSP (about 70%) levels. This proves the extent of human activities, especially combustion processes (see .3.2.3.3), in the variations of these emissions. An opposite example can be found in the "agriculture" sector emissions (Figure 13). In this case, TSP and PM 10 were found to have an increasing trend, whereas PM 2.5 showed a relative decrease when approaching 2010. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 PM2.5 emissions in tonnes (=1000 kg)

I

Waste

Solvent and product use

I.3.3. French anthropogenic emissions

For this section, the data was extracted from the EEA (EEA(a), 2012). On the European level, a decreasing pattern was adopted by most anthropogenic emissions in general, with some exceptions in certain sectors. However, since this project is conducted in France, it is necessary to study the evolution of the anthropogenic emissions on the national level, and compare it to the European situation in order to uncover any changes on the local scale. The same list of sectors that was previously studied will also be included in this following analysis.

I.3.3.1. TSP, PM 10 , and PM 2.5 emissions

The evolution of anthropogenic emissions for the three major classes of PM is similar to that of the European situation for PM 10 and PM 2.5 emissions (Figure 14). On the other hand, TSP decreased less severely in France when compared to Europe in general. 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in tonnes (=1000 kg) 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in tonnes (=1000 kg) TSP PM10 PM2.5 On the other hand, As, Cd, Cr emissions decreased in a moderated fashion, when compared to Ni and Pb (Figure 16 and Figure 17). These latter decreased more gradually till 2010. Finally, Se emissions showed almost stable levels between 1990 and 2010 (Figure 17). 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in Kt (=1000 tonnes) TSP PM10 PM2 .5 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in Kt (=1000 tonnes)

Cu Zn 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in Kt (=1000 tonnes) 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in Kt (=1000 tonnes)

As

Cr

Se Ni that TSP levels increased in 2009 -2010 to match 1998 and 1991 values (Figure 18), whereas the "solvent and product use" TSP emissions increased above the 1990 value (Figure 19). 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Emissions in t (=1000 kg) 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Emissions in t (=1000 kg)

Energy production and distribution Energy use in industry Non-road transport Solvent and product use Waste 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in t (=1000 kg)

Energy production and distribution Road transport Commercial, institutional and households Industrial processes Agriculture 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in t (=1000 kg)

Energy use in industry

Non-road transport Solvent and product use Waste The limit values for this size fraction of atmospheric PM are defined at the European level, but also by the US EPA and WHO. In this latter, PM 10 limit was set at 20 µg/m 3 as an annual mean value, and 50 µg/m 3 for the 24 hours mean value (WHO, 2006). On the other hand, the US EPA has set PM 10 limit values to 50 and 150 µg/m 3 for the annual and daily (24 hours average) limits respectively.

PM 10 limit values were also defined in Europe to 40 and 50 µg/m 3 as annual and 24 hours average respectively without crossing the 35 daily exceedance limit. These regulations 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in t (=1000 kg) 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Emissions in t (=1000 kg) [START_REF] Collet | PM10 : la France poursuivie en justice par l'UE pour non-respect des normes de qualité de l'air[END_REF].

France is not the only country that exceeds these limits. In 2010, about 21% of the urban population in the European Union was exposed to PM 10 levels that exceed the limit value. The frequency of exposure to above limit values varied between 18% and 41% in 2001.

These variations reflected the changes caused by meteorological conditions (EEA(b), 2012).

I.4.2. PM 2.5 limit values PM 2.5 limit values are set and enforced in the USA. Historically, the determination of limit values for this size range of PM evolved from high values to lower ones, after coupling assessments on human health problems linked to the exposure to these particles. For example, the United States National Ambient Air Quality Standards (NAAQS) changed the limit value for PM 2.5 (24 hour) from 65 µg/m 3 (1997) to 35 µg/m 3 (2006). However, their annual average value was stabilized to 15 µg/m 3 (USEPA, 2012).

The World Health Organization (WHO) followed the US EPA steps and had many interim targets for PM 2.5 annual mean concentrations (35, 25, and 15 µg/m 3 ) as well as for the 24-hour concentrations (75, 50, and 37.5 µg/m 3 ), before settling with 10 and 25 µg/m 3 as guideline values for PM 2.5 annual and 24-hour concentrations respectively (WHO, 2006).

As for the European Union, the Directive "2008/50/EC" published the targets values and limit values for PM 2.5 (Directive-2008/50/EC, 2008). In the latter, PM 2.5 annual average limit values of 25 µg/m 3 and 20 µg/m 3 were defined as objectives to be met in January, 2015 and 2020 respectively.

In the year 2010, French legislation on suspended PM was modified to consider PM sizes below 10 µm. Hence, since 2010, PM 2.5 levels are being monitored in regions populated by more than 100 000 inhabitants. The monitoring sites in those regions are located in an urban background location. Many PM 2.5 limit values were converted from the European to the French legislation (Decree-n°2010-1250(Decree-n°2010- , 2010)). Thus a target value was set at 25 µg/m 3 , which should be met in 2015. Meanwhile, the limit value will evolve yearly following a decreasing order. In 2010, the value was set at 29 µg/m 3 , then 28 µg/m 3 in 2011, 27 µg/m 3 in 2012, and 26 µg/m 3 for 2013 and 2014.

Based on annual reports on air quality in France between 2007 and 2011, the annual averages of PM 2.5 is found to range between 15 and 25 µg/m 3 in some French regions [START_REF] Meddtl | Bilan de la qualité de l'air en France en 2010: et des principales tendances observées au court de la période 2000-2010[END_REF]. Few exceeding limit values were identified in large cities such as Lyon and Paris in 2009. Furthermore, a seasonal variation of the particles concentrations was identified: the highest levels were recorded under dry anticyclone weather conditions that usually favor pollutants accumulation. Such episodes are encountered particularly during winter and the beginning of spring seasons.

More concerns about the respect of limit values of PM in the ambient air have been raised in France in which limit values for PM 10 are regularly being broken; therefore the question remains: what about the levels and sources of PM 2.5 in the NPdC region?

Furthermore, our project will deal with the chemical composition of PM 2.5 , including an extended list of metal species from which some have guidelines values. These guidelines will be discussed in the forthcoming section.

I.4.3. Metals guideline values

Current atmospheric standards for metals are of a great importance to this work, since it will be used to evaluate the results and compare them to international and national standards.

The WHO, EPA, EU, and the French government addressed the problem regarding the concentrations of certain inorganic pollutants in the atmosphere, specifically some elements that have observable adverse health effects and are emitted from anthropogenic activities.

Guidelines values were set for these elements: As, Cd, Cr, Pb, Mn, Ni, and V.

The adverse health effects of these metals are detailed in the Air quality guidelines for Europe [START_REF] Who | Air quality guidelines for Europe -2nd Edition[END_REF], in which "As" is found to increase incidence of lung cancer in occupationally exposed groups, and therefore is considered carcinogenic. "Cd" and "Cdcompounds" are classed as Group 1 human carcinogens. Its inhalation is found to cause renal alterations as well. On the other hand, "Cr" represents a different case, where "Cr (III)" is an essential element for humans and animals, so the problem resides in the load of "Cr (VI)" in the environment. This latter is found to be toxic and carcinogenic.

Furthermore, "Pb" can cause nerve problems in adults, hearing impairment and disturb

Vitamin-D metabolism in children. Smaller age groups are found to be more sensitive to this metal, like children whose cerebral system can be specifically affected. On the other hand, some exposure tests on animals proved that Pb loads alters hearing functions and the ability to comprehend. However, it is extremely difficult to prove that the same effects exist in humans.

Nevertheless, researches have proven through epidemiological studies on school children that the exposition to Pb resulted in developmental (psychomotor system) or intellectual troubles as well as behavioral problems. In this latter, the observed consequences were hyperactivity, lack of concentration, and impulsivity (INSERM, 1999).

In the case of "Mn", its accumulation can cause pneumonitis, pneumonia, reproductive dysfunction, and even neurotoxicity (at a low-level exposure). However, "Ni" can increase the risk of lung and nasal cancer. This was observed under exposure within Ni refining industries.

In the mean time, "V" contamination is found to be highly related to the inhaled dose. An exposure to low doses can cause irritations of the upper respiratory tract, which is considered as minor effects when compared to chronic bronchitis and pneumonitis that can result from exposure to high concentrations [START_REF] Who | Air quality guidelines for Europe -2nd Edition[END_REF].

As for the guidelines values, Table 2 summarizes the available data taken from different sources. However, few terms need to be defined like "Exposure LV", which means the exposure limit value (measured per day) not to be exceeded. Another used term is "Target", which herein means the annual average objective to be attained for a better air quality. Annual average (a,c) Exposure LV (a) 3 months average (b) A closer look at Table 2 shows that a large list of elements is missing as well as implemented limit values for As, Cr, and V. This last case can be explained by the difficulties that researchers encounter when defining thresholds for adverse health effects [START_REF] Who | Air quality guidelines for Europe -2nd Edition[END_REF].

The remaining trace elements that are not on the list constitute a large list, and highlight the importance of our work, in which we will be able to monitor elements concentration range and their sources.

I.5. Background on fine PM studies

In this section, we will inspect some of the works that we could find and that deal with the subject of atmospheric PM pollution. The early studies on the fine fraction of PM were mostly conducted to unfold the formation related to direct emissions or gas-to-particles conversion processes. In the 90's period, more focus was laid on the chemical composition in relation with the formation mechanisms of fine PM. In the same period, a lot of attention was given to the toxicology of fine PM and on the sources of emissions through biological and chemical testing. The evolution of fine particles studies continued in the same upward direction from 2000 till recently, with more emphasis on the chemical composition as a source tracer, but also on the smaller fractions like: fine, ultra-fine, and nano particles.

Therefore in the following section, we will discuss the advancements in fine PM research. and NO 2 [START_REF] Bricard | Formation of atmospheric ultrafine particles and ions[END_REF]. In the same period more work was conducted also on the formation of particles through nucleation processes [START_REF] Yue | The formation of ultrafine aerosols through nucleation on ions process[END_REF]. On the other hand, the health effects of the deposition of ultrafine PM in the lungs was being studied in that period by [START_REF] Boffa | Deposition in the lungs of ultrafine particles[END_REF] which is one of the oldest articles that we found on the inhalation effects of particles, and emphasized the importance of studying the toxicity of PM.

Deeper chemical investigations can be found in the studies conducted in the 80's, especially some works conducted with the objective of tracing elements that are problematic such as Mn [START_REF] Davis | Origins of manganese in air particulates in California[END_REF], and "Pb, V, Ni, Mn and Fe" ( [START_REF] Paul | The new jersey project on airborne toxic elements and organic substances (ATEOS): a summary of the1981 summer and 1982 winter studies[END_REF] and [START_REF] John | Mass and elemental composition of fine and coarse particles in six U.S. cities[END_REF]). Toxicity issues had also been discussed in that period, such as the carcinogenicity of atmospheric Benzo-a-Pyren studied by Perera [START_REF] Perera | Carcinogenicity of airborne fine particulate benzo(a)pyrene: an appraisal of the evidence and the need for control[END_REF].

I.5.2. Chemical composition and sources of fine PM

Additional interests in the chemical composition of the fine fraction of PM started in the 90's. The issues of formation and evolution of these PM in the atmosphere, the emissions sources and toxicity were extensively studied during this period.

The formation of particles in the atmosphere was the subject of many studies ( [START_REF] Bigg | A mechanism for the formation of new particles in the atmosphere[END_REF]; [START_REF] James | A density functional theory study of the hydrates of NH3-H2SO4 and its implications for the formation of new atmospheric particles[END_REF]; and [START_REF] Liisa | Effects of the increased UV radiation and biogenic VOC emissions on ultrafine sulphate aerosol formation[END_REF]), when others worked on the changes in the chemical composition of these particles [START_REF] Veli-Matti | Substitution of chloride in sea-salt particles by inorganic and organic anions[END_REF] with aging mechanisms [START_REF] Kotzick | The effects of aging processes on critical supersaturation ratios of ultrafine carbon aerosols[END_REF]. On the other hand, the use of mathematical modeling started to emerge in that period in order to understand particles emissions using positive matrix factorization [START_REF] Kaarle | The investigations of aerosol particle formation in urban background area of Helsinki[END_REF]. Also, receptor modeling was used in Germany to distinguish between three sources of PM 2.5 emissions: road dust, secondary aerosols, and vehicle combustion [START_REF] Pohlmann | Receptor modelling of urban aerosol[END_REF].

The sources of PM emissions were the subject of many studies, from which transport related emissions were highlighted as an important source of particles ( [START_REF] Antonio | On-road emissions of particulate polycyclic aromatic hydrocarbons and black carbon from gasoline and diesel vehicles[END_REF] and [START_REF] Lidia | A study of the horizontal and vertical profile of submicrometer particles in relation to a busy road[END_REF]). Furthermore, wood combustion particles characteristics were studied in [START_REF] Gaegauf | Characterization of wood combustion particles: morphology, mobility, and photoelectric activity[END_REF], who found that combustion air supply plays an essential role in the amount of PAHs emissions.

Studies on the inorganic chemical composition of fine PM fractions were also available in that period. Some of these studies were conducted in big cities in the US [START_REF] Huges | Physical and chemical caraterisation of atmospheric ultrafine particles in the Los Angeles area[END_REF] and in China [START_REF] Guor-Cheng | Characterization of chemical species in PM2.5 and PM10 aerosols in suburban and rural sites of central Taiwan[END_REF]. In this latter, PM 10 bound heavy metals concentrations were found higher during the day when compared to the night time. Also, PM 2.5 was found to be the major part of day time PM 10 , and they were found in high concentration in a suburban area compared to a rural one. On the other hand more investigation on the metallic content of PM 2.2 was conducted in the Los Angeles area [START_REF] Huges | Physical and chemical caraterisation of atmospheric ultrafine particles in the Los Angeles area[END_REF], in which Fe concentration was found to be most prominent, followed by K, Na, Ti, Zn and Cr.

The analysis of a detailed chemical composition of fine PM is very important when searching for sources and their impacts. Metals, ions, and carbon contents uncovered in recent studies on fine PM will be detailed hereafter in order to explain the variety of anthropogenic contribution in fine PM loads.

I.5.2.1. Sources of metals

Metallic compositions of fine PM is highly related to nearby as well as distant sources.

Metals cocktail within this fraction of aerosols can present a quantitative and mostly a qualitative variety. These fluctuations are mostly linked to the emissions of metals from anthropogenic sources. Therefore, quantitative variations of elements in PM can be explained once the source of emissions is identified. However, this last task is complicated, especially when the studied region presents a mixture of many anthropogenic sources. The difficult section in such studies is to be able to determine which metal or group of metals is resulting from which activity, knowing that in most cases, different human activities enrich PM with similar metals. Therefore, the variations in metals concentrations in PM 2.5 based on their origins of emissions will be discussed in this section using results from studies conducted around Europe.

I. 5.2.1.1. Crustal Also known as mineral dust (see section I.2.2.1.1), crustal particles can be emitted directly with wind suspending soil minerals into the air, or from anthropogenic activities resuspending this sediment from the ground. Many elements can be considered as tracers of mineral dust, from which: Al, Ca, Fe, Si, Ti and Mg were categorized as soil dust elements ( [START_REF] Wu | Source apportionment of PM2.5 and selected hazardous air pollutants in Seattle[END_REF]; (Ilacqua, et al., 2007). However, each study has to follow other sources contributions, which might have common tracers with the previously mentioned elements. In some studies, Al and Si oxides correlations pointed at this type of fine dust particles [START_REF] Gotschi | Elemental composition and reflectance of ambient fine particles at 21 European locations[END_REF], but also Ti and Ca may be considered tracers if found correlated [START_REF] Cyrys | Elemental composition and sources of fine and ultrafine ambient particles in Erfurt, Germany[END_REF].

I.5.2.1.2. Traffic exhaust emissions

In traffic exhaust emissions, carbon content is always found important along with other metals that are considered as tracers of direct exhaust emissions. Between these metals: Cu, K, Mn, Zn, Pb, Ba, Sb, Br, As, V, and Cd are mostly used to trace traffic direct emissions ( (Moloi, et al., 2002); [START_REF] Mazzei | Characterization of particulate matter sources in an urban environment[END_REF]; [START_REF] Beslic | Relationship between 4day air mass back trajectories and metallic components in PM10 and PM2.5 particle fractions in Zagreb air, Croatia[END_REF]; (Ilacqua, et al., 2007)). One problem in determining the tracers for traffic emissions is the fact that one element can correlate with many others, displaying different scenarios of anthropogenic contribution. In some studies, long range transport (LRT) traffic emissions were identified in fine PM, in which As, Cl, K, S, and EC were well correlated (Ilacqua, et al., 2007). I. Under this category we regroup brakes of vehicles and tyre road wear, and also resuspended dust particles (see section I. 5.2.1.1). In the first group, Cu -Cd correlations seems to be a commonly used tracers for brakes of vehicles emissions ( [START_REF] Beslic | Relationship between 4day air mass back trajectories and metallic components in PM10 and PM2.5 particle fractions in Zagreb air, Croatia[END_REF] and [START_REF] Rajsic | Evaluation of the levels and sources of trace elements in urban particulate matter[END_REF]). However, Fe can also be considered as a tracer [START_REF] Vallius | Source apportionment of urban ambient PM2.5 in two successive measurement campaigns in Helsinki, Finland[END_REF] along with Ba and Mn [START_REF] Birmili | Trace metal concentrations and water solubility in size-fractionated atmospheric particles and influence of road traffic[END_REF] of brakes emissions. Furthermore, Sb -Cu correlations were used to identify brake linings in Spain (Moreno, et al., 2006) and in the United Kingdom [START_REF] Gietl | Identification of brake wear particles and derivation of a quantitative tracer for brake dust at a major road[END_REF], whereas Cr -Cu correlations were used for the same purpose in a study conducted in Amsterdam [START_REF] Boogaard | Contrast in air pollution components between major streets and background locations: Particulate matter mass, black carbon, elemental composition, nitrogen oxide and ultrafine particle number[END_REF].

On the other hand, tyres road abrasion can be traced by following Zn correlations [START_REF] Vallius | Source apportionment of urban ambient PM2.5 in two successive measurement campaigns in Helsinki, Finland[END_REF], or Ba -Mn correlations (Moreno, et al., 2006). In fact, high Zn concentrations in the tyres can be explained since the element is introduced in the vulcanization process during tyres manufacturing.

Finally, traffic related re-suspended dust particles were traced using Zn to Mn and Al to Fe correlations [START_REF] Rajsic | Evaluation of the levels and sources of trace elements in urban particulate matter[END_REF]. Another example can be found in [START_REF] Vallius | Source apportionment of urban ambient PM2.5 in two successive measurement campaigns in Helsinki, Finland[END_REF], in which a correlation between Fe and Mn concentrations was found to be a tracer of crustal traffic re-suspended dust. Also Cu, Zn, and Fe correlations pointed at traffic emissions (Vallius, et al., 2005) as well as Sb, Cu, Fe, and Zn correlations can be used as specific tracers of traffic emissions ( (Pakkanen, et al., 2001); [START_REF] Strak | Variation in characteristics of ambient particulate matter at eight locations in the Netherlands -The RAPTES project[END_REF]; and (Salvador, et al., 2012)).

I. For this source of emissions, studies seem to have some sort of a consensus on the use of Ni -V correlations to uncover this contribution source ( [START_REF] Cyrys | Elemental composition and sources of fine and ultrafine ambient particles in Erfurt, Germany[END_REF]; (Ilacqua, et al., 2007) and [START_REF] Mazzei | Characterization of particulate matter sources in an urban environment[END_REF]). On the other hand, heavy oil combustion can be identified by linking SO 2 emissions to Ni and V [START_REF] Vallius | Source apportionment of urban ambient PM2.5 in two successive measurement campaigns in Helsinki, Finland[END_REF]. In some cases however, Ni was associated to Mo and Co in PM 2.5 to point at oil combustion source of PM emissions (Pakkanen, et al., 2001).

I.5.2.1.5. Coal combustion

Arsenic was found to be one common element that is mostly used to trace coal combustion. It can also be correlated to Hg [START_REF] Freitas | Gravimetric and chemical features of airborne PM10 and PM2.5 in mainland Portugal[END_REF], to Se to trace residential coal combustion (Salvador, et al., 2012), to Cd and Pb [START_REF] Schleicher | Temporal variability of trace metal mobility of urban particulate matter from Beijing -A contribution to health impact assessments of aerosols[END_REF], or even to a list of metals: Cd, Bi, Tl, K, Zn, and Pb, to trace aged coal combustion aerosols (Pakkanen, et al., 2001). In other studies, unique metals in PM can be used as tracers of coal combustion process, like "S" [START_REF] Cyrys | Elemental composition and sources of fine and ultrafine ambient particles in Erfurt, Germany[END_REF] and "Se" (Ilacqua, et al., 2007) for example.

I.5.2.1.6. Long range transport (LRT)

Even though this origin of PM can be identified by studying ionic composition of the particles, complementary methods can still be used to uncover this source. For example, a combination of metals was studied by Ilacqua (Ilacqua, et al., 2007), in which LRT combined with traffic sources was identified by the presence of As, Zn, Pb and K. Finally, a correlation between Pb, Sb, and Cd was used to identify LRT sources in (Pakkanen, et al., 2001).

I. 5.2.1.7. Biomass combustion Tracing the biomass combustion emissions using metals concentrations is more difficult since one major metal is used in order to identify biomass or wood combustion: K. The problem reside within the use of this element to trace many other sources as well, especially industrial combustions and works, which will be discussed later. Potassium was used as marker of biomass combustion in Helsinki (Ilacqua, et al., 2007) and in Estonia [START_REF] Orru | Elemental composition and oxidative properties of PM2.5 in Estonia in relation to origin of air massesresults from the ECRHS II in Tartu[END_REF] as well. Furthermore, it can be used also to trace wood combustion emissions [START_REF] Vallius | Source apportionment of urban ambient PM2.5 in two successive measurement campaigns in Helsinki, Finland[END_REF]. Finally, to be able to better determine wood or biomass combustion sources, we must consider organic species (like organic carbon "OC") as well as the presence of high K content in PM. Also, between the different organic components, levoglucosan has been proved to be a suitable marker for biomass combustion in PM [START_REF] Favez | Evidence for a significant contribution of wood burning aerosols to PM2.5 during the winter season in Paris, France[END_REF]. I. 5.2.1.8. Industry Industrial emissions tracing can be one of the most challenging tasks when studying PM sources in the environment. The problem is within the complexity of separating the many different origins of industrial emissions. In an industrial complex, many activities may load the atmosphere with PM that has a close chemical composition, especially heavy metals combinations. For example, Pb and Cr correlations were used to trace industrial gasoline and oil refinery sources ( [START_REF] Gotschi | Elemental composition and reflectance of ambient fine particles at 21 European locations[END_REF] and [START_REF] Rajsic | Evaluation of the levels and sources of trace elements in urban particulate matter[END_REF]). In Madrid, industrial activities were identified by the presence of Cr, Ni, Pb, and Zn (Salvador, et al., 2012). In some cases, a combination of many elements (Zn, Mn, K, and Fe) with gases measurements (SO 2 ) can be used to trace industrial and incineration activities (Vallius, et al., 2005).

We will discuss hereafter examples about tracing some industrial activities that are contributing significantly to PM loads: 5.2.1.8.i. Steel works In tracing steel works emissions, [START_REF] Oravisjarvi | Source contributions to PM2.5 particles in the urban air ofa town situated close to a steel works[END_REF] found that K can be used to trace sintering works, Zn to trace smelting, and Cu for mechanical works. On the other hand:

I.
Pb, Zn, Mn, Cd, and As were found to constitute a signature for steel works activities in Spain (Moreno, et al., 2006).

Also by combining the chemical composition of PM 2.5 in the vicinity of a steel plant along with Factor Analysis-Multiple Linear Regression model (FA-MLR), [START_REF] Oravisjarvi | Source contributions to PM2.5 particles in the urban air ofa town situated close to a steel works[END_REF] have found that Ca, Cd, K, Na, Pb and Cl were correlated. Therefore, they can be used as tracers for sintering works. The same reasoning was used to trace the influence of steelmaking plant emissions by using Co, Fe, Li, Mn, Zn and F as tracers.

On the other hand, [START_REF] Dall'osto | A study of the size distributions and the chemical characterization of airborne particles in the vicinity of a large integrated steelworks[END_REF] studied the size distributions and the chemical characterization of PM in a region affected by a large integrated steelworks. They confirmed the presence of Fe, Pb, Zn and Ni when being under industrial influences, and the concentration of these metals in the micron and sub-micron modes.

Furthermore, SEM-EDX technique was used to uncover iron-rich spherical particles loads in blast furnaces and steel making plant emissions ( (Moreno, et al., 2006) and (Hleis, et al., 2013)). This feature can also be used to trace industrial activities as well.

Finally, coke plants emissions seems to be signed by OC, EC, SO

, As, and Se ( [START_REF] Weitkamp | Fine particle emission profile for a large coke production facility based on highly time-resolved fence line measurements[END_REF][START_REF] Jiun-Horng | Chemical constituents in particulate emissions from an integrated iron and steel facility[END_REF]).

I. Nickel can be used to trace this source of PM [START_REF] Freitas | Gravimetric and chemical features of airborne PM10 and PM2.5 in mainland Portugal[END_REF]. In addition to Ni, other metals can be used together to trace petrochemical PM emissions, such as V, Pb, Zn, Cr, and Co (Moreno, et al., 2006).

I. 5.2.1.8.iii. Metallurgy and Pigment industry Metallurgic activities were found to increase the concentrations of As, Cu, Pb, and Zn in PM 2.5 in Spain, whereas Cr, Mo, Ni, and Co along with As and Cu constituted a fingerprint for pigment industry (Moreno, et al., 2006), as well as Ti which is concentrated in the primary product of pigment industries.

I.5.2.1.8.iv. Cement industry

On the influence of the cement industry, Al, Si, Ca, SO 4

2-

, K, Mg, and Sr can be considered as highly associated in atmospheric PM originated from cement factories [START_REF] Santacatalina | Impact of fugitive emissions in ambient PM levels and composition: A case study in Southeast Spain[END_REF]. Nevertheless, the abundance of mineral species in this profile makes the separation between cement production and re-suspended soil dust a very difficult task. On the other hand, [START_REF] Gutierrez-Cañas | Source signature and HM's Enrichment in PM2.5 Emission from Clinker Production[END_REF] proceeded by studying emissions raw feed enrichment factor, and found that Cd, Pb, As, Cu, Sb, and Tl were mostly enriched compared to clinker raw feed. The same study explained the existence of a difference in metals emissions from this type of industrial activity related to solid fuel conversion processes.

I. 5.2.1.8.v. Glassmaking industry Particulate matter emissions from this type of industries contain numerous elements. A first group includes Al, Ti, Ca, Mg, Fe, Mn, Sr, Rb and Ba, which are concentrated in the ore used to produce glass ( (Rampazzo, et al., 2008) and [START_REF] Mantovan | Correlations among inorganic elements in particulate matter (PM10) in urban area of Venezia-Mestre[END_REF]). Other elements can also be emitted from other levels within the production line, especially after using specific elements for product composition improvement and/or for coloring (Li, Zn, Ag, Se, Cd, Sb and Pb). After Mantovan [START_REF] Mantovan | Correlations among inorganic elements in particulate matter (PM10) in urban area of Venezia-Mestre[END_REF], As, Se and Cd can serve as a "finger print"

of the glassmaking factory in Murano (Italy).

I.5.2.1.9. Ships, ship yards, and harbor Up to our knowledge, few studies dealt with the emissions from shipping transportation and/or harbor activities. Ship yard activities were traced by (Moloi, et al., 2002) using Ca -Ni correlations. In Helsinki (Finland), a combination of black carbon (BC), Zn, K, Ni, and V were used to identify oil combustion and ships emissions (Pakkanen, et al., 2001). It seems that Ni and V are commonly used to trace heavy oil combustion and ships activities that might include oil combustion emissions itself. Finally, Fe, Ni, V, Ca, and Ba were linked to use of petrol and/or oils [START_REF] Moldanová | Characterisation of particulate matter and gaseous emissions from a large ship diesel engine[END_REF].

I.5.2.2. Sources and contribution of ions in PM 2.5

Ionic composition plays a major role in defining the anthropogenic contributions in the load of fine PM 2.5 , but also to the chemical composition of these particles. In many studies around the world, soluble metals and other ions were analyzed to uncover natural and human contributions in PM emissions. Between the different ions present in the aerosols, Cl -, Na Pakkanen, et al., 2001) and (Salvador, et al., 2012)) or Mg and K (Ilacqua, et al., 2007).

Within this category of aerosols we can distinguish between "fresh sea salt" and "aged sea salt". The first type is directly emitted from the sea, in which ions such as sea-SO 4

2-

, Na + , Cl -, Mg 2+ , K + and Br -are concentrated in the aerosol with proportions similar to the ones found in sea water. However, once fresh sea salt passes over a polluted region, the relatively acidic atmosphere created by the anthropogenic emissions (such as HNO 3 and H 2 SO 4 ) in the atmosphere of the region will force a reaction between these acidic species and the sea salt.

This reaction results in the decrease in the concentrations of Cl -and Br -, which will be transformed into gaseous HCl and HBr [START_REF] Delmas | Physique et chimie de l'atmosphère[END_REF]. On the other hand, the reaction will increase anions such as NO 3 -and SO 4 2-in this secondary aerosol, also known in that phase as "aged sea salt".

I.5.2.2.2. Traffic related ions

In the use of ions to determine contributions, NO 3 -ions were found to point at traffic sources [START_REF] Gotschi | Elemental composition and reflectance of ambient fine particles at 21 European locations[END_REF]. Ionic loads can also result from distant sources and get to specific sites with LRT. Within this category, non-sea SO 4 2-ions constitute a signature for traffic-LRT [START_REF] Oravisjarvi | Source contributions to PM2.5 particles in the urban air ofa town situated close to a steel works[END_REF]. In another study, ion rich LRT aerosols were identified in a rural environment with an important SO 4 2-and NH 4 + load (Pakkanen, et al., 2001).

I.5.2.2.3. Secondary inorganic aerosols

Secondary inorganic aerosols were identified in Spain, where SO 4

2-

, NH 4 + , and V constituted the secondary sulfates aerosols (Salvador, et al., 2012). In this same study, secondary nitrates were found to be constituted mainly by NO 3 -and NH 4 + ions. Also, within the category of secondary sulfates, non-sea SO 4 2-are found abundant and can point at regional sources of pollution (Vallius, et al., 2005).

Finally, the contribution of ions in fine PM 2.5 mass can fluctuate depending on the sources, but it is always found exceeding that of metals. For example, SO 4 2-and NO 3 combined to Na + and NH 4 + was found to contribute in about 70% of PM 2.5 [START_REF] Yin | Pragmatic mass closure study for PM1.0, PM2.5 and PM10 at roadside, urban background and rural sites[END_REF]. On the other hand, a 47% contribution of SO 4 2-/ NO 3 -/ NH 4 + in PM 2.15 was found to be generated from diesel traffic emissions [START_REF] Hitzenberger | Size distribution of black (BC) and total carbon (TC) in Vienna and Ljubljana[END_REF]. Also, many studies proved that ionic loads in fine PM are not exclusively generated locally, but mostly on a regional level. In some cases rural sites may have higher levels that distant light traffic sites in the same region. Researchers have found that SO 4 2-/ NO 3 -/ NH 4 + proportion in fine PM had increased from 36%, to 38%, and rose to 42% in "light-traffic", "traffic-industrial", and rural site respectively (Pateraki, et al., 2012). The reason behind the elevation of ions levels at the rural site was linked to regional pollution sources instead of the local sources. Furthermore, SO

, NH 4 + , and K + ions were found to constitute over 85% of PM 2.5 in Lecce, southern Italy (Perrone, et al., 2011). Thus we can conclude the importance of surveying ionic species concentrations in PM 2.5 to be able to explain the load of particles as well as to trace local and distant source emissions. Its importance doesn't close down at this level, but it expands to the fact that these chemical forms constitute a major part of fine PM, as discussed earlier in this section, when compared to metals whose contribution in the PM mass is found relatively smaller.

I.5.2.3. Sources of carbon

The carbon content is divided between two major types: organic carbon (OC) and elemental carbon (EC). The latter is also known as black carbon or soot. The sum of OC and EC content in PM gives the total carbon content (TC). EC and OC can be emitted directly and simultaneously into the atmosphere from combustion sources, whereas OC can have other origins such as photochemical reaction of gaseous precursors in the atmosphere. The variations between TC, OC, and EC in relation to the influence of pollution sources will be discussed hereafter.

TC content in PM 2.5 fluctuates depending on nearby sources of pollution. It can vary from 23% to 37% in some cases [START_REF] Yin | Pragmatic mass closure study for PM1.0, PM2.5 and PM10 at roadside, urban background and rural sites[END_REF]. In Lecce, southern Italy, TC made about 29% (with a max getting to 50% in some samples) of PM 2.5 (Perrone, et al., 2011). In this same study, OC and EC concentrations ranged between 2 -13 µg/m 3 and 0.4 -5 µg/m 3 respectively. Furthermore, EC and OC contents were investigated in 6 European cities in 2006 (Sillanpaa, et al., 2006) to find that these components constituted the major load of chemicals in PM 2.5 , with OC always having the highest contribution when compared to EC: 31% (OC) to 9% (EC) in Duisburg; 54% (OC) to 5.7% (EC) in Prague; 23% (OC) to 5.4% (EC) in Amsterdam; 46% (OC) to 8.4% (EC) in Helsinki; 21% (OC) to 7.6% (EC) in Barcelona; 35% (OC) to 6.6% (EC) in Athens (Sillanpaa, et al., 2006).

OC can be found more concentrated than EC in PM 2.5 when sampling is located next to a traffic site. This was verified in (Pateraki, et al., 2012), where three types of sites was studied: light traffic, traffic and industry, and a distant control site. In this latter, OC and EC contents were 11% and 4% respectively, showing that at the less influenced site OC was higher than EC. However, at the light traffic site, OC and EC were found to constitute 18% and 3%, which verifies the effect of traffic on OC concentrations. However, the second site influenced by industrial emissions as well as traffic, EC concentrations rose to almost match the OC content (OC = 17% and EC = 15%) showing that traffic emissions contributed in OC when industrial emissions raised EC concentrations.

In another study, a comparison between diesel and gasoline traffic was studied [START_REF] Hitzenberger | Size distribution of black (BC) and total carbon (TC) in Vienna and Ljubljana[END_REF]. Diesel traffic was found to increase EC/TC ratio when compared to gasoline traffic ratio.

Finally, a mixture of combustion emissions can also be identified using carbon concentrations. For example, good EC correlations with Zn, K, V, and Ni pointed at local traffic and ships emissions contributions in Helsinki (Pakkanen, et al., 2001).

I.5.3. Health effects of fine PM inhalation

Studies about the health effects of PM inhalation focused on many adverse effects from which asthma, increase in lung cell permeability, oxidative stress on DNA, and cardiovascular / respiratory / cerebrovascular diseases were the most important. Furthermore, and as discussed earlier in section I.4.3, the effects of inhalation can also lead to the appearance of chronic diseases such as cancer. On the other hand, focus has also been given to the inhalation mode of particles, mostly to outdoor and indoor exposure scenarios.

London smog in 1952 marked a starting point of the interest in the relationship between pollution and health effects. Afterwards, researches on the health impacts of particles inhalation increased significantly specially in the 90's. Important associations were found between PM 10 , black smoke, and sulfate concentrations and the prevalence of symptoms of the lower respiratory tract in children living in Netherlands [START_REF] Saskia | Acute effects of urban air pollution on respiratory health of children with and without chronic respiratory symptoms[END_REF]. On the other hand, severe inflammatory responses and extensive lung injuries were observed in mouse lung as a response to the soluble fraction of collected ultrafine particles [START_REF] Ian | Pulmonary toxicity of an atmospheric particulate sample is due to the soluble fraction[END_REF].

Furthermore, investigations on the effect of traffic related emissions were conducted to find that cough, wheeze, runny nose, and doctor diagnosed asthma were significantly reported for children living within 100 m from a freeway [START_REF] Patricia | Motor Vehicle Exhaust and Chronic Respiratory Symptoms in Children Living near Freeways[END_REF]. In addition, carbon black particles toxicity on rodent lung epithelial cells was found increasing with the increase in the total surface and the decrease in the size of carbon particles [START_REF] Murphy | Bioreactivity of carbon black and diesel exhaust particles to primary Clara and type II epithelial cell cultures[END_REF].

On the exposure to fine PM, outdoor personal exposure was linked to the transportation mode in use. Passengers in cars and buses were found to be more exposed to high concentrations of PM compared to cyclers and people walking on sidewalks [START_REF] Kaur | Determinants of personal exposure to PM2.5, ultrafine particle counts, and CO in a transport microenvironment[END_REF], to conclude that the location and duration of exposure is a factor to be considered. Furthermore, indoor versus outdoor exposure to PM was studied in [START_REF] Buonanno | Tracheobronchial and alveolar dose of submicrometer particles for different population age groups in Italy[END_REF]. In this study, females were more exposed to PM while cooking, when men were exposed in working time, and finally children were mostly exposed to emissions from transportation traffic.

Combustion related PM exposure were associated to respiratory mortality (PM 2.5 ), and cardiovascular and cerebrovascular mortality (PM 1 ). Furthermore, red-ox metals content of this fine PM contributed in the oxidative stress of human cells [START_REF] Perez | Size fractionate particulate matter, vehicle traffic, and case-specific daily mortality in Barcelona, Spain[END_REF].

Traffic exposure and effects of traffic emitted particles were extensively studied. On this subject, traffic exhaust was found to induce DNA oxidative stress, and was verified using 8-hydroxydesoxyguanosine (8-OHdG) as a tracer for the effects of such emissions [START_REF] Lai | Exposure to traffic exhausts and oxidative DNA damage[END_REF]. Furthermore, emissions from combustion in vehicles were found to increase the risk of asthma in children (up to 14 years of age). Concentrations of PM 2.5 , EC and OC, and SO 4 2increased the risk of asthma by 6%, 7.4%, and 2.1% respectively in that age group [START_REF] Anderson | Particulate matter and daily mortality and hospital admissions in the west midlands conurbation of the United Kingdom: associations with fine and coarse particles, black smoke and sulphate[END_REF]. Also, Zn and S rich PM emitted with tire debris were found to alter the morphology and proliferation of alveolar cell line A549, accumulate in human liver cell line HepG2, and cause malformations in X. Laevis frog embryos [START_REF] Gualtieri | Impact of tire debris on in vitro and in vivo systems[END_REF].

Respiratory and cardiovascular diseases were found linearly linked to exposure of elderly people to LRT -PM 10 , whereas pediatric asthma was associated to the exposure to NO and accumulation mode PM emitted mainly from traffic [START_REF] Andersen | Size distribution and total number concentration of ultrafine and accumulation mode particles and hospital admissions in children and the elderly in Copenhagen, Denmark[END_REF]. Another example on traffic effects can be observed in Spain, where a linear relationship was found between PM 2.5 , NO, circulatory problems, heart disease, and cardiovascular problems [START_REF] Maté | Short-term effect of fine particulate matter (PM2.5) on daily mortality due to diseases of the circulatory system in Madrid (Spain)[END_REF]. Also, the inhalation of smoke was found to increase the levels of Clara Cell protein CC16, which is an indicator of lung cell permeability [START_REF] Timonen | Daily variation in fine and ultrafine particulate air pollution and urinary concentrations of lung Clara cell protein CC16[END_REF].

On a global European level, a recent study on the effects of fine PM was conducted in 25 European cities. In its summary report [START_REF] Aphekom | Summary report of the Aphekom project 2008-2011[END_REF], many results were illustrated on the effects of reducing PM 2.5 emissions, exposure to PM 2.5 , and SO 2 emissions on population health and the economy in Europe. This works outcome showed that a decrease to a level of 10 µg/m 3 in PM 2.5 concentration (WHO annual mean; see section I.4.2) could add up to 22 months of life expectancy for adults older than 30 years of age, depending on the city in which they are mostly exposed. On the other hand, exceeding the WHO guideline for this size fraction will eventually lead to 19000 deaths, 15000 of which from cardiovascular diseases, and about €31.5 billion in health and related costs. The study also showed the positive feedback of sulfur reduction in fuels in 20 cities, in which 2200 premature deaths valued at €192 million were prevented. And finally, the report summarized the effects of PM inhalation on the human body (Figure 24). The NPdC region (12414 km²), which is concerned by this study is located on the northern coastline border of France (Figure 25). The coastal side of the region stretches between two regional seas: English Channel and North-Sea. The coastal side of this area is called the "Littoral Côte d'Opale", and the whole region is known for its high industrial and agricultural activities, but also for the high urbanization characterized by a dense population [START_REF] Atmo | Synthese -Bilan Annuel[END_REF]. Its location close to the sea makes winter temperatures relatively acceptable, with a high average of wind velocity. This climatic condition favors pollutants dispersion, except when sea breeze occurs, during which pollutants accumulate above the coastal zone. Finally, transportation routes, high activity areas, and cities represent about 13% of the NPdC terrain surface, which proportionally is the most urbanized region (8.3%) when compared to the rest of France (ATMO, 2011).

I.6.1. Emissions of PM in NPdC

In this section we will discuss the emissions of particles from anthropogenic sources in the NPdC region, with emphasis on recent data, as well as on the emissions in different cities within the region, particularly: Dunkerque, Boulogne-sur-Mer and Saint-Omer. These three cities are the main focus of this study, and are subjected to different kinds of pollution sources.

The declared total industrial dust emissions in the NPdC region represented in Figure 26 shows a gradual decrease in the scale of emissions between 2004 and 2009, after which we can observe a light increase. Figure 27 shows dust emissions evolution for each of the most important industries in NPdC.

By analyzing the chart, we notice that the emissions are relatively decreasing for ArcelorMittal and EDF, when it is fluctuating around a stable level for SRD and Al Dk. In addition, we should note that Arcelor Mittal, SRD, and Al Dk are all located in Dunkerque.

This proves the importance of Dunkerque's site as a first choice for air quality study. The first three represent the sites that are the subject of this study, whereas "Bethune" is an inland rural site less influenced by pollution sources and is similar to "Saint-Omer", and finally "Lille" is the most urbanized city between the others in the NPdC region.

Dunkerque is found to have the highest PM 10 mean concentration between the other cities except in 2009, when "Lille" and "Saint-Omer" had higher values. Dunkerque is a highly industrialized site, which explains the elevation in the PM 10 mean concentrations over the years to pass over the levels at "Lille", which is largely more urbanized than "Dunkerque". However, the decrease in 2009 might be related to world economic crisis that highly affected the industrial sector. The lack of data in "Saint-Omer" before 2006 makes us wonder about its comparison to "Bethune" and "Boulogne-sur-Mer". However, the data collected in 2007, 2008 and 2009 shows that the city is more concentrated in PM 10 when compared to the other two. In January 2007, a new PM 10 measuring methodology was adopted and installed in different air monitoring networks. This modification aimed at adjusting the French monitoring results to match the criteria defined in the European method by considering the amount of volatile compounds for the concentration measurements. The change affected daily and annual concentrations that were found higher. This increase stands behind the elevation beyond limit values.

I.6.2.2. PM daily limit exceedance

In France, the daily PM 10 limit value (50 µg/m 3 ) was the subject of intensive monitoring in the past few years. It has been noticed that the 35 days of exceedance per year allowed were exceeded in many French regions like Ile de France, Rhone-Alpes, Alsace, Provence

Alpes Côte d'Azur and Nord Pas de Calais (Figure 29). The situation was relatively alleviated in 2010, which can be partially explained by the meteorological conditions that favored atmospheric particles dispersion.

However, exceedance episodes were identified on a regional level in NPdC, from which 14 days of particulate pollution have lead to raise the alarm of the "procédure d'information et de recommendation (PIR)". In 2011, a total of 35 days of PIR were detected during winter and the beginning of spring.

I.6.2.3. Physicochemical characteristics of PM and its origins

The "Littoral Côte d'Opale" region is highly industrial as well as urbanized, therefore is influenced by many atmospheric PM pollution sources. The emissions of these PM can originate from different sectors such as residential, industrial, transport, natural… etc, but also natural emissions which are mostly emitted by sea spray due to the coastal aspect of the region. Many studies have been conducted in the region in order to uncover the chemical composition of these emissions, but also to understand their possible toxicity on humans.

These studies were conducted majorly within the research strategy of the "Institut de

Recherches en Environnement Industriel" (IRENI), which constitutes a multidisciplinary research program that is focused on the study of air quality by following three major research axes:

-Air quality: metrology, modeling and remediation On the chemical composition of PM in the "Littoral Côte d'Opale" region, [START_REF] Ledoux | EPR investigations of Mn2+,Fe3+ ions and carbonaceous radicals in atmospheric particulate aerosols during their transport over the eastern coast of the English Channel[END_REF] and (Ledoux, et al., 2006) studied the chemical properties of paramagnetic Fe 3+ and Mn 2+ in aerosols collected in Wimereux and Boulogne-sur-Mer respectively. The latest showed that paramagnetic species concentrations increased with wind coming from the continental sector. On the other hand, more focus was given to the city of Dunkerque due to the multiple pollution sources present in and around the city. Hence, using Electronic Paramagnetic Resonance (EPR), Fe 3+ ions were found in agglomerated form within aerosols coming from the industrial wind sector. In the same time, the study evidenced that agglomerated iron was found within the coarse PM fraction compared to the iron clusters found in the fine fraction [START_REF] Ledoux | EPR investigation of iron in size segregated atmospheric aerosols collected at Dunkerque, Northern France[END_REF]. Atmospheric aerosols concentrations in Dunkerque were found higher in winter when compared to summer (Ledoux, et al., 2006).

-
Furthermore, metals such as Al, Ba, Fe, Pb, and Ti were found highly concentrated with winds originating from the industrial sector in the summer and with urban wind sector in the winter. In the same study, it was found that 27% of iron was mainly resulting from industrial activities, whereas Zn high concentrations originated from a continental sector. Finally, LRT aerosols were found to highly contribute in the loads of SO Other studies were directed towards uncovering specific tracers of steelmaking ((Lamaison, 2006) and (Hleis, 2010)). Moreover, these studies focused on the physicochemical characteristics of samples collected at the sources of emissions as well as in the ambient air under the same sources sectors. In addition, the evaluation of the impacts of these industrial emissions on ambient air levels of PM was evaluated using receptor modeling namely: positive matrix factorization (Lamaison, 2006) and non-negative matrix factorization (Hleis, 2010). These studies uncovered a moderate contribution of industrial sources in the levels of PM 10 . On the other hand, the modeling proved that the loads of certain metals (Fe, Mn, Pb and Cd) are majorly resulting from industrial contributions.

In addition, [START_REF] Cazier | Sampling analysis and characterization of particles in the atmosphere of rural, urban and industrial areas[END_REF] found that PM 2.5 collected in Dunkerque were loaded with Zn that is originated from nearby industries and tyre wear, but also with Mn from Fe-Mn production site nearby, Ca in CaCO 3 form, and Mg-K from crustal and marine sources.

The toxicity of airborne PM 2.5 was studied in Dunkerque to find that industrial and motor traffic related PM increased oxidative stress and inflammatory responses on L132 epithelial lung cells [START_REF] Garçon | Dunkerque city air pollution particulate matter-induced cytotoxicity, oxidative stress and inflammation in human epithelial lung cells (L132) in culture[END_REF]. On the other hand, reactive oxygen species as well as protein secretion of inflammatory mediators were increased due to the exposure of BEAS-2B human bronchial cells to PM 2.5 collected in Dunkerque. These reactive oxygen species point at an increase in oxidative damage and bronchial inflammation respectively [START_REF] Dergham | Prooxidant and proinflammatory potency of air pollution particulate matter (PM2.5-0.3) produced in rural, urban, or industrial surroundings in human bronchial epithelial cells (BEAS-2B)[END_REF].

I.7. Hypothesis and objectives

The NPdC region is a section of the French zones exhibiting high levels of suspended particles which are regularly recorded by official air monitoring network stations. On the other hand, the recorded atmospheric levels of PM 10 show that concentrations increased above the limit values many times since 2007, in particular during the winter season and at the beginning of spring. This seasonal increase was also observed in other regions in France, such as in Ile de France and Rhône-Alpes. For these reasons, at the beginning of 2011, the European Committee ranked France in a contentious situation due to the disrespect of the environmental legislation concerning the daily average limit value. In addition to the European context, there is an increasing need to understand the reason behind these above limit values levels on the French national level. Therefore, it is necessary to assemble enough scientific data in order to provide answers for the questions raised on both national and European levels.

In parallel, the legislation on the limit values of PM 2.5 (Directive-2008/50/EC) evolved since 2010 and will be roofed to 25 µg/m 3 in January 1 st , 2015. In addition, we remind that an objective of 10 µg/m 3 (annual mean) is under scope as a long term atmospheric level to be attainted for an adequate protection of human health.

Until now, few studies had been conducted on the exposition level to fine PM in NPdC cities. Furthermore, the continuous monitoring of PM 2.5 concentration trends is not yet matched by chemical analysis. Hence, a real deficiency in the knowledge on the chemical composition of this fraction is factual, especially in the region where the exposition to regular road traffic, domestic or industrial emissions is the most prominent.

In this study, we intend to study the chemical composition and origins of PM 2.5 in average sized cities located next to the littoral facade of the region (Dunkerque, Boulognesur-Mer and Saint-Omer). In fact, up to our knowledge, no studies were conducted on the chemical composition and origins of PM 2.5 in this region especially in Boulogne-sur-Mer (coastal) and in Saint-Omer (40 km inland) except some studies in Dunkerque.

There are many reasons standing behind our choice of these three cities:

-Dunkerque is an urban-industrial site that could be influenced by urban, industrial and marine sectors.

-Boulogne-sur-Mer represents a urban background coastal city that is subjected to a main marine influence, but does not include any industrial PM emitters.

-Saint-Omer is an urban center located inland and could be influenced by marine wind intrusions similarly to the first two sites. The city encloses an industrial site and is surrounded by vast agricultural lands.

The principal objectives of this work aim at:

-Acquiring knowledge on the chemical composition of PM 2.5 , like ionic species, major metallic and trace elements and carbonic fraction.

-Identifying different particles sources of emissions -Constructing spatial/chronological trends of PM composition and comparing the levels of exposition from one city to another.

-Evaluating the contribution of natural versus anthropogenic levels in the load of atmospheric suspended PM 2.5 .

In order to accomplish these objectives, this study was divided into three successive actions performed following this sequence:

II.1. Introduction

This chapter includes a detailed explanation of the materials and methods used in this study in order to achieve the obj ectives mentioned in chapter I. It begins with a description of the Nord-Pas-de-Calais region as well as the chosen sampling sites for this study.

A second section will follow, and includes explanations on the used sampling materials, details on the functioning of the high volume sampler, the used sampling filters and the acquisition of meteorological data.

The third section of this chapter revolves around the analysis materials and procedures that were used in order to determine the chemical composition of the collected PM 2.5 samples.

In total, four different methods were used for the quantification of a large list of chemical species in the collected samples. The chapter will be concluded by the sampling protocol validation experiment.

II.2. Sampling sites

Our proj ect scope is to study PM 2.5 levels and origins in the Nord-Pas-de-Calais (NPdC) region. The logical choice was to select a group of cities located at a certain distance from each other and subj ected to a combination of pollution sources including similar and different types.

The main site under focus in this work is located in Dunkerque (about 222417 inhabitants), followed by another site in Boulogne-sur-Mer (about 118932 inhabitants) and a third in Saint-Omer (about 64000 inhabitants), and finally all three sites can be found illustrated in Figure 1.

II.2.1. Dunkerque

This city is heavily industrialized and located on the coastline of the north of France, and is limited by the French-Belgian border from the east. However, the sampling site is located within the city center, next to the "Lamartine" university building of the Université du Littoral Côte d'Opale (Lat.: 51.036323; Lon.: 2.379408). The sampling site was chosen specifically in a way that allows us to sample under the effects of urban and industrial emissions (Figure 2). In summary, depending on the wind direction, this sampling site can be subj ected to a possible influence of a mixture of PM sources: the urban sector extends on a wide range (60°-290°) in some sectors of which appears an Electric steel plant (30°-70°) and an integrated steel works (260°-290°) influences. Finally, the northern sector represents the marine sector influence. Our sampling point was located at 10m elevation from the ground level. It was installed on the rooftop of the "Lamartine" building to prevent sampling of any punctual events localized at the street ground level.

II.2.2. Boulogne-sur-Mer

The second sampling point is located in Boulogne-sur-Mer city and called "Musée" (Lat.: 50.723461; Lon.: 1.608783). Boulogne-sur-Mer is a coastal fishing and touristic site located at about 70 to 80 km south-west of Dunkerque (Figure 3).

The marine influence through sea salt emissions can affect the site from almost all directions except the SE sector. Direct marine emissions can arrive to the sampling location from the nearby marine sector (180°-45°). In addition, seaport activities can also influence the city from the western side (250°-310°). However, emissions influence from Dunkerque's industrial-urban site is a possibility that cannot be discarded. Thus, NE air masses can hold marine, urban-industrial and continental types of emissions. Furthermore, the 70°-180° sector can be a source of continental air mass from inland Europe. Finally, the remaining 180°-250° The sampling at each site was realized using an automatic High-Volume Sampler (HVS) made by DIGITEL®: the DA-80 (Figure 5-(A)). The sampler is equipped with field housing for additional protection in the field, and is characterized by an automatic filter changer, a precision flow meter coupled to a photoelectric cell (flow rate sensor) that regulates the turbine suction power by monitoring a floater in the flow meter. The sampler is also equipped with thermal resistances to regulate the inside temperature of the machine as well as the sampling head in which the separation of the PM sizes occurs. Heating is used in the machine to prevent any possible malfunction resulting from freezing when temperatures drop below zero. Finally, the DA80 is also capable of recording outside temperatures and atmospheric pressure in addition to sampling data such as date, sampling start and end time, air flow correction coefficients, pressure correction coefficients, and most importantly the real sampling volume taking into account sampling temperature and pressure. Metallic grids were used to stabilize the DA-80 that was firmly attached to. The grids held about 150 kg (15 x 10 kg) of cement blocks made specifically to maintain a stable position when subj ected to heavy winds (Figure 6).

ISW

II.3.2. Sampling filters

The choice of filters was linked to the obj ectives of the proj ect. It is highly related to the analytical procedure and the chemical species that are aimed at in this work which include a list of elements, water soluble ions and total carbon. Therefore, we were forced to consider two main filters matrices: cellulose and quartz fiber filters. The first type is used for elements and ions analysis, when the other is typically used for carbon quantification. However, cellulose based samples cannot be used for carbon analysis since the carbon loaded matrix of the filter will bias our results. On the other hand, we found through chemical analysis that unwashed quartz fiber filters have high contents of metals incrusted within the fibers (see Table 8 in section II.5), and hence are not washable with acids. Therefore, we were obliged to use both types of filters simultaneously for sampling.

II.3.2.1. Cellulose filters contamination reduction

Unwashed W hatman® 41 cellulose filters (150 mm) have significant concentrations of many elements (up to 15 μg/ g in the case of Fe for example). These filters need to be washed before sampling and therefore were treated as described by (Ledoux, et al., 2006) This latter is an inland station located next to our site in Saint-Omer.

As for the PM 2.5 hourly average concentrations (in µg/ m 3 ), the data was retrieved from website of Atmo-NPdC (http: / / www.atmo-npdc.fr), which can be found in the information section on daily recording. W e should note that PM 2.5 data was available only for Dunkerque, whereas PM 10 was retrieved for all three sites.

II.3.4. Filters preparation before sampling

Our obj ectives force us to consider W hatman® 41 cellulose filters and W hatman® QMA quartz microfiber filters as substrates for PM sampling. Consequently, we found that combining the two types in one filter can be used as a support for sampling. This filter is constituted of half cellulose filter glued using an adhesive tape to a half quartz fiber filter.

This resolves the analytical problem by giving us one half of a filter as cellulose for elemental and soluble ions analysis and the other as quartz fiber for carbon quantification (Figure 7). This mixture resolved the analytical issue but conversely created another problem that concerns the volume of air flux through each part of the filter. Usually, when sampling occurs using a homogeneous filter (one type of fiber), PM deposition is considered to be uniform on the filters surface because air is passing regularly through the surface. This fact could not be adopted in our study since the filter is made from two different matrices, and obviously one side will have higher flux than the other as can be clearly seen in one of our samples.

Furthermore, the collected particles are deposited on the surface of the quartz fiber filter, but they penetrate deeply into the fibers of the cellulose filter (Figure 8). One consequence of this difference in air fluxes is the accumulation of PM on one side more than the other, which will eventually leads to an under-or over-estimation of the concentrations depending on the analyzed chemical species.

a b c

Finally, in order to fix this problem, we proposed a method that has been validated and described in section II.5.

II.4. Analysis of inorganic components and total carbon

In this work, an important list of chemical species was analyzed which includes the following elements: Ag, Al, As, Ba, Bi, TC, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Nb, Ni, P, Pb, Rb, Sb, Sc, Sn, Sr, Te, Ti, Tl, V and Zn. In addition, the following list of water soluble ions was also analyzed: NH 4 + , Cl -, NO 3 -and SO 42-

. The analysis of the elements was realized using Inductively Coupled Plasma-Mass spectrometry (ICP-MS) and Inductively Coupled Plasma Atomic Emission spectroscopy (ICP-AES), whereas the analysis of total carbon was achieved using a CHNS/ O microanalysis. Finally, the water soluble ions were quantified using ion chromatography. These techniques, along with their respective analytical conditions are detailed in the upcoming sections.

II.4.1. Inductively Coupled Plasma-Mass Spectrometer (ICP-MS)

This method was used in our study in order to quantify a list of elements that is usually found in trace amounts in the atmosphere. It requires dissolution of the collected particles in order to be introduced in the analyzer in liquid form.

II.4.1.1. Samples preparations

Using a scalpel blade, approximately 250 mg (about 1/ 8 th ) of each sampled cellulose and quartz fiber filter were cut and placed into a PTFE flask, then digested with 5.5mL of HNO 3 / HF/ HClO 4 (4: 1: 0.5 v/ v/ v, Suprapur®, Merck®) at 120°C for 4h. The acids were then evaporated at 170°C for 4h using heating wells, and ultrapure water was added to the residue and heated at 90°C for 1h. In order to perform this digestion, we used a DigiPREP MS® digestion kit that allows the digestion of 48 samples simultaneously.

Finally, the cooled solution was diluted with MilliQ® water up to 10 mL added with 0.2% of nitric acid and filtered on PTFE membrane (0.45 µm) prior to analysis.

II.4.1.2. ICP-MS principles and characteristics

Metals analysis was realized using inductively coupled plasma-mass spectrometer (ICP-MS) coupled with a collision reaction cell to minimize polyatomic interferences during the measurements. The analysis begins with the sample inj ection in the analyzer, and then the solution containing the elements passes through a nebulizer which transforms it into fine droplets and inj ects it in the plasma torch in a flux of argon gas. The argon gas is burned with the droplets at 8000⁰C which is sufficient to ionize all the atoms in the sample by eliminating one peripheral electron from each. Inside the torch, the sample goes through different phases:

liquid, solid, gas, atom then ion. The latter enters a quadrupole unit that separates in a fraction of a second the ions depending on their mass to charge ratio (m/ z). Hence, each ion quits the quadrupole at a certain specific time. Finally, the concentration relative to each element that hits the detector is proportional to its quantity in the sample and is measured by the mass spectrometer in counts per second.

Many polyatomic species such as ArC + , ArO + , NaAr + , CaAr + , and MgAr + can disrupt the measurements due to the fact that they have the same m/ z ratio than that of other analyzed elements. For that reason, we usually break polyatomic species by using the following collision gases: helium, hydrogen and air. These gases are inj ected one at a time in a Collision

Reaction Cell (CRC) located between the plasma torch and the mass spectrometer. This section of the instrumentation is also known as Collision Reaction Interface (CRI). The gas collides with polyatomic species, breaks them and continues its path. Collision gases selection is based on their efficiency from a thermodynamic point of view, and also on the atomic mass.

Smaller masses reduce the loss of sensitivity due to atoms dispersion during the collisions.

Table 1 illustrates the characteristics of our ICP-MS method. For the analysis we used a Varian® 820-MS, with a Collision Reaction Interface (CRI) in which we used helium as collision gas in addition to air used to quantify elements with minimum polyatomic interferences. Data treatment was realized using Varian® ICP-MS expert v2.1 b105 software.

Standard solutions for ICP-MS calibration were prepared from a multi-element solution (10 ppm). The dilutions were achieved using MilliQ® ultra pure water, and the volumes were measured by weighting the flasks at each addition to obtain higher precision. Calibration solutions were also acidified with 0.2% HNO 3 Suprapur®.

The Detection Limits (DL) calculated for the studied elements given in Table 1 are equal to three times the standard deviation value calculated from 10 blank solution measurements. All the considered elements gave above DL concentration values, except Se which was always below DL.

II.4.2. Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES)

ICP-AES was used to quantify some elements that are usually found in moderate to high concentrations in the atmosphere and hence do not require any additional sensitivity in the detection procedure. Our ICP-AES instrument is the iCAP 6000 Series from Thermo SCIENTIFIC®. The functioning mode of the instrument is similar to the ICP-MS Varian® 820-MS described above, but the chemical species detection is different.

The samples should be introduced in the analyzer in liquid form. Therefore, the same solutions obtained after samples mineralization (see section II.4.1.1) were analyzed with this technique. Each sample is inj ected and then sprayed with a nebulizer inside a plasma torch where the elements of the sample change phases starting from liquid, passing through solid and gas, and ending in an excited atom form. Instantly after the transformation, the excited atom will return to its initial non-excited status and emit a ray that is characteristically linked to the atom. The group of rays emitted from the excited atoms is transferred inside a dispersive system and then diverged to separate each ray by its wavelength. The diverged rays are redirected to face a plate formed by photomultiplier that increase the wavelengths One inconvenience can be encountered when using this method is the presence of many energy levels for atomic excitation: thus one atom can emit many rays (having different wavelengths) while going back to non-excited status depending on which high energy level he is found at after excitation. The selection of rays was based on the capacity of these latter to give the highest intensity for each element and on the minimum interferences between them.

The list of elements quantified using this technique, along with the respective analytical conditions are illustrated in (Table 2). In the case of Fe, Mg and Na, the concentration was the average concentration found between the two highest intensity rays. The instrument used for the analysis can also detect emitted rays using two modes: radial detection and direct axial detection. The latter is the most sensible between the two methods, and hence it is selected for our analysis.

II.4.3.1. Samples preparations

Approximately 30 mg of each QMA quartz fiber filter was weighted using a Mettler Toledo® microbalance with a 0.01 mg precision. This weighted fraction of the filter is enclosed in two (25 x 25 mm) Thermo Scientific® tin foils using laboratory tweezers. The samples are then arranged in an automatic sampler that is piloted by the analyzer software (Thermo Scientific® Eager Experience version 1.2).

II.4.3.2. CHNS-O Analyz er principles and characteristics

The quartz reactive column is composed of a combustion area at the top, which is a few centimeters of empty space that is heated up to 900°C to allow the combustion of the introduced sample. Afterwards, a 10 mm layer of quartz wool separates between the combustion zone and a few centimeters layer of cooper oxide granules. This latter is separated from cooper metallic bars (up to 10 cm) by another 10 mm layer of quartz wool. At the end of the cooper bars, another 25 mm of quartz wool are laying on the bottom of the column. A constant helium flux passes through the column and plays the role of carrier gas.

The sample enclosed in tin foils is introduced into the heated combustion side of the column were tin rapidly atomize, thus increasing the temperature around the sample up to 1800°C approximately. This reaction occurs in the heated combustion area at the top of the column. At this high temperature, all carbon content of the sample in the capsules is atomized.

Total released carbon is then oxidized into CO 2 after passing through the oxidation section formed by cooper oxide granules. The excessive oxygen is captured in the cooper bars which are the reduction section of the column. Then CO 2 passes with helium gas vector into a gas chromatography column (used for the separation of C, H, N, and S), to continue afterwards into the detector: a katharometer. The detection at this level is measured by the difference in the thermal conductivity between the carrier gas and the formed CO 2 .

Calibration of the analyzer was realized using soil (2.29% w/ w carbon) and cystine (C 6 H 12 NO 4 S 2 ; 30.05% w/ w carbon) certified reference materials. The calibration details are summarized in Table 3. The DL for TC analysis was calculated using the standard deviation (SD) equation applied on 10 measurements of Tin paper blanks TC content: DL = 3 x SD.

The DL of our analyzer was 0.00367 mg of carbon, which is verified to be lower than the contents of all analyzed samples in this study.

II.4.4.1. Samples preparations

Anions and cations were extracted from a piece of the cellulose filter (about 1/ 6 th ) that is sliced using a scalpel blade, then weighted and placed in the bottom of a beaker and covered with a small quantity of Milli-Q® ultrapure water (2 to 3 mL). The beaker is placed in an ultrasonic bath for 15 min, and the leachate containing the ions is filtered using a syringe coupled to a Sartorius® cellulose acetate membrane (porosity 0.45 μm). The leachate is then placed in a polyethylene flask. The filter was submerged again in few milliliters of ultrapure water Milli-Q® before undergoing another ultrasonic bath and filtration. The same procedure was repeated three times successively. The final volume of the gathered extracted solutions in the flask is finally adj usted up to 20 mL by adding ultrapure water, and the resulting solution is conserved in the fridge at 4⁰C until analysis.

II.4.4.2. Ion chromatography principles and characteristics

Simultaneous analysis of water soluble anions (F -, Cl -, NO ) was realized using a Dionex DX 100® coupled to a Dionex ICS 900® ion chromatography column, after water leaching of a part of the collected cellulose filter.. The information given by the conductivity meter was treated using CHROMELEON® software.

For anion analysis, a solution made from Na 2 CO 3 and NaHCO 3 (3.5 mM and 1 mM respectively) is used as mobile phase. It passes in the chromatographic column in a 1.2 mL/ min solution flux. An electrochemical suppressor is used to decrease the mobile phase signal. On the other hand, an initial anion stock solution of 500 mg/ L was prepared using EMSURE® MERCK NaF, NaCl, NaNO 3 , Na 3 PO 4 and Na 2 SO 4 salts. The calibration range covered a range between 0.5 and 100 ppm including 1, 2.5, 5, 10, 25 and 50 ppm.

A solution of methane sulfonic acid (CH 3 SO 3 H, 20 mM) is used as mobile phase in the soluble anions analysis, in a 1.2 mL/ min eluent flux. An electrochemical suppressor is also used to neutralize the conductivity of this mobile phase. Calibration of the anion column was realized using a stock solution (500 mg/ L) prepared from NH 4 Cl salt from EMSURE® MERCK. The etalon covered the same concentration range used for anions analysis.

Finally, a succession of blanks analysis enabled us to estimate the detection limit of our analyzers for both anions and cations. This detection limit was calculated for each ion based on the standard deviation of the results of 10 ultra pure water Milli-Q® blank samples following the same equation used for TC detection limit: DL = 3 x SD. The results showed that F -and PO 4 3-ions could not be detected, whereas for the rest of the ions the DL are summarized in Table 4. Using the same method described in section II.4.1.1, our SRM-1648 certified dust sample was digested and prepared for elemental analysis for a selection of elements. Table 5 shows the details about this analysis, in which the recovered values proved to be highly acceptable. On the other hand, TC certified soil and cystine samples were also analyzed using our CHNS-O analyzer. Soil dust and cystine crystals were weighted and enclosed in tin capsules before analysis, following the same protocol used for our samples analysis. The results summarized in Table 6 show very good recovered values, which prove that our analysis conditions are optimal. Finally, the SRM-1648 was also used to validate our water soluble anions analysis protocol. From the certified material, 2.2 mg of particles were deposited on a cellulose filter.

The latter was treated with ultrapure water following the protocol described in section II.4.4.1. The solution was analyzed for Cl -and SO 4 2-contents, and the results are summarized in Table 7. Nitrates and ammonium contents could not be analyzed because the SRM-1648 certifies a total nitrogen percentage only.

II.5. Sampling protocol validation

In the previous section, we described the reasons behind the use of a dual type of filters combined in one for our sampling campaign. The use of this montage will eventually leads to a difference in PM deposition on each half of the filter. The turbine used in our DA-80 creates a stable flux in the sampling system, which will be divided unequally between the cellulose and quartz fiber part.

II.5.1. Methodology

In order to correct for this PM deposition difference, we were obliged to find a method that allows us to calculate the exact volume that passes through each half of the filter. The idea was to determine the mass of a reference element that has been collected on each of the two parts of the filter (quartz fiber and cellulose); the collected mass is directly proportional to the volume of filtered air. The reference element must be present in low quantities in both washed cellulose and glass fiber filters, but also relatively abundant in the PM in order to better determine how the PM is distributed between the two parts of the filter.

W ashed cellulose filter blank values are found very low: all trace metals contents are below the DL (see section II. 3.2.1). This is not the case for the QMA quartz fiber filters which contain a large variety of trace metals (Table 8). Among those, the choice of the reference element is guided by considering its high abundance in the atmosphere compared to its filter content. For example, Ag, Bi, Co and Nb showed interesting low contents in the filter but they exist in trace concentrations in ambient air. Consequently, Cu was selected as the reference element. After chemical analysis of each part of the filter, we quantified the masses of Cu sampled in the cellulose part (Cu cell ) and in the quartz fiber part (Cu QMA ) of the mixed filters.

Thus, the total mass of collected Cu (Cu tot ) is the sum of Cu cell + Cu QMA , and is directly linked to the total sampled air volume. In these conditions, the volume of air passing through the cellulose part of the mixed filter (V cell ) is equal to:

V cell = Cu cell Cu tot ×V tot Equation ( 1)
Similarly, the volume of air passing through the QMA quartz fiber part is given by:

V QMA = Cu QMA Cu tot ×V tot Equation ( 2)
The calculated V cell is used to determine elements and water soluble ions atmospheric concentrations quantified from the cellulose half of the filter, whereas V QMA is used to determine the atmospheric TC concentrations which are measured using the glass fiber half of our mixed filter.

II.5.2. Results

In order to validate the proposed methodology, a sampling campaign was realized using two DA-80 samplers in parallel for a period of seven days, beginning from 05/ 12/ 2010 till 05/ 19/ 2010. One of the samplers was loaded with 15 washed cellulose filters (see section II.3.2.1) and the second sampler was loaded with 15 of our laboratory made mixed quartzcellulose fiber filters (see section II.3.4). The two samplers were placed next to each other in Dunkerque's site and launched at the same time for the same sampling period. Two samples of PM 2.5 per day (12 hours each) were collected for a total of 14 samples that can be used to validate the protocol.

Atmospheric concentration values obtained using the mixed quartz-cellulose filter according to the previously described methodology, will be compared to the atmospheric concentration values obtained using the whole cellulose filter considered as reference.

The quantification of the Cu masses in the cellulose and QMA parts of the mixed filters shows different concentrations as illustrated in Table 9. This proves and confirms that the volumes penetrating through each section of the filter are not equal. The application of Equation ( 1) and Equation ( 2) allowed the determination of the filtered air volume through each part: V cell and V QMA (Table 9). This last result shows that V cell and V QMA are not only different when compared to each other, but also not equal between the samples. The difference can be explained by the fact that the surface of each part of the mixed filter is never exactly equal to the other. Sometimes we can have more cellulose surface than QMA in one filter, when in contrary we can sometimes have larger QMA surface than cellulose in another. Finally, we compared the atmospheric elemental concentrations (in ng/ m 3 ) determined from the mixed filter using V cell with the ones found by analyzing a part of the complete cellulose filter sample using V tot . Figure 9 illustrates this comparison for some of the less abundant elements which quantification is the most problematic. The figure shows that the atmospheric concentrations obtained using the mixed filter are very close to those found using the whole cellulose filter. The recovery appears to be between 89% for Bi and Sn and 106%

for Ag, which is very acceptable considering that studied elements are present at very low concentration values. Thus, we can conclude that the methodology proposed here allows a precise estimation of the elemental atmospheric concentrations using mixed quartz-cellulose filters and Cu as reference element. The sampling protocol is then fully validated.

LIST OF FIGURES

LIST OF TABLES

III.1.Introduction

Tracing metals, ions and total carbon sources is found to be of a great importance as highlighted in Chapter I. Following the detailed explanation about the sampling and analytical method in chapter II, we present and discuss the results herein. The encountered meteorological conditions will be detailed for each of the studied sites: Dunkerque, Boulognesur-Mer, and Saint-Omer. The result interpretations will incorporate four chemical species groups:

-

;

-Total Carbon (TC) which includes both Organic "OC" and Elemental Carbon "EC".

However, in this chapter, TE elements will be considered all together as a global trace elements concentration. The detailed analysis of this group will be achieved in Chapter IV.

An inter-site comparison between the average concentrations found in each site of simultaneous sampling will be included in this chapter. Also the average concentrations found for the above mentioned list of elements will be compared to other results found in other studies conducted in specific sites in Europe. Furthermore, a diurnal evolution of each chemical species in ME, TC, and SI will be studied and compared within each site. And finally, a study of chemical species enrichment in relation to wind directions will be conducted using concentration roses.

III.2.Meteorology in the "Littoral Côte d'Opale" coastline

The typical climate of this region is oceanic temperate, with modest seasonal variations and almost regular precipitation at the end of the year, between mid autumn and till the end of winter.

W ind fluxes can hit the region from all four sides, with each direction presenting its own characteristics and can influence local weather:

-Under a western flux, the region is found situated on the axis of atmospheric depression circulation coming from the Atlantic Ocean. In this case, wind speed can be found high if not stormy, followed by precipitation (high humidity).

-Under a southern flux, air masses that pass over France can reach to the region.

These air masses are found to heat ambient air in winter and can drastically increase temperatures in summer causing heat waves. In this case, the region can sometimes be considered as one of the hottest spots in France, with temperatures reaching 30°C, even on the coastline.

-Northern flux winds are usually coming from the North Sea. These fluxes represent typical northern cold air masses. In this case, temperatures are found at their lowest levels, which might lead to hail or snow fallout in the region during winter season.

-A dry and sunny weather can be observed in spring and summer seasons under eastern flux, but it can also lead to a glacial weather during winter with a cloudless sky.

Furthermore, the dominant winds are from the NE and SW sector in general with different properties. W ind blowing from the NE sector comes along with anticyclone conditions. Typically, the weather is found to be stable, without precipitation, and with low wind speed leading in most cases to the accumulation of pollutants in the atmosphere. On the other hand, winds from the SW sector are linked to atmospheric depression conditions. They are usually characterized by high wind velocity and abundant precipitation that leads to a rapid dispersion and rainout and/ or washout of atmospheric pollutants respectively.

Monthly mean temperatures and rainfall accumulation data retrieved from www.lameteo.org database for the past 30 years (1981-2010) are illustrated in Figure 1. Saint-Omer data were not available.

The trend shows that the lowest recorded temperature (about 5°C) occurs during J anuary-February and the highest was encountered during J uly-August (around 18°C).

Furthermore, rainfall monthly accumulations were the highest during October and November. This glimpse on the regional climate resumes only a generalized on the Littoral Cote d'Opale weather, which reflects the climatic situation of the NPdC region. However, local differences may be found when we focus on a specific time period or on a specific site.

Therefore the following section will expose the meteorology observed during the sampling periods.

III.3.Meteorology encountered during the sampling campaigns

During the sampling periods, wind velocity and direction, precipitation, and temperature measurements were retrieved from Mé té o France and from our onsite weather stations, and used to draw wind roses, precipitation trends and temperature trends for the sampling sites. W ind velocities were divided into four maj or classes illustrated in Table 1. For very low speed winds, wind velocity is extremely low and therefore a specific wind direction cannot be defined. As for precipitation, its accumulation (sum of precipitation heights recorded in mm) will be discussed per wind sector (45° each).

Finally, temperature trends retrieved from the DA80 automatic sampler will be presented in the form of trend lines for each site per campaign. These graphs will display temperature evolution during two daily periods: the first one from 00h to 12h (midnightnoon) and the second from 12h -24h (noon -midnight).

III. This campaign was characterized by rainfall accumulation of 69 mm (Dunkerque) and 49 mm (Boulogne-sur-Mer) during the sampling period. When compared to the usual rainfall recorded at these sites, the results are found below the historical averages calculated for the same period, which were 98 and 123 mm for Dunkerque and Boulogne-sur-Mer respectively (www.lameteo.org). Furthermore, the average temperatures were 1°C (Dunkerque) and 2°C

(Boulogne-sur-Mer) and are much lower than 7 °C which is usually found at these sites (www.lameteo.org).

During this first sampling campaign, precipitation and temperature were found different than the usual averages of the region: the climate was colder and dryer. Therefore, in the following sections we will detail our recorded meteorological conditions at the sampling sites.

III.3.1.1.Wind, precipitation and temperature conditions in Dunkerque

A closer look at Dunkerque's winter wind rose (Figure 2) shows that most frequent wind directions were E, followed by S, NE and NW. An examination of wind velocity shows that high speed wind came from all previously mentioned sectors with a frequency of 22%.

Furthermore, moderate speed wind (4 to 8 m/s) was recorded coming from E, S and NW sectors (48% frequency). Finally, low speed wind was found coming from S and E directions (25% frequency), when very low speed wind was almost absent in Dunkerque during this campaign (5% frequency). We should note that the recording shows only NE winds, which does not accord with the usually expected NE and SW dominant wind tendencies. On the other hand, total precipitation in Dunkerque during this period was 69 mm.

Figure 3 illustrates in details the evolution of precipitation in relation to wind direction. We can distinguish that rain was poured heavily with winds coming from directions between W and NE (270° -0° -45°). Furthermore, winds from the S to W (180° -270°) directions contributed in less rainfall than the previously described sectors. The lowest rainfall quantity can be observed with E to S winds (90° -180°).

However, a comparison between wind speed and rainfall concludes that high speed winds are not necessarily related to precipitation in this period. Temperature fluctuations in Dunkerque in this period were ranging from +8 to -8°C (Figure 4). These trends prove that our campaign was conducted under real winter weather conditions. In addition, snow episodes were recorded for this period between November 26 th -December 2 nd and December 17 th -19 th of 2010. These dates are marked by subzero temperatures. Local variations show slightly higher temperatures in the 12h-24h period when compared to 00h-12h ones. Normally, higher differences should be expressed between the two periods, but the observed small differences can be explained by the proximity of the coastal city to the sea, which moderate extreme temperatures variations in the 24 hours period. Precipitation recordings show that rainy weather was not linked to a specific wind speed at this site (Figure 6). Furthermore, rainfall quantity was found higher when associated to winds between N and E sectors (0° -90°) from one side, and between SW and W sectors Finally, rainfall trend in Boulogne-sur-Mer is found slightly different than in Dunkerque. Temperature fluctuations at Boulogne-sur-Mer were found similar to those found in Dunkerque for this period (Figure 7). The range was limited to a maximum of +8°C and a minimum of -6°C and constant slightly higher temperatures were also observed in the 12h-24h period when compared to 00h-12h ones. As found for Dunkerque, snow episodes were observed at Boulogne-sur-Mer between November 29 th -December 4 th and December 16 th -25 th of 2010. Average wind speed (in m/s) Rainfall (in mm)

III

Wind direction

Rainfall

Wind speed To resume all, we found that the winter campaign (11/18/2010 -12/31/2010) is characterized by:

-Low temperatures: the values were 1°C in Dunkerque and 2°C in Boulogne-sur-Mer, which are much less than the 7°C average found at these sites for the past 30 years.

-Dominating wind directions of NW, NE and S were recorded instead of the usually dominating NE and SW sectors.

-Precipitation levels were also below the usual seasonal trend, but also marked with snowing events. On the other hand, average temperatures during this campaign were 11°C in Dunkerque and Saint-Omer, which are higher than the 8°C found as an average for the NPdC region in previous years. In addition, no snowing events are usually recorded during this period of the year, which was also the case during our sampling period. Finally, we conclude that during our spring sampling campaign, the climate was dryer and warmer than the usual.

III.3.2.Spring campaign

III.3.2.1.Wind, precipitation and temperature conditions in Dunkerque

Dunkerque was mostly subj ected to wind coming from directions ranging between N-NE and E-NE, and few winds coming from the S and SW sectors (Figure 8). These winds were mostly of moderate speed (46%) followed by low speed (30%), very low speed (15%), and high speed type (9%). This latter was frequently blowing from the NE direction. An illustration of the variations in rainfall and wind speed by sector of 45° can be found in Figure 9, in which we can notice that rainfall occurred mostly with winds from the NW to NE sectors (315° -0° -45°). In the rest of the sectors, precipitation was very low and did not increase much beyond 2 mm with 180° -225° winds, proving that we were sampling under a relatively dry weather. On the other hand, temperatures were found to be extremely fluctuating in this period (Figure 10), from low levels at the beginning, to higher ones before the end of the campaign.

Globally, temperatures measured at the sampling site ranged between 0°C and 23°C, with an average found to be close to 10°C. These fluctuations show that we were able to sample under different weather conditions: winter to spring conditions in the beginning, typical spring weather, and summer weather (between 04/19 and 04/22/2011). The difference between the cold (00h-12h) and the warm (12h-24h) period of the day were clearly expressed in the recordings when compared to the winter campaign daily temperature fluctuations. In Saint-Omer, the dominant wind was blowing essentially from the NE and SW directions (Figure 11). The highest frequency (38%) was found for low speed, followed by moderate speed (32%), very low (27%) and finally high speed (3%). However, the difference between Saint-Omer and the rest of the sites is found in the frequency of very low speed winds: 27% versus 5% to 15% respectively. This last observation emphasizes the fact that in Saint-Omer we sampled for a significant amount of time under maj or stagnant atmospheric condition. On the other hand, precipitation is found to be highly associated to winds coming from the S-W quarter (180° -270°), whereas N sector winds did not carry the same load of rain (about 3.5 mm). The rest of the sectors rainfall was found negligible (Figure 12). Average wind speed (in m/s) Rainfall (in mm)

Wind direction

Rainfall

Wind speed

Finally, temperatures at Saint-Omer are found to be relatively higher than those recorded at Dunkerque for the same period (Figure 13). The 12h-24h temperatures fluctuated between +6°C and +24°C, and in the same time the 00h-12h temperatures were fluctuating between +2°C and +16°C. On the other hand, a resemblance between Saint-Omer and Dunkerque temperature trends can be noticed at the level of the separation between cold (00h-12h) and warm period (12h-24h) of the day, during which the separation was amplified at Saint-Omer. This difference can be related to the fact that Saint-Omer is an inland site, and cannot benefit from the effect of the sea in moderating the variations of daily temperatures. To resume all, we found that the spring campaign (03/10/2011 -04/30/2011) is characterized by:

-High temperatures: 10°C in Dunkerque and 11°C in Saint-Omer, which are higher than the average found for these sites for the past 30 years (8°C).

Furthermore, temperatures higher than 20°C were recorded during the second half of the campaign, which occurred under clear sky sunny days.

-A NE dominant wind direction associated with dry atmosphere, but also SW winds sometimes accompanied by some rainfall. These situations are almost in accordance with the usual seasonal observations. In the case of Atmo, the measurements were taken by weighting procedures. However, our total analyzed PM 2.5 average concentration is lower than the one found with Atmo data. Our total is the sum of all the analyzed chemical species and doesn't consider other elements like oxygen concentrations, knowing that a lot of elements are found in oxide forms in the particles. Therefore, the difference between the two resides within the measurement and calculation method. We should also note that PM The total contribution of ME in the total average accounted for about 3.2% compared to the 3.3% contribution coming from Cl -only. The latter is the less concentrated ion within the SI group, proving the extent of ions contribution (61.1%) compared to the remaining elements in the load of PM 2.5 . On the other hand, average TC accounted for 35.3% of total analyzed PM 2.5 , and represents the highest average when compared individually to the other analyzed components.

As for Boulogne-sur-Mer, the results show that ME represent the same tendencies as the ones found in Dunkerque, with Na levels being higher than K, followed by Fe, Ca, Mg and Al. In the case of SI, the contribution in the total analyzed PM 2.5 average was also high (63.2%), when in the same time TC contribution decreased to 33.6% when compared to Dunkerque, leaving NO 3 -with the highest contribution (35.1%). Also, TE average in Boulogne-sur-Mer is found to be low when compared to Dunkerque's average.

The average concentrations of SI, TC, ME and TE between Dunkerque and Boulognesur-Mer are compared herein (Figure 14 and Figure 15) in order to examine the variations in the chemical composition from one sampling site to another in the region. For SI and TC, the values representing Dunkerque are found slightly higher than the ones representing Boulogne-sur-Mer for all the compared species except NO 3 -(Figure 14).

Furthermore, the comparison of ME and TE averages illustrated in Figure 15 shows a similarity with the ones observed above. Dunkerque's values for all maj or metals were always found higher than in Boulogne-sur-Mer except Fe. We can also notice that in Dunkerque, Fe and TE levels were almost 2 and 1.5 fold higher than their respective levels in Boulogne-sur-Mer. For the winter campaign, the comparison between the concentrations of maj or components of PM 2.5 in Dunkerque and Boulogne-sur-Mer showed that:

-ME concentrations were comparable between the two sites with few exceptions.

-Highest concentrations were reserved to TC (5625-6198 ng/m 3 ) and NO 3 -(5707-5872 ng/m 3 ), followed by SO 4 2-(2449-2521 ng/m 3 ) and NH 4 + (1706-1914 ng/m 3 ). The sum of these four species accounted for about 93% of the total analyzed species in PM 2.5 .

-Fe, Ca, Al and Mg elements tend to be in higher concentrations in Dunkerque than in Boulogne-sur-Mer.

III.4.2.Spring campaign averages comparison

Table 3 summarizes the average concentrations of the analyzed chemical species in the samples taken from Dunkerque and Saint-Omer during the spring campaign. We notice that total PM 2.5 chemical species average concentration is found slightly higher at Saint-Omer.

When compared to the winter campaign, Dunkerque's spring values for ME show a decrease in the levels of K from 136 to 98.9 ng/m 3 . The succession during the spring campaign in decreasing order of metals average concentrations in Dunkerque becomes: Na, Fe, K, Ca, Mg and Al. Furthermore, SI contribution in fine PM 2.5 at Dunkerque was found to be 75.6%, with major nitrate (≈ 50%) content. As for TE, the atmospheric average concentration is found higher than during the winter season, whereas TC values decreased from 6198 ng/m 3 in winter to 4727 ng/m 3 in spring. These differences will be extensively discussed in section III.6.

In the case of Saint-Omer, average concentrations between ME elements have similar trends than the ones observed in Boulogne-sur-Mer during winter, with Finally, j ust like in the case of the first campaign, a significant difference was found between our total analyzed PM 2.5 and the measured PM 2.5 mass by Atmo. This latter exceeds the daily limit value of 25 µg/m 3 defined by the WHO (WHO, 2006) and 28 µg/m 3 defined as limit value for 2011 in France.

[ Na] > [ K] > [ Fe] > [ Ca] > [ Mg] > [ Al] .
Further investigations on the trends of Atmo PM 2.5 (for Dunkerque) and PM 10 (for Dunkerque, Boulogne-sur-Mer and Saint-Omer) during the two sampling periods will be detailed later in this chapter.

The comparison of SI and TC between the two sites exhibited a different scene than the one we observed in the winter campaign (Figure 16). Some ions were found at the same level in the two sites (Cl -and SO 4 2-

), whereas NH 4 + and TC were higher at the inland site of Saint-Omer, and only NO 3 -was found higher in Dunkerque. The comparison of ME concentrations between the two sites of the spring campaign revealed higher values for Dunkerque except in the case of K, for which Saint-Omer average concentration was found higher (Figure 17). In particular, Fe levels were doubled at the coastal industrialized site, where in the same time TE value was about 25% higher in Dunkerque. -Relatively higher values for maj or elements in Dunkerque.

-The highest concentration was observed in the case of NO . The cumulative value of these species represented about 95% of the total PM 2.5 analyzed species.

-All ME except K are more concentrated in Dunkerque than in Saint-Omer.

III.5.Average concentrations comparison at a European scal e

III.5.1.Location and characteristics of the comparison sites

The average concentrations are compared to other values found in similar studies accomplished at different sites in Europe (Figure 18). These sites were chosen depending on the pollution sources that affects them and the climate. All the selected studies focused on the Geographically, the sites are distributed between northern Europe (Finland, Sweden, Belgium, Netherlands and the United Kingdom), middle Europe (Switzerland and Hungary)

and southern Europe (Spain, Italy and Greece).

Meteorological conditions play an important role in the chemical composition of fine PM (Seinfeld & Pandis, 2006). Therefore, within the chosen list of cities for comparison some are located in areas exhibiting similar climatic conditions when compared to our region like: In addition to the climate characteristics, we also explain the urban distinctiveness of the comparison sites as well as the maj or anthropogenic influences affecting them in Table 4.

Some cities are located within regions that represent an urban density and population similar to those encountered in megacities (> 1 million inhabitants). Others are found less dense with a population ranging between a hundred thousand and one million inhabitants.

As for our sites: Dunkerque region (city and agglomeration) hosts about 223000 inhabitants, compared to 119000 inhabitants in Boulogne-sur-Mer and 64000 inhabitants in Saint-Omer. (Pakkanen, et al., 2001) Sweden Goteborg Urban + Industrial (Moloi, et al., 2002) United Kingdom West Midlands Urban background (Harrison & Yin, 2010) Netherlands Rotterdam Urban [START_REF] Mooibroek | Source apportionment and spatial variability of PM2.5 using measurements at five sites in the Netherlands[END_REF] Belgium Ghent Urban + Industrial (steelworks) [START_REF] Viana | Comparative chemical mass closure of fine and coarse aerosols at two sites in south and west Europe: Implications for EU air pollution policies[END_REF] Switzerland Basel Urban (Ilacqua, et al., 2007) " Zurich Urban [START_REF] Hueglin | Chemical characterisation of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland[END_REF] Hungary Budapest Urban [START_REF] Szigeti | Chemical characterisation of PM2.5 fractions of urban aerosol collected in Budapest and Istanbul[END_REF] Italy Salento Urban background (distant traffic) (Perrone, et al., 2011) " Venice Urban (+ sea traffic) + Light industry [START_REF] Stortini | An evaluation of the PM2.5 trace elemental composition in the Venice Lagoon area and an analysis of the possible sources[END_REF] " Milan Urban (city center) [START_REF] Lonati | Maj or chemical components of PM2.5 in Milan (Italy)[END_REF] Spain Cantabria region Urban + Industry [START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF] " Barcelona Urban background + Industrial [START_REF] Viana | Comparative chemical mass closure of fine and coarse aerosols at two sites in south and west Europe: Implications for EU air pollution policies[END_REF] " Madrid Urban (Salvador, et al., 2012) " Onda Urban background + Industrial [START_REF] Querol | Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources[END_REF] Greece Athens Urban + Industrial (Pateraki, et al., 2012) " Athens Urban (Ilacqua, et al., 2007) III.5.2.Comparison of ME, SI, and TC average concentrations . Finally, the TC group will be represented by carbon and the last group will enclose the rest of the maj or elements: Al, Ca, Fe and K.

III.5.2.1.M arine maj or el ements

In this section, Na, Cl -and Mg will be considered as sea salt elements. The average concentrations for Na and Cl -found in our study were similar from one site to another, except for Mg which is found higher in Dunkerque when compared to Boulogne-sur-Mer and Saint-Omer.

The Na values are found comparable to geographically close sites like Raahe, Vallila and Rotterdam. Higher Na concentrations can be found in south European sites: Onda, Venice and Athens, but also in Cantabria region. This is probably due to the proximity of these sites to the sea and therefore they are subj ected to higher wind circulation originating from the sea and loaded with marine particulates.

Within this type of comparison, we could not pass by the conclusions of a study that compiled sodium concentration data in 89 European sites to trace sea [START_REF] Manders | Sea salt concentrations across the European continent[END_REF].

This work focused on PM 10 sea salts emitted in high concentrations from the sea, which highlights this latter as a maj or source of suspended particles. Hence, stretching along the facade of the North Sea, average concentration was about 5 µg/m 3 , whereas lower concentrations were found for inland European sites. Concentrations ranged between 2 and 5 µg/m 3 in inland sites, almost 300 km away from the sea, and decreased furthermore when moving deeper inland.

Zurich Na concentration is found lower than our results, and can be explained by the location of the city in the middle of Europe and away from the coastline. West Midlands and

Basel represented exceptionally higher Na levels compared to our sites even though the first site is geographically close, and the second is located further inland in Europe. West Midland is located within the island of the United Kingdom and can be hit by winds carrying sea salts from all directions. As for Basel, the high atmospheric salt content might be linked to the use of salt to defreeze road pavements in winter. This is probably the only logical explanation to this high salt content, since Basel is an inland site in middle Europe. During dry periods, crystal salt will be found in dust form on the ground. This salt is probably re-suspended into the atmosphere by traffic activities. The dates during which sampling occurred in that study (Ilacqua, et al., 2007) confirm our suggestion on the origins of the atmospheric salt, since sampling was conducted between winter 1997 and spring 1998.

On the other hand, Cl -average concentrations are also found similar from one site to another in our study, but when compared to the other sites, it was found to be comparable to the averages of Athens and Cantabria region and higher than the rest. The low values found in coastal sites around Europe can be explained by chloride depletion phenomenon that is frequently encountered after sea Cl -reaction with acidic emissions from anthropogenic sources. Actually, freshly emitted sea salts carry a certain Cl -load that is related to Na concentration ([ Cl -] = 1.789 [ Na] ; 1.789 is the mass ratio Cl/Na in sea water). When the fresh sea salt aerosol passes over an atmosphere loaded with anthropogenic emissions (such as NO x and SO 2 ) that transform into acidic species, a reaction between these species and sea salts will occur which leads to replacing Cl -with sulfates or nitrates. This will eventually lead to a decrease in the amount of Cl -in fresh sea aerosol which is also called "chloride depletion"

described also in (Seinfeld & Pandis, 2006). The aerosols referred to as "sea salt" at the beginning will be known as "aged sea salt" after these chemical changes.

However, in our study we noticed that Cl -/Na ratios ranged between 2.3 and 2.7 from one site to another. This observation pushes us to consider other sources of Cl -than the marine one, a point that was also raised in a previous work conducted in our laboratory (Ledoux, et al., 2006). Ratios of Cl -/Na in the particles were compared between different granulometric sizes of particles collected using a cascade impactor, to conclude that the ratio tend to be higher in smaller sized particles (≤ 2.5 µm). This conclusion was explained by considering additional sources of Cl -other than marine. In fact, ratios rose up to 10 with winds blowing from the East sector, whereas the air mass originated from continental Europe.

Cl -ions can be generated by many anthropogenic activities in this case like combustion related emissions (biomass and incineration), or sintering in industrial emissions (Hleis, et al., 2013). Furthermore, Ledoux in 2006 demonstrated that a part of Cl -ions could be neutralized by ammonium ions (Ledoux, et al., 2006).

Finally, Mg average concentrations were doubled at Dunkerque when compared to the two remaining sites of our study. Mg values found in Vallila, Raahe and Zurich are comparable to those found for Boulogne-sur-Mer and Saint-Omer, and low when compared to

Dunkerque's values. The latter was found comparable to the averages found in Rotterdam, and low when compared to the rest of the comparison studies, most of which are located in southern Europe.

III.5.2.2.SIA maj or ions

Secondary inorganic components appear to be predominant in PM 2.5 in the three studied sites. This observation is also verified on the European scale as well [START_REF] Putaud | A European aerosol phenomenology -3: Physical and chemical characteristics of particulate matter from 60 rural, urban and kerbside sites across Europe[END_REF].

III.5.2.2.1.Nitrates

Comparable nitrates average concentrations were found between Dunkerque and Boulogne-sur-Mer during the winter season, whereas during the spring campaign, Dunkerque's average was found much higher than Saint-Omer's. Furthermore, the levels encountered in our samples are higher than all the comparison sites except for Milan in the cold season, in which NO 3 -levels peaked to slightly cross the 20 µg/m 3 barrier.

III.5.2.2.2.Sulfates

Sulfates average concentrations are found comparable between our sites during the sampling campaigns. The average concentrations are also comparable to Vallila, West Midlands, Rotterdam, Ghent, Budapest and Madrid. With the exception of Vallila, the rest of these comparison cities are much more urbanized than our studied ones, including Dunkerque.

However, sulfate levels in Cantabria region exhibited relatively lower levels than the ones found in our sites for both winter and spring periods. Finally, higher sulfate levels were found mostly in the studies in the Mediterranean zone, especially Spain, Italy and Greece.

III.5.2.2.3.Ammonium

The highest average concentration for NH 4 + was found at Saint-Omer, which is an inland city, followed by Dunkerque and Boulogne-sur-Mer. The spring period showed higher concentrations than the winter one in our samples. Furthermore, the results are comparable to most of the comparison sites after omitting Saint-Omer high value during the spring period.

The latter was only comparable to Milan high average concentration.

For the remainder of our sites, the values are found similar to the geographically close studies like Rotterdam, Ghent and Zurich. Finally, lower values are noticed in northern Europe (Vallila), but also in big cities towards the south such as Madrid, Budapest and Athens.

III.5.2.3.Total Carbon ( TC)

The winter period samples are characterized by the highest TC concentrations, with a maximum average reached at Dunkerque. This latter was found comparable to the values found in Zurich, Ghent (during the cold period) and Athens. For the rest of our sites, the values for TC are found comparable to Rotterdam, West Midlands and Barcelona.

Higher averages are found in big cities like Barcelona, Budapest, Madrid, Salento and Milan. In the latter, a high peak of TC concentration is reached, in the warm period, which makes more than the double of Dunkerque winter season high value. These cities exhibit higher traffic densities than the rest of the sites, including ours.

Finally, relatively low TC averages are encountered in Cantabria, Onda and Ghent (warm period) when compared to our study results. Rotterdam, Helsinki and in Budapest. Crustal enrichment was found to be behind the high Fe levels at Onda, whereas steelworks were possibly behind the high Fe concentration at Raahe.

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K is found to fluctuate depending on the seasons. It is found higher in winter compared to spring. This correlates well with the fact that K is an indicator for biomass combustion, but also for other sources as well. Therefore, when compared to other sites, our values are found to be comparable to the values found for Vallila, Rotterdam, Cantabria, West Midlands, Goteborg and Helsinki. Higher values in Zurich and Basel were linked to biomass combustion. In the same time, high crustal enrichment was found responsible of K increase in Onda and Venice. As for Raahe, high K levels were linked to the waste gas dust in the steel works sintering plant.

Finally, we can conclude that the elemental average concentrations measured in the atmosphere of the three sites in the NPdC region are comparable to those measured in other European sites. However, observed differences could be explained by the specific geographical position and/or the difference in population densities between the reference sites.

III.6.Diurnal evol ution of M E, SI, and TC

In this section we will discuss the daily evolution of ME, SI, and TC concentrations in PM 2.5 samples during both the winter and spring campaigns. Some elements and ionic species were separated in groups, for which the concentration is calculated by the sum of each element or ions concentration included within the group. The evolution of these groups will be compared and will include: sea salts, secondary inorganic aerosols (SIA), TC, and the rest of the ME group.

Sea salts concentrations in our sampled PM are evaluated by adding the concentrations of the analyzed typical sea elements and ions. To accomplish this goal, we calculated the sea salt concentration of ME elements using Na concentration as a reference with respect to sea water composition. Na is considered here as totally generated by sea spray, and therefore its presence is considered as a marker of sea originated aerosols. Following this principle, the sea salt (ss-) and non sea salt (Nss-) elemental concentrations, expressed in ng/m 3 , are calculated as follows:

From (Seinfeld & Pandis, 2006):

- From [START_REF] Wang | Water soluble ions of atmospheric aerosols in three New Zealand cities: seasonal changes and sources[END_REF]:

-[ ss-Ca 2+ ] = 0.0385[ Na] and [ Nss-Ca] = [ Ca] -[ ss-Ca 2+ ] -[ ss-K + ] = 0.037[ Na] and [ Nss-K] = [ K] -[ ss-K + ] -[ ss-SO 4 2- ] = 0.2516[ Na] and [ Nss-SO 4 2- ] = [ SO 4 2- ] -[ ss-SO 4 2- ]
Following this method, sea salt concentration for each sample is calculated by summing the sea salt components of each sample:

-[ sea salt] = [ Na] + [ ss-Cl -] + [ ss-Mg 2+ ] + [ ss-Ca 2+ ] + [ ss-K + ] + [ ss-SO 4 2- ]
Furthermore, SIA form another interesting group which concentrations are calculated following this method:

-[ SIA] = [ NH 4 + ] + [ NO 3 - ] + [ Nss-SO 4 2- ]
Finally, the rest of the elements including: Fe, Al, Nss-Ca, Nss-K, Nss-Mg and TC are also included in this section for temporal evolution study. Fe and Al evolutions will be compared with the evolution of Nss-Ca and Nss-Mg respectively, because these elements usually points at a crustal source of PM but also can point at industrial sources in the region.

We lay this possibility as we acquired information in previously conducted studies about our regional air quality, especially in Dunkerque ( (Hleis, 2010) and (Ledoux, et al., 2006)). Also, TC and Nss-K variations will be studied together as they are considered as important tracers for combustion processes.

III.

97.9

In Dunkerque for example, 79.5% from total Mg average concentration is ss-Mg 2+ .

Similarly, 73.4% of total Cl -, 18.4% of Ca, 6.6% of K, and 2.4% of SO 4 2-average concentrations was sea salt.

Boulogne-sur-Mer samples follow similar patterns for the percentage of contribution of each element total concentration. About 94.1% of Mg concentrations is found as sea salts, while 65.1, 17.9, 6.1, and 2.1% constitute ss-Cl -, ss-Ca 2+ , ss-K + , and ss-SO 4 2-respectively from their corresponding totals. These calculations confirm that Cl -ions are not only related to a marine origin as suggested earlier (26.6% and 34.9% of Cl -is in Nss-Cl -form).

As for the diurnal evolution, we can notice a resemblance in the temporal evolution of calculated sea salt concentrations between the two sites (Figure 19), with a ground level constantly below 300 ng/m 3 interrupted with peaks reaching 3500 ng/m 3 . This similarity proves the presence of regional influence of sea spray in this coastal region. For example, some peaks of sea salt concentrations were observed in both sites and occurring simultaneously with sea sector winds (N-W winds in 12/12/2010 and N-E winds in 12/23-25/2010). However, sea salt peaks were not chronologically synchronized all the time between the two sites. Some peaks were observed in Dunkerque (11/22-26/2010 and 12/16-17/2010) and were not matched by others in Boulogne-sur-Mer. The latter exhibited similar peaks also in 12/14 and 12/15/2010. This shows that punctual differences are present between the two sites, which refer to the presence of local influences linked to the position of the site on the coast, as well as regional influences, found on a lower frequency. Therefore, we can conclude that sea salt concentrations during the winter campaign exhibited temporal variability instead of geographical. For Dunkerque's site, these observations can be partially related to steelmaking type of emissions. In fact, a previous study demonstrated that Fe and Ca were largely used for the production of iron. Emitted particulates from the different installations are therefore constituted from these elements (Hleis, et al., 2013) and have an impact on the chemical composition of atmospheric PM. In our study, it is essential to determine if these Fe and Ca For Nss-Mg concentrations, we noticed that some values remains at the zero level, which proves that in these cases ss-Mg concentration was equal to the total Mg content. This also shows that Mg loads are resulting mainly from marine origins. In addition, independent peaks also appears in Dunkerque and Boulogne-sur-Mer, which suggest local sources of Mg enrichment, especially in the case of Dunkerque where Nss-Mg peaks are found higher and more frequent than in Boulogne-sur-Mer. In this section we will discuss the evolution of TC and Nss-K, which can both be tracers of combustion processes ( (Sillanpaa, et al., 2006); (Pateraki, et al., 2012)), during the sampling period. Figure 22 illustrates these variations, and shows that TC patterns are similar to a certain extent between Dunkerque and Boulogne-sur-Mer. The maximum values reached at the two sites proves that the emission source impact is similar, which leads to the possibility of having a same source of TC emissions located at both sites.

On the other hand, and under a much smaller scale, diurnal Nss-K fluctuations followed By comparing TC and Nss-K concentrations evolution at each site, we can notice that it is homogeneous with some rare exceptions. This observation indicates that these species concentrations evolution represent mainly a regional tendency. Local specificity can be responsible for some differences rarely observed between the two sites (peaks occurring between 12/26 and 12/28/2010 at Boulogne-sur-Mer).

Finally, it is important to mention that high TC and K concentrations were observed during a very cold period of the campaign stretching from 11/27/2010 to 12/4/2010. This phenomenon was also observed at the end of the sampling campaign but in less intense peaks, but during which low temperatures were coupled to snowing events. These high levels of TC and K could be partially due to residential heating emissions that increased during these episodes. The percentages of sea salt and non sea salt contents for some ME and SI are found fluctuating between high and low values in the samples of the spring campaign (Table 7).

98.3

In Dunkerque ss-Cl -constituted about 75.2% of total Cl -average concentration. The rest of the values were 67.3, 14.2, 9, and 2.1% for ss-Mg 2+ , ss-Ca 2+ , ss-K + , and ss-SO 4 2respectively. On the other hand, Saint-Omer results showed different percentages, in the sense that Mg had the highest sea salt fraction with an 86.3% of its total average concentration in the form of ss-Mg 2+ . As for the rest of the chemical species, we found 66, 14.1, 6.5, and 1.7 % for ss-Cl -, ss-Ca 2+ , ss-K + , and ss-SO 4 2-respectively.

The diurnal evolution of the calculated sea salt concentrations was similar between the coastal (Dunkerque) and inland (Saint-Omer) sites (Figure 24). This observation proves the presence of significant amounts of sea salts in Saint-Omer despite the distance between the site and the marine facade. Individual peaks of sea salts can be identified at both sites, but at higher frequency in Dunkerque. In each case, an examination of the meteorological conditions showed that the increase in sea salts concentration is related to high wind activity (≥ 7m/s), mostly under the SW or N-NE sectors and sometimes accompanied with wind gusts. The evolution of Al and Nss-Mg concentrations is studied following the same resolution used in section ( III. 6.1.3). The evolution of Al concentrations is different between Dunkerque and Saint-Omer (Figure 26). The tendencies are found to fluctuate independently between the two sites, proving that Al concentrations in the atmosphere of these areas are linked to local sources. Furthermore, higher peaks of Al can be identified at the Dunkerque sampling site when compared to the peaks found at Saint-Omer.

On the other hand, Nss-Mg concentrations were found extremely low to almost absent in the maj ority of the samples in both sites, suggesting that Mg have mainly marine origins.

However, high peaks of Nss-Mg can be observed in many cases at Dunkerque, whereas it was found in only two successive samples at Saint-Omer. This means that local sources of Mg are more frequently present in Dunkerque. The diurnal evolution of these combustion processes tracers is displayed in Figure 27.

The trend of TC shows slightly higher values for Saint-Omer in general when compared to Dunkerque. The global trend at the two sites is highly similar with only few disruptions pointing at local carbon emissions.

The same observation can be noticed in the case of Nss-K. High peaks found at Saint-Omer where matched on a smaller scale at Dunkerque. However, other Nss-K peaks were found in isolated cases at Dunkerque, which are probably generated by local sources at the site. Another high peak can be observed in both the coastal and inland site before the end of the sampling campaign, proving that at these dates a regional Nss-K pollution phenomenon occurred. To be able to identify above limit peaks of fine PM episodes that occurred during our concentration which is defined here as the sum of all analyzed chemical species concentration in each sample.

III.7.1.1.Winter campaign above l imit-val ue peaks

The evolution of PM 2.5 and PM 10 at Dunkerque and Boulogne-sur-Mer during the winter campaign period is illustrated in Figure 29.

A closer look at the trends shows that above limit peaks of PM 2.5 recorded by Atmo follow the same pattern of our total analyzed fine particles. Atmo-PM 2.5 values are found sometimes higher than those for PM 10 in Dunkerque. This can be explained by the fact that the measurements were taken at two different sites: in Malo-les-Bains (East) and Saint-Polsur-Mer (West) respectively. Malo-les-Bains is probably more exposed to PM than Saint-Polsur-Mer. On the other hand, the evolution of both PM classes in Dunkerque is also found similar to the one measured at Boulogne-sur-Mer for PM 10 . This shows a global regional trend that controls the daily variations of PM levels. Furthermore, in Boulogne-sur-Mer, a high similarity between the trends of Atmo-PM 10 and our total analyzed PM 2.5 can be clearly seen in Figure 29. These correlations allow us to rely on these trends for the selection of above limit peaks of PM. However, we have noticed that total PM 2.5 was higher than total PM 10 for certain dates. This is abnormal and could not be explained at this level, but we suspect that the cause of this difference is related to the fact that PM 2.5 and PM 10 monitoring stations are located at two different sites.

The limit values for both PM classes, 50 µg/m 3 for PM 10 and 25 µg/m 3 for PM 2.5 , were exceeded many times in Dunkerque as well as in Boulogne (Figure 29). In parallel, similar variations occurred in other measuring stations in the region (www.atmo-npdc.fr). This proves that the load of PM is increasing regionally. Hence, we were able to identify high fine PM loaded samples individually at Dunkerque and Boulogne-sur-Mer, which are consisted of 44 samples (22 days): 21, 22, 27, 28 -30 for November, in addition to 1 -4, 6, 7, 19 -22 and 26

-31 during December 2010. We will be investigating the chemical composition of these selected samples later in this chapter. A similarity can be noticed in the case of Atmo-PM 10 and our PM 2.5 collected in Saint-Omer. The two trends follow each other especially in high PM episodes, during which limit values were exceeded. These above limit peaks correspond to specific PM 2.5 samples taken from Saint-Omer. These samples were identified as in the case of Dunkerque to find that they match in Dunkerque and in Saint-Omer.

A total of 29 days (58 samples) is chosen for chemical species contribution analysis.

During March the dates are: 12, 14, 15 -17, 19 -22 and 24 -28. As for April we identified the following dates of PM 2.5 peaks: 7, 15 -25 and 28 -30. The detailed analysis of the chemical composition of these selected samples will be realized in the following section. , NO 3 -and NH 4 + seem to contribute the most in the total PM 2.5 concentration during the episodes. The rest of the analyzed species contribution is found to be negligible when compared to the above discussed ones.

The contribution in the total PM 2.5 for certain species at Boulogne-sur-Mer had a different aspect when compared to the respective case observed in Dunkerque (Figure 31).

For example, TC is found linked to the increase in total analyzed PM 2.5 except during 11/29/2010 and 12/7/2010 in Dunkerque and Boulogne-sur-Mer respectively. NO 3 -and NH 4 + contribution to the load can be evidenced in almost all the peaks. Finally, SO 4 2-was found increasing with the total analyzed PM 2.5 . The rest of the analyzed metals followed the same pattern as the one observed in Dunkerque as their contribution was insignificant compared to the previously discussed ones. Finally, we note that we could not compare the contributions for the 29 th , 30 th and 31 st samples because the sampling campaign in Boulogne-sur-Mer ended on the 28 th .

III.7.2.1.2.Detailed interpretation of PM 2.5 above limit values for the winter campaign

Our obj ectives in this section are to examine under which conditions these above limit episodes are occurring, and establish a link between these episodes and wind directions and/or temperatures in the studied sites. In order to backup our analysis, we have regrouped in Figure 32 the diurnal evolutions of the recorded temperatures, PM 2.5 levels recorded by Atmo, TC, SIA and Al corresponding to each of the previously selected samples. In addition, Figure 33 regroups some air mass backward traj ectories drew using HYSPLIT transport and dispersion model from the NOAA-Air Resources Laboratory [START_REF] Draxler | Hybrid Single-Particle Lagrangian Integrated Trajectory[END_REF] online server, to

illustrate some examples that can uncover some PM origins, especially during long range transport mechanisms. The event takes place during an episode of dry climate, and starts when daylight and nightfall temperatures drops below 0°C, all the way to a minimum of -7°C encountered in the 2 nd of December 2010. At the beginning of this episode, we can notice the inversion in wind direction: initially under NW direction, the air masses changed direction to pass over East of France before returning to the NPdC region (Figure 33).

The region in that period kept being subj ected to S-E winds, but PM 2.5 levels dropped significantly in the middle of this episode (November 30 th , 2010). Associated to this phenomenon is some temporary wind reinforcement from the East sector (between 9 and 10 m/s).

In both Dunkerque and Boulogne-sur-Mer, the episodes could be explained by the concentrations in inorganic secondary aerosols and total carbon. However, it is important to mention that the evolution of these species is not the same: SIA concentrations increased more than TC at the beginning, followed by an increase in TC concentrations (reaching 15 µg/m 3 in December 2 nd 2010) but remaining under SIA high loads. This observation is found occurring with the minimum recorded ambient temperature. Therefore, heating activities could be a source of TC emissions during this event and could also contribute to the above limits peaks. Furthermore, this suggestion can be backed up by the increase in potassium levels, which is considered as a tracer of biomass combustion.

The episode ends by the end of the 4 th of December, marked by a large increase in temperature and rainfall occurrence. III.7.2.1.2.iii.Episodes on the 6 th and 7 th of December 2010

This episode is shorter and less intense than the one observed between the 27 th of November and the 4 th of December 2010. Meteorological conditions during these two days exhibited average temperatures of 0°C, sometimes lightly negative, and an N-E sector winds.

The backward traj ectory shows slow inland air mass movement that is directed towards Belgium and Holland. Then it returns to our regions under the influence of NE winds. This behavior is found similar for our two sites (Dunkerque and Boulogne-sur-Mer).

The episode terminates at the beginning of high speed wind event, followed by a shift in wind direction to the N-W sector and an increase in the temperature. This episode is also observed under a cold dry weather conditions, whereas winds were originating from the S-E sector. In a similarity between this episode and the one of the 2 nd of December, TC concentrations increased noticeably (December 26 th and 27 th ), and can be attributed to the increase in domestic heating activities. We can also notice that the air mass is originating from the South of France and crosses the region of "Ile de France" before settling in ours.

III
From all the previous episodes analysis we can conclude that each one from the winter campaign was characterized by similar meteorological conditions. The exceedance of the limit value was encountered under dry weather, with cold winds blowing from N-E to S-E continental sectors. Another interpretation can be applied herein considering the Long Range Transport of particles also evidenced by air mass back traj ectories.

The analysis of the chemical composition proves that these episodes can be explained by the increase in SIA and TC concentrations. SIA are well known to be associated with long range transport particles, whereas TC is mostly originated by local sources. Furthermore, when the weather exhibits low temperature weather conditions, the high concentrations of TC could be due to an important use of domestic heating, which can contribute significantly to PM 2.5 loads.

III

III.7.2.2.2.Detailed interpretation of above limit values for the winter campaign

Using the same strategy used for the winter campaign, we examined the meteorological conditions encountered during the spring episodes, which raised the level of PM 2.5 to reach high values between 60 and 70 µg/m 3 . Figure 35 illustrates the diurnal evolution of the temperatures, Atmo-PM 2.5 levels, TC, SIA and Al in Dunkerque and Saint-Omer sites during the spring campaign. Six air mass backward traj ectories are also given in Figure 36 that confirms the global meteorological situation occurring during some of the above limit samples. In this episode, the limit threshold was crossed when wind direction shifted to the N-E sector in 14 April. Backward traj ectories show the arrival of air masses to our sites that had already crossed over Holland and Belgium before that (Figure 36).

The following days were marked by a gradual decrease in wind speed to get to stagnant conditions, especially at Saint-Omer. Furthermore, we observed that temperatures increased to reach summer values between 18 and 14 April. During these days, daylight average temperatures were around 20°C, which is an unusual episode since these temperatures are above the seasonal averages that are encountered in the region.

As for the concentration evolution, a particular phenomenon was observed and deserves a detailed explanation. In fact, diurnal fine PM evolution shows three successive peaks: 15, 20 and 23 April. The chemical species evolution shows that SIA concentrations increase is well synchronized with the elevation of fine PM 2.5 loads, j ust like the previous cases. However, it seems that TC, Al, Ca and Fe presented similar evolutions characterized by a progressive augmentation. This increase got to a maximum during April 21 st then the levels decreased in the following days. The aspect of the trends for Al, Ca and Fe points at an accumulation phenomenon in the air, which is not totally surprising if we consider the stagnant meteorological conditions recorded during this period (dry weather, almost complete absence of wind and above normal seasonal average temperatures).

This accumulation could not explain the SIA evolution during this episode due to the degree of stability of SIA, and mostly ammonium compounds which are temperature dependant. The stability of suspended NH 4 NO 3 particulates in a gas is governed by the direction of the chemical equilibrium (Seinfeld & Pandis, 2006):

NH 4 NO 3(s) = NH 3(g) + NHO 3(g) ∆ r G° = -92.67 kJ /mol
In this reaction, ∆ r G° corresponds to the Gibbs function (free enthalpy) of the reaction in standard conditions and under 25°C. If we consider the link between the equilibrium constant K and ∆ r G°, it is possible to demonstrate that K decreases by a factor of 1000 when temperature increases from 0°C to 25°C. This proves that ammonium nitrate is less stable under high temperatures, and thus tends to decompose into its primary gaseous species.

The SIA concentration increases dramatically to reach its maximum under N-E sector winds, when in the mean time temperatures remain under 20°C. However, despite the stable meteorological conditions, we observe a decrease in SIA accompanied by an increase of temperatures beyond 20°C. This decrease can be explained by ammonium nitrate decomposition as previously explained.

Our explanation can be backed up by the fact that SIA levels in Saint-Omer were lower than those of Dunkerque during April 21 st and 22 nd , even though average temperatures were higher in the first site than the second.

Equally, we observed at the end of the episode another temperature increase (in April 24 th ) followed by an important atmospheric cooling during the following days. This temperature gradient can also have an effect on SIA concentration evolution. In this case, we can consider that SIA precursors exist in the air, but the relatively high temperatures kept the precursors from transforming into particulate form, especially particulate ammonium nitrate.

On the opposite, when temperatures started to drop gradually (in April 24 th under N to N-E sector winds), the conditions for the transformation of precursors into particulate ammonium nitrate became favorable, thus the increase in its concentration was observed. In parallel, we observed an entrance of continental air mass which was located over northern Germany 48 hours earlier.

Lower levels of fine PM were again encountered as of April 25 th 2011, with wind origin maintained from the N to N-NE sectors and speed between 6 to 9 m/s.

To resume the spring campaign PM 2.5 above limit value increases, we noticed that there are two conditions linked to these episodes:

-N-E sector winds, corresponding to inland air masses, under which the increase in PM loads is related to higher SIA and TC concentrations.

-Extremely low wind activities leading to accumulation of fine particles in the atmosphere.

-High atmospheric temperatures decreased the contribution of ammonium nitrates in the PM concentrations.

III.8.Concentration evol ution by wind sector

After the diurnal evolution which helped in determining regional versus local pollution sources in the sampled sites, another possible analysis is discussed hereafter. It analyzes the relationship between wind sectors and chemical species concentrations at the studied sites of each campaign. Therefore, wind roses are drawn for each ME, SI, and for TC as well, which determine concentration variations in relation to wind directions.

For each sample, we were able to associate an average wind direction and sector:

-The average wind direction represents the average of recorded wind directions in the 12 hours sampling period that corresponds to one sample.

-The defined wind sector is the sector (in degrees) in which the recorded wind directions fluctuated for the same 12 hours as above for each sample. For example, in the case of a sample for which the recorded wind directions ranged between 40° and 80° has a defined wind sector of 40°-80°.

In addition, certain samples were excluded from the calculations especially:

-The samples for which wind directions were extremely variable (sector range increased beyond 90°). For these samples we could define neither a wind sector nor direction.

-The samples for which no wind direction could be recorded (stagnant winds having 0 m/s speed).

Hence, the concentration roses were drawn based on the following principle:

-The wind rose was decomposed in sectors of 10° each, which define 36 possible wind directions: 0°, 10°, 20°… 340°, 350°.

-The wind sector of each analyzed species that ranges between D x -D y is divided into wind directions as follows: D x , D x +10, D x +20…, D y , to each of which is associated the concentration of the analyzed species in the considered sample.

-After applying the same method to all the collected samples, we can then find for each wind direction the concentrations of the chemical species found when wind was blowing from that same direction but for the whole campaign.

-The geometric averages of these concentrations for each of the 36 wind directions will serve then to draw a "radar" chart type that represents the concentration rose.

The following example (Table 8) summarizes our used method for Al, considering 4 samples only:

- This method was applied to all the analyzed chemical species in our study, which allowed us to draw the concentration roses for all these species and for the two sampling campaigns. This also helped us to identify which wind directions were contributing in the increase of certain species. Boulogne-sur-Mer (Figure 37). These wind directions originated from the sea put in evidence that these elements are indeed in sea salt form. Sea salts are expected to be linked to SW sector winds for both sites [START_REF] Ledoux | Les aerosols particulaires atmospheriques sur le Dunkerquois: caracterisations chimiques, physiques, spectroscopiques et evaluation de leur toxicité[END_REF]. In this winter campaign, we must recall that meteorological conditions shifted than the normal seasonal variations, and specifically with a much less SW sector winds.

III.8.1.2.SIA maj or ions origins

The highest concentration of the different SIA components are found associated to a maj or SE and SW wind directions in Dunkerque, and E direction in the case of Boulogne-sur-Mer (Figure 38). These directions represent the continental sector influence, as discussed in the previous part in which the E-SE wind directions were associated to inland air masses holding LRT particles. On the opposite side, species like NO 3 -, SO 4 2-and NH 4 + exhibited the lowest contributions when subj ected to marine sector winds. However, in Dunkerque, contributions to these ions can also be identified coming from the NW side that includes the ISW site as well as the seafront.

On the other hand, in addition to the urban sector participation in the loads of SIA in Saint-Omer, we can notice a high peak pointing at the N and a lower one from the SE direction, particularly in the case of SO 4 2-ions. The first one indicates a possible contribution from Dunkerque's industrial site whereas the second sets the possibility of a nearby glass factory participation in Saint-Omer.

III.8.2.3.Combustion el ements origins: TC and K

Combustion tracers concentration roses show similar aspects between TC and K at both coastal and inland site (Figure 43). This observation is in accordance with a previously demonstrated link between these two chemical species. In Dunkerque however, TC and K maximum concentrations were linked to winds blowing from the SE sector, which points at an urban origin, but also from the NW sector that encloses the industrial site. In particular, high K concentrations are recorded when wind was blowing from the 270-290° direction and could be linked to the ISW sintering unit emissions as it was found to be in a published work realized by D. Hleis (Hleis, et al., 2013). Whereas at Saint-Omer, the concentrations of TC and K were both linked to many wind directions, mainly SE, W-NW, and E-NE. However, some K increase can be noticed also coming with N winds.

III.8.2.4.Fe, Ca, and Al origins

The 260° to 290° wind sector is found to highly influence the concentrations of Fe, Ca, and Al in Dunkerque as it is demonstrated in Figure 44. This examination agrees with the Fe -Ca relation suggested in section III.6.1.2, and proves that the ISW activities may influence Fe, Ca and in a less extent Al atmospheric concentrations when wind blows from the western directions. Smaller concentrations of these elements are also found related to the both SE and SW urban sectors.

In addition to the urban sector moderate participation, Fe, Ca and Al also had a common enriching SE sector in Saint-Omer, from which the industrial influence of the nearby glass factory might be the reason behind it. However, it should be noted that under SE winds effect, meteorological conditions favor the accumulation of atmospheric pollutants. Thus, air masses can be heavily loaded in elements which explain the high concentrations found under these conditions. Furthermore, Fe rose peaked from the N direction as well proving that the possibility of Dunkerque's industrial emissions can be identified as a source of this element enrichment.

III.9.Discussion of the resul ts

A comparison of the concentrations found in this study for Dunkerque, Boulogne-sur-Mer and Saint-Omer was achieved internally between the sites, but also to other European studies in order to classify the NPdC region. We were able to find that for each element, the studied sites exhibited comparable, high, and low values depending on the characteristics of the local activities and geographical position. Furthermore, the diurnal evolution between some analyzed species, regrouped in marine elements, SIA ions, Fe and Ca, Al and Nss-Mg, TC and K, enabled the differentiation between regional and local sources of pollution. And finally, a detailed analysis of each species concentration in relation to wind direction was realized using concentration roses in order to point at the direction of the emissions sources.

Fe

The following sections will discuss in details the significance of this previous analysis, to be able to point at specific possibilities of regional and local atmospheric pollution sources.

Sampling during the winter campaign (11/18/2010-12/31/2010) took place in Dunkerque and Boulogne-sur-Mer simultaneously. The weather during that period was characterized with lower than usual temperatures and rainfall, but most importantly wind directions influenced the sampling site with different sources of fine PM. In Dunkerque, continental sector winds were the most abundant, followed by industrial and marine mixed sector, then finally by less frequent marine sector winds. However, Boulogne-sur-Mer was affected by only two maj or wind sectors: the continental inland winds and less frequent marine sector winds.

Total PM 2.5 element average concentrations show that Dunkerque had higher values for all ME elements, TC, and ions except for NO 3 -which results were comparable between the two sites.

The spring campaign was held between 03/10/2011 and 04/30/2011 in Dunkerque and Saint-Omer. Meteorological conditions were characterized with below seasonal averages values, with an exceptionally lasting anticyclone episode during which average diurnal temperature increased beyond 20°C. As for wind activities, the situation was closer to the normal seasonal observations with N-E sector winds in the dry period, or SW winds accompanied with less that seasonal average rainfall level during the sampling period.

In this campaign, the concentrations of TC, NO 3 -and SO 4 2-were found comparable between the two sampling sites. However, NH 4 + concentrations were found higher in Saint-Omer, whereas the average concentrations of Fe, Ca and Mg were higher in Dunkerque.

It is important to mention that during winter and spring campaigns, diurnal evolutions of species concentrations had similar tendencies at the three studied sites, very much linked to the encountered meteorological conditions. This illustrates that the local PM 2.5 concentration trends follows the fluctuations of a regional atmospheric baseline. To this we can add some local emissions impact observed in the differences in the concentrations of certain species between the sites.

Deeper investigations in the sea salt components lead us to find a minor difference between Dunkerque and Boulogne-sur-Mer, which are both regional coastal sites. On the European level, the results showed comparable values except when compared to some Mediterranean sites in which the sea salt fraction was highly concentrated. This difference can be explained by wind circulation pattern of the Mediterranean region. Concentration roses for sea salt elements proved the marine origins of these elements since they were found abundant with wind directions coming from the marine sector. Unsynchronized peaks of sea salts were identified in Dunkerque and Boulogne-sur-Mer to demonstrate that local variations exist in this natural source of emissions, probably due to the position of the seafront.

Anthropogenic activities were identified by the analysis of Fe and Ca concentration averages and fluctuations. The inter-sites comparison shows that Fe was higher at Dunkerque than in Boulogne-sur-Mer, whereas Ca was found comparable between the two sites, but remaining in slightly higher concentrations in Dunkerque. This observation is common since industrial activities in Dunkerque might be responsible of Fe enrichment. Dunkerque is known to host one of the largest integrated steel works plant in Europe, as well as other industrial activities that can enrich the atmosphere with these elements. However, the comparison on the European level showed that the values found in our study can be compared to the ones found in the case of big cities, in which iron levels were found to be resulting from traffic, but it is still lower than the values found in other industrialized sites in Europe. Daily

Fe concentration evolution shows that high levels of Fe identified as punctual peaks are observed regularly. During the peaks, Fe levels increase well beyond the average value then decrease to low levels afterwards. Meanwhile, Ca levels at Dunkerque are found linked to Fe levels, which is not the case at Boulogne-sur-Mer. Finally, concentration roses confirm the industrial origins of Fe and Ca at Dunkerque with slight participation from the marine and continental sectors. On the other hand, the origins in Boulogne-sur-Mer are found to be within the inland continental sector for Fe and Ca.

Aluminum concentration levels were found to be comparable between Dunkerque and Boulogne-sur-Mer, but lower than the European comparison sites. The diurnal evolution of Al shows that it is not linked to a regional tendency. Traffic can be a maj or source of Al enrichment through non-point source emissions (like dust re-suspension), but also can be emitted by industrial activities from point and non-point sources. Concentration roses of Al at the two coastal sites proves the participation of the traffic sources coming from the city center at the two sites, but also the industrial site influence at Dunkerque and the marine sector at Boulogne-sur-Mer.

TC and K concentrations were also compared to find that their levels at the three sites are comparable to most of the large north European cities and industrialized sites, but lower when compared to megacities like Budapest and Milan. In this case combustion processes, including traffic, participated in the loads of TC. Furthermore, the daily successive evolution of these two elements shows that TC is affected by local emissions at the sites, especially with the presence of unsynchronized peaks between the sites of each campaign. These sources can range from traffic to residential at Boulogne-sur-Mer. The industrial source can be added to these sources in the case of Dunkerque. Concentration roses came to confirm that TC and K sources at Dunkerque are maj orly from the continental sector, meaning that the effect of the city is clear with some minor participation from the industrial, especially for K, and marine sectors. The same observation can be noted in Boulogne-sur-Mer for both elements, but with an absence of industrial participation. Possible marine sources of TC and K can be related to marine transportation activities that are high in the region.

NO 3 -, SO 4 2-and NH 4 + concentrations of the SIA group were also analyzed and compared. SIA levels are found comparable between the three sites, and their diurnal evolution shows regional background pollution effect since the concentration levels and trends between the coastal sites are similar. However, NO 3 -concentrations are found to be higher than in European sites except for Milan, in which extremely high concentration levels are probably due to accumulation phenomenon. Furthermore, SO 4 2-concentrations were found comparable to megacities levels in central Europe as well as in northern European sites.

This indicates that the studied sites are clearly affected by regional as well as local pollution sources. A similar observation can be found in the case of NH 4 + , which is found higher than nearby sites but lower than some Mediterranean ones. NO 3 -is mostly emitted in traffic dense areas as direct pipe emissions in the form of gaseous NO x that react in the atmosphere and convert to form ions in particles under low temperature environments. As for SO 4 2-ions, it results from the conversion of SO 2 generated mostly by industrial activities like: petrochemical, cement factories, steelmaking…etc. These conditions are fulfilled in the region, as well as in north Europe in general. More confirmation on the source of SIA can be laid down through the analysis of the concentration roses. These latter clearly show that the highest SIA concentrations are encountered under continental sector winds. This origin involves the urban activities as well as the long range transport phenomenon as main SIA sources.

PM 2.5 concentration levels above the 2015 limit value (25 µg/m 3 ) were recorded in air quality surveillance stations during the sampling campaigns. The analysis of the chemical composition of the collected PM 2.5 allowed the identification of some species with very high concentrations.

During the winter campaign, episodes during which PM 2.5 increased beyond the limit value were encountered under dry and cold weather, and also under N-E to S-E sectors winds which carried continental air masses. In parallel, we observed an increase in TC and SIA (specifically NO 3 -and NH 4 + ) concentrations.

Spring campaign episodes can be explained by:

-Particles accumulation situation encountered during stagnant winds and dry weather.

-Winds blowing from the N-E under dry weather and cold temperatures leading to the increase in SIA concentrations.

The observation of high SIA concentrations under the N-E sector deserves a profound interpretation. In fact, the results obtained simultaneously in two sampling sites demonstrate that SIA have not been generated locally. It is well known that secondary inorganic components can be transported to long distances within continental air masses. In the case of ammonium nitrate for example, NO x and NH 3 precursors can be emitted by combustion processes and agriculture respectively. Therefore, it is normal to find that precursors are emitted in large quantities in inland European sites.

NO x emitted into the atmosphere evolve into HNO 3 as a result of photochemical reactions. NH 3 reacts faster than NO x in the atmosphere to give NH 4 + ions by undergoing acid-base reactions. This transformation can be limited when acidic species concentrations like HNO 3 is low ( (Seinfeld & Pandis, 2006) and [START_REF] Kim | Formation of secondary serosols over Europe: comparison of two gas-phase chemical mechanisms[END_REF]).

Inside the NPdC region under study, air masses arriving from the N-E sector could have possibly crossed over the eastern European countries, northern Germany, Holland and Belgium before hitting the NPdC region as it was deduced from air mass back traj ectories analysis. It is important to note that many studies have demonstrated that NO 3 -and NH 4 + concentrations in Holland and Belgium are between the highest in Europe [START_REF] Kim | Formation of secondary serosols over Europe: comparison of two gas-phase chemical mechanisms[END_REF].

This phenomenon can be explained by the fact that NOx and SO 2 emissions are also the highest in northern Europe. They are not only emitted from heavy urbanized and industrialized areas, but also from marine transportation sources in the North-Sea and crossing the Straight of Dover, which is one of the most frequently used around the world: 700 to 800 ships/day (Pré fecture-Maritime, 2013). In addition, NH 3 emissions are important themselves in these northern European countries. In France, three sectors contribute in ammonia emissions into the atmosphere, between which, agricultural activities is responsible of about 97%, followed by road transport (2%) and industries (1%) [START_REF] Citepa | Emissions dans l'air en France métropol: substances relatives à l'acidification[END_REF].

Agricultural activities emissions are actually generated by the application of mineral fertilization of the soil. This latter depends also on soil pH, the level of fertilization and some additional variables such as soil temperature and wind speed [START_REF] Hamaoui-Laguel | Les emissions d'ammoniac par les activites agricoles: impactes sur la qualite de l'air[END_REF]. These emissions can also result from animal farming. This situation is similarly found in neighbor European countries like in Belgium and Holland, in which agricultural activities are important.

The abundance of NH 4 + and NO 3 -in northern European is explained by the rapid transformation of emitted ammonia into ammonium nitrate.

Finally, we remind that average temperatures in northern Europe are lower than those recorded in the south. This climatic condition is to be considered regarding the stability of particulate ammonium nitrate, which is found in higher concentrations in the relatively colder climates of northern Europe as explained before in this chapter. On the opposite side, in high temperatures regions, ammonium nitrate is unstable. For example, nitrate ions in Mediterranean countries are more likely to be found associated to Ca 2+ , giving that Ca(NO 3 ) 2 is less temperature dependent [START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF]. We were able to show that PM 2.5 concentrations evolved by following similar tendencies from one site to another, which proves the fluctuations of an atmospheric baseline level on the regional scale. These phenomena can be explained by ionic species (NO ) and total carbon (TC) concentrations variations. The cumulative contribution of these species constitutes about 93% of PM 2.5 total concentration in winter, and about 95% of the total in spring. The diurnal trends of these species were also found very variable and heavily linked to meteorological conditions.

III.10.Concl usion

During the two sampling campaigns, the PM 2.5 averages in Dunkerque were 24.8 µg/m 3 in winter and 33.1 µg/m 3 in spring. Daily above limit (25 µg/m 3 ) values were recorded many times (22 out of 44 days during winter, and 29 out of 49 days in spring). These high concentrations were observed under stable anticyclone conditions and with E winds, which favors the influx of continental air masses. In addition, significant and relatively higher TC concentrations were detected during winter episodes, and are found linked to the increased use of heating. On the other hand, a stable above limit increase in fine PM during spring was accentuated by stable meteorological conditions and almost a total absence of wind activities, leading to an eventual accumulaFinally, the diurnal study of Fe, Ca, Nss-K, Mg and Al concentration variations from one site to the other enabled us to reveal local contributions in Dunkerque, which are probably linked to industrial activities as evidenced by the Fe concentration roses and in less extent with Ca and K, all of which pointed at the ISW wind direction.

LIST OF TABLES

IV.1.Introduction

After the detailed explanation of ME, SI and TC fluctuations and sources identification that was achieved in Chapter III, we continue the interpretation of the findings that followed the chemical analysis of the PM 2.5 samples by boarding the subj ect of the trace elements group (TE). This group includes the following elements: Ag, As, Ba, Bi, Cd, Co, Cr, Cu, Mn, Nb, Ni, P, Pb, Rb, Sb, Sc, Sn, Sr, Te, Ti, Tl, V and Zn. Therefore, this chapter will include a first section which deals with the comparison between the TE average concentrations measured at the three sampling sites. We will also discuss the difference between our findings and those found in other sites around Europe in order to uncover the position of the three selected regional sites. Afterwards, the "Industrial Impact" will be discussed to reveal which elements were mostly influenced by the industrial activities. This last step will then be completed by an Enrichment Factor analysis for both campaigns, which will also help to determine the anthropogenic influence compared to the natural crustal source of metals.

However, a statistical analysis will be applied to find which metals are mostly correlated to each other. This will form a first step in uncovering the origins of these trace metals. A concentration rose analysis will be added to this last section to confirm the origins of metals in relation to wind sectors. The chapter will then be concluded by an analysis of elemental ratios and the identification of signature elements that point at TE specific sources PM 2.5 in the studied sites.

IV.2. El emental average concentrations and contributions

IV.2.1.Winter campaign (November -December 2010)

The average concentrations and contributions to the total analyzed TE of each TE element are illustrated in Table 1. Total TE concentration in Dunkerque is 1.6 fold higher than the one found for Boulogne-sur-Mer (86.21 versus 50.34 ng/m 3 ).

Furthermore, Zn, Pb, P and Mn average concentrations in Dunkerque ranged between 7

and 39 ng/m 3 and accounted for about 81.5% of the total analyzed TE concentration, followed by 9.7% formed by V, Cr, Sn, Sb, Ti, As and Ba. These last group elements showed average concentrations fluctuating between 0.9 and 1.7 ng/m 3 . Ni and Cu average concentrations (3.23 ng/m 3 and 2.74 ng/m 3 respectively) participated in 7% of the total. Finally, the rest of the TE group contributed in 1.8% only and showed average concentrations ranging between 0.02 and 0.45 ng/m 3 . On the other hand, Boulogne-sur-Mer trace elements contribution in total analyzed TE concentration shows that Zn, Pb, P and Mn which average concentrations ranged between 4

and 19 ng/m 3 , account for 78% of total TE. On the other hand, Cu, Ni, Ti, V, Sb, Sn and Ba (average concentrations between 0.9 and 1.7 ng/m 3 ) contributed in 18% of total analyzed TE concentration. Furthermore, 4% was the quota of the remaining trace elements which included average concentrations ranging between 0.02 and 0.46 ng/m 3 .

Finally, we noticed in Boulogne-sur-Mer and Dunkerque that the average concentrations of Pb, Mn, Ni, As and Cd did not increase beyond WHO, USEPA and the European directive limit and target values during this campaign. In fact, the obtained average concentrations were all found below the annual mean limit values of 250, 150, 20, 6 and 5 ng/m 3 respectively.

In addition to the contribution of each element in total TE concentration, we also discuss hereafter the difference in the concentrations between Dunkerque and Boulogne-sur-Mer for each of the TE metals (Figure 1). In this section, the elements are divided between the ones found higher, moderately higher and comparable average concentrations at one site when compared to the other. Hence we find that Zn, P, Pb, Mn, Ni, Cu, V, Cr and As concentrations are much higher in Dunkerque compared to Boulogne-sur-Mer. Furthermore, Rb, Cd, Ag and Sn average concentrations are found moderately higher in Dunkerque, while Co, Ba, Sr, Sc, Bi, Nb, Te and Tl average concentrations are found comparable between the two sites. Finally, the remaining elements represented by Ti and Sb were found more concentrated on average in Boulogne-sur-Mer than in Dunkerque. Additional interpretation on the origins of these elements will be given later in this chapter as well as in chapter V. A summary of the average concentrations and contributions of TE calculated for our spring campaign fine PM samples can be found in Table 2. We are able to distinguish that total average TE concentration in Dunkerque is about 1.3 fold higher than the average in Saint-Omer (99.95 versus 75.95 ng/m 3 ). Total Te concentration values are also higher than the winter campaign ones. However, in a similar fashion as the winter campaign, the average concentrations of Pb, Mn, Ni, As and Cd regulated TE did not cross the WHO, USEPA and the European directive limit and target values during this campaign. Furthermore, Dunkerque's spring results show that Zn, P, Pb and Mn contribute in about 78.4% of total TE, followed by V, Cu, Ni and Ti (15.4%), then Cr, Sn, Sb, Ba and As (4.6%), and the remaining elements constitutes 1.6% only, which is more or less the same distribution as observed in winter.

On the other hand, 68% of total average TE concentration in Saint-Omer is constituted from Zn, Pb, P and Mn, whereas Cu, Ni, Ti, V, Sb, Sn and Ba represent 26%. Finally, the rest of the elements contribution summed a remaining 6% of the total TE average.

The comparison between the TE levels found in Dunkerque and those found in Saint-Omer reveals a different scenario than the one observed for Dunkerque and Boulogne-sur-Mer during the winter campaign. More TE elements are found concentrated in the inland site than the coastal industrialized site (Figure 2).

Dunkerque average concentrations exceeded Saint-Omer's for Zn, P, Mn, V, Ni, Cr and Sr. Comparable levels were found for Co, Ag, Nb, Te and Tl at the two sites. Meanwhile, elemental concentration of As, Cu, Pb, Sb, Sn ,Ti, Rb, Cd and Bi are found moderately higher in Saint-Omer. Finally, this latter is found to have higher concentration levels for the remaining TE elements. In this section, the data used for comparison will be extracted from similar studies conducted around Europe (Figure 3). Geographically, the sites are distributed between northern Europe (Finland, Netherlands and the United Kingdom), middle Europe (Switzerland and Hungary) and southern Europe (Spain, Italy, and Greece). These studies include common PM 2.5 trace elements measurements. The characteristics of some selected sampling sites were detailed in chapter III, when many others were not.

Therefore, we will illustrate the details about each study used hereafter with a small summary about the maj or influences encountered at these locations (Table 3).

I V. 3. 2. 1. Alkali and Alkaline-earth metals

This group includes some metals that are usually considered in a moderate frequency in PM 2.5 studies. Between our sites, Dunkerque displayed comparable concentration values for Ba in winter and spring when compared to Boulogne-sur-Mer and Saint-Omer respectively.

As for the rest of the European studies, our values are found slightly higher than the nearby site of Raahe, but much lower than the rest of the studies. On the other hand, Rb average concentration was found to be the highest in Saint-Omer, followed by Dunkerque winter concentration. When compared to other studies, these values are comparable to mid European sites (Zurich and Basel), but much lower than other industrialized sites (Onda and Raahe).

In general, these two elements (Ba and Rb) can be found in crustal dust [START_REF] Wedepohl | The composition of the continental crust[END_REF] . Higher levels from one site to another can be explained by important dust resuspension, and/or important industrial activities which include raw ore manipulations.

Actually, this situation can be observed in Dunkerque, as well as in Raahe and Onda.

Finally, Sr average concentrations in Dunkerque is higher than in the other sites for the two campaigns, and is comparable to some values between the nearby sites studies (Raahe and Vallila). However, this metal is found in relatively higher concentrations in the remaining comparison sites.

I V. 3. 2. 2. d-block elements

This group includes a list of metals that is extensively analyzed in similar studies around the European continent. Our first observation regarding this group revolves around the elements that have legislated concentration limit values in the atmosphere (Cd, Mn and Ni).

Average concentrations are found lower in our study than the limits discussed in Chapter I (section I.4.3.) and we also notice that there is no case of limit values rupture recorded in the European sites either.

When compared to these studies, Ag concentrations are found to be generally higher than the values found in nearby sites and much lower than the rest of the comparison sites in Spain and Italy. Co concentration levels are comparable between our sites in each campaign, but when compared to most studies, the values are found lower. On the other hand, Cr average concentrations are found higher in Dunkerque than the remaining sites in our study.

These averages are also higher than Huelva's, comparable to Onda and Budapest results, and finally lower than the rest of the comparison sites.

Furthermore, highest Cd average found in Saint-Omer was found to be comparable to Basel, Onda and Barcelona levels. However, the average was lower than in Huelva and Llodio, but much lower than the urbanized Venice area. In parallel, Cu averages followed Cr variations between our sampling sites, but were found higher than Cantabria's value (except for Boulogne-sur-Mer average), comparable to some north European sites, and finally much lower than the rest of the comparison sites. On the other hand, Mn levels in Dunkerque show higher and comparable averages when compared to most studies. However, Raahe, Llodio and Cantabria's Mn averages exceeded in many folds the values of our industrialized site.

This is in accordance with the industrial characteristics of these sites.

Dunkerque's Ni averages are also found higher than the remaining sites of our campaigns, but also higher than most of the comparison sites in Europe as well. Few exceptions can be noticed for sites like Llodio and Venice in which Ni concentration peaked well above our region values. In an opposite direction, Ti average concentrations in Saint-

Omer is found to be the highest between the three studied sites, which can be resulting from crustal matter sources in that area. Nonetheless, this high Ti concentration value is found to be much lower than those found in Europe with very few exceptions. Furthermore, V average at Dunkerque during spring is found to be the highest between our campaigns, it is also the highest when compared to most European studies, with the exceptions of the following heavy urbanized sites: Rotterdam, Barcelona, Venice and Athens, and the industrial site of Llodio in Spain. As for high Zn average concentration in Dunkerque, it is found higher than half of the comparison studies results, including some urban-industrial sites, but also very much lower than other heavy urbanized and industrialized sites.

Finally, Nb and Sc averages for comparison were down to one study for each metal.

Both elements are found very much lower than the ones found in the two comparison studies.

I V. 3. 2. 3. Other metals, metalloid and non-metals

Within this group of elements, our first concern focuses on the elements that have legislated concentration limit values in the atmosphere (As and Pb). Average concentrations found in our study are lower than the limits discussed in Chapter I (section I. 4.3.). We can also notice that Onda's industrial site as well as the urban site of Athens presented As concentration levels beyond the limit of 6 ng/m 3 .

On the other hand, Bi and Te average concentrations are found higher in our sampling sites than in the other comparison ones. P concentrations show higher values for Dunkerque in both winter and spring campaigns, but also lower than in other sites in Europe.

Furthermore, the highest Sn average concentration can be observed in Saint-Omer, when the other values of our campaigns are found to be almost comparable between each other. But when compared to other sites, Saint-Omer value was higher and comparable in almost all the sites except for Llodio's value which was very much higher than our study's.

Additionally, Tl average concentration showed a peak value in Dunkerque during the winter season, followed by Saint-Omer's average that was comparable to Dunkerque's spring one.

However, our values tend to be higher than the ones observed on the European level.

Moreover, As highest average concentration is uncovered in Saint-Omer inland site. It is found higher than most north European sites (except in Helsinki andBasel "1997-1998") and lower than the rest of the comparison sites which includes heavily industrialized and urbanized cities. On the other hand, Saint-Omer has also the highest Pb concentration between our sites. This concentration is higher and comparable to the values found in other European cities that exhibit maj or urban influences, but also is much lower than the concentrations of Pb in industrial sites (Onda and Llodio) as well as in heavy urbanized ones (Barcelona and Athens). Finally, Sb concentration levels followed to a certain extent the trend of Pb for our sites from which Saint-Omer held the highest average for this element. This average was still higher than most of the European comparison sites, including highly urbanized cities values (Budapest), but was also comparable and low when weighed against the heavy industrialized site of Tarragona and the heavy urbanized site of Barcelona respectively.

I V. 4. I ndustrial I mpact analysis

The Industrial Impact calculation method was used by [START_REF] Puxbaum | A dual sit e st udy of PM2. 5 and PM10 ae rosol che mist ry in t he large r re gion of Vie nna[END_REF] and reproduced by [START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF] to quantify the impact of a certain anthropogenic sector on the total load of chemical species in atmospheric PM. Originally this method was used to evaluate the urban impact on the chemical composition of atmospheric aerosols using two sets of chemical concentration data: reference site data (background) and impacted site data.

The source of impact we are searching for in this section is industrial, therefore we will be calculating this impact in Dunkerque (urban-industrial) using Boulogne-sur-Mer as a reference site (urban background). Both sites are influenced by many common sources (urban, marine and secondary inorganic aerosols) with the exception of the industrial sector that is present only in Dunkerque. The choice of taking into account Boulogne-sur-Mer site as reference can be verified to a certain extent by the averages found for TE at that site (Table 1).

The calculations will allow us to estimate the percentage of the industrial activities contribution in Dunkerque's TE load. Therefore, we will consider the winter campaign during which simultaneous sampling was accomplished between Dunkerque and Boulogne-sur-Mer.

However, this method could not be applied for the spring campaign since the choice of a reference site was impossible due to the presence of industrial facilities at both sites and the detection of high concentrations in many trace elements. In addition, the possible influence of Dunkerque industrial site emissions at Saint-Omer cannot be discarded as mentioned in the upcoming section IV.5 and section IV.7.2.2.

The estimation procedure was extracted from [START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF] but the symbols we modified from the original source in order to define our impact and reference in Equation 1illustrated below.

I I (%) = I L -U L × 100 I L Equation 1
In the equation we define:

-I.I. as the industrial impact (in %); affected by a large uncertainty. In this case, the observed differences from one site to the other are not significant. These values can be resulting from possible non urban and non-industrial contributions at the reference site during the sampling period. The rest of the results indicate that high industrial impact can be observed for As, Cd, Cr, Cu, Ni, P and Zn compared to relatively less but still significant impact for Mn, Pb, Rb, Sn and V.

Using the same approach, it is important to mention that I.I. were calculated for maj or elements such as Al, Ca and Fe which was evaluated to 26%, 12% and 43% respectively. It is known that these elements in addition to other trace elements such as Pb, Mn and Cd can be generated by the industrial activities linked to the metallurgic factory in Dunkerque. However, it is important to uncover if the particularly high Zn industrial impact value is limited to one or many industrial sources.

Further investigations will complete this analysis in the upcoming section that includes an enrichment factor followed by correlations and concentration rose analysis, in order to introduce an additional trustworthy method to determinate the most important sources of emissions.

I V. 5. Enrichment Factor analysis

In the previous section, we evaluated the impact of the industry on the loads of TE in PM 2.5 at the urban-industrial site of Dunkerque. However, another evaluation criterion can be added as an additional step in the estimation of anthropogenic activities. This parameter is the enrichment factor which allows us to determine which element is more concentrated at one site when compared to a natural crustal source. It is widely used in the literature ((Torfs & Van Grieken, 1997); (Chester, et al., 1999) and [START_REF] Saliba | Ef f e ct of local and long-range t ransport e missions on t he e le me nt al composit ion of PM10-2.5 and PM2. 5 in Be irut[END_REF]), because it enables the differentiation between a natural source of emissions (soil crust in our case) for a certain element and the additional sources for the same element (without differentiating between these latter).

Between the studies that use enrichment factor method, we can notice that different elements can be used as reference, between which: Ti, Al, Fe, Si, Sr and Ba are commonly used. The principal selection criterion is embodied in the fact that the reference element should not be emitted in considerable quantities by any nearby anthropogenic source in the studied region. By following this method, we discarded Ti, Al and Fe, which are emitted by industrial sources in Dunkerque. On the other hand, silicon was not analyzed in this study, which leaves us two choices: Sr and Ba. The first can be present partially in marine aerosols, which is not the case for Ba.

Hence, the enrichment factor for each of the TE list was calculated using Ba upper crust and aerosol content as references. This element is present in high concentrations (668 ppm) in the upper crust [START_REF] Wedepohl | The composition of the continental crust[END_REF], and is not significantly emitted from any known nearby anthropogenic sources which makes it the best choice for a reference element in our case. Te enrichment could not be calculated in our study because of a lack in information about its crustal concentration.

The concentrations for the remaining TE elements in the upper crust were retrieved from [START_REF] Wedepohl | The composition of the continental crust[END_REF] and used in the following equation: Equation 2With:

-[ X] aerosol as the average concentration of the element in our samples (in ng/m 3 ); Ba was used as reference element in our study.

Significant enrichment for any given element can be considered starting from an EF value that increases beyond 10. Finally, the results of the calculations are illustrated and discussed hereafter for each campaign separately.

IV.5.1.Winter campaign

Figure 4 illustrates the EF results for the winter campaign which shows that enrichment is not significant for Co, Mn, Rb, Sc, Nb, Sr and Ti in both Dunkerque and Boulogne-sur-Mer. In this latter, Cr (EF≈10) and P (EF<10) were also not enriched in comparison to

Dunkerque's values. On the other hand, Bi and Sb were found to be highly enriched with EF values similar between our two sites. For the rest of the elements, EF values vary depending on the site. In Dunkerque, they are always higher than those found for Boulogne-sur-Mer, especially for Ag, As, Cd, Cr, Cu, Ni, P, Pb and Zn which indicates the presence of an anthropogenic contribution in Dunkerque.

Finally, these observations can be compared to the industrial impact calculations discussed earlier (section IV.4) to find that both calculations lead to similar results.

I V. 6. Global TE correlations study

In this section we will board the relationship between each of the TE concentrations in all the samples. This comparison will allow us to define which elements are correlated, as well as the possible origins of these elements by comparing their mean ratios to ones found in the literature for specific sources. Therefore, correlation coefficients (R 2 ) and slopes (y = ax) tables and scatter plot matrices (SPLOM) were built using Microsoft® Excel 2007 and SYSTAT® 10 respectively. The data sets used in this step include all species concentrations of all samples for a given sampling site regardless of the emission source or wind sector. The calculat e d corre lat ion coe f f icie nt value s show t hat many e le me nt s are f ound ve ry we ll corre lat e d t o e ach ot he r whe n R 2 ≥ 0.60. Howe ve r, ot he r e le me nt s are f ound mode rat e ly corre lat e d wit h an ave rage coe f f icie nt 0.5 0 < R 2 < 0. 60, whe n t he re st of TE e le me nt s are we akly or not corre lat e d at all.

The corre lat ion coe f f icie nt s and t he corre sponding slope value s obt aine d using t he wint e r campaign sample s are illust rat e d in Table 6. The upcoming sub se ct ion will de ve lop t he analysis of t he re sult s illust rat e d in t he t able s above . Spe cial at t e nt ion will be give n t o t he prof ile s of ce rt ain ant hropoge nic e missions of bot h urban and indust rial nat ure s.

I V. 6. 1. Oil combust ion TE

In the literature, Ni and V are extensively discussed with emphasis on "oil combustion"

as a primary source of e missions. This t ype of e missions can be ide nt if ie d in pe t roche mical indust rial e missions, wit h V/ Ni rat ios ranging be t we e n 1. 4 ( Folt e scu, e t al. , 1996) , 1. 5 t o 1. 9

( Swie t licki & Kre j ci, 1996) , and 2. 34 ( Lamaison, 2006) . Oil combust ion can also be use d in re side nt ial he at ing which e missions cont ain V/ Ni rat io around 0. [START_REF] Orks Cited Adachi | Charact e riz at ion of he avy me t al part icle s e mbe dde d in t ire dust[END_REF] . The same st udy analy z e d t ire t re ad and dust t o f ind t hat t he f irst is mainly compose d of Zn, whe re as t he se cond is e nriche d in Al, K and Ca, and re pre se nt re suspe nde d soil dust . On the other hand, Dunkerque's industrial site has been the focus of a recent re se arch t he sis t hat de alt wit h part iculat e e missions ( Lamaison, 2006) . This lat t e r ide nt if ie d a Cu/ Sb rat io value of 3.6 in an are a af f e ct e d by a mixt ure of indust rial and t raf f ic e missions.

Furt he rmore , highe r Cu/ Sb rat ios we re ide nt if ie d having 5.6 and 5.3 value s t hat we re calculat e d using che mical dat a f rom PM sample s t ake n in Swe de n f rom a road t unne l and a maj or road re spe ct ive ly ( Hj ort e nkrans, e t al. , 2007) . I n addit ion, a Cu/ Sb rat io value of 2.5

was calculat e d in 2009 t o t race brake pads using che mical dat a f rom PM 2. 5 sample s ( Dongarrà , e t al. , 2009) .

Our re sult s allow us t o de t e rmine Cu/ Sb rat io value s of 1. 60, 0. 56, 2. 19 and 0. 67 in Dunke rque ( wint e r), Boulogne -sur-Me r, Dunke rque ( spring) and Saint -Ome r re spe ct ive ly.

The slope s of t he se e le me nt s in spring we re not re port e d in Table 7 be cause of t he we ak corre lat ion coe f f icie nt s be t we e n t he se t wo e le me nt s. Howe ve r, a close r e xaminat ion of t he rat ios shows t hat t he e f f e ct of brake linings could be obse rve d most ly in Dunke rque during spring, and slight ly during wint e r, but ve ry low in t he re st of t he sampling sit e s ( Appe ndix-I I ) .

W he e l rubbe r is most ly conce nt rat e d wit h Zn ( Adachi & Tainosho, 2004) as we ll as Cu, Pb, Mn, Co, Ni and Cd [START_REF] Thorpe | Source s and prope rt ie s of non-e xhaust part iculat e mat t e r f rom road t raf f ic: A re vie w[END_REF]

I V.7.Concentration evolution by wind sector

Af t e r t he ide nt if icat ion of t he maj or corre lat ions and slope s comparison t o lit e rat ure , we proce e d he re af t e r by obse rving t he maj or wind se ct ors cont ribut ing in t he loads of t he se TE.

This st e p is achie ve d t hrough t he analysis of calculat e d ge ome t ric conce nt rat ion rose s, which we re obt aine d for e ach sit e in bot h campaigns f ollowing t he same calculat ions de t aile d in Chapt e r I I I ( se ct ion I I I . 8. ) . Howe ve r, une xplainable rose s we re f ound f or some e le me nt s in t he sample s and should be analyz e d care f ully due t o t he ir e xce pt ionally sharp f orm. The se e le me nt s are : Bi and Ti in Dunke rque during wint e r ( Figure 6), As and Ti in Boulogne -sur-Me r ( Figure 7), Tl in Dunke rque during spring ( Figure 8) and Nb in Saint -Ome r ( Figure 9).

The common f act or re lat ing t he se e le me nt s le ading t o t he e xce pt ional shape of t he ir rose s is t he f act t hat most sample s pre se nt e d ve ry low conce nt rat ions unde r most wind dire ct ions e xce pt f or t he dire ct ions ide nt if ie d in t he rose s.

Globally, t he rose s show t hat f ine PM apport ionme nt is re lat e d t o a mixt ure of wind se ct ors at most sit e s in our st udy. To be able t o e xplain t he loading of t he se e le me nt s, t he int e rpre t at ion will conside r t he e le me nt s linke d by t he ir wind se ct or origins: urban, crust al, marine or indust rial. One e le me nt can originat e f rom many se ct ors de pe nding on wind dire ct ion and f re que ncie s, but one se ct or can also re pre se nt a mixt ure of many source s. The se ct or de pe nde nt approach will not be e xclusive since we pre viously ide nt if ie d se ve ral corre lat ion combinat ions, which will also be t ake n in conside rat ion he re in. t he e le ct ric st e e l plant locat e d in Le f f rinckoucke . On t he ot he r hand, t he urban e mission source s are re groupe d unde r t he urban se ct or st re t ching f rom t he W , passing t hrough t he S and e nding at t he E dire ct ion. This se ct or e nclose s f re que nt light and rare he avy t raf f ic e missions, in addit ion t o t he re side nt ial e missions source s. Finally, t o t he se ant hropoge nic act ivit ie s we add t he nat ural source s, re pre se nt e d he re in by marine ( se ct or N-NE) and/ or soil dust re -suspe nsion t hrough t ransport act ivit ie s. Dunke rque TE conce nt rat ion rose s show t hat t he e le me nt s can be carrie d by winds blowing f rom many dire ct ions, and t hus are f ound ge ne rat e d by many source s of e missions.

Howe ve r, we st ill we re able t o divide t he m int o many groups de t e rmine d by t he wind se ct or which induce t he highe st conce nt rat ions ( Table 8) . The f irst group ( including Ag, As, Ba, Bi, Cd, Co, Cr, Cu, Nb, Ni, Pb, Rb, Sb, Sn, Tl and V) e xhibit s it s highe st conce nt rat ions unde r t he large E t o W se ct or (90° -270°) . This se ct or e nclose s urban, t ransport and cont ine nt al e missions ( ne ighboring re gions and count rie s) . The W se ct or (260° -280°) also share d a large list of e le me nt s e missions wit h t he int e grat e d st e e lworks sit e in Dunke rque ( Ag, As, Cd, Co, Cu, Mn, Pb, Rb, Tl, V and Zn) .

Also, we cannot e xclude a possible inf lue nce in t he le ve ls of Mn and Pb coming f rom a ne arby iron-mangane se st e e l work f act ory locat e d in t he same se ct or ( Hle is, 2010) .

High conce nt rat ions are obse rve d unde r t he NE-E wind se ct or ( 50° -100 °) f or t he t hird group which e nclose s: Ba, Cd, Cr, Cu, Nb, Pb and Zn. The possible inf lue nce s of an e le ct ric st e e l plant locat e d in Le f f rinckoucke , 5 km away t o t he e ast e rn side of t he sampling point , is conside re d he re as a possibilit y t hat can e xplain t he aspe ct of t he conce nt rat ion rose . The variat ion of TE at mosphe ric conce nt rat ions de pe nding on wind dire ct ions in Boulogne -sur-Me r is illust rat e d in Figure 7. mixe d inf lue nce can ge ne rat e be t we e n t he S and SW dire ct ions. Finally , indust rial inf lue nce s can arrive by a re gional scale t ransport int o Boulogne -sur-Mer's atmosphere from two ve ry close dire ct ions: t he f irst is t he NE-E t hat point s at t he marine -urban-indust rial sit e of Dunke rque , and t he se cond is t he E dire ct ion which st re t che s t o Saint -Ome r t hat e xhibit s an urban-indust rial inf lue nce . The re f ore , t he analysis of t he rose s should conside r t he se dire ct ions wit h prude nce .

Sources of emissions W ind sector Corresponding elements

The conce nt rat ion rose s illust rat e d in Figure 7 The conce nt rat ion rose s calculat e d using TE conce nt rat ions f or Dunkerque's spring campaign are illust rat e d in Figure 8. During this spring campaign, Dunkerque's wind sectors playe d an import ant role in t he ide nt if icat ion of spe cif ic source apport ionme nt f or TE. The pe rmane nt mixt ure of t he source s at t his indust rial coast al sit e impose s a similar approach of rose analysis as t he one s adopt e d be f ore . The re f ore we proce e d he re af t e r by dividing t he e le me nt s int o groups de pe nding on t he ir origins.

Dunke rque sampling sit e can be inf lue nce d by many source s of PM e missions t hat we re pre viously de t aile d in t his chapt e r ( se ct ion I V. 7. 1. 1) . A f irst obse rvat ion can be drawn f rom t he rose s and is t he f act t hat t he indust rial and urban se ct ors we re most cont ribut ing in t he high TE conce nt rat ions in t he spring pe riod. The nat ural source s we re limit e d t o Sr inf lue nce f rom t he marine nort he rn se ct or. The e le me nt s we re re groupe d by se ct or of origins in Table 10.

I V.7.2.2.Saint-Omer spring concentration roses

Saint -Ome r spring conce nt rat ion rose s displaye d in Figure 9 shows a compilat ion of many wind se ct ors cont ribut ion in t he le ve ls of TE conce nt rat ions. This sit e is charact e riz e d by it s inland ge ographic posit ion, which se parat e s it f rom t he coast al e nvironme nt e ncount e re d in t he case s of Dunke rque and Boulogne -sur-Me r. Howe ve r, t he possibilit y of any nat ural cont ribut ions e xist s part icularly for t he soil dust , which can be re suspe nde d wit h t he urban act ivit ie s in t he cit y.

On t he ot he r hand, t he inf lue nce s of t he indust rial act ivit ie s should not t o be ne gle ct e d at t his sit e . The pre se nce of a ne arby glass making f act ory ( SE side of t he sampling sit e ) might re pre se nt t he light -indust ry inf lue nce in t he PM loads wit hin t he are a. The se cond indust rial inf lue nce conce rns t he he avy indust rie s locat e d in Dunke rque , which can part icipat e wit h winds blowing f rom t he N-NE se ct or.

The conce nt rat ion rose s ( Figure 9) show t hat t hre e maj or wind se ct ors are re sponsible of t he highe st conce nt rat ions: SE ( 120° -160°) corre sponding t o glassmaking f act ory, N t o E quart e r ( 0° -110°) related to Dunkerque's distant industrial emissions, and finally the SW to E se ct or ( 230° -90°) . The urban inf lue nce can be adde d t o t his list as it is e ncount e re d f rom all around t he sampling sit e . cont ribut ion was also re f ine d he re since t he se ct ors f rom which we have an e xclusive urban inf lue nce are large r t han t he one s obse rve d in t he re maining t wo sit e s. Furt he rmore , t he conce nt rat ion rose s allowe d us t o conside r a large possibilit y of e ach sites emissions influences on each other. Dunkerque's steel industry ( Ag, As, Cd, Cu, Mn, Pb, Rb and Zn) and fue l oil combust ion ( Ni, V and Co) could be cont ribut ing in t he load of ce rt ain e le me nt s in Saint -Ome r. Howe ve r, t he indust rial se ct or is in most case s found t o be mixe d t o ot he r se ct ors ( marine or urban f or e xample ) .

I n orde r t o analy z e spe cif ic indust rial inf lue nce s wit hin a give n sit e , and also f rom one sit e t o anot he r, we will e xamine a list of e le me nt s t hat is f ound share d unde r t he indust rial inf lue nce s in t he upcoming part . I n addit ion t o t his f irst se le ct ion, we will be using t he conce nt rat ions f ound in ce rt ain se le ct e d sample s only. The se sample s will be chose n base d on t he ave rage wind dire ct ion t hat point s only at t he indust rial source s which inf lue nce s are unde r st udy.

I V. 7. 3. Corre lat ions and slope s unde r indust rial inf lue nce s I n orde r t o dist inguish t he e f f e ct of t he indust rial inf lue nce s one f rom t he ot he r, a spe cif ic se le ct ion of PM 2. 5 sample s colle ct e d unde r winds blowing f rom indust rial e mission source s dire ct ions was achie ve d f or e ach sit e . The indust rial e f f e ct s will be st udie d care f ully in an at t e mpt t o de t e rmine a spe cif ic rat io be t we e n t he e le me nt s t hat could be conside re d as a t race r of such e missions. W e choose t o limit our discussion in t his se ct ion t o a list of e le me nt s showing a signif icant incre ase in conce nt rat ion unde r combine d indust rial inf lue nce s, wit hout any dist inct ion be t we e n t he sit e s: Fe , K, Zn, Cr, Mn, Ni, Cu, As, Rb, Ag, Cd, Sn, Sb, Tl and Pb.

For a give n sit e , all t he se e le me nt s are not ne ce ssarily indust rial t race rs, which is t he pre cise re ason f or which we will be able t o propose a spe cif ic e le me nt al rat io f or a give n e mission source . W he n e xamining complicat e d case s whe re mixe d source s are pre se nt , a combinat ion of rat ios could be sugge st e d t o dif f e re nt iat e one source f rom t he ot he r.

The se le ct ion of PM 2. 5 sample s unde r indust rial inf lue nce s was achie ve d using t he fol lowing me t hod: a) For Dunke rque sample s t he se le ct ion include d t wo se ct ors:

• The sample s unde r 250° -320° wind se ct or unde r t he int e grat e d st e e lworks inf lue nce . Be t we e n t he se sample s, we se le ct e d t he one s e xhibit ing Fe and K conce nt rat ions above 100 ng/ m 3 .

• Unde r t he inf lue nce of t he e le ct ric st e e l making f act ory f rom t he N-NE se ct or, t he conce nt rat ions of Zn, Pb, Cr and Cu we re f ound t o be part icularly highe r t han t he t ot al ave rage conce nt rat ion of t he whole campaigns in Dunke rque ( all se ct ors combine d) . I n a le sse r e xt e nt , t he re mark can also be give n for Fe , K, Mn, As and Sn ( Table 13). The se re sult s se e m t o be cohe re nt whe n conside ring t he nat ure of t he annual e missions de clare d by t he indust ry ( E-PRTR, 2012) and are e qually cohe re nt wit h t he lit e rat ure ( Sammut , e t al. , 2006) . Howe ve r, t his obse rvat ion could not be f urt he r de ve lope d since t he re are not e nough sample s t o make a re pre se nt at ive conclusion.

.Conclusion

The st udy of t race e le me nt s conce nt rat ions ( Ag, As, Ba, Bi, Cd, Co, Cr, Cu, Mn, Nb, Ni, P, Pb, Rb, Sb, Sc, Sn, Sr, Te , Ti, Tl, V and Zn) in Dunke rque , Boulogne -sur-Me r and Saint -Ome r during t he sampling campaigns showe d t hat :

-Ele me nt s like Zn, Pb, P and Mn we re f ound in re lat ive ly high conce nt rat ions in e ach sit e , st art ing wit h Zn ( 18. 2 -43.9 ng/ m 3 ) , Pb ( 9. 6 -16.71

V.1. I ntroduction

W e have de monst rat e d in pre vious chapt e rs ( I I I and I V) t hat Dunke rque , Boulogne -sur-Me r and Saint -Ome r are subj e ct e d t o dif f e re nt nat ural and ant hropoge nic inf lue nce s. The se inf lue nce s have a dire ct e f f e ct on t he che mical composit ion of PM 2. 5 , which was e vide nce d qualit at ive ly, but not pre cise ly quant if ie d. This chapt e r include s t he f inal se ct ion t hat aims at t he ide nt if icat ion of t he source s t hat are cont ribut ing in t he PM 2. 5 che mical composit ion and t he quant if icat ion of t he ir re spe ct ive cont ribut ions.

Dunke rque , Boulogne -sur-Me r and Saint -Ome r are locat e d on t he coast and inland of t he NPdC re gion and are subj e ct e d t o dif f e re nt source s t hat can be re sponsible f or f ine PM 2. 5 e missions. Be t we e n t he se source s, t he marine one is e vide nce d in all t hre e sit e s, e ve n t he inland one of Saint -Ome r. I n addit ion, crust al dust ge ne rat e d f rom soil e rosion make s anot he r nat ural source of PM. The urban act ivit y is also found common be t we e n t he t hre e sit e s, and is re pre se nt e d mainly by t raf f ic and some dome st ic act ivit ie s. I n addit ion t o t he nat ural and ant hropoge nic source s me nt ione d e arlie r, indust rial act ivit ie s are pre se nt in Dunke rque and Saint -Ome r. At t he f irst sit e , an int e grat e d st e e lworks ( I SW ) as we ll as an e le ct ric st e e l plant indust ry locat e d on t he e ast e rn borde r of Dunke rque ( in Le f f rinckoucke ) , can bot h be ide nt if ie d in some PM 2. 5 sample s. Howe ve r, at t he inland sit e of Saint -Ome r, t he cont ribut ion of Arques' glassmaking f act ory in f ine PM 2. 5 re le ase is ve rif ie d as we ll as t he inf lue nce of t he re lat ive ly dist ant I SW locat e d in Dunke rque .

Following t he che mical analysis of t he inorganic f ract ion and t ot al carbon, and af t e r t he de t aile d e xploit at ion of t he che mical re sult s in orde r t o de t e rmine t he possible origins of t he colle ct e d PM 2. 5 , we t ake he re af t e r anot he r pat h t hat use s st at ist ical t re at me nt of our che mical dat a t hrough mode ling. I t use s che mical dat a t o f e e d a mat rix f act oriz at ion mode ling algorit hm in orde r t o ide nt if y and quant if y t he cont ribut ions of e ach e mission source in t he Howe ve r, using PMF v. 3.3 prove d t hat an incre ase in t he input siz e e ve nt ually le ads t o an incre ase in t he mode l t ime consumpt ion. I n addit ion, t he same ve rsion doe s not allow any cont rol on t he init ializ at ion and e nding crit e ria. Thus, t he re maining e sse nt ial parame t e r use d in t he PMF is t he inclusion of me asure me nt unce rt aint ie s in t he calculat ions which st abiliz e t he solut ion.

Many re asons push us t owards t he use of NMF as an alt e rnat ive t o PMF. Dif f e re nt for ms of NMF we re de ve lope d de pe nding on t he choice of minimiz at ion crit e rion. I t is an algorit hm e xt e nsive ly use d by mat he mat icians f or signal analysis and it s Mat lab® code s are e asily acce ssible . The usual NMF mode ls do not conside r unce rt aint ie s f or e ach me asure me nt .

The re f ore , we chose t o de ve lop our own ve rsion of t he NMF which int e grat e s t he se unce rt aint ie s in t he calculat ion proce ss.

V.2. Early development and use of receptor modeling

The use of multivariate receptor modeling started in the 60's with I. H. Billford ( Viana, e t al. , 2008) . I n t his e quat ion we conside r t hat :

-X is t he mat rix of obse rvat ions which siz e is "n x m" wit h "n" and "m" as t he numbe r of sample s and analyz e d spe cie s re spe ct ive ly. X is e xpre sse d in t he case of at mosphe ric e nvironme nt s st udie s in ng/ m 3 . I f we conside r an e le me nt x ij f rom t he mat rix, it will re pre se nt t he conce nt rat ion of t he spe cie s "j" of t he sample "i".

-G is t he cont ribut ion mat rix "n x p" wit h "n" as t he numbe r of t he sample s and "p" as t he numbe r of source s. W he n applie d in e nvironme nt al st udie s, a give n e le me nt "g ik " is me asure d in µg/ m 3 , and re pre se nt s t he mass cont ribut ions of source "k" in sample "i".

-F is t he prof ile s mat rix const it ut e d f rom "p x m", wit h "p" as t he numbe r of source s and "m" as t he numbe r of st udie d spe cie s. A give n e le me nt f rom t he mat rix "f kj " is e xpre sse d in ng/ µg, and re pre se nt s t he mass of a spe cie s "j" in t he t ot al mass of spe cie s in source "k".

-E is t he e rror mat rix of "n x m" dime nsion and e xpre sse d similarly t o t he mat rix of obse rvat ions ( X). W it hin t his mat rix, a random se le ct ion of a variable "e ij " re pre se nt s t he e rror of re const it ut ion re sult ing f rom t he f act oriz at ion of spe cie s "j" in t he sample "i".

I n ge ne ral, t he numbe r of source s "p" is much lowe r t han t he numbe r of sample s "n"

and analy z e d spe cie s "m". The choice of t he source s numbe r de pe nds e sse nt ially on t he apriori inf ormat ion on t he st udie d sit e .

V. Howe ve r, in e nvironme nt al scie nce s, t he dat a can vary t re me ndously , which induce s t he liaison be t we e n e ach spe cie s me asure me nt and an unce rt aint y. He nce , in addit ion t o mat rix X we add mat rix Σ of unce rt aint ie s in which a give n e le me nt "σ ij " is associated to the observation matrix, and represents the error on species "j" associated value in sample "i".

Therefore the model searches for the minimal error "e ij " and includes measurement uncertainties "σ ij " under the constraints of having (g ik ≥ 0) and (f kj ≥ 0). Thus, the minimization criterion is given by: Equation 3Following this last calculation method, the modeling approach confronts many problems, between which we cite the sensibility of the model to the initialization that deeply af f ect s t he result s. The init ial Paat ero approach [START_REF] Paatero | Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values[END_REF] was based on optimizing through calculations the results by alternately searching for matrix G and F. The first version was based on the research of matrix G with a constant matrix F and vice versa.

Another method enhanced the calculation procedure by adding a third step which refine G and F directly by transforming negative values to "0".

I n 1997, an updated version of positive factorization (PM F v.2) was introduced, which was used extensively in many scientific domains. However, inconveniences remained in this last version, especially when matrix dimensions increased an important number of local minima appears ( [START_REF] Paatero | Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values[END_REF] and [START_REF] Paatero | Least squares formulation of robust non-negative factor analysis[END_REF]).

V.4.3. Non-negative matrix factorization (NM F)

Similar to the principles of positive factorization, the NM F use the approached factorization of X = GF under positivity constraints and minimizing the following criteria under the constraints of having (g ik ≥ 0) and (f kj ≥ 0): Equation 4W e can notice the similarity between this algorithm and the one used in the case of PM F (Equation 3). The difference between the two resides in the positivity constraint applied on each element of matrices F and G (meaning that sources cannot have negative concentrations or negative source contribution) as well as the lack of measurements uncertainties consideration in the NM F. This latter appears to be an important setback in the receptor modeling of chemical data. On the other hand, the Non-negative M atrix Factorization has many advantages when compared to PM F: NM F is lighter than PM F in terms of calculation time and accessible M ATLAB® scripts, which can easily be modified. Finally, the goal was to develop a model calculation and implement it, therefore we naturally turned to the NM F method.

V.4.4. W eighted NM F By default, the NM F algorithm does not consider any differed variance data in the calculations. Therefore, constructing a new version of the NM F that considers individual variances on the data input was essential and forms an important extension to the model. This upgrade is realized in the "weighted NMF" based algorithm which enables the consideration of individual measurement uncertainties in the calculations ( (Hleis, 2010); (Delmaire, et al., 2010a ) and [START_REF] Delmaire | Factorization matricielle non negative sous contraintes[END_REF]). The uncertainties are inserted in the model in a matrix form, and the calculation equation turns to the following form:

Equation 5
Furthermore, the weighted NM F algorithm has been modified in order to take into account the a-priori knowledge on the source chemical composition by applying constraints.

The first type is the "equality" constraint which defines the presence or absence of an element the source profile, and eventually forces its concentration to a specific value. On the other hand, the second type called "inequality" constraint classifies an element order of abundance between the sources (for example [ X] source-1 > [ X] source-2 ). Additional information about our weighted NM F model is given in (Hleis, 2010) and [START_REF] Delmaire | Factorization matricielle non negative sous contraintes[END_REF]. The following section includes detailed explanations on the entry data needed to perform a successful weighted NM F calculation. I n some samples, some elements concentrations were not acquired due to technical reasons or because their values were below the detection limit (DL). I f the reading is below DL, the value in the matrix is replaced by DL ÷ 2. On the other hand, if the concentration data is missing due to technical reasons, the missing data is then replaced with the mean concentration of the species in that campaign ( [START_REF] Polissar | Atmospheric aerosol over Alaska: Elemental composition and sources[END_REF], [START_REF] Kim | I mproving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization[END_REF] and [START_REF] Liu | Atmospheric aerosol over two urbanrural pairs in the southeastern United States: chemical composition and possible sources[END_REF]).

V. 5. 1. 2. Matrix of measurements uncertainties (matrix Σ)

Similar and identical in size as matrix X, this matrix includes the values of relative measurements uncertainties (%) related to the analysis and sampling process. W e should note that for below DL concentration values in matrix X, the uncertainty was raised to 100 %. On the other hand, for matrix X missing values, the uncertainty is raised to 400% ( [START_REF] Polissar | Atmospheric aerosol over Alaska: Elemental composition and sources[END_REF], [START_REF] Kim | I mproving source identification of Atlanta aerosol using temperature resolved carbon fractions in positive matrix factorization[END_REF] and [START_REF] Liu | Atmospheric aerosol over two urbanrural pairs in the southeastern United States: chemical composition and possible sources[END_REF]. 2 7 .2 3 7 .0 2 8 8 .2 4 16 6 .92 3 1.3 5 2 7 2 .2 2 … 2 3 .16 41.44 5 6 .1 16 0 .96 14.2 6 10 8 .3 4 … 40 .0 9 8 1.7 7 4.16 12 0 .3 3 13 .0 2 8 3 The matrix of initialization (F init ) serves as a starting point to launch the calculations.

The initialization represents a crucial point when using factorization methods [START_REF] Frangkou | Review of the current evaluation methodologies used for source apportionment applications in EU countries[END_REF]. F init matrix values should be carefully defined in order to avoid reaching any local minimum and to ensure the best quality in the obtained results. This matrix contains chemical profiles of each source of emissions (Figure 4). Hence, a line represents the chemical The data used to fill the F init matrix were selected based on numerous studies conducted using modeling or CM B methods for source apportionment analysis.

… … … … … … … … … …
-Combustion 1: including fossil fuel combustion (coal, diesel, gasoil, and oil). I n this profile we also regroup tailpipe emissions from transportation, which encloses road traffic, heavy industrial and marine transportation.

-Combustion 2: representing the combustion of biomass materials such as wood logs, grass and green wood pellets.

-I SW -fugitive: is the integrated steel works non point sources emission in Dunkerque.

-I SW -sintering stack: is the main point-source emission of the integrated steel works plant in Dunkerque.

V. 5. 1. 4. M atr ix of "e qual ity" c ons tr aints

The first constraints used by NM F are the equality constraints, which consist of two matrices. The first one (Figure 5) signals the presence (cell value = 1) or absence (cell value = 0) of the constraint. I n this last case, even if the second matrix contains a certain value, the model will not consider it.

Figur e 5: Equal ity c ons tr aints f ir s t matr ix s howing the l oc ation of s ome c ons tr aints in the pr of il e s

On the other hand, when cell value = 1, the second matrix defines the value of the constraint for a selected chemical species within the profile. This defined value is considered at each step of the calculation and constraint the model to find this value in the final F matrix result. The values that can be inserted in the matrix should range between 0 and 1000, knowing that the final result is automatically normalized to 1000, but will always respect the constrained values. The use of such constraints is unfavorable because it limits the flexibility of the model in finding the results by itself. Furthermore, it shifts from the advantage of having the ability to guide the model calculations to the inconvenience of hardly constraining the model, and forcing it to search certain results.

AlCa Fe K M g Na

… Profil A 1 1 1 0 0 0 … Profil B 1 1 1 1 1 1 … Profil C 0 0 0 0 0 0 … Profil D 1 1 1 1 1 1 … Profil E 0 0 0 0 1 1 … … … … … … … …

V. 5. 1. 5. M atr ix of "ine qual ity" c ons tr aints

Less rigid than the "equality" constraints, the inequality constraints can also be used in the weighted NM F to softly guide the calculations without forcing any values in the final results. An example of the "inequality" constraint matrix is illustrated in Figure 7.

For each chemical species, we have the ability to assign an order of abundance for each species according to the source profile. The abundance classification starts with "1" and can get to a maximum that corresponds to the total number of profiles under study. On the other hand, inequality can be removed for any given species in any profile by putting "0" in the corresponding case. Hence, "0" is not considered in the matrix as the lowest level of abundance but an absence of the inequality constraint. Finally, a chemical species can also have comparable levels between different profiles, for this reason it is sufficient to insert the same number many times in the column. For example, in the matrix sample illustrated in These inequality constraints are favored when compared to the "equality" ones because of their flexibility and lightness on the calculations. I n fact, these constraints do not force the NM F model to calculate under stringent conditions; instead, it pushes only towards the respect of an order of abundance of species between different source profiles.

V. 6. W e ighte d NM F ope r ating c onditions

The use of the weighted NM F allows us to acquire chemical profiles as well as their contributions to PM 2.5 composition. The profiles allow us to identify the emission sources of PM 2.5 , whereas the second represents an estimation of the identified sources impacts on the total analyzed PM 2.5 value.

W e have demonstrated at the beginning of this chapter that PM 2.5 collected at the three selected sites exhibits similar but also different influences. Thus the NM F calculations using each site dataset should identify similar as well as different source profiles between these sites. However, similar profiles between the sites could be attributed to similar local sources such as local traffic, but they could also be linked to common regional sources. Additional specific local sources can be added if found by the model. This profile buildup is actually defined in the literature as the Lenschow method [START_REF] Lenschow | Some ideas about the sources of PM 10[END_REF]. This latter considers that the atmospheric PM concentrations are resulting from the addition of many contributions: regional background + urban background + local traffic + local industries +…etc. The regional background contribution is considered to be originating from large scale 2 1 0 1 0 0 2 … Profil F 4 4 6 2 0 0 3 … Profil G 3 3 3 5 0 1 4 … Profil H 2 1 3 1 0 0 1 … Profil I 5 1 5 1 0 0

Al Ca Fe K Mg Na Ag … Profil A 1 1 0 2 1 3 0 … Profil B 1 1 1 2 1 2 0 … Profil C 6 4 4 3 0 0 1 … Profil D 2 1 0 1 0 0 2 … Profil E
3 … Profil J 2 2 2 4 0 0 1 … Profil K 1 2 5 1 2 0 1 … … … … … … … … …
emissions, to which is added the local traffic. This last source can also be considered as an urban background stage, but at the local sampling region level.

However, in order to evidence a regional versus local scale contributions, the use of PM 2.5 chemical data obtained simultaneously in two different sites is an essential step. I t is made possible through our sampling strategy described in Chapter I I , thus we will achieve this phase by comparing source profiles identified in two sites for the same period. The results of the NM F modeling will be examined to uncover these profiles buildup, in case they existed. Furthermore, the model will allow us to quantify the impact of each of the identified sources, which will eventually lead to uncovering the contribution of regional and local influences.

The strategy used while performing NM F calculations is based on increasing the number of sources until the identification of an additional source profile is not possible. I n parallel, constraints were considered in the form of a matrix that was initially built relying on a-priori knowledge on the sources profiles. The "equality" constraints were used in very few cases in order to exclude some species from certain profiles (in this case their values were forced to "0" in the final output). On the other hand, the "inequality" constraints were maj orly used to guide the model to refine the final results.

The operational strategy was based on three calculation procedures, considering three datasets respectively, each of which corresponds to one sampling site and is independent from the season. Furthermore, considering our a-priori knowledge on the local sources influencing the site, the preliminary NM F calculations showed that the best results can be obtained when considering 8, 11 and 10 source profiles for Boulogne-sur-M er, Dunkerque and Saint-Omer respectively. Table 2 summarizes the characteristics of the data sets used to run the NM F. The 8 source profiles were identified using Boulogne-sur-M er data in the beginning of this search. Then followed a second step in which we used Dunkerque's data, which allowed us to increase the number of profiles to 11. The third step allowed us to determine the 10 source profiles found for Saint-Omer by re-launching the NM F with 8 sources as starting point (identified at the first step), and then finally adding 2 new source profiles.

Finally, under these conditions, the developed weighted NM F calculations gave interesting results, which will be discussed extensively hereafter.

V. 7. Ve r if ic ation of c al c ul ations r e l iabil ity

Before discussing the results of the weighted NM F, we verified that the calculations are not biased by comparing the modeled data to the observed data. I n order to accomplish this task, we recalculated the concentrations of each chemical species, while considering the contributions (matrix G) and the profiles (matrix F) of sources. I n fact, the matrix multiplication of G and F allows us to calculate the modeled concentrations of each analyzed species based on the NM F calculations: these ones are named "calculated" concentrations. On the other hand, matrix X represents the observed concentrations obtained by direct chemical analysis of the samples. The verification of calculations reliability can be realized on two scales:

-Global: by summing, for each sample, all the calculated concentrations for each species, we are able to acquire the total calculated concentration of analyzed species in the PM 2.5 samples. The same procedure can be done using the observed data, and thus we obtain the total observed concentrations in PM 2.5 .

Then by comparing the "total calculated concentrations" to the "total observed concentrations", it becomes possible to evaluate the reliability on a global level.

W hen plotted, if "total calculated" versus "total observed" shows a linear relationship with a slope equals to 1, that means that the total PM 2.5 mass has been correctly modeled, globally speaking.

-Specific: this phase is realized by comparing, for each species, the "calculated concentrations with the "observed" ones. In general, a slope equals 1 means no data reconstruction problems occurring. W hereas if the slope in superior to 1, the calculated concentrations are overestimated, and inversely, when the slope is below 1, the calculated concentrations are then underestimated. This specific species by species check can point at specific problems in data reconstruction.

Finally, it is always recommended to consider the reconstruction of each species by itself, because when following the global checking method, an overestimated species concentration can compensate an underestimated one to give a final appearance of good correlations, when in reality they are not.

V.7.1. NM F calculated versus observed data for Boulogne-sur-M er

The comparison between total NM F calculated and total observed species concentrations in PM 2.5 for Boulogne-sur-M er winter campaign is illustrated in Figure 8. The results shows that the total calculated concentrations are very well correlated (R 2 =0.97) with the total observed concentrations. The slope value is 0.982, which means that the global reconstruction error for total species concentration is below 2%. On the other hand, the specific comparison gave similar results for maj or ions, TC, and some of the most concentrated metals like: K, M g and Na (Figure 9). Highly concentrated species are well reconstructed, which partially explains the closeness of our total calculated and observed species concentrations. TC not correct or that the species is not correctly represented in the source profiles. Therefore, the identification of the source profiles should not be based on the presence of such species.

Slopes values did not exceed 1.01 (value for NH 4 +

), proving that the model we used does not overestimate the species concentrations and consequently the total PM 2.5 concentrations.

V.7.2. NM F calculated versus observed data for Dunkerque

The total calculated concentrations in Dunkerque in both winter and spring correlated very well (R 2 = 0.99) with the corresponding total observed concentrations (Figure 10). The slope value (1.006) proves that the total species concentration in PM 2.5 is also very well estimated. Nevertheless, in the case of As, M n, and Cd, some tendencies seem to exist only for the lowest concentrations, whereas at high concentrations these tendencies disappear. I n a similar procedure, the total calculated concentrations in Saint-Omer were found very well correlated (R 2 = 1.00) to the total observed concentrations as illustrated in Figure 12. The slope between the two correlated data equals 0.997, which proves that the total calculated species concentration in PM 2.5 is perfectly modeled. On the other hand, as observed in the case of Boulogne-sur-M er, ions and TC calculated and observed data were well correlated, with a slope value close to 1 (Figure 13). Other metals are also found well correlated except for Ag, As, Co, M n and Ti, which are found less correlated with the corresponding observed concentrations.

Between these elements, Cd and M n shows good reconstruction tendencies at low concentrations, whereas in the case of As and Ti, some values are reconstructed well for some samples, and in the same time not reconstructed at all for others. This indicates a lack of As and Ti in some source profiles, which prevents the correct modeling of As and Ti concentrations in some PM 2.5 samples. After running the NM F under the conditions described in section V.6, we were able to identify eight profiles shared between Boulogne-sur-M er, Dunkerque and Saint-Omer. The profiles are illustrated in Figure 14.

V. 8. 1. 1. Se a-s al ts

The "Seasalts" profile did not show many differences between the three sites. The presence of high loads of Na and Cl -, as well as some maj or metals (Ca, K and M g) and sulfate ions constituted a signature of this profile. On the other hand, the "Aged Sea-salts" profile shows more elemental loads than the "Sea-salts", especially trace elements like Ni, Pb, Sr and V. However, some differences in some ions levels can be found between the sites. For example, additional sulfate can be found in the profile of Dunkerque, meanwhile more nitrates are found in the profile of Saint-Omer whereas more ammonium are found in the profile of Boulogne-sur-M er.

V. 8. 1. 2. Cr us tal

The "Crustal" profile showed similarities in the maj or elements loads and some differences on a minor scale for trace elements and some ionic species. M aj or elements like Al, Ca, Fe, K, and M g are found the highest, a normal observation since these elements, which is in accordance with the profiles found in the literature (Table 1). Another important observation concerns Ba levels, which are found almost equal between the sites and is the highest for all three in this "crustal" profile when compared to the remaining common profiles. Sr and Ti also characterize crustal sources; however, the absence of Ti in the profile of Boulogne-sur-M er was unexpected. The reason behind this result might be very possibly linked to the poor correlations of this element with others at this site (see Appendix-I I ).

V. 8. 1. 3. Se c ondar y I nor ganic Ae r os ol s ( SI A)

Generally, this profile includes "Secondary Sulfates" and "Secondary Nitrates" which are mainly constituted with sulfates and nitrates respectively, associated to ammonium. I n this study, this last hypothesis is found respected in the identified profiles. I n addition, these latter contained K, Pb, Zn and NH 4 + which are found in moderate to high amounts.

Differences between the sites are found at first in the "Secondary Sulfates" profiles, in which Saint-Omer exhibited relatively non negligible TC and NO 3 levels. I n the same profile, Dunkerque exhibited higher Cl -levels compared to the remaining sites. Correspondingly, the "Secondary Nitrates" is also displaying similarities and differences between the three sites.

Similar levels are found for NO On the other hand, the comparison showed also some differences between the profiles.

For example, Ni and V were present in "Combustion 1" showing the impact of oil combustion in this profile ( (Vallius, et al., 2005), [START_REF] Amato | Quantifying road dust resuspension in urban environment by M ultilinear Engine: comparison with PM F2[END_REF][START_REF] Schaap | Source apportionment and spatial variability of PM 2.5 using measurements at five sites in the Netherlands[END_REF]). On the opposite, K levels are found much higher in the second combustion profile, which is a signature of biomass combustion ((Sillanpa, et al., 2006) and (Pateraki, et al., 2012)).

I n addition to the differences between the combustion profiles, other distinctions can be noted within each profile but between the three sites. For example, Ca, Ni, Sr, V and Cl -in "Combustion 1" are higher in Dunkerque and Saint-Omer than in Boulogne-sur-M er.

M oreover, differences in "Combustion 2" profile are highlighted in the case of NH . The first is found higher in Dunkerque and Saint-Omer, whereas the second was present only in Saint-Omer's profile.

V. 8. 1. 5. Tr af f ic non-e xhaus t

This profile is a typical maj or and trace element containing profile, without any ionic or carbon species contributions (see Table 1). Our results illustrated in Figure 14 shows that this characteristic is respected in our NM F model. Between the three sites, the common features of this profile is found expressed by the levels of Al, Fe, K, Cd, Cu, M n, Ni, Pb, Sb and Zn. The differences were limited to the remaining elements, especially Ca (absent in Dunkerque), Sn (high values in Dunkerque) and V (low levels in Boulogne-sur-M er). Three supplementary profiles were identified in Dunkerque, to increase the total number of identified profiles in this site to 11 in total. These local specific profiles are identified as the integrated steelworks main emissions (I SW -fugitive and I SW -sintering stack) and the nearby electric steel plant emissions.

The first two conserved almost the same chemical characteristics of sources samples.

High levels of Fe, Ca, Al, Zn, Na, Pb and M n were respected as well as the presence of nitrates and sulfates in the fugitive emissions. On the other hand, we found that the absence of M g in the two I SW profiles constitutes an unexpected result as its presence was anticipated (Hleis, et al., 2013). Furthermore, the sintering stack emissions profile respected to a certain extent the chemical composition of these emissions with the presence of Fe, K, Pb, Cd, Rb, Cl -and NH 4 + (Hleis, et al., 2013), but with some differences as well: Ca, M g, Na and TC are absent in the calculated sintering stack profile. These results remain however in accordance with another study in which the application of PM F revealed the same elements that characterized the sintering activities at the same site [START_REF] Alleman | PM 10 metal concentrations and source identification using positive matrix factorization and wind sectoring in a French industrial zone[END_REF].

These differences between our knowledge on the sources composition and the calculated ones remain moderate since the species exhibiting these variations do not constitute the basis of these profiles determination.

El e c tr ic s te e l pl ant

Finally, the electric steel plant calculated profile respected to a high extent the chemical composition, as we know it, for this type of emissions: Zn, Fe, M g, Ca, M n, Pb, Cr and Cu are all found high to fairly concentrated in the calculated profile, which is in accordance with our knowledge on this profile (see Table 14 in Chapter I V). One difference can be noted in this case which is the absence of Al in the calculated profile. However, the final result remains satisfactory.

V.8. winter contributions displayed a slightly different scenario (Figure 18). Combustion emissions contributed the most at this site during winter (44.4%), followed by secondary nitrates (34.3%), secondary sulfates (14.4%), sea-salts and aged sea-salts (2.9% and 1.9% respectively). Finally, the integrated steelworks average contribution was about 1.6%, including fugitive (0.5 %) and sintering stack (1.1%) sources, whereas the remaining 0.5 % was resulting from crustal, traffic non-exhaust and electric steelplant average contributions combined. For this last source, the contribution is 0.3% .

The analysis of these results shows that in Dunkerque, the main sources contributions are estimated to be 93.2% in total compared to 92.0% in Boulogne-sur-M er. I n fact, these values are found very close to each other, which prove that the 3 sources responsible of these contributions constitute the maj or part of PM 2.5 measured at the two sites: secondary sulfates, secondary nitrates and combustion sources. Dunkerque is more populated than Boulogne-sur-M er, and combustion sources contribution is ranked 1 st (44%) between the rest (Figure 18), whereas it is ranked 2 nd (35.9 %) in Boulogne-sur-M er after secondary nitrates (Figure 17). The reason behind this difference could be linked to the presence of heating related combustion emissions during winter, which can also be correlated to very low temperatures recorded during that period. A comparison between these observations and the respective ones of the spring season should confirm this hypothesis.

Nitrates and sulfates secondary aerosol sources are another maj or contributor to the PM 2.5 load. The secondary inorganic aerosol, which encloses both profiles, can be considered as the first contributor at both sites since its contribution is about 50%. These results are in accordance with the diurnal evolution study of SI A and TC concentrations held in Chapter I I I .

M arine sources contributions are evidenced at both sites, which is not surprising due to their coastal location. Crustal and traffic non-exhaust sources gave marginal contributions to the PM 2.5 load. Finally, the industrial activities located in Dunkerque had an impact estimated to 1.9% during winter. This relatively low value must be carefully considered since the industrial emissions could have a specific effect on some elemental concentrations due to the nature of the industrial activities.

V. 9. 1. 2. Spr ing c ampaign ave r age c ontr ibutions

The spring campaign average contributions in Dunkerque (Figure 19) show a maj or contribution from the secondary nitrates profile (61.8%). The combustion profile participated in 20.3%, followed by secondary sulfates (12.3%) and the marine particles (4.1%). The remaining 1.5% contributions are found divided between crustal, traffic non-exhaust and industrial profiles (1.2%). For this latter, the I SW contribution appears to be 1% (0.5% for each of the I SW sources) and the electric steel plant contribution is equal to 0.2%. A slightly different scenario can be observed for the contributions in Saint-Omer site.

Secondary nitrates held the first position with 57.4% average contribution in the total fine PM load, followed by combustion (21.2%), then secondary sulfates (17.6 %). M arine contributions were also present in this inland site, and estimated to 3.2% (2.6% for sea-salts and 0.6% for aged sea-salts). The remaining profiles contributions were estimated to 0.6%, in which the distant I SW and the glassmaking sources contributed in 0.2% and 0.03% respectively. To begin with, the high impact of secondary nitrates in PM 2.5 load is found to be between 57% and 62% of the total PM . These contributions were followed by the combustion source ones which were about 20% in both sites. W hen compared to the winter season, the combustion sources contributions in Dunkerque decreases from 45% (winter) to 20% (spring).

This difference could be linked to the decrease in the use of heating combustion in the spring season during which high temperatures were recorded. Hence, the hypothesis proposed in the previous section is confirmed. Conversely, secondary nitrates held the third place with 12% and 18% contributions in PM 2.5 concentrations depending on the site.

Secondary inorganic aerosols appear mainly in the form of nitrates in spring, when its distribution between nitrates and sulfates profiles is much less defined in winter. However, the high SI A contributions found in spring can be highly related to the accumulation phenomenon that often occurred during the spring campaign.

The marine sources were identified in both sites having similar contributions: 4.1% for the coastal site versus 3.2% for the inland one. The proximity of the source of emissions to the sampling site explains that difference, j ust like in the case of the I SW influence which contributions were found to be 1% in Dunkerque and 0.2% in Saint-Omer.

Finally, two additional remarks can be noted concerning the used strategy that gave the contribution results:

Sour c e s c ontr ibutions in Saint-Ome r

Sea-salts Aged sea-salts Crustal Secondary Sulfates Secondary Nitrates I SW Combustion 1+2

Traffic non-exhaust Glassmaking -The results obtained for Dunkerque in winter are comparable to those for Boulogne-sur-M er, and similarly for the spring results between Dunkerque and Saint-Omer. However, the results for Boulogne-sur-M er are found very different when compared to those found for Saint-Omer. I n addition, the fact that the calculations were performed using Dunkerque's winter and spring data, and only winter or spring data for the remaining sites, proves the good performance of the weighted NM F model in defining the source profiles and their relative contributions.

-The amount of data used to perform the calculations was different in each time, but globally included: 86, 173 and 98 samples for Boulogne-sur-M er, Dunkerque and Saint-Omer respectively, as well as 28 species concentrations and 8 to 11 sources profiles. I t is well known that the quality of calculations results is directly linked to the amount of used data as well as to the number of variables and sources. The weighted NM F model was able to provide coherent results between the sites of each campaign, even when running under the different above mentioned conditions. This proves the good performance of the model.

V.9.2. Diurnal contribution trends I n order to facilitate the interpretation as well as the visibility of the charts, in this section, "Seasalts" and "Aged sea-salts" will be regrouped in a global "sea-salts" source, "Secondary nitrates" and "Secondary sulfates" in "SIA" source, "Combustion 1" and "Combustion 2" in "Combustion" source, and finally "I SW -fugitive" with "I SW -sintering stack" and "Electric steel plant" in "Industry" source.

V. 9. 2. 1. W inte r c ampaign diur nal s our c e c ontr ibutions

Dunke r que ( winte r )

Seasalts Crustal Secondary I norganic Aerosols Traffic nonexhaust Combustion I ndustry I n Boulogne-sur-M er, crustal and traffic non-exhaust contributions barely existed, whereas Sea-salts contributions are found low but noticeable most of the sampling period with some exceptions, during which the contribution increased to 5 µg/ m 3 . These exceptions are found mainly when the total concentration is low, which is in agreement with the situation under a marine air flux. M oreover, the contribution value of the marine source is very similar between the two sites, with high and low values found synchronized.

This seems to indicate that the marine source does not contribute on the local scale, but mostly on a regional one. On the other hand the mostly regional SI A and mostly local combustion source profiles are found contributing in the maj ority of PM 2.5 atmospheric concentrations, especially during pollution episodes. I t should also be noted that in some cases of above PM 2.5 limit values measurements, the SI A source can be found exclusively involved in the increase in PM 2.5 concentrations.

The contributions in Dunkerque's case followed to a high extent the trends of Boulogne-sur-M er, in the sense where sea-salts and crustal evolved on a low level, and SI A and combustion represented the maj ority of the contributions. One difference between the two sites is the presence of industrial contributions in Dunkerque, which contributions appear episodically and remained low. The industrial source is not directly involved in the increase in the PM 2.5 load to above limit values.

V. 9. 2. 2. Spr ing c ampaign diur nal s our c e c ontr ibutions

I n Dunkerque and Saint-Omer, during the spring campaign, "seasalts", "crustal", "traffic non-exhaust" and "industrial" contributions followed a similar hilly shaped evolution (Figure 23 and Figure 24). High and low concentration periods are found synchronized, but some minor differences existed between the sites in each concentration period. SI A trends showed very high daily contributions in the total PM 2.5 loads, whereas combustion contributions decreased when compared to the previous observations of the winter period as shown in section V.9.1.2. The 25 µg/ m3 limit value is broken very often j ust by taking the SI A source by itself. On the other hand, the diurnal evolution of "sea-salts" source indicates that it contributes episodically to the PM 2.5 concentration. As during the winter campaign, this evolution is synchronized between the two sites, despite the fact that Saint-Omer is 30 km away from the coastline. The sea-salts source seems to contribute at a regional level as in the case of SI A source. The combustion profiles diurnal evolution does not show any large variations between the samples. I n the spring season, the combustion source can be considered as essentially related to traffic. This latter can be considered as a local source having a constant emission factor. On the other hand, the daily variations could be explained by the fluctuations of the meteorology (rainfall and wind) which favors, depending on the circumstances, the accumulation or dispersion of traffic exhaust emissions. Furthermore, crustal, traffic non-exhaust and industry exhibited very low daily contributions except during some days taken in Dunkerque.

Finally, this diurnal trend study agrees with the stage divisions mentioned in Lenschow method [START_REF] Lenschow | Some ideas about the sources of PM 10[END_REF] about the presence of the regional background (sea-salts, crustal and SI A in our study), topped by an urban background (traffic non-exhaust and mainly combustion), and then on a higher level by local sources (industries). W e can also conclude that the regional and urban background profiles contribute the most in the total PM 2.5 concentrations found in this study.

V.9.3. Average source contributions to the chemical species I n this section we will discuss the origins of each analyzed species between the identified source profiles. By considering the mean contribution and the composition of each source, we were able to determine which source contributes most in the load of a given species. I n other words, we were able to identify the main origins of a given species during this period. On the other hand, we should remind that certain elements had bad reconstructions. I t is possible that these elements are not perfectly positioned in the identified source profiles. Therefore, the contributions of different sources in the load of these specific elements should be carefully analyzed, and the origins distribution in this case has a higher uncertainty.

V. 9. W e will start our analysis by looking at the chemical species affected by a small number of sources, like in the case of TC and K which are found resulting from the two combustion profiles. In addition, V is found to be generated by the "Combustion 1" profile which also contributed in an important part of Cd, Co, Cr, Cu, Ni and Sn. Sulfates and nitrates are also found originated maj orly from secondary sulfates and nitrates respectively. These two last profiles are found responsible of the maj ority of NH 4 + also. On the other hand, "traffic nonexhaust" profile is found to contribute in most of Ti, As, Ag and Sb. Furthermore, Na, M g and Cl -are found divided between "sea-salts" and "aged seasalts", with more contributions in the first two elements from this last profile. The "crustal" profile contributed maj orly in Al, Ca, Fe, Ba and M n. However, Pb, Rb and Zn loads are found generated by almost all profiles except those related to natural emissions (Sea-salts, Aged sea-salts and crustal). Finally, the Aged sea-salts and secondary nitrates profiles contributed in the maj ority of Sr loads.

These results representations are important as an additional examination of the model distribution of the analyzed chemical species between the sources. Ti repartition for example shows that Ti concentrations are explained by only one source. However, in section V.7.1, bad correlations were evidenced between Ti reconstructed and observed values, hence we conclude that the model did not find sufficient source combination possibilities in the case of Ti concentrations. I n Dunkerque, the contributions in each species total load showed some different scenarios (Figure 26), and the elements are found well distributed between the sources. The "sea-salts" profile contributed in most of Na and Cl -. On the other hand, "Aged sea-salts"

profile is found to contribute importantly in Na but also in Ca, Co and Sr loads and maj orly in total Mg. The "Crustal" profile participated in important percentages of Al, Ca, Ba and Ti loads. Meanwhile, "Secondary sulfates" profile is found contributing in almost all SO Finally, the industrial influences are found to enrich most of the elements. For example, the I SW -fugitive profile is contributing in the maj or loads of Fe, As and M n, as well as partial percentages of Al, Ca, Ag, Cr, Cu, Ni Pb, Rb, V and Zn. The input of the I SWsintering stack is found to be more segregated between K, Ag, Cd, Pb, Rb and Cl -loads. Thus, the final industrial profile of the "Electric steel plant" contributes the most in Zn, Cr, Cu and M n loads. These results are not incompatible with the low contribution found for "industries" Electric steel plant source in the analysis of PM 2.5 . I n fact, industrial sources contribute at a low level to the total PM 2.5 concentrations, but at a high level to specific metals concentrations that are directly linked to industrial activities.

V. 9. Finally, the source profiles used to calculate these source contributions in spring were the same as the ones used in winter. Thus, the difference between the main origin of each element (Figure 26 The case of Saint-Omer's contributions (Figure 28), a difference can be noticed in the distributions. Sea-salts contributed in Na, M g and most of Cl -, when in the same time the "Aged sea-salts" profile was contributing in most of Mg loads, followed by Sr then Na and Ca. The "Crustal" profile was as usual participating in large percentages of Al, Ti and Ca loads, followed by moderate contributions in the case of Fe, Ba and Rb. The "Secondary sulfates" profile contributed in the loads of SO The "Combustion 1" profile is found responsible for most of V and Ni loads, and part of Co and Sb concentrations at this site. Meanwhile, the "Combustion 2" profile shows a maj or contribution in TC and K loads followed by a less intense participation in the loads of Ba and Rb.

Finally, the industry in Dunkerque (I SW ) participated maj orly in the load of Ag, followed by more moderate contributions in the concentrations of Fe, As, Cd, Cr, M n and Rb in Saint-Omer. However, the "Glassmaking" influence was exhibited largely in the case of Sn, pursued by less severe contributions in Cu, Cr and Sb loads. The previous list of elements appears to be source specific, which explains the high contributions found when searching for

V. 10. Conc l us ion

The use of the weighted NM F model considering our chemical data evidenced 12 source profiles divided between natural and anthropogenic: sea-salts, aged sea-salts, secondary sulfates, secondary nitrates, crustal, traffic non-exhaust, combustion 1 (close to fossil fuel combustion), combustion 2 (close to biomass combustion), and finally, four additional industrial profiles, starting with I SW -fugitive, followed by I SW -sintering stack, electric steel plant and glassmaking. The study of the contributions of these different sources showed seasonal variations.

Hence, the winter campaign was characterized by a high contribution in PM 2.5 loads from combustion sources (44.5% in Dunkerque and 35.9% in Boulogne-sur-M er). The contribution of this source decreases to about 20%, in Saint-Omer and Dunkerque, during the spring campaign, which is a possible consequence of the decrease in the need for heating. Secondary inorganic aerosols constituted the essential mass of measured PM 2.5 , which in addition to combustion sources form 92.0% (Boulogne-sur-M er) and 96.2% (Saint-Omer) of the measured PM 2.5 concentration.

The diurnal evolution study of the different sources identified at the sites that were sampled simultaneously (Boulogne-sur-M er / Dunkerque and Saint-Omer / Dunkerque) showed a synchronized evolution of SI A and marine salts contributions. Furthermore, we have demonstrated that the levels of contributions of these two sources were not linked to local sources, instead it was found to be associated to a regional background origin, which fluctuates in time. The variation of this non local background level is found responsible of the variation of PM 2.5 concentrations. The PM 2.5 concentrations beyond the limit values appear directly linked to the increase of this regional background level. Consequently, the actions taken in order to decrease the levels of PM 2.5 should stretch to cover a larger scale since local emission sources are slightly contributing to PM 2.5 loads (an exception should be considered concerning local heating during winter).

The industrial sources are found to directly contribute in fewer amounts of PM 2.5 measured mass: 1 to 2% in Dunkerque and about 0.2% in Saint-Omer. However, these sources can sensibly change the sampled PM 2.5 composition, and notably the concentrations of certain elements such as Fe, Zn, M n, Pb, Cd, Sn, Cr, As, Ag, Cu and Rb in Dunkerque as well as in Saint-Omer. I n fact, we have observed that industrial origins are maj orly behind these elements loads. Nevertheless, we must not forget the indirect role that these industrial sources play on the concentration levels of PM 2.5 in the region, especially through NO x and SO 2 emissions, which are precursors of secondary inorganic aerosols formation.

The main obj ectives of this study were to compare the levels and the chemical composition of atmospheric PM 2.5 in cities located in the vicinity of the littoral of the Nord-Pas-de-Calais region, and to identify the different sources that are impacting the load of these atmospheric particulates.

I n order to achieve these obj ectives, this work was divided into three successive sections:

1) The achievement of two PM 2.5 sampling campaigns in Dunkerque, Boulognesur-M er and Saint-Omer. I n details, the sampling procedure was accomplished following two sampling campaigns plan (winter and spring), applied simultaneously at two sites and in a 12 hours/ sample method:

winter campaign: held between November 16 th and December 31 st of 2010 in Dunkerque and Boulogne-sur-M er;

spring campaign: held between M arch 10 th and April 30 th of 2011 in Dunkerque and Saint-Omer.

2) A profound chemical characterization of the samples was achieved, with a scope aiming at the determination of ionic species, maj or elements, trace elements and total carbon concentrations. The diurnal and geographical variations were analyzed while considering meteorological data and air masses origins. A detailed examination of element concentrations allowed to evidence elemental ratios that can be adopted as tracers of certain emission sources.

3) Particle sources identification was achieved by the use of a receptor model that is based of a non-negative matrix factorization method. This section aimed at the identification of emission source profiles as well as the quantification of their relative contributions in the samples.

Concentration levels and chemical composition of PM 2.5

During the two sampling periods, the average concentrations of PM 2. + and Total Carbon) exhibited similar diurnal variations from one site to the other in each period. The difference in the concentrations of these species from one site to the other is relatively low, which has been attributed to the variability of a regional background level, and found heavily related to meteorological conditions.

Concentrations above 25 µg/ m 3 were recorded in many occasions in winter as well as in spring. These high concentrations were encountered mainly under stable anticyclone conditions and East sector winds, which boost the arrival of air masses of continental origins.

I n addition, significant high carbon concentrations were measured during cold episodes and were attributed to the important use of heating systems. I nversely, during the spring period, high concentrations episode was maintained by stable meteorological conditions and stagnant winds. These high concentrations can hence be related to accumulation phenomenon as well as the increase in SI A formation which is a consequence of precursor release (NO x and SO 2 ).

The influence of industrial emissions on the chemical composition of PM 2.5

Significant concentration differences were observed in the case of metallic and some trace elements. I n particular, the comparison between Dunkerque and Boulogne-sur-M er data uncovered the industrial emissions impact at Dunkerque, especially when comparing the levels of Fe, Al, Ca, Zn, M n, Cd, Cr, Cu, Ni, V and As. This observation was confirmed by an elemental enrichment factor study (EF). Using this approach, the comparison between Dunkerque and Saint-Omer had not only confirmed the impact of industrial emissions in Dunkerque, but also revealed significant enrichments for Sn, Sb, Pb, Cd and As at both sites.

Furthermore, by examining the elemental concentration roses, it was possible to point at certain sectors under which metallic and other trace elements concentrations were found relatively high. This examination allowed the identification of industrial activities that could be responsible for these high concentrations. I n addition, a selection of certain samples in which these influences are identified allowed us to calculate elemental ratios that can be considered as characteristic of the sources:

the emissions from the integrated steelworks and electric steel plant industries in Dunkerque are found characterized by specific elemental ratios of Zn/ Fe, Zn/ M n, Cr/ Cd and Pb/ Cd, which allow the distinction between these source;

the emissions from the glassmaking industry in Saint-Omer can be identified by specific ratios of Sn/ Cr, Sn/ Sb, Cu/ Cd and As/ Ag.

Finally, by following the observations of the concentration roses, air mass back traj ectories and the examination of elemental ratios, we proved that the integrated steelworks emissions located in Dunkerque influence the metallic concentrations of PM 2.5 air masses in Saint-Omer.

Identification and evaluation of sources contributions using a source-receptor model

The acquired chemical composition data of PM 2.5 was used as input data in a sourcereceptor model. The used software was developed by our partners of the LI SI C (ULCO).

Essentially, it revolves around a weighted version of a non-negative matrix factorization method (NM F), which considers individual measurement uncertainties, in the sense where the model relies more on the concentrations affected by low uncertainties. I n addition, this model allows the consideration of a-priori knowledge on the sources, which is included in the calculations in the form of constraints.

Based on the sources that were identified using our PM 2.5 chemical composition data (chapter I V and V), we were able to introduce constraints in the model calculations, which was essentially in the form of an order of abundance given to the elements between the different source profiles.

The model output analysis allowed us to determine 8 common source profiles shared between the three sampling sites:

marine aerosols: composed from fresh sea-salts and aged sea-salts;

secondary inorganic aerosols: divided into secondary sulfates and secondary nitrates;

crustal source;

combustion sources: composed of two profiles in which total carbon is dominant;

traffic non-exhaust profile.

Additional profiles were also identified following the use of Dunkerque and Saint-Omer data, which reflects the impacts of the industrial emissions located in these sites. Three industrial source profiles were identified in Dunkerque, two of which were attributed to the integrated steelmaking activities (one profile representing the point emissions in the sintering unit and another representing the fugitive emissions), and the third one was found to represent an electric steel making plant emissions. I n Saint-Omer, two additional profiles were obtained: a first one that corresponds to a glassmaking industry emissions, whereas the second represents the emissions from the integrated steelworks located in Dunkerque.

These industrial sources are found to have small contributions to the particle average concentrations. Nevertheless, we maintain the fact that some of these sources emit particles charged with metallic and trace elements, some of which have low legislated limit values (Cd and As). Hence, it has been demonstrated that the origin of Fe, Zn, M n, Pb, Cd, Sn, Cr, As, Ag, Cu and Rb is maj orly industrial, located in Dunkerque and Saint-Omer.

The secondary inorganic aerosols represent the highest contributions, specifically for secondary nitrates, in a similar fashion to the carbon rich particles emitted by combustion sources. The comparison between winter and spring campaign contributions showed accentuated seasonal variations. The highest impact of combustion sources during winter can be mainly explained by the use of heating processes. I n spring, it is the secondary inorganic aerosols that exhibited the highest contributions, which can explain the high concentration levels of PM 2.5 during this period (around 33 µg/ m 3 ).

By comparing the contribution results between the two studied sites, during the same period, we found that the contribution variations of SI A and marine aerosols are similar. The contribution trends of these sources are found linked to regional scale phenomenon instead of a local one. This emphasizes the importance of considering large scale pollution reduction measures, on the national and international level, as recommended by the European Union. I n addition, we insist on the fact that these measures should also consider the reduction in the emissions of gaseous pollutants: SO 2 , NO x and NH 3 , which are precursors of fine particles formation. Abstract :

Appendices

The main obj ectives of this study were to acquire a better knowledge on the exposure level to fine PM 2.5 particles and on their chemical composition and sources, in three cities located on the littoral facade of the Nord-Pas-de-Calais region. The particles were collected following two sampling campaigns held between November 2010 and April 2011 ("winter" campaign in Dunkerque and Boulogne-sur-Mer; "spring" campaign in Dunkerque and Saint-Omer). The chemical composition of the collected PM 2.5 was determined through the quantification of maj or elements, trace elements, water soluble ions and total carbon. For the two considered sampling periods, PM 2.5 concentrations and chemical composition trends followed similar tendencies at each site. Local sources influence was evidenced throughout a comparison of the chronological evolution and concentration roses of some maj or and trace elements between the sites. This analysis allowed the suggestion of specific elemental ratios, which can be used as tracers of some anthropogenic sources. Finally, the use of a source-receptor model, based on a non-negative matrix factorization (NM F) method, allowed the identification of the main PM 2.5 sources as well as the evaluation of their relative contributions in each of the studied sites.

Figure 1 :

 1 Figure 1: Schematic representation of aerosols size distribution in relation with formation mechanisms[START_REF] Whitby | Fine particles[END_REF] ......................................................................................................................................... Figure2: Flow chart of possible formation reactions of different nitrates and sulfates chemical species; taken from[START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF] who reproduced it from[START_REF] Matsumoto | Formation and dissociation of atmospheric particulate nitrate and chloride: an approach based on phase equilibrium[END_REF],[START_REF] Cited Alastuey | Monitoring of atmospheric particulate matter around sources of secondary inorganic aerosol[END_REF],[START_REF] Pakkanen | Study of formation of coarse particle nitrate aerosol[END_REF], and[START_REF] Hewitt | Handbook of Atmospheric Science: Principles and Application[END_REF] which represent (a), (b), (c), and (d) respectively. ........ Figure3: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in Europe from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 4: Anthropogenic emissions trends of Cu, Pb and Zn in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 5: Anthropogenic emissions trends of As, Cd, Cr, Se, and Ni in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................ Figure 6: Emissions trends of TSP by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) .............. Figure 7: Emissions trends of TSP emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 8: Emissions trends for PM 10 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............ Figure 9: Emissions trends of PM 10 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 10: Emissions trends for PM 2.5 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ......... Figure 11: Emissions trends of PM 2.5 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 12: Emissions trends emitted from the "non-road transport" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) .................................................................................................................................Figure 13: Emissions trends emitted from the "agriculture" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................................................ Figure 14: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in France from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 15: Anthropogenic emissions of Cu and Zn in France between 1990 and 2010 in Kt (EEA(a), 2012) ..... Figure 16: Anthropogenic emissions of As, Cd, and Pb in France between 1990 and 2010 in Kt (EEA(a), 2012) ..................................................................................................................................................................... Figure 17: Anthropogenic emissions of Cr, Se, and Ni in France between 1990 and 2010 in Kt (EEA(a), 2012) Figure 18: Emissions trends for TSP emitted from: road transport, commercial/institutional and household, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) Figure 19: Emissions trends for TSP emitted from: energy production and distribution, energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 20: Emissions trends for PM 10 emitted from: energy production and distribution, commercial-institutional and households, road transportation, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................. Figure 21: Emissions trends for PM 10 emitted from: energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .................... Figure 22: Emissions trends for PM 2.5 emitted from: energy production and distribution, commercial-institutional and households, road transportation, and industrial processes sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 23: Emissions trends for PM 2.5 emitted from: energy use in industry, non-road transportation, solvent and product use, agriculture, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .Figure 24: Summary of the major adverse health effects of PM inhalation on the human body (Aphekom, 2011) ..................................................................................................................................................................... Figure 25: Map of France (upper right corner) and the "Nord Pas de Calais" region; reproduced from (CarteFrance, 2012) and (Dalet, 2012) ........................................................................................................ Figure 26: Declared industrial PM emissions trend from 2004 till 2010 in the NPdC region (DREAL, 2011) .... Figure 27: Some of the industrial dust emissions in the NPdC department; SRD = Société de Raffinerie de Dunkerque, Al Dk = Rio Tinto Dunkerque, EDF = Electricité De France (Bouchain), Cargill SAS (Haubourdin), E.ON -Central (Hornaing), Arc International (Arques); source (DREAL, 2011) .............. Figure 28: Annual mean concentration of PM 10 in Dunkerque, Boulogne-sur-Mer, Saint-Omer, Bethune, Lille, and in µg/m 3 ; source (Ministère de l'Écologie, 2012) ................................................................................. Figure 29: Map of the regions (in red) representing more than 35 days of PM 10 daily limit (50 µg/m 3 ) exceedance between 2007 and 2009 in France (Source: LCSQA) .................................................................................

  Figure 1: Schematic representation of aerosols size distribution in relation with formation mechanisms[START_REF] Whitby | Fine particles[END_REF] ......................................................................................................................................... Figure2: Flow chart of possible formation reactions of different nitrates and sulfates chemical species; taken from[START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF] who reproduced it from[START_REF] Matsumoto | Formation and dissociation of atmospheric particulate nitrate and chloride: an approach based on phase equilibrium[END_REF],[START_REF] Cited Alastuey | Monitoring of atmospheric particulate matter around sources of secondary inorganic aerosol[END_REF],[START_REF] Pakkanen | Study of formation of coarse particle nitrate aerosol[END_REF], and[START_REF] Hewitt | Handbook of Atmospheric Science: Principles and Application[END_REF] which represent (a), (b), (c), and (d) respectively. ........ Figure3: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in Europe from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 4: Anthropogenic emissions trends of Cu, Pb and Zn in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 5: Anthropogenic emissions trends of As, Cd, Cr, Se, and Ni in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................ Figure 6: Emissions trends of TSP by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) .............. Figure 7: Emissions trends of TSP emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 8: Emissions trends for PM 10 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............ Figure 9: Emissions trends of PM 10 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 10: Emissions trends for PM 2.5 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ......... Figure 11: Emissions trends of PM 2.5 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 12: Emissions trends emitted from the "non-road transport" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) .................................................................................................................................Figure 13: Emissions trends emitted from the "agriculture" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................................................ Figure 14: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in France from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 15: Anthropogenic emissions of Cu and Zn in France between 1990 and 2010 in Kt (EEA(a), 2012) ..... Figure 16: Anthropogenic emissions of As, Cd, and Pb in France between 1990 and 2010 in Kt (EEA(a), 2012) ..................................................................................................................................................................... Figure 17: Anthropogenic emissions of Cr, Se, and Ni in France between 1990 and 2010 in Kt (EEA(a), 2012) Figure 18: Emissions trends for TSP emitted from: road transport, commercial/institutional and household, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) Figure 19: Emissions trends for TSP emitted from: energy production and distribution, energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 20: Emissions trends for PM 10 emitted from: energy production and distribution, commercial-institutional and households, road transportation, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................. Figure 21: Emissions trends for PM 10 emitted from: energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .................... Figure 22: Emissions trends for PM 2.5 emitted from: energy production and distribution, commercial-institutional and households, road transportation, and industrial processes sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 23: Emissions trends for PM 2.5 emitted from: energy use in industry, non-road transportation, solvent and product use, agriculture, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .Figure 24: Summary of the major adverse health effects of PM inhalation on the human body (Aphekom, 2011) ..................................................................................................................................................................... Figure 25: Map of France (upper right corner) and the "Nord Pas de Calais" region; reproduced from (CarteFrance, 2012) and (Dalet, 2012) ........................................................................................................ Figure 26: Declared industrial PM emissions trend from 2004 till 2010 in the NPdC region (DREAL, 2011) .... Figure 27: Some of the industrial dust emissions in the NPdC department; SRD = Société de Raffinerie de Dunkerque, Al Dk = Rio Tinto Dunkerque, EDF = Electricité De France (Bouchain), Cargill SAS (Haubourdin), E.ON -Central (Hornaing), Arc International (Arques); source (DREAL, 2011) .............. Figure 28: Annual mean concentration of PM 10 in Dunkerque, Boulogne-sur-Mer, Saint-Omer, Bethune, Lille, and in µg/m 3 ; source (Ministère de l'Écologie, 2012) ................................................................................. Figure 29: Map of the regions (in red) representing more than 35 days of PM 10 daily limit (50 µg/m 3 ) exceedance between 2007 and 2009 in France (Source: LCSQA) .................................................................................

Figure 7 :

 7 Figure 1: Schematic representation of aerosols size distribution in relation with formation mechanisms[START_REF] Whitby | Fine particles[END_REF] ......................................................................................................................................... Figure2: Flow chart of possible formation reactions of different nitrates and sulfates chemical species; taken from[START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF] who reproduced it from[START_REF] Matsumoto | Formation and dissociation of atmospheric particulate nitrate and chloride: an approach based on phase equilibrium[END_REF],[START_REF] Cited Alastuey | Monitoring of atmospheric particulate matter around sources of secondary inorganic aerosol[END_REF],[START_REF] Pakkanen | Study of formation of coarse particle nitrate aerosol[END_REF], and[START_REF] Hewitt | Handbook of Atmospheric Science: Principles and Application[END_REF] which represent (a), (b), (c), and (d) respectively. ........ Figure3: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in Europe from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 4: Anthropogenic emissions trends of Cu, Pb and Zn in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 5: Anthropogenic emissions trends of As, Cd, Cr, Se, and Ni in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................ Figure 6: Emissions trends of TSP by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) .............. Figure 7: Emissions trends of TSP emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 8: Emissions trends for PM 10 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............ Figure 9: Emissions trends of PM 10 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 10: Emissions trends for PM 2.5 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ......... Figure 11: Emissions trends of PM 2.5 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 12: Emissions trends emitted from the "non-road transport" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) .................................................................................................................................Figure 13: Emissions trends emitted from the "agriculture" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................................................ Figure 14: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in France from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 15: Anthropogenic emissions of Cu and Zn in France between 1990 and 2010 in Kt (EEA(a), 2012) ..... Figure 16: Anthropogenic emissions of As, Cd, and Pb in France between 1990 and 2010 in Kt (EEA(a), 2012) ..................................................................................................................................................................... Figure 17: Anthropogenic emissions of Cr, Se, and Ni in France between 1990 and 2010 in Kt (EEA(a), 2012) Figure 18: Emissions trends for TSP emitted from: road transport, commercial/institutional and household, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) Figure 19: Emissions trends for TSP emitted from: energy production and distribution, energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 20: Emissions trends for PM 10 emitted from: energy production and distribution, commercial-institutional and households, road transportation, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................. Figure 21: Emissions trends for PM 10 emitted from: energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .................... Figure 22: Emissions trends for PM 2.5 emitted from: energy production and distribution, commercial-institutional and households, road transportation, and industrial processes sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 23: Emissions trends for PM 2.5 emitted from: energy use in industry, non-road transportation, solvent and product use, agriculture, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .Figure 24: Summary of the major adverse health effects of PM inhalation on the human body (Aphekom, 2011) ..................................................................................................................................................................... Figure 25: Map of France (upper right corner) and the "Nord Pas de Calais" region; reproduced from (CarteFrance, 2012) and (Dalet, 2012) ........................................................................................................ Figure 26: Declared industrial PM emissions trend from 2004 till 2010 in the NPdC region (DREAL, 2011) .... Figure 27: Some of the industrial dust emissions in the NPdC department; SRD = Société de Raffinerie de Dunkerque, Al Dk = Rio Tinto Dunkerque, EDF = Electricité De France (Bouchain), Cargill SAS (Haubourdin), E.ON -Central (Hornaing), Arc International (Arques); source (DREAL, 2011) .............. Figure 28: Annual mean concentration of PM 10 in Dunkerque, Boulogne-sur-Mer, Saint-Omer, Bethune, Lille, and in µg/m 3 ; source (Ministère de l'Écologie, 2012) ................................................................................. Figure 29: Map of the regions (in red) representing more than 35 days of PM 10 daily limit (50 µg/m 3 ) exceedance between 2007 and 2009 in France (Source: LCSQA) .................................................................................

Figure 9 :

 9 Figure 1: Schematic representation of aerosols size distribution in relation with formation mechanisms[START_REF] Whitby | Fine particles[END_REF] ......................................................................................................................................... Figure2: Flow chart of possible formation reactions of different nitrates and sulfates chemical species; taken from[START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF] who reproduced it from[START_REF] Matsumoto | Formation and dissociation of atmospheric particulate nitrate and chloride: an approach based on phase equilibrium[END_REF],[START_REF] Cited Alastuey | Monitoring of atmospheric particulate matter around sources of secondary inorganic aerosol[END_REF],[START_REF] Pakkanen | Study of formation of coarse particle nitrate aerosol[END_REF], and[START_REF] Hewitt | Handbook of Atmospheric Science: Principles and Application[END_REF] which represent (a), (b), (c), and (d) respectively. ........ Figure3: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in Europe from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 4: Anthropogenic emissions trends of Cu, Pb and Zn in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 5: Anthropogenic emissions trends of As, Cd, Cr, Se, and Ni in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................ Figure 6: Emissions trends of TSP by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) .............. Figure 7: Emissions trends of TSP emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 8: Emissions trends for PM 10 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............ Figure 9: Emissions trends of PM 10 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 10: Emissions trends for PM 2.5 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ......... Figure 11: Emissions trends of PM 2.5 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 12: Emissions trends emitted from the "non-road transport" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) .................................................................................................................................Figure 13: Emissions trends emitted from the "agriculture" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................................................ Figure 14: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in France from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 15: Anthropogenic emissions of Cu and Zn in France between 1990 and 2010 in Kt (EEA(a), 2012) ..... Figure 16: Anthropogenic emissions of As, Cd, and Pb in France between 1990 and 2010 in Kt (EEA(a), 2012) ..................................................................................................................................................................... Figure 17: Anthropogenic emissions of Cr, Se, and Ni in France between 1990 and 2010 in Kt (EEA(a), 2012) Figure 18: Emissions trends for TSP emitted from: road transport, commercial/institutional and household, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) Figure 19: Emissions trends for TSP emitted from: energy production and distribution, energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 20: Emissions trends for PM 10 emitted from: energy production and distribution, commercial-institutional and households, road transportation, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................. Figure 21: Emissions trends for PM 10 emitted from: energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .................... Figure 22: Emissions trends for PM 2.5 emitted from: energy production and distribution, commercial-institutional and households, road transportation, and industrial processes sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 23: Emissions trends for PM 2.5 emitted from: energy use in industry, non-road transportation, solvent and product use, agriculture, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .Figure 24: Summary of the major adverse health effects of PM inhalation on the human body (Aphekom, 2011) ..................................................................................................................................................................... Figure 25: Map of France (upper right corner) and the "Nord Pas de Calais" region; reproduced from (CarteFrance, 2012) and (Dalet, 2012) ........................................................................................................ Figure 26: Declared industrial PM emissions trend from 2004 till 2010 in the NPdC region (DREAL, 2011) .... Figure 27: Some of the industrial dust emissions in the NPdC department; SRD = Société de Raffinerie de Dunkerque, Al Dk = Rio Tinto Dunkerque, EDF = Electricité De France (Bouchain), Cargill SAS (Haubourdin), E.ON -Central (Hornaing), Arc International (Arques); source (DREAL, 2011) .............. Figure 28: Annual mean concentration of PM 10 in Dunkerque, Boulogne-sur-Mer, Saint-Omer, Bethune, Lille, and in µg/m 3 ; source (Ministère de l'Écologie, 2012) ................................................................................. Figure 29: Map of the regions (in red) representing more than 35 days of PM 10 daily limit (50 µg/m 3 ) exceedance between 2007 and 2009 in France (Source: LCSQA) .................................................................................

Figure 24 :

 24 Figure 1: Schematic representation of aerosols size distribution in relation with formation mechanisms[START_REF] Whitby | Fine particles[END_REF] ......................................................................................................................................... Figure2: Flow chart of possible formation reactions of different nitrates and sulfates chemical species; taken from[START_REF] Arruti | Regional evaluation of particulate matter composition in an Atlantic coastal area (Cantabria region, northern Spain): spatial variations in different urban and rural environments[END_REF] who reproduced it from[START_REF] Matsumoto | Formation and dissociation of atmospheric particulate nitrate and chloride: an approach based on phase equilibrium[END_REF],[START_REF] Cited Alastuey | Monitoring of atmospheric particulate matter around sources of secondary inorganic aerosol[END_REF],[START_REF] Pakkanen | Study of formation of coarse particle nitrate aerosol[END_REF], and[START_REF] Hewitt | Handbook of Atmospheric Science: Principles and Application[END_REF] which represent (a), (b), (c), and (d) respectively. ........ Figure3: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in Europe from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 4: Anthropogenic emissions trends of Cu, Pb and Zn in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 5: Anthropogenic emissions trends of As, Cd, Cr, Se, and Ni in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............................................................................................................................................ Figure 6: Emissions trends of TSP by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) .............. Figure 7: Emissions trends of TSP emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 8: Emissions trends for PM 10 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ............ Figure 9: Emissions trends of PM 10 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 10: Emissions trends for PM 2.5 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012) ......... Figure 11: Emissions trends of PM 2.5 emitted from "waste" and "solvent and product use" sectors in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ...................................................................................... Figure 12: Emissions trends emitted from the "non-road transport" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) .................................................................................................................................Figure 13: Emissions trends emitted from the "agriculture" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................................................ Figure 14: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in France from 1990 to 2010 in Kt (EEA(a), 2012) ............................................................................................................................................................ Figure 15: Anthropogenic emissions of Cu and Zn in France between 1990 and 2010 in Kt (EEA(a), 2012) ..... Figure 16: Anthropogenic emissions of As, Cd, and Pb in France between 1990 and 2010 in Kt (EEA(a), 2012) ..................................................................................................................................................................... Figure 17: Anthropogenic emissions of Cr, Se, and Ni in France between 1990 and 2010 in Kt (EEA(a), 2012) Figure 18: Emissions trends for TSP emitted from: road transport, commercial/institutional and household, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) Figure 19: Emissions trends for TSP emitted from: energy production and distribution, energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 20: Emissions trends for PM 10 emitted from: energy production and distribution, commercial-institutional and households, road transportation, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ............................................................................................................. Figure 21: Emissions trends for PM 10 emitted from: energy use in industry, non-road transportation, solvent and product use, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .................... Figure 22: Emissions trends for PM 2.5 emitted from: energy production and distribution, commercial-institutional and households, road transportation, and industrial processes sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) ................................................................................................................................. Figure 23: Emissions trends for PM 2.5 emitted from: energy use in industry, non-road transportation, solvent and product use, agriculture, and waste sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012) .Figure 24: Summary of the major adverse health effects of PM inhalation on the human body (Aphekom, 2011) ..................................................................................................................................................................... Figure 25: Map of France (upper right corner) and the "Nord Pas de Calais" region; reproduced from (CarteFrance, 2012) and (Dalet, 2012) ........................................................................................................ Figure 26: Declared industrial PM emissions trend from 2004 till 2010 in the NPdC region (DREAL, 2011) .... Figure 27: Some of the industrial dust emissions in the NPdC department; SRD = Société de Raffinerie de Dunkerque, Al Dk = Rio Tinto Dunkerque, EDF = Electricité De France (Bouchain), Cargill SAS (Haubourdin), E.ON -Central (Hornaing), Arc International (Arques); source (DREAL, 2011) .............. Figure 28: Annual mean concentration of PM 10 in Dunkerque, Boulogne-sur-Mer, Saint-Omer, Bethune, Lille, and in µg/m 3 ; source (Ministère de l'Écologie, 2012) ................................................................................. Figure 29: Map of the regions (in red) representing more than 35 days of PM 10 daily limit (50 µg/m 3 ) exceedance between 2007 and 2009 in France (Source: LCSQA) .................................................................................

Figure 1 :

 1 Figure 1: Schematic representation of aerosols size distribution in relation with formation mechanisms (Whitby & Cantrell, 1976)

Figure 2 :

 2 Figure 2: Flow chart of possible formation reactions of different nitrates and sulfates chemical species; taken from (Arruti, et al., 2011) who reproduced it from (Matsumoto & Tanaka, 1996), (Alastuey, et al., 2004), (Pakkanen, 1996), and (Hewitt & Jackson, 2005) which represent (a), (b), (c), and (d) respectively.

Figure 3 :

 3 Figure 3: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in Europe from 1990 to 2010 in Kt (EEA(a), 2012)

Figure 4 :

 4 Figure 4: Anthropogenic emissions trends of Cu, Pb and Zn in Europe between 1990 and 2010 in Kt (EEA(a), 2012)

Figure 5 :

 5 Figure 5: Anthropogenic emissions trends of As, Cd, Cr, Se, and Ni in Europe between 1990 and 2010 in Kt (EEA(a), 2012)

Figure 6 :Figure 7 :

 67 Figure 6: Emissions trends of TSP by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012)

Figure 8 :Figure 9 :

 89 Figure 8: Emissions trends for PM 10 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012)

Figure 10 :Figure 11 :

 1011 Figure 10: Emissions trends for PM 2.5 by sector in Europe between 1990 and 2010 in Kt (EEA(a), 2012)

Figure 12 :Figure 13 :

 1213 Figure 12: Emissions trends emitted from the "non-road transport" sector in Europe between 1990 and 2010 in tonnes (EEA(a), 2012)

Figure 14 :

 14 Figure 14: Anthropogenic emissions trends of TSP, PM 10 , and PM 2.5 in France from 1990 to 2010 in Kt (EEA(a), 2012)

Figure 15 :

 15 Figure 15: Anthropogenic emissions of Cu and Zn in France between 1990 and 2010 in Kt (EEA(a), 2012)

Figure 16 : 1 .

 161 Figure 16: Anthropogenic emissions of As, Cd, and Pb in France between 1990 and 2010 in Kt (EEA(a), 2012)

Figure 18 :Figure 19 :

 1819 Figure 18: Emissions trends for TSP emitted from: road transport, commercial/institutional and household, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012)

Figure 20 :Figure 21 :

 2021 Figure 20: Emissions trends for PM 10 emitted from: energy production and distribution, commercialinstitutional and households, road transportation, industrial processes, and agriculture sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012)

Figure 22 :Figure 23 :

 2223 Figure 22: Emissions trends for PM 2.5 emitted from: energy production and distribution, commercialinstitutional and households, road transportation, and industrial processes sectors; in France between 1990 and 2010 in tonnes (EEA(a), 2012)

I. 5 . 1 .

 51 Early studies Between the 70's and 80's period, most research subjects concerning atmospheric PM revolved around the formation mechanisms, penetration in the respiratory tract, industrial emissions analysis, early toxicity studies and chemical composition. Within the research axis of understanding the formation of PM, a lot of studies went to analyze the chemical composition of fine and ultrafine particles. Bricard et al, in 1977 worked on the formation of new ultrafine particles from gases in a search to better understand the nucleation mode of particles considering the presence of SO 2

Figure 24 :

 24 Figure 24: Summary of the major adverse health effects of PM inhalation on the human body (Aphekom, 2011)

Figure 25 :

 25 Figure 25: Map of France (upper right corner) and the "Nord Pas de Calais" region; reproduced from (CarteFrance, 2012) and (Dalet, 2012)

Figure 26 :

 26 Figure 26: Declared industrial PM emissions trend from 2004 till 2010 in the NPdC region (DREAL, 2011)

Figure 27 :

 27 Figure 27: Some of the industrial dust emissions in the NPdC department; SRD = Société de Raffinerie de Dunkerque, Al Dk = Rio Tinto Dunkerque, EDF = Electricité De France (Bouchain), Cargill SAS (Haubourdin), E.ON -Central (Hornaing), Arc International (Arques); source (DREAL, 2011)

Figure 29 :

 29 Figure 29: Map of the regions (in red) representing more than 35 days of PM 10 daily limit (50 µg/m 3 ) exceedance between 2007 and 2009 in France (Source: LCSQA)

  Health effects of atmospheric pollution -Legislative and socio-economic impacts All these actions are conducted by scientists related to many higher education establishments of the NPdC region: Université de Lille 1, Université de Lille 2, Université d'Artois, Université du Littoral Côte d'Opale and École des Mines de Douai.

Figure 1 :

 1 Figure 1: Satellite imagery showing the three sampling sites cities locations

Figure 2 :

 2 Figure 2: Satellite imagery showing "Lamartine" sampling site and the major PM sources influences

  hold urban-marine mixed influences. The sampling point in Boulogne-sur-Mer is located at 7m from the ground level to prevent the sampling of any promptly produced local emissions.

Figure 3 :

 3 Figure 3: Satellite imagery showing "Musée" sampling site and the maj or PM sources influences

Figure 4 :

 4 Figure 4: Satellite imagery showing "Ribot" sampling site and the maj or PM sources influences

Figure 5 :

 5 Figure 5: ( A) HVS DA-80 in its field housing; ( B) Longitudinal section of the PM 2.5 sampling head used with the DA-80 for sampling, the air passing in the bottom section ( red arrow) holds PM 2.5 only.

Figure 6 :

 6 Figure 6: Photo of the DA-80 high volume sampler location in Dunkerque showing the stabiliz ing metallic grid on the floor holding about 150 kg of cement.

Figure 7 :

 7 Figure 7: Filter preparation using quartz fiber filter ( a) glued using an adhesive tape ( b) to a cellulose fiber filter ( c)

Figure 8 :

 8 Figure 8: Example of a sample taken using the DA-80; the darker side represents the quartz semi-filter when the other lighter one is the cellulose semi-filter; adhesive tape can be seen in the middle.

Table 1 :

 1 List of the elements analyz ed by ICP-MS, their calibration ranges and

  intensity and transform it into electric signals corresponding to each element. The electric signals are captured and proj ected as peaks that are proportional to the corresponding elements concentrations. The electric signal treatment was realized by Thermo® iTEVA-Control Center v 2.5.0.84.

Table 2 :

 2 List of elements analyz ed by ICP-AES, their calibration ranges and filters were used for total carbon analysis. For this latter, we used a FLASH 2000 Organic Elemental Analyzer from Fisher Scientific®. The analyzer is equipped with two chromatography columns: one for C, H, N and S analysis, whereas the other is for O detection.

Figure 9 :

 9 Figure 9: Atmospheric concentrations ( ng/ m 3 ) calculated using the mixed quartz -cellulose fiber filters versus atmospheric concentration determined using the whole cellulose filter for a selection of elements

  filter results (ng/ m3) W hole cellulose filter reults (ng/ m3) Chromium CHAPTER III: Concentrations trends and statistical analysis of Maj or Elements (ME), Soluble Ions (SI), and Total Carbon (inter campaign ..................................................................................................... III.3.1.1. W ind, precipitation and temperature conditions in Dunkerque ..................................... III.3.1.2. W ind, precipitation and temperature conditions in Boulogne-sur-Mer ......................... III.3.2. Spring campaign ................................................................................................... III.3.2.1. W ind, precipitation and temperature conditions in Dunkerque ................................... III.3.2.2. W ind, precipitation and temperature conditions in Saint-Omer .................................. inter campaign averages comparison ................................................................ III.4.2. Spring campaign averages comparison ................................................................. III.5. Average concentrations comparison at a European scale ................................... III.5.1. Location and characteristics of the comparison sites ............................................ III.5.2. Comparison of ME, SI, and TC average concentrations ....................................... III.5.2.1. Marine maj or elements ................................................................................................ III.5.2.2. SIA maj or ions ........................................................................................................

  3. Total Carbon (TC) ....................................................................................................... III.5.2.4. Other ME metals .......................................................................................................... inter campaign ................................................................................................... III.6.1.1. Sea salts diurnal evolution ........................................................................................... III.6.1.2. Fe and Nss-Ca diurnal evolution .................................................................................. III.6.1.3. Al and Nss-Mg diurnal evolution ................................................................................ III.6.1.4. TC and Nss-K diurnal evolution .................................................................................. III.6.1.5. SIA diurnal evolution................................................................................................... III.6.2. Spring campaign ................................................................................................... III.6.2.1. Sea salts diurnal evolution ........................................................................................... III.6.2.2. Fe and Nss-Ca diurnal evolution .................................................................................. III.6.2.3. Al and Nss-Mg diurnal evolution ................................................................................ III.6.2.4. TC and Nss-K diurnal evolution .................................................................................. III.6.2.5. SIA diurnal evolution................................................................................................... III.7. Analysis of above limit peaks of PM 2.5 ................................................................... III.7.1. Identification of above limit-value peaks of PM 2.5 ............................................... III.7.1.1. W inter campaign above limit-value peaks ................................................................... III.7.1.2. Spring campaign above limit-value peaks ................................................................... III.7.2. Detailed analysis of above limit peaks of PM 2.5 ................................................... III.7.2.1. PM 2.5 winter campaign episodes .................................................................................. III.7.2.1.1. General observation ............................................................................................. III.7.2.1.2. Detailed interpretation of PM 2.5 above limit values for the winter campaign ...... III.7.2.2. PM 2.5 spring campaign episodes .................................................................................. III.7.2.2.1. General observations ............................................................................................ III.7.2.2.2. Detailed interpretation of above limit values for the winter campaign ................ inter campaign ................................................................................................... III.8.1.1. Maj or marine element origins ...................................................................................... III.8.1.2. SIA maj or ions origins ................................................................................................. III.8.1.3. Combustion elements origins: TC and K ..................................................................... III.8.1.4. Fe, Ca, and Al origins .................................................................................................. III.8.2. Spring campaign ................................................................................................... III.8.2.1. Maj or marine element origins ...................................................................................... III.8.2.2. SIA maj or ions origins ................................................................................................. III.8.2.3. Combustion elements origins: TC and K ..................................................................... III.8.2.4. Fe, Ca, and Al origins .................................................................................................. ................................................................................................................ III.11. Works Cited .............................................................................................................

Figure 1 :

 1 Figure 1: Temperatures and rainfall monthly average variations for the NPdC region from 1981 to 2010 .......... Figure 2: W ind rose at Dunkerque during the winter campaign (data source: Mé té o France) .............................. Figure 3: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the winter campaign (data source: Mé té o France) ................................................................................................ Figure 4: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the winter campaign ...Figure 5: W ind rose in Boulogne-sur-Mer during the winter campaign (data source: Mé té o France)..................Figure 6: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Boulogne-sur-Mer during the winter campaign (data source: Mé té o France) .....................................................................................Figure 7: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Boulogne-sur-Mer during the winter campaign .............................................................................................................................................................Figure 8: W ind rose at Dunkerque during the spring campaign (data source: Mé té o France). ...........................Figure 9: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the spring campaign (Source: Mé té o France)...................................................................................................... Figure 10: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the spring campaign Figure 11: W ind rose at Saint-Omer during the spring campaign (data source: Mé té o France). ........................ Figure 12: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Saint-Omer during the spring campaign (Source: Mé té o France). ......................................................................................... Figure 13: Evolution of temperatures (in °C) at Saint-Omer during the spring campaign ..................................Figure 14: SI and TC average concentrations at Dunkerque and Boulogne-sur-Mer ......................................... Figure 15: ME and TE average concentrations at Dunkerque and Boulogne-sur-Mer ....................................... Figure 16: SI and TC average concentrations at Dunkerque and Saint-Omer .................................................... Figure 17: ME and TE average concentrations at Dunkerque and Saint-Omer .................................................. Figure 18: Comparison sites location on the European map (modified from (Dalet, 2012)) .............................. Figure 19: Diurnal evolution of sea salts concentrations (ng/ m 3 ) in Dunkerque and Boulogne-sur-Mer during the winter campaign ..................................................................................................................................................Figure 20: Diurnal evolution of Fe and Nss-Ca concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 21: Diurnal evolution of Al and Nss-Mg concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 22: Diurnal evolution of TC and Nss-K concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 23: Diurnal evolution of SIA, NH 4

Figure 5 :

 5 Figure 1: Temperatures and rainfall monthly average variations for the NPdC region from 1981 to 2010 .......... Figure 2: W ind rose at Dunkerque during the winter campaign (data source: Mé té o France) .............................. Figure 3: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the winter campaign (data source: Mé té o France) ................................................................................................ Figure 4: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the winter campaign ...Figure 5: W ind rose in Boulogne-sur-Mer during the winter campaign (data source: Mé té o France)..................Figure 6: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Boulogne-sur-Mer during the winter campaign (data source: Mé té o France) .....................................................................................Figure 7: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Boulogne-sur-Mer during the winter campaign .............................................................................................................................................................Figure 8: W ind rose at Dunkerque during the spring campaign (data source: Mé té o France). ...........................Figure 9: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the spring campaign (Source: Mé té o France)...................................................................................................... Figure 10: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the spring campaign Figure 11: W ind rose at Saint-Omer during the spring campaign (data source: Mé té o France). ........................ Figure 12: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Saint-Omer during the spring campaign (Source: Mé té o France). ......................................................................................... Figure 13: Evolution of temperatures (in °C) at Saint-Omer during the spring campaign ..................................Figure 14: SI and TC average concentrations at Dunkerque and Boulogne-sur-Mer ......................................... Figure 15: ME and TE average concentrations at Dunkerque and Boulogne-sur-Mer ....................................... Figure 16: SI and TC average concentrations at Dunkerque and Saint-Omer .................................................... Figure 17: ME and TE average concentrations at Dunkerque and Saint-Omer .................................................. Figure 18: Comparison sites location on the European map (modified from (Dalet, 2012)) .............................. Figure 19: Diurnal evolution of sea salts concentrations (ng/ m 3 ) in Dunkerque and Boulogne-sur-Mer during the winter campaign ..................................................................................................................................................Figure 20: Diurnal evolution of Fe and Nss-Ca concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 21: Diurnal evolution of Al and Nss-Mg concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 22: Diurnal evolution of TC and Nss-K concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 23: Diurnal evolution of SIA, NH 4

Figure 6 :

 6 Figure 1: Temperatures and rainfall monthly average variations for the NPdC region from 1981 to 2010 .......... Figure 2: W ind rose at Dunkerque during the winter campaign (data source: Mé té o France) .............................. Figure 3: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the winter campaign (data source: Mé té o France) ................................................................................................ Figure 4: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the winter campaign ...Figure 5: W ind rose in Boulogne-sur-Mer during the winter campaign (data source: Mé té o France)..................Figure 6: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Boulogne-sur-Mer during the winter campaign (data source: Mé té o France) .....................................................................................Figure 7: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Boulogne-sur-Mer during the winter campaign .............................................................................................................................................................Figure 8: W ind rose at Dunkerque during the spring campaign (data source: Mé té o France). ...........................Figure 9: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the spring campaign (Source: Mé té o France)...................................................................................................... Figure 10: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the spring campaign Figure 11: W ind rose at Saint-Omer during the spring campaign (data source: Mé té o France). ........................ Figure 12: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Saint-Omer during the spring campaign (Source: Mé té o France). ......................................................................................... Figure 13: Evolution of temperatures (in °C) at Saint-Omer during the spring campaign ..................................Figure 14: SI and TC average concentrations at Dunkerque and Boulogne-sur-Mer ......................................... Figure 15: ME and TE average concentrations at Dunkerque and Boulogne-sur-Mer ....................................... Figure 16: SI and TC average concentrations at Dunkerque and Saint-Omer .................................................... Figure 17: ME and TE average concentrations at Dunkerque and Saint-Omer .................................................. Figure 18: Comparison sites location on the European map (modified from (Dalet, 2012)) .............................. Figure 19: Diurnal evolution of sea salts concentrations (ng/ m 3 ) in Dunkerque and Boulogne-sur-Mer during the winter campaign ..................................................................................................................................................Figure 20: Diurnal evolution of Fe and Nss-Ca concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 21: Diurnal evolution of Al and Nss-Mg concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 22: Diurnal evolution of TC and Nss-K concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 23: Diurnal evolution of SIA, NH 4

Figure 7 :

 7 Figure 1: Temperatures and rainfall monthly average variations for the NPdC region from 1981 to 2010 .......... Figure 2: W ind rose at Dunkerque during the winter campaign (data source: Mé té o France) .............................. Figure 3: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the winter campaign (data source: Mé té o France) ................................................................................................ Figure 4: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the winter campaign ...Figure 5: W ind rose in Boulogne-sur-Mer during the winter campaign (data source: Mé té o France)..................Figure 6: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Boulogne-sur-Mer during the winter campaign (data source: Mé té o France) .....................................................................................Figure 7: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Boulogne-sur-Mer during the winter campaign .............................................................................................................................................................Figure 8: W ind rose at Dunkerque during the spring campaign (data source: Mé té o France). ...........................Figure 9: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the spring campaign (Source: Mé té o France)...................................................................................................... Figure 10: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the spring campaign Figure 11: W ind rose at Saint-Omer during the spring campaign (data source: Mé té o France). ........................ Figure 12: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Saint-Omer during the spring campaign (Source: Mé té o France). ......................................................................................... Figure 13: Evolution of temperatures (in °C) at Saint-Omer during the spring campaign ..................................Figure 14: SI and TC average concentrations at Dunkerque and Boulogne-sur-Mer ......................................... Figure 15: ME and TE average concentrations at Dunkerque and Boulogne-sur-Mer ....................................... Figure 16: SI and TC average concentrations at Dunkerque and Saint-Omer .................................................... Figure 17: ME and TE average concentrations at Dunkerque and Saint-Omer .................................................. Figure 18: Comparison sites location on the European map (modified from (Dalet, 2012)) .............................. Figure 19: Diurnal evolution of sea salts concentrations (ng/ m 3 ) in Dunkerque and Boulogne-sur-Mer during the winter campaign ..................................................................................................................................................Figure 20: Diurnal evolution of Fe and Nss-Ca concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 21: Diurnal evolution of Al and Nss-Mg concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 22: Diurnal evolution of TC and Nss-K concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 23: Diurnal evolution of SIA, NH 4

Figure 8 :

 8 Figure 1: Temperatures and rainfall monthly average variations for the NPdC region from 1981 to 2010 .......... Figure 2: W ind rose at Dunkerque during the winter campaign (data source: Mé té o France) .............................. Figure 3: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the winter campaign (data source: Mé té o France) ................................................................................................ Figure 4: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the winter campaign ...Figure 5: W ind rose in Boulogne-sur-Mer during the winter campaign (data source: Mé té o France)..................Figure 6: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Boulogne-sur-Mer during the winter campaign (data source: Mé té o France) .....................................................................................Figure 7: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Boulogne-sur-Mer during the winter campaign .............................................................................................................................................................Figure 8: W ind rose at Dunkerque during the spring campaign (data source: Mé té o France). ...........................Figure 9: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the spring campaign (Source: Mé té o France)...................................................................................................... Figure 10: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the spring campaign Figure 11: W ind rose at Saint-Omer during the spring campaign (data source: Mé té o France). ........................ Figure 12: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Saint-Omer during the spring campaign (Source: Mé té o France). ......................................................................................... Figure 13: Evolution of temperatures (in °C) at Saint-Omer during the spring campaign ..................................Figure 14: SI and TC average concentrations at Dunkerque and Boulogne-sur-Mer ......................................... Figure 15: ME and TE average concentrations at Dunkerque and Boulogne-sur-Mer ....................................... Figure 16: SI and TC average concentrations at Dunkerque and Saint-Omer .................................................... Figure 17: ME and TE average concentrations at Dunkerque and Saint-Omer .................................................. Figure 18: Comparison sites location on the European map (modified from (Dalet, 2012)) .............................. Figure 19: Diurnal evolution of sea salts concentrations (ng/ m 3 ) in Dunkerque and Boulogne-sur-Mer during the winter campaign ..................................................................................................................................................Figure 20: Diurnal evolution of Fe and Nss-Ca concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 21: Diurnal evolution of Al and Nss-Mg concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 22: Diurnal evolution of TC and Nss-K concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 23: Diurnal evolution of SIA, NH 4

Figure 9 :

 9 Figure 1: Temperatures and rainfall monthly average variations for the NPdC region from 1981 to 2010 .......... Figure 2: W ind rose at Dunkerque during the winter campaign (data source: Mé té o France) .............................. Figure 3: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the winter campaign (data source: Mé té o France) ................................................................................................ Figure 4: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the winter campaign ...Figure 5: W ind rose in Boulogne-sur-Mer during the winter campaign (data source: Mé té o France)..................Figure 6: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Boulogne-sur-Mer during the winter campaign (data source: Mé té o France) .....................................................................................Figure 7: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Boulogne-sur-Mer during the winter campaign .............................................................................................................................................................Figure 8: W ind rose at Dunkerque during the spring campaign (data source: Mé té o France). ...........................Figure 9: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the spring campaign (Source: Mé té o France)...................................................................................................... Figure 10: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the spring campaign Figure 11: W ind rose at Saint-Omer during the spring campaign (data source: Mé té o France). ........................ Figure 12: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Saint-Omer during the spring campaign (Source: Mé té o France). ......................................................................................... Figure 13: Evolution of temperatures (in °C) at Saint-Omer during the spring campaign ..................................Figure 14: SI and TC average concentrations at Dunkerque and Boulogne-sur-Mer ......................................... Figure 15: ME and TE average concentrations at Dunkerque and Boulogne-sur-Mer ....................................... Figure 16: SI and TC average concentrations at Dunkerque and Saint-Omer .................................................... Figure 17: ME and TE average concentrations at Dunkerque and Saint-Omer .................................................. Figure 18: Comparison sites location on the European map (modified from (Dalet, 2012)) .............................. Figure 19: Diurnal evolution of sea salts concentrations (ng/ m 3 ) in Dunkerque and Boulogne-sur-Mer during the winter campaign ..................................................................................................................................................Figure 20: Diurnal evolution of Fe and Nss-Ca concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 21: Diurnal evolution of Al and Nss-Mg concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 22: Diurnal evolution of TC and Nss-K concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 23: Diurnal evolution of SIA, NH 4

Figure 20 : 4 +

 204 Figure 1: Temperatures and rainfall monthly average variations for the NPdC region from 1981 to 2010 .......... Figure 2: W ind rose at Dunkerque during the winter campaign (data source: Mé té o France) .............................. Figure 3: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the winter campaign (data source: Mé té o France) ................................................................................................Figure 4: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the winter campaign ...Figure 5: W ind rose in Boulogne-sur-Mer during the winter campaign (data source: Mé té o France)..................Figure 6: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Boulogne-sur-Mer during the winter campaign (data source: Mé té o France) .....................................................................................Figure 7: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Boulogne-sur-Mer during the winter campaign .............................................................................................................................................................Figure 8: W ind rose at Dunkerque during the spring campaign (data source: Mé té o France). ...........................Figure 9: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Dunkerque during the spring campaign (Source: Mé té o France)...................................................................................................... Figure 10: Evolution of 00h-12h and 12h-24h temperatures (in °C) at Dunkerque during the spring campaign Figure 11: W ind rose at Saint-Omer during the spring campaign (data source: Mé té o France). ........................ Figure 12: Variation of precipitation (in mm) by wind sector of 45° in relation to wind speed at Saint-Omer during the spring campaign (Source: Mé té o France). ......................................................................................... Figure 13: Evolution of temperatures (in °C) at Saint-Omer during the spring campaign .................................. Figure 14: SI and TC average concentrations at Dunkerque and Boulogne-sur-Mer ......................................... Figure 15: ME and TE average concentrations at Dunkerque and Boulogne-sur-Mer ....................................... Figure 16: SI and TC average concentrations at Dunkerque and Saint-Omer ....................................................Figure 17: ME and TE average concentrations at Dunkerque and Saint-Omer .................................................. Figure 18: Comparison sites location on the European map (modified from (Dalet, 2012)) .............................. Figure 19: Diurnal evolution of sea salts concentrations (ng/ m 3 ) in Dunkerque and Boulogne-sur-Mer during the winter campaign ..................................................................................................................................................Figure 20: Diurnal evolution of Fe and Nss-Ca concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 21: Diurnal evolution of Al and Nss-Mg concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 22: Diurnal evolution of TC and Nss-K concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign .................................................................................................................................................. Figure 23: Diurnal evolution of SIA, NH 4 + , NO 3 -and SO 4 2-concentrations in Dunkerque and Boulogne-sur-Mer during the winter campaign ................................................................................................................................. Figure 24: Diurnal evolution of sea salts concentrations in Dunkerque and Saint-Omer during the spring campaign ............................................................................................................................................................. Figure 25: Diurnal evolution of Fe and Nss-Ca concentrations in Dunkerque and Saint-Omer during the spring campaign ............................................................................................................................................................. Figure 26: Diurnal evolution of Al and Nss-Mg concentrations in Dunkerque and Saint-Omer during the spring campaign ............................................................................................................................................................. Figure 27: Diurnal evolution of TC and Nss-K concentrations in Dunkerque and Saint-Omer during the spring campaign ............................................................................................................................................................. Figure 28: Diurnal evolution of SIA concentrations in Dunkerque and Saint-Omer during the spring campaign ............................................................................................................................................................................ Figure 29: Evolution of Atmo PM 10 and PM 2.5 recorded data and the total analyzed species in Dunkerque and Boulogne-sur-Mer during the winter campaign (red lines = limit values of 25 µg/ m 3 (PM 2.5 ) and 50 µg/ m 3 (PM 10 )) ................................................................................................................................................................ Figure 30: Evolution of Atmo PM 10 and PM 2.5 recorded data and the total analyzed species in Dunkerque and Saint-Omer during the spring campaign (red lines = limit values of 25 µg/ m 3 (PM 2.5 ) and 50 µg/ m 3 (PM 10 )) ..Figure 31: Chemical composition of selected PM 2.5 samples exhibiting above limit value peaks in Dunkerque and Boulogne-sur-Mer during the winter campaign ........................................................................................... Figure 32: Diurnal evolution of the temperature, PM 2.5 level, TC, SIA and Al concentrations (ng/ m 3 ) in PM 2.5 during winter .......................................................................................................................................................Figure 33: Air mass backward traj ectories drew using HYSPLIT (Draxler & Rolph, 2012) for three selected samples (November 28 th , December 7 th and 29 th 2010) in Dunkerque (above three) and Boulogne-sur-Mer (below); Height: 100m AGL, Duration: 48 hrs, Start: 1100 UTC, Vertical motion: isobaric, New traj ectory every: 2 hrs, Max traj ectories: 6. ......................................................................................................................... Figure 34: Chemical composition of PM 2.5 above limit value samples in Dunkerque and Saint-Omer during the spring campaign ..................................................................................................................................................

Figure 31 :

 31 Chemical composition of selected PM 2.5 samples exhibiting above limit value peaks in Dunkerque and Boulogne-sur-Mer during the winter campaign ........................................................................................... Figure 32: Diurnal evolution of the temperature, PM 2.5 level, TC, SIA and Al concentrations (ng/ m 3 ) in PM 2.5 during winter .......................................................................................................................................................

Figure 33 :

 33 Air mass backward traj ectories drew using HYSPLIT[START_REF] Draxler | Hybrid Single-Particle Lagrangian Integrated Trajectory[END_REF] for three selected samples (November 28 th , December 7 th and 29 th 2010) in Dunkerque (above three) and Boulogne-sur-Mer (below); Height: 100m AGL, Duration: 48 hrs, Start: 1100 UTC, Vertical motion: isobaric, New traj ectory every: 2 hrs, Max traj ectories: 6. ......................................................................................................................... Figure 34: Chemical composition of PM 2.5 above limit value samples in Dunkerque and Saint-Omer during the spring campaign ..................................................................................................................................................

Figure 35 :

 35 Figure 35: Diurnal evolution of the temperature, PM 2.5 level, TC, SIA and Al concentrations (ng/ m 3 ) in PM 2.5 during spring ....................................................................................................................................................... Figure 36: Air mass backward traj ectories drew using HYSPLIT (Draxler & Rolph, 2012) for three selected samples (March 15 th , April 16 th and 24 th 2011) in Dunkerque (above three) and Saint-Omer (below); Height: 100m AGL, Duration: 48 hrs, Start: 1100 UTC, Vertical motion: isobaric, New traj ectory every: 2 hrs, Max traj ectories: 6 ....................................................................................................................................................... Figure 37: Maj or marine elements concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................. Figure 38: SIA maj or ions concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................................Figure 39: TC and K concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign .Figure 40: Fe, Ca, and Al concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................................ Figure 41: Sea salt elements concentration roses for Dunkerque and Saint-Omer during the spring campaign .Figure 42: SIA species concentration roses for Dunkerque and Saint Omer during the spring campaign .......... Figure 43: TC and K concentration roses for Dunkerque and Saint-Omer during the spring campaign .............Figure 44: Fe, Ca, and Al concentration roses for Dunkerque and Saint-Omer during the spring campaign .....

  Figure 35: Diurnal evolution of the temperature, PM 2.5 level, TC, SIA and Al concentrations (ng/ m 3 ) in PM 2.5 during spring ....................................................................................................................................................... Figure 36: Air mass backward traj ectories drew using HYSPLIT (Draxler & Rolph, 2012) for three selected samples (March 15 th , April 16 th and 24 th 2011) in Dunkerque (above three) and Saint-Omer (below); Height: 100m AGL, Duration: 48 hrs, Start: 1100 UTC, Vertical motion: isobaric, New traj ectory every: 2 hrs, Max traj ectories: 6 ....................................................................................................................................................... Figure 37: Maj or marine elements concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................. Figure 38: SIA maj or ions concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................................Figure 39: TC and K concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign .Figure 40: Fe, Ca, and Al concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................................ Figure 41: Sea salt elements concentration roses for Dunkerque and Saint-Omer during the spring campaign .Figure 42: SIA species concentration roses for Dunkerque and Saint Omer during the spring campaign .......... Figure 43: TC and K concentration roses for Dunkerque and Saint-Omer during the spring campaign .............Figure 44: Fe, Ca, and Al concentration roses for Dunkerque and Saint-Omer during the spring campaign .....

  Figure 35: Diurnal evolution of the temperature, PM 2.5 level, TC, SIA and Al concentrations (ng/ m 3 ) in PM 2.5 during spring ....................................................................................................................................................... Figure 36: Air mass backward traj ectories drew using HYSPLIT (Draxler & Rolph, 2012) for three selected samples (March 15 th , April 16 th and 24 th 2011) in Dunkerque (above three) and Saint-Omer (below); Height: 100m AGL, Duration: 48 hrs, Start: 1100 UTC, Vertical motion: isobaric, New traj ectory every: 2 hrs, Max traj ectories: 6 ....................................................................................................................................................... Figure 37: Maj or marine elements concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................. Figure 38: SIA maj or ions concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................................Figure 39: TC and K concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign .Figure 40: Fe, Ca, and Al concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................................ Figure 41: Sea salt elements concentration roses for Dunkerque and Saint-Omer during the spring campaign .Figure 42: SIA species concentration roses for Dunkerque and Saint Omer during the spring campaign .......... Figure 43: TC and K concentration roses for Dunkerque and Saint-Omer during the spring campaign .............Figure 44: Fe, Ca, and Al concentration roses for Dunkerque and Saint-Omer during the spring campaign .....

  Figure 35: Diurnal evolution of the temperature, PM 2.5 level, TC, SIA and Al concentrations (ng/ m 3 ) in PM 2.5 during spring ....................................................................................................................................................... Figure 36: Air mass backward traj ectories drew using HYSPLIT (Draxler & Rolph, 2012) for three selected samples (March 15 th , April 16 th and 24 th 2011) in Dunkerque (above three) and Saint-Omer (below); Height: 100m AGL, Duration: 48 hrs, Start: 1100 UTC, Vertical motion: isobaric, New traj ectory every: 2 hrs, Max traj ectories: 6 ....................................................................................................................................................... Figure 37: Maj or marine elements concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................. Figure 38: SIA maj or ions concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................................Figure 39: TC and K concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign .Figure 40: Fe, Ca, and Al concentration roses for Dunkerque and Boulogne-sur-Mer during the winter campaign ............................................................................................................................................................................ Figure 41: Sea salt elements concentration roses for Dunkerque and Saint-Omer during the spring campaign .Figure 42: SIA species concentration roses for Dunkerque and Saint Omer during the spring campaign .......... Figure 43: TC and K concentration roses for Dunkerque and Saint-Omer during the spring campaign .............Figure 44: Fe, Ca, and Al concentration roses for Dunkerque and Saint-Omer during the spring campaign .....

Figure 1 :

 1 Figure 1: Temperatures and rainfall monthly average variations for the NPdC region from 1981 to 2010

Figure 2 :

 2 Figure 2: Wind rose at Dunkerque during the winter campaign ( data source: M é té o France)

Figure 3 :

 3 Figure 3: Variation of precipitation ( in mm) by wind sector of 45° in rel ation to wind speed at Dunkerque during the winter campaign ( data source: M é té o France)

Figure 4 :

 4 Figure 4: Evol ution of 00h-12h and 12h-24h temperatures ( in °C) at Dunkerque during the winter campaign

  .3.1.2.Wind, precipitation and temperature conditions in Boul ogne-sur-M erBoulogne-sur-Mer wind rose (Figure5) shows that wind originated maj orly form the NE-E, SE-S and NW-N sectors. High speed wind (7% frequency) comes mainly from the N and W directions, whereas moderate speed wind originates from the E-NE (52%), S-SE and N-NW sectors. Low speed wind was also recorded coming from the E-NE and S-SE sectors in much smaller frequencies (31%). Finally, very low speed (stagnant) wind was encountered in 10% of the period.

Figure 5 :

 5 Figure 5: Wind rose in Boul ogne-sur-M er during the winter campaign ( data source: M é té o France)

(

  225° -270°) on the other. For the rest of the wind sectors, fluctuations in rainfall ranged 6 mm, except for the E-SE sector in which precipitation levels are negligible.

Figure 6 :

 6 Figure 6: Variation of precipitation ( in mm) by wind sector of 45° in rel ation to wind speed at Boul ogne-sur-M er during the winter campaign ( data source: M é té o France)

Figure 7 :

 7 Figure 7: Evol ution of 00h-12h and 12h-24h temperatures ( in °C) at Boul ogne-sur-M er during the winter campaign

Figure 8 :

 8 Figure 8: Wind rose at Dunkerque during the spring campaign ( data source: M é té o France) .

Figure 9 :

 9 Figure 9: Variation of precipitation ( in mm) by wind sector of 45° in rel ation to wind speed at Dunkerque during the spring campaign ( Source: M é té o France)

Figure 10 :

 10 Figure 10: Evol ution of 00h-12h and 12h-24h temperatures ( in °C) at Dunkerque during the spring campaign

Figure 11 :

 11 Figure 11: Wind rose at Saint-Omer during the spring campaign ( data source: M é té o France) .

Figure 12 :

 12 Figure 12: Variation of precipitation ( in mm) by wind sector of 45° in rel ation to wind speed at Saint-Omer during the spring campaign ( Source: M é té o France) .

Figure 13 :

 13 Figure 13: Evol ution of temperatures ( in °C) at Saint-Omer during the spring campaign

PM 2 .

 2 5 mass fluctuations recorded by the air quality surveillance network of Atmo were retrieved from the "bulletin quotidien" page within the network web site (http://www.atmonpdc.fr). A PM 2.5 average concentration of 24860 ng/m 3 was calculated for Dunkerque's winter campaign using Atmo data. It is found close to the limit value of 25 µg/m 3 (24 hours average) defined by theWHO (WHO, 2006) and the European Union (target value for 2015).

Figure 14 :

 14 Figure 14: SI and TC average concentrations at Dunkerque and Boul ogne-sur-M er

Figure 15 :

 15 Figure 15: M E and TE average concentrations at Dunkerque and Boul ogne-sur-M er

Figure 16 :

 16 Figure 16: SI and TC average concentrations at Dunkerque and Saint-Omer

Figure

  Figure 17: M E and TE average concentrations at Dunkerque and Saint-Omer

PM 2 .

 2 5 chemical composition in relation to different emission sources (direct and indirect traffic, natural (sea salt), industrial …etc).

Figure 18 :

 18 Figure 18: Comparison sites l ocation on the European map ( modif ied f rom ( Dal et, 2012) )

  [ ss-Cl -] = 1.789[ Na] and [ Nss-Cl -] = [ Cl -] -[ ss-Cl -] -[ ss-Mg 2+ ] = 0.1205[ Na] and [ Nss-Mg] = [ Mg] -[ ss-Mg 2+ ] (the maximum values of [ ss-Mg 2+ ] was forced to equalize total [ Mg] instead of exceeding it).

Figure 19 : 6 . 1 . 2 .

 19612 Figure 19: Diurnal evol ution of sea sal ts concentrations ( ng/ m 3 ) in Dunkerque and Boul ogne-sur-M er during the winter campaign

Figure 20 :

 20 Figure 20: Diurnal evol ution of Fe and Nss-Ca concentrations in Dunkerque and Boul ogne-sur-M er during the winter campaign

III. 6 . 1 . 3 .

 613 related to the steel-making industry. An answer to this question will be developed in chapter IV and V. Al and Nss-M g diurnal evol utionFollowing the same logic used in the case of Fe and Nss-Ca, we compared Al and Nss-Mg concentrations evolution during our winter sampling period (Figure21). The diurnal variation of Al concentrations in Dunkerque and Boulogne-sur-Mer is not similar, which suggests the influence of a local phenomenon. In general, Al is known to be generated by different sources of PM emissions, but many scientists classify this element as originating from soil dust. In this case, its presence in the atmosphere can be related to dust resuspension. Furthermore, in urban environments, these dust emissions can be encountered mainly in the vicinity of transportation related traffic and/or in certain areas exhibiting particular wind turbulences. These phenomena can partially explain the temporal Al fluctuations at both sites. However, the identification of higher Al concentrations peaks frequency in Dunkerque when compared to Boulogne-sur-Mer shows the participation of local Al emissions at the first site. The same observation can be noted for Fe and Ca, which high concentrations can be related to the emissions from the steelmaking industry in Dunkerque.

Figure 21 :

 21 Figure 21: Diurnal evol ution of Al and Nss-M g concentrations in Dunkerque and Boul ogne-sur-M er during the winter campaign

aFigure 22 :

 22 Figure 22: Diurnal evol ution of TC and Nss-K concentrations in Dunkerque and Boul ogne-sur-M er during the winter campaign

4 +Figure 23 : 4 +

 4234 Figure 23: Diurnal evol ution of SIA, NH 4 + , NO 3 and SO 4 2concentrations in Dunkerque and Boul ognesur-M er during the winter campaign

Figure 24 :

 24 Figure 24: Diurnal evol ution of sea sal ts concentrations in Dunkerque and Saint-Omer during the spring campaign

  Figure 26: Diurnal evol ution of Al and Nss-M g concentrations in Dunkerque and Saint-Omer during the spring campaign

Figure 27 :Figure 28 : 5 III. 7 . 1 .

 2728571 Figure 27: Diurnal evol ution of TC and Nss-K concentrations in Dunkerque and Saint-Omer during the spring campaign

  winter and spring campaign, we turned to Atmo air monitoring network data for the daily evolution of PM 2.5 and PM 10 . Atmo sites for the measurements of PM 2.5 and PM 10 mass in Dunkerque are located in Malo-les-bains (East side) and Saint-Pol-sur-Mer (West side) respectively, whereas in Boulogne-sur-Mer and Saint-Omer, Atmo installed only one automatic PM 10 measuring station per site. Data on PM 2.5 and PM 10 mass were acquired from Atmo web site (http://www.atmo-npdc.fr), which allowed drawing their daily evolution and hence identifying the daily peaks of PM levels at our sites. In the following segment we will discuss the method for selecting the dates during which episodes of PM pollution occurred, as well as explain the main chemical species contribution in the total analyzed PM 2

Figure 29 : 2 .

 292 Figure 29: Evol ution of Atmo PM 10 and PM 2.5 recorded data and the total anal yz ed species in Dunkerque and Boul ognesur-M er during the winter campaign ( red l ines = l imit val ues of 25 µg/ m 3 ( PM 2.5 ) and 50 µg/ m 3 ( PM 10 ) )

Figure 30 : 2 . 5 IIIFigure 31

 302531 Figure 30: Evol ution of Atmo PM 10 and PM 2.5 recorded data and the total anal yz ed species in Dunkerque and Saint-Omer during the spring campaign ( red l ines = l imit val ues of 25 µg/ m 3 ( PM 2.5 ) and 50 µg/ m 3 ( PM 10 ) )

Figure 31 :

 31 Figure 31: Chemical composition of sel ected PM 2.5 sampl es exhibiting above l imit val ue peaks in Dunkerque and Boul ognesur-M er during the winter campaign

Figure 32 :Figure 33 :

 3233 Figure 32: Diurnal evol ution of the temperature, PM 2.5 l evel , TC, SIA and Al concentrations ( ng/ m 3 ) in PM 2.5 during winter

Figure 35 :

 35 Figure 35: Diurnal evol ution of the temperature, PM 2.5 l evel , TC, SIA and Al concentrations ( ng/ m 3 ) in

3 - 3 - 3 - 3 Tabl

 3333 Sample 1: 20°-40° sector; Al concentration: 100 ng/m Sample 2: 0°-20° sector; Al concentration: 40 ng/m Sample 3: 20°-50° sector; Al concentration: 70 ng/m Sample 4: 50°-90° sector; Al concentration: 30 ng/m

Finally, after drawingSE

  the concentration roses we were able to notice that wind activities in Dunkerque (winter) and Boulogne-sur-Mer from the S to E sector were at very low frequencies, which forces us to consider this fact in the analysis of the roses especially for the -, and Mg are considered to have a predominant marine origin in this section.Concentration roses of our sites during the winter campaign show that Na, Cl -and Mg are maj orly observed under the N sector in Dunkerque, and under NE and SW sectors in

Figure

  Figure 37: M aj or marine el ements concentration roses f or Dunkerque and Boul ogne-sur-M er during the winter campaign

Figure 38 : 8 . 1 . 3 .

 38813 Figure 38: SIA maj or ions concentration roses f or Dunkerque and Boul ognesur-M er during the winter campaign

Figure 39 :

 39 Figure 39: TC and K concentration roses f or Dunkerque and Boul ogne-sur-M er during the winter campaign

FigureFigure 42 :

 42 Figure 40: Fe, Ca, and Al concentration roses f or Dunkerque and Boul ogne-sur-M er during the winter campaign

  Figure 43: TC and K concentration roses f or Dunkerque and Saint-Omer during the spring campaign

Figure 44 :

 44 Figure 44: Fe, Ca, and Al concentration roses f or Dunkerque and Saint-Omer during the spring campaign

  Two sampling campaigns, realized simultaneously in Dunkerque and Boulogne-sur-Mer and Dunkerque and Saint-Omer respectively, allowed us to understand the evolution of PM 2.5 concentrations as well as to analyze the fine PM situation in the studied region. The winter campaign (11/18/2010 -12/31/2010) was realized in Dunkerque and Boulogne-sur-Mer, followed by the spring campaign (03/10/2011 -04/30/2011) in Dunkerque and Saint-Omer.

Figure 1 :Figure 3 :Figure 4 :

 134 Figure 1: Winter TE average concentrations in Dunkerque and Boulogne-sur-Mer ........................................... Figure 2: Spring TE average concentrations in Dunkerque and Saint-Omer ...................................................... Figure 3: Comparison sites location on the European map (modified from (Dalet, 2012)) ................................Figure 4: Winter campaign EF values for TE elements using Ba as reference in Dunkerque and Boulogne-sur-Mer ......................................................................................................................................................................Figure 5: Spring campaign EF values for TE elements using Ba as reference in Dunkerque and Saint-Omer ...Figure 6: TE concentration roses for Dunkerque during the winter campaign ...................................................Figure 7: TE concentration roses for Boulogne-sur-Mer during the winter campaign .......................................Figure 8: TE concentration roses for Dunkerque during the spring campaign ....................................................Figure 9: TE concentration roses for Saint-Omer during the spring campaign ................................................... Figure 10: Air mass backward traj ectories of selected samples showing the N-NE sector influence of Dunkerque in Saint-Omer ......................................................................................................................................................

Figure 5 :

 5 Figure 1: Winter TE average concentrations in Dunkerque and Boulogne-sur-Mer ........................................... Figure 2: Spring TE average concentrations in Dunkerque and Saint-Omer ...................................................... Figure 3: Comparison sites location on the European map (modified from (Dalet, 2012)) ................................Figure 4: Winter campaign EF values for TE elements using Ba as reference in Dunkerque and Boulogne-sur-Mer ......................................................................................................................................................................Figure 5: Spring campaign EF values for TE elements using Ba as reference in Dunkerque and Saint-Omer ...Figure 6: TE concentration roses for Dunkerque during the winter campaign ...................................................Figure 7: TE concentration roses for Boulogne-sur-Mer during the winter campaign .......................................Figure 8: TE concentration roses for Dunkerque during the spring campaign ....................................................Figure 9: TE concentration roses for Saint-Omer during the spring campaign ................................................... Figure 10: Air mass backward traj ectories of selected samples showing the N-NE sector influence of Dunkerque in Saint-Omer ......................................................................................................................................................

Figure 6 :

 6 Figure 1: Winter TE average concentrations in Dunkerque and Boulogne-sur-Mer ........................................... Figure 2: Spring TE average concentrations in Dunkerque and Saint-Omer ...................................................... Figure 3: Comparison sites location on the European map (modified from (Dalet, 2012)) ................................Figure 4: Winter campaign EF values for TE elements using Ba as reference in Dunkerque and Boulogne-sur-Mer ......................................................................................................................................................................Figure 5: Spring campaign EF values for TE elements using Ba as reference in Dunkerque and Saint-Omer ...Figure 6: TE concentration roses for Dunkerque during the winter campaign ...................................................Figure 7: TE concentration roses for Boulogne-sur-Mer during the winter campaign .......................................Figure 8: TE concentration roses for Dunkerque during the spring campaign ....................................................Figure 9: TE concentration roses for Saint-Omer during the spring campaign ................................................... Figure 10: Air mass backward traj ectories of selected samples showing the N-NE sector influence of Dunkerque in Saint-Omer ......................................................................................................................................................

Figure 7 :

 7 Figure 1: Winter TE average concentrations in Dunkerque and Boulogne-sur-Mer ........................................... Figure 2: Spring TE average concentrations in Dunkerque and Saint-Omer ...................................................... Figure 3: Comparison sites location on the European map (modified from (Dalet, 2012)) ................................Figure 4: Winter campaign EF values for TE elements using Ba as reference in Dunkerque and Boulogne-sur-Mer ......................................................................................................................................................................Figure 5: Spring campaign EF values for TE elements using Ba as reference in Dunkerque and Saint-Omer ...Figure 6: TE concentration roses for Dunkerque during the winter campaign ...................................................Figure 7: TE concentration roses for Boulogne-sur-Mer during the winter campaign .......................................Figure 8: TE concentration roses for Dunkerque during the spring campaign ....................................................Figure 9: TE concentration roses for Saint-Omer during the spring campaign ................................................... Figure 10: Air mass backward traj ectories of selected samples showing the N-NE sector influence of Dunkerque in Saint-Omer ......................................................................................................................................................

Figure 8 :

 8 Figure 1: Winter TE average concentrations in Dunkerque and Boulogne-sur-Mer ........................................... Figure 2: Spring TE average concentrations in Dunkerque and Saint-Omer ...................................................... Figure 3: Comparison sites location on the European map (modified from (Dalet, 2012)) ................................Figure 4: Winter campaign EF values for TE elements using Ba as reference in Dunkerque and Boulogne-sur-Mer ......................................................................................................................................................................Figure 5: Spring campaign EF values for TE elements using Ba as reference in Dunkerque and Saint-Omer ...Figure 6: TE concentration roses for Dunkerque during the winter campaign ...................................................Figure 7: TE concentration roses for Boulogne-sur-Mer during the winter campaign .......................................Figure 8: TE concentration roses for Dunkerque during the spring campaign ....................................................Figure 9: TE concentration roses for Saint-Omer during the spring campaign ................................................... Figure 10: Air mass backward traj ectories of selected samples showing the N-NE sector influence of Dunkerque in Saint-Omer ......................................................................................................................................................

Figure 9 :

 9 Figure 1: Winter TE average concentrations in Dunkerque and Boulogne-sur-Mer ........................................... Figure 2: Spring TE average concentrations in Dunkerque and Saint-Omer ...................................................... Figure 3: Comparison sites location on the European map (modified from (Dalet, 2012)) ................................Figure 4: Winter campaign EF values for TE elements using Ba as reference in Dunkerque and Boulogne-sur-Mer ......................................................................................................................................................................Figure 5: Spring campaign EF values for TE elements using Ba as reference in Dunkerque and Saint-Omer ...Figure 6: TE concentration roses for Dunkerque during the winter campaign ...................................................Figure 7: TE concentration roses for Boulogne-sur-Mer during the winter campaign .......................................Figure 8: TE concentration roses for Dunkerque during the spring campaign ....................................................Figure 9: TE concentration roses for Saint-Omer during the spring campaign ................................................... Figure 10: Air mass backward traj ectories of selected samples showing the N-NE sector influence of Dunkerque in Saint-Omer ......................................................................................................................................................

Figure 1 :

 1 Figure 1: Winter TE average concentrations in Dunkerque and Boulogne-sur-Mer

2 .

 2 Spring campaign (March -April 2011)

Figure 2 : 1 .

 21 Figure 2: Spring TE average concentrations in Dunkerque and Saint-Omer

Figure 3 :

 3 Figure 3: Comparison sites location on the European map ( modif ied f rom ( Dalet, 2012) )

-

  I.L. as the industrial level (= the concentration at the industrial site in ng/m 3 ); -U.L. as the urban level (= the concentration at the urban reference site in ng/m 3 ); Thus the average concentrations found in Dunkerque are used as I.L., whereas those measured in Boulogne-sur-Mer are used as U.L.. The calculations lead to uncover the contribution of the industrial activity in Dunkerque summarized in

-

  [ X] crustal as the average concentration of the element in the upper crust (in ppm); -[ REF] aerosol as the reference element concentration in our samples (in ng/m 3 ); -[ REF] crustal as the reference element concentration in the upper crust (in ppm).

Figure 4 :Figure 5 :

 45 Figure 4: Winter campaign EF values f or TE elements using Ba as ref erence in Dunkerque and Boulognesur-Mer

  Figure6illust rat e s t he re sult s of rose calculat ions f or 20 TE during t he wint e r campaign in Dunke rque . This sit e is charact e riz e d by t he mixt ure of ant hropoge nic act ivit ie s as we ll as t he pre se nce of nat ural source s ( marine and crust al) t hat can e mit f ine PM. The indust rial se ct or in Dunke rque ( W -NW ) is const it ut e d of a mixt ure of he avy indust rie s t hat include s many source s of e missions ( iron ore sint e ring, coke making plant , blast f urnace s, st e e lworks, oil re f ine ry, me t allurgy…etc). I n addit ion, t he E-NE se ct or might carry e le me nt s coming f rom

Figure 6 :

 6 Figure 6: TE concentration roses for Dunkerque during the winter campaign

  -Nb-Sb-Ba-Cd-Co-Cu-Pb-Sb Urban + I nt e grat e d st e e lworks W -NW Ag-As-Cd-Cu-Mn-Pb-Rb-Tl-V-Co-Sr-Zn Urban + Ele ct ric st e e l plant NE-E Cd-Cr-Pb-Zn-Nb-Cu-Ba Fossil f ue l combust ion SSW -SW W V-Ni I V.7.1.2.Boulogne-sur-Mer winter concentration roses

Figure 7 :

 7 Figure 7: TE concentration roses for Boulogne-sur-Mer during the winter campaign

Figure 8 :

 8 Figure 8: TE concentration roses for Dunkerque during the spring campaign

Figure 9 :

 9 Figure 9: TE concentration roses for Saint-Omer during the spring campaign

•

  The 30° -60° wind se ct or unde r Ascome t al e le ct ric st e e l plant inf lue nce , f rom which we se le ct e d t he one s showing Zn conce nt rat ions above 50 ng/ m 3 . b) For Saint -Ome r sample s we chose : • The sample s unde r 360 -40° wind se ct or ( Dunke rque st e e lworks inf lue nce ) and e xhibit ing Fe and K conce nt rat ions above 100 ng/ m 3 . The sample s unde r 100 -160° wind se ct or ( Saint -Ome r glass making f act ory) and showing Sn conce nt rat ions highe r t han 20 ng/ m 3 . The sample s in Dunke rque and Saint -Ome r we re se le ct e d using wind dire ct ions re corde d during t he sampling campaign. Howe ve r, addit ional conf irmat ion on t he indust rial st e e lworks inf lue nce s at Saint -Ome r was achie ve d by drawing air mass back-t raj e ct orie s using t he Air Re source s Laborat ory ( ARL) HYSPLI T t raj e ct ory int e rne t base d mode l ( Draxle r & Rolph, 2012

Figure 10 :

 10 Figure 10: Air mass backward traj ectories of selected samples showing the N-NE sector influence of Dunkerque in Saint-Omer

  Figure1: Classif icat ion base d on a-priori knowle dge re quire me nt s of me t hods use d t o de t e rmine source s apport ionme nt using re ce pt or mode ls ( e xt ract e d f rom( Viana, e t al. , 2008) which re produce d it f rom ( Schaue r, e t al. , 2006) ) ................................ ................................ .......................... ................ 226 Figure 2: A se ct ion of a mat rix X including spe cie s conce nt rat ions in ng/ m 3 ....................... .... 231 Figure 3: A section from a matrix Σ of uncertainties expressed in % ........................... ................ 231 Figure 4: I llust rat ion of a se ct ion of F init mat rix ................................ ........................... ................. 232 Figure 5: Equalit y const raint s f irst mat rix showing t he locat ion of some const raint s in t he prof ile s . 234 Figure 6: Equalit y const raint s se cond mat rix showing t he value s of some const raine d e le me nt s in some source prof ile s ................................ ................................ ................................ ....................... ..... 235 Figure 7: I ne qualit y const raint s mat rix showing t he orde r of abundance of some e le me nt s .............. 236 Figure 8: Comparison be t we e n t ot al NMF calculat e d and t ot al obse rve d PM 2. 5 spe cie s conce nt rat ions ( ng/ m 3 ) in Boulogne -sur-Me r during t he wint e r campaign ................................ ...................... . 239 Figure 9: Comparison be t we e n NMF calculat e d ( Y axis) and obse rve d ( X axis) spe cie s conce nt rat ions ( ng/ m 3 ) in Boulogne -sur-Me r during t he wint e r campaign ................................ ...................... . 240 Figure 10: Comparison be t we e n t ot al NMF calculat e d and t ot al obse rve d PM 2. 5 spe cie s conce nt rat ions ( ng/ m 3 ) in Dunke rque during bot h wint e r and spring campaigns ............................. .................. 241 Figure 11: Comparison be t we e n NMF calculat e d ( Y axis) and obse rve d ( X axis) spe cie s conce nt rat ions ( ng/ m 3 ) in Dunke rque during bot h wint e r and spring campaigns ..................... 242 Figure 12: Comparison be t we e n t ot al NMF calculat e d and t ot al obse rve d PM 2. 5 spe cie s conce nt rat ions ( ng/ m 3 ) in Saint -Ome r during t he spring campaign ................................ ......................... ............ 243 Figure 13: Comparison be t we e n NMF calculat e d ( Y axis) and obse rve d ( X axis) spe cie s conce nt rat ions ( ng/ m 3 ) in Saint -Ome r during t he spring campaign ............................ ................. 244 Figure 14: Eight common NMF prof ile s ide nt if ie d in t he t hre e sampling sit e s ...................... . . 247 Figure 15: I ndust rial NMF prof ile s ide nt if ie d in Dunke rque during bot h wint e r and spring campaigns ................................ ................................ ................................ ............................... .................... 248 Figure 16: I ndust rial NMF prof ile s ide nt if ie d in Saint -Ome r during t he spring campaign ................ 249 Figure 17: Ave rage cont ribut ions of source s in t he PM 2. 5 load in Boulogne -sur-Me r during wint e r . 251 Figure 18: Ave rage cont ribut ions of source s in t he PM 2. 5 load in Dunke rque during wint e r ............. 252 Figure 19: Ave rage cont ribut ions of source s in t he PM 2. 5 load in Dunke rque during spring .............. 253 Figure 20: Ave rage cont ribut ions of source s in t he PM 2. 5 load in Saint -Ome r during spring ............. 254 Figure 21: Diurnal t re nds of source cont ribut ions in Boulogne -sur-Me r during wint e r ( sampling e nde d on De ce mbe r 28 t h , 2010) ................................ ................................ ............................ .................. 256 Figure 22: Diurnal t re nds of source s cont ribut ions in Dunke rque during wint e r ( missing dat a be t we e n De c. 7 t h and 9 t h are cause by sampling proble ms) ................................ .......................... ............... 257 Figure 23: Diurnal t re nds of source s cont ribut ions in Dunke rque during spring ( March 15t h and 16 t h missing dat a are cause d by sampling proble ms; t he gap be t we e n April 24 t h and 26 t h re pre se nt s out lie rs discarde d due t o une xplainable high K conce nt rat ions) ................................ ....................... ...... 259 Figure 24: Diurnal t re nds of source s cont ribut ions in Saint -Ome r during t he spring campaign ( t he missing sample s of April 16 t h , a nd be t we e n t he 24 t h and t he 26 t h are out lie rs discarde d due t o une xpl ainable highPb and K conce nt rat ions re spe ct ive ly) ................................ ...................... . 260 Figure 25: NMF source s pe rce nt age cont ribut ions t o t he t ot al load of e ach analyz e d spe cie s in t he sample s t ake n f rom Boulogne -sur-Me r during wint e r ................................ ........................ ........ 262 Figure 26: NMF source s pe rce nt age cont ribut ions t o t he t ot al load of e ach analyz e d spe cie s in t he sample s t ake n f rom Dunke rque during wint e r ................................ ............................ ................. 263 Figure 27: NMF source s pe rce nt age cont ribut ions t o t he t ot al load of e ach analyz e d spe cie s in t he sample s t ake n f rom Dunke rque during spring................................ ............................ .................. 265 Figure 28: NMF source s pe rce nt age cont ribut ions t o t he t ot al load of e ach analyz e d spe cie s in t he sample s t ake n f rom Saint -Ome r during spring ................................ ............................ ................. 266

t

  ot al colle ct e d PM 2. 5 . This se ct ion of t he st udy was accomplishe d by t he combine d e f f ort s of t he "Unité de Chimie Environnementale et Intéractions sur le Vivant" ( UCEI V) f or t he che mical aspe ct s, and t he "Laboratoire d'Informatique, Signal et Image de la Côte d'Opale" ( LI SI C) f or comput ing aspe ct s, which are two research units within the "Université du Littoral Côte d'Opale". The mat rix f act oriz at ion mode l t hat will be use d in t his chapt e r was de ve lope d by Dr. Gille s DELMAI RE and Dr. Gille s ROUSSEL ( LI SI C) , and is base d on a we ight e d non-

(

  Billf ord & Me e ke r, 1967) , which was f ollowe d by t he cre at ion of t he che mical e le me nt balance ( curre nt ly known as chemical mass balance "CMB") that was realized in the 70's ( W inche st e r & Nif ong, 1971) ; ( Mille r, e t al. , 1972) and ( Frie dlande r, 1973)

Figure 1 :

 1 Figure 1: Classification based on a-priori knowledge requirements of methods used to determine sources apportionment using receptor models ( extracted from ( Viana, et al., 2008) which reproduced it from ( Schauer, et al., 2006) )

V. 5 .

 5 Appl ic ation of we ighte d NM F f or s our c e appor tionme nt V.5.1. Preparation of input data I n order to perform a calculation of matrix G and F following the previously explained method, many data matrices are required: V. 5. 1. 1. M atr ix of s pe c ie s c onc e ntr ations ( matr ix X)The columns of this matrix correspond to each analyzed chemical component concentration values (in ng/ m 3 ) in the samples. On the other hand, a line represents the concentration values of all analyzed species for a given sample. Figure2illustrates an example of a matrix of species concentrations.

Figur e 2 :

 2 Figur e 2: A s e c tion of a matr ix X inc l uding s pe c ie s c onc e ntr ations in ng/ m 3

Figur e 3

 3 Figur e 3: A section from a matrix Σ of unc e r taintie s e xpr e s s e d in %

Figur e 4 :

 4 Figur e 4: I l l us tr ation of a s e c tion of F init matr ix

Figur e 6 :

 6 Figur e 6: Equal ity c ons tr aints s e c ond matr ix s howing the val ue s of s ome c ons tr aine d e l e me nts in s ome s our c e pr of il e s

Figure 7 ,Figur e 7 :

 77 Figure 7, "inequality" constraints were applied on Al to define that [ Al] in sources A and B are inferior to [ Al] in sources D, E, H, J , which themselves are inferior to [ Al] in source G. No classification is given for sources holding the same value of "inequality" constraints, like in the cases of [ Al] in sources A and B on one hand, and [ Al] in sources D, E, H, J on the other.

Figur e 8 :

 8 Figur e 8: Compar is on be twe e n total NM F c al c ul ate d and total obs e r ve d PM 2. 5 s pe c ie s c onc e ntr ations ( ng/ m 3 ) in Boul ogne -s ur -M e r dur ing the winte r c ampaign

Figur e 9 :

 9 Figur e 9: Compar is on be twe e n NM F c al c ul ate d ( Y axis ) and obs e r ve d ( X axis ) s pe c ie s c onc e ntr ations ( ng/ m 3 ) in Boul ogne -s ur -M e r dur ing the winte r c ampaign

Figur e 10 :

 10 Figur e 10: Compar is on be twe e n total NM F c al c ul ate d and total obs e r ve d PM 2. 5 s pe c ie s c onc e ntr ations ( ng/ m 3 ) in Dunke r que dur ing both winte r and s pr ing c ampaigns

  Figur e 11: Compar is on be twe e n NM F c al c ul ate d ( Y axis ) and obs e r ve d ( X axis ) s pe c ie s c onc e ntr ations ( ng/ m 3 ) in Dunke r que dur ing both winte r and s pr ing c ampaigns

Figur e 12 :

 12 Figur e 12: Compar is on be twe e n total NM F c al c ul ate d and total obs e r ve d PM 2. 5 s pe c ie s c onc e ntr ations ( ng/ m 3 ) in Saint-Ome r dur ing the s pr ing c ampaign

  Figur e 13: Compar is on be twe e n NM F c al c ul ate d ( Y axis ) and obs e r ve d ( X axis ) s pe c ie s c onc e ntr ations ( ng/ m 3 ) in Saint-Ome r dur ing the s pr ing c ampaign

Figur e 14 :

 14 Figur e 14: Eight c ommon NM F pr of il e s ide ntif ie d in the thr e e s ampl ing s ite s

Figur e 15 :

 15 Figur e 15: I ndus tr ial NM F pr of il e s ide ntif ie d in Dunke r que dur ing both winte r and s pr ing c ampaigns

Figur e 16 :

 16 Figur e 16: I ndus tr ial NM F pr of il e s ide ntif ie d in Saint-Ome r dur ing the s pr ing c ampaign

Figur e 17 :

 17 Figur e 17: Ave r age c ontr ibutions of s our c e s in the PM 2. 5 l oad in Boul ogne -s ur -M e r dur ing winte r

  Figur e 18: Ave r age c ontr ibutions of s our c e s in the PM 2. 5 l oad in Dunke r que dur ing winte r

Figur e 19 :

 19 Figur e 19: Ave r age c ontr ibutions of s our c e s in the PM 2. 5 l oad in Dunke r que dur ing s pr ing

Figur e 20 :

 20 Figur e 20: Ave r age c ontr ibutions of s our c e s in the PM 2. 5 l oad in Saint-Ome r dur ing s pr ing

Figure 21 and

 21 Figure 21 and Figure 22 illustrates the sample by sample winter contributions trends in Boulogne-sur-M er and Dunkerque respectively.

ConcentrationsFigur e 22 :

 22 Figur e 22: Diur nal tr e nds of s our c e s c ontr ibutions in Dunke r que dur ing winte r ( mis s ing data be twe e n De c . 7 th and 9 th ar e c aus e by s ampl ing pr obl e ms )

Figur e 23 :Figur e 24 :

 2324 Figur e 23: Diur nal tr e nds of s our c e s c ontr ibutions in Dunke r que dur ing s pr ing ( M ar c h 15 th and 16 th mis s ing data ar e c aus e d by s ampl ing pr obl e ms ; the gap be twe e n Apr il 24 th and 26 th r e pr e s e nts outl ie r s dis c ar de d due to une xpl ainabl e high K c onc e ntr ations )

3 . 1 .

 31 Figure 25 illustrates these contributions for Boulogne-sur-M er during the winter campaign.

Figur e 25 :

 25 Figur e 25: NM F s our c e s pe r c e ntage c ontr ibutions to the total l oad of e ac h anal yz e d s pe c ie s in the s ampl e s take n f r om Boul ogne -s ur -M e r dur ing winte r

  , the first combustion profile (Combustion 1) is found to enrich mainly V and Ni, when in the same time the second combustion profile (Combustion 2) is contributing in a large amount of K, Ag, Ba, Co, NH 4 + and TC. Nevertheless, trace elements such as Cr, Sb and Sn are found enriched by the contributions of the "traffic non-exhaust" profile.

Figur e 26 :

 26 Figur e 26: NM F s our c e s pe r c e ntage c ontr ibutions to the total l oad of e ac h anal yz e d s pe c ie s in the s ampl e s take n f r om Dunke r que dur ing winte r

3 . 2 .

 32 Spr ing c al c ul ate d ave r age s our c e c ontr ibutions to the s pe c ie s total l oadsUsing the spring campaign data, the NM F calculated sources contributions to the species show that in Dunkerque (Figure27), the "Sea-salts" profile participated essentially in the loads of Na and Cl -. On the other hand, Ca, M g, Na, Co and Sr are found affected by the contributions of the "Aged sea-salts" profile. The "Crustal" profile contributed in most of Al and Ti, and partially in Ca and Ba loads. Nevertheless, trace elements such as Cr, Sb and Sn are found enriched by the contributions of the "traffic non-exhaust" profile.On the other hand, the "Secondary sulfates" contribution is found limited to almost all SO 42-concentrations and to a small fraction of the NH 4 + loads. W hereas the "Secondary nitrates" largely contributed in NO 3 -and NH 4 + , and moderately in TC, Pb, Cu, Co and Cd.Furthermore, the "Combustion 1" profile contributed essentially in most of V and Ni contents, in the time when "Combustion 2" was enriching TC, K, Ag, Ba and NH 4 + .The industrial profiles followed almost exact contribution patterns as in the case of the winter campaign for Dunkerque. A small difference can be noted for the contributions of I SW -fugitive in Al loads and of I SW -sintering stack in Cd loads, which was slightly lower than the winter respective contributions.

  and Figure 27) is only due to the different contribution values. The contribution of a local industrial source could vary when local meteorological conditions, such as wind directions, also vary. Consequently, the difference between winter and spring campaigns in Dunkerque can be explained by the different meteorological conditions recorded during the two seasons.

Figur e 27 :

 27 Figur e 27: NM F s our c e s pe r c e ntage c ontr ibutions to the total l oad of e ac h anal yz e d s pe c ie s in the s ampl e s take n f r om Dunke r que dur ing s pr ing

  Sn, Pb, Cu, Co, Cd and K. Furthermore, trace elements such as As, Cd, Cr, M n, Pb, Sb and Zn are found enriched by the contributions of the "traffic non-exhaust" profile, which marked a different behavior when compared to the usual contributions found for the other sites.

Figur e 28 :

 28 Figur e 28: NM F s our c e s pe r c e ntage c ontr ibutions to the total l oad of e ac h anal yz e d s pe c ie s in the s ampl e s take n f r om Saint-Ome r dur ing s pr ing

Between 8

 8 and 11 sources that contribute to the load of atmospheric PM 2.5 were identified in different sites. The first 8 non industrial sources were identified at Boulogne-sur-M er. I n Dunkerque, 3 steelmaking sources are added to the first 8 identified profiles. Finally in Saint-Omer, 2 industrial sources: local glassmaking and Dunkerque's steel works were detected in addition to the first 8 non-industrial sources.

-

  5 were 24.8 µg/ m 3 in winter and 33.1 µg/ m 3 in spring, based on Atmo Nord-Pas-de-Calais official air quality monitoring network recordings.W hen comparing these data to the chemical composition of the collected particles, we found that maj or species concentrations (NO 3

1 1

 1 I : Scatter Plot M atrix (SPLOM ) of TE correlationsDunkerque winter TE matrix correlations graphs (part 1) The diagonal histograms represent the "Density histograms" which shows the number of samples (Y axis) versus their value range (X axis)

  .........................................................................................................................................Figure

Table 2 :

 2 ......................................................... 19 Guideline values for metals in the Atmosphere ...................................................................................... 34

  Directive-2005/0183) in Europe as well as in France. However, the limit values set by the European directive for ambient air PM 10 are being exceeded in France to an extent where the European Union obligated France to face the EU justice court for not respecting the limit values of PM 10 in 2011

	are already enforced since January 2005 (
	Energy use in industry	Non-road transport	Solvent and product use	Agriculture	Waste

Table 2 : Guideline values for metals in the Atmosphere

 2

Figure 28: Annual mean concentration of PM 10 in Dunkerque, Boulogne-sur-Mer, Saint-Omer, Bethune, Lille, and in µg/m 3 ; source (Ministère de l'Écologie, 2012)

		3500
	Dust emissions in tonnes /year	500 1000 1500 2000 2500 3000
		0
		2004	2005	2006		2007	2008	2009	2010
		Arcelor Mittal	SRD	Al Dk	EDF	Cargill SAS	E.ON -Central	Arc International
			Finally, Boulogne-sur-Mer annual mean decreased significantly
	between 2007 and 2009.

II.1. Introduction .................................................................................................... II.2. Sampling sites ................................................................................................. II

  .2.1. Dunkerque .................................................................................................... II.2.2. Boulogne-sur-Mer ........................................................................................ II.2.3. Saint-Omer ...................................................................................................

	1) Sampling of PM 2.5 in three cities of the NPdC region: Dunkerque, Boulogne-CHAPTER II:
	sur-Mer and Saint-Omer. In details, two mid-daily sampling campaigns (winter Materials and Methods used for Sampling and spring) will be realized, each of which in two sites simultaneously: a. Winter campaign: will be achieved in Dunkerque and Boulogne-sur-Mer and Analysis
	b. Spring campaign: will be achieved in Dunkerque and Saint-Omer
	2) Detailed chemical characterization of the samples, with a focus on ionic species,
	major elements, trace elements and total carbon. The variations interpretations,
	including spatial/chronological trends, will be achieved taking into account the
	meteorological conditions as well as the air masses origins.
	3) Identification of emissions sources by means of receptor modeling: a method
	based on a non-negative matrix factorization principle. This latter was
	developed by Dr G. Delmaire and Dr G. Roussel (LISIC, ULCO), who are
	experts in signal analysis and with which the UCEIV laboratory collaborates to
	offer its expertise in the environmental approach as well as the chemical tracing
	of the sources. This type of models is widely used in source apportionment
	studies. On one hand, it identifies emissions source profiles and quantifies the
	contributions of these profiles.

II.3. Materials used for sampling .......................................................................... II

  .3.1. DIGITEL® High Volume Sampler DA-80 .................................................. II.3.2. Sampling filters ............................................................................................ II.3.2.1. Cellulose filters contamination reduction .......................................................... II.3.2.2. Quartz fiber filters contamination reduction ...................................................... II.3.3. Meteorological, PM 10 and PM 2.5 data acquisition ........................................ II.3.4. Filters preparation before sampling ..............................................................

II.4. Analysis of inorganic components and total carbon ................................... II

  .4.1. Inductively Coupled Plasma-Mass Spectrometer (ICP-MS) ....................... II.4.1.1. Samples preparations ......................................................................................... II.4.1.2. ICP-MS principles and characteristics .............................................................. II.4.2. Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) .. II.4.3. CHNS-O Analyzer ....................................................................................... II.4.3.1. Samples preparations ......................................................................................... II.4.3.2. CHNS-O Analyzer principles and characteristics ............................................. II.4.4. Ion Chromatography (IC) ............................................................................. II.4.4.1. Samples preparations ......................................................................................... II.4.4.2. Ion chromatography principles and characteristics ........................................... II.4.5. Analysis of certified standards .....................................................................

II.5. Sampling protocol validation ........................................................................ II

  .5.1. Methodology ................................................................................................ II.5.2. Results ..........................................................................................................

II.6. Works Cited ....................................................................................................

  Satellite imagery showing the three sampling sites cities locations ......................................................Figure 2: Satellite imagery showing "Lamartine" sampling site and the major PM sources influences ...............Figure 3: Satellite imagery showing "Musée" sampling site and the major PM sources influences .....................

	LIST OF FIGURES
	Figure 1: Figure 4: Satellite imagery showing "Ribot" sampling site and the major PM sources influences ......................
	Figure 5: (A) HVS DA-80 in its field housing; (B) Longitudinal section of the PM 2.5 sampling head used with the
	DA-80 for sampling, the air passing in the bottom section (red arrow) holds PM 2.5 only. ...................................
	Figure 6: Photo of the DA-80 high volume sampler location in Dunkerque showing the stabilizing metallic grid
	on the floor holding about 150 kg of cement. .......................................................................................................
	Figure 7: Filter preparation using quartz fiber filter (a) glued using an adhesive tape (b) to a cellulose fiber filter
	(c) ..........................................................................................................................................................................
	Figure 8: Example of a sample taken using the DA-80; the darker side represents the quartz semi-filter when the
	other lighter one is the cellulose semi-filter; adhesive tape can be seen in the middle. ........................................
	Figure 9: Atmospheric concentrations (ng/ m 3 ) calculated using the mixed quartz-cellulose fiber filters versus
	atmospheric concentration determined using the whole cellulose filter for a selection of elements .....................
	LIST OF TABLES

Table 1 :

 1 List of the elements analyzed by ICP-MS, their calibration ranges and detection limits ........................

Table 2 :

 2 List of elements analyzed by ICP-AES, their calibration ranges and detection limits ............................

Table 3 :

 3 Calibration range for TC analysis ...........................................................................................................

Table 4 :

 4 Detection limits (DL) found for the analyzed ions .................................................................................

Table 5 :

 5 Summary of NIST SRM-1648 analysis using ICP-MS and ICP-AES ....................................................

Table 6 :

 6 Summary of soil (certificate 133317) and cystine (certificate 134139) total carbon analysis ................

Table 7 :

 7 Summary of NIST SRM-1648 certified anions analysis .........................................................................

Table 8 :

 8 Trace elements contents in QMA quartz fiber filter (in ng/ g of filter) ....................................................

Table 9 :

 9 Mass of Cu sampled on the cellulose part and on the quartz fiber part of the mixed filter and corresponding air volume passing through ...........................................................................................................

II.3.2.2. Quartz fiber filters contamination reduction

	Petri dishes and stored in a freezer (-20°C). This treatment lowers the Fe contamination under
	0.1 μg per filter, and contents of all other metals are lower than the detection limits.
	W hatman® QMA quartz microfiber filters (150 mm) were sealed inside handmade
	aluminum foil pouches, one pouch for each filter. After opening one side of the pouches,
	filters were heated up to 450°C in a closed oven for 48 hours in order to decrease carbon
	content to below the detection limit. Afterwards, each filter was conserved inside its original
	pouch until sampling.
	II.3.3. Meteorological, PM 10 and PM 2.5 data acquisition
	Meteorological data included rainfall occurrence and quantity as well as wind
	directions, speed and gusts were acquired from Mé té o France (Convention ULCO/ Mé té o
	France). The stations in which the data was recorded are located in Dunkerque (station n°
	59183001), Boulogne-sur-Mer (station n° 62160001) and Radinghem (station n° 62685001).
	for Fe
	analysis. Filters are submerged in 0.5M HCl and 0.5M HNO 3 for 24h, then rinsed with
	ultrapure water (MilliQ®, Millipore; resistivity=18.2 MΩ.cm). Then, filters are dried under a
	laminar flow hood (Class 100, US Federal Standard 209a), and hermetically kept in cleaned

Table 3 : Calibration range for TC analysis

 3

	Soil	TC content ( µg)	Cystine	TC ( µg)
	Standard 1	4	Standard 1	19
	Standard 2	8	Standard 2	60
	Standard 3	14	Standard 3	138
	Standard 4	20	Standard 4	155
	Standard 5	23	Standard 5	256

Table 4 : Detection limits ( DL) found for the analyz ed ions

 4

	Ions	NH 4 +	F -	Cl -	NO 3	-	PO 4	3-	SO 4	2-
	DL (ppm)	0.00296	ND*	0.00296	0.00008	ND*	0.00009

*ND: not detected II.

[START_REF]Industrial Impact anal ysis[END_REF]

.5. Analysis of certified standards

In order to validate our sample preparation procedure as well as the reliability of our measuring technique, we analyzed a certified urban particulate matter sample (NIST SRM-1648) using ICP-MS, ICP-AES and Ion Chromatography, as well as soil (Thermo Scientific® certificate 133317) and cystine (Thermo Scientific® certificate 134139) reference materials using the CHNS-O analyzer.

Table 5 : Summary of NIST SRM-1648 analysis using ICP-MS and ICP-AES

 5

	Elements	Analyz er	Obtained values % SD	Certified values % SD	Recovery %
	Al	ICP-AES	3,437 ± 0,127	3,42 ± 0,11	101
	Ba	ICP-AES	0,071 ± 0,002	0,074	-	96
	Ca	ICP-AES	5,956 ± 0,190	-	-	-
	Fe	ICP-AES	3,894 ± 0,030	3,91 ± 0,10	100
	K	ICP-AES	1,032 ± 0,002	1,05 ± 0,01	98
	Mg	ICP-AES	0,781 ± 0,023	0,800 ± 0,014	98
	Na	ICP-AES	0,406 ± 0,014	0,425 ± 0,002	96
	Sr	ICP-AES	0,021 ± 0,001	-	-	-
	Zn	ICP-AES	0,436 ± 0,006	0,476 ± 0,014	92
	Cu	ICP-MS	0,059 ± 0,001	0,061	-	97
	Mn	ICP-MS	0,081 ± 0,003	0,079	-	103
	Ti	ICP-MS	0,392 ± 0,006	0,400	-	98
	Pb	ICP-MS	0,692 ± 0,007	0,655 ± 0,008	106
	V	ICP-MS	0.015 ± 0.002	0.014 ± 0.0003	104

Table 6 : Summary of soil ( certificate 133317) and cystine ( certificate 134139) total carbon analysis

 6

	Reference	Measured ( mg)	SD	Certified ( mg) Recovered ( % )
	Soil TC	0.01092	± 0.00026	0.01086	101
	Cystine TC	0.06451	± 0.00122	0.06437	100

Table 7 : Summary of NIST SRM-1648 certified anions analysis

 7

	Ion		Measured ( % )	SD (%)	Certified ( % )	Recovered ( % )
	Cl -		0.47	± 0.02	0.45	104
	SO 4	2-	14.1	± 0.5	15 *	94

*Certified to 5% as "S", converted to 15% as SO

4 2-

Table 8 : Trace elements contents in QMA quartz fiber filter ( in ng/ g of filter)

 8

	Element	ng/ g
	Ag	34
	As	261
	Bi	119
	Cd	128
	Co	40
	Cr	1050
	Cu	162
	Mn	724
	Nb	53
	Ni	302
	Pb	375
	Rb	409
	Sb	458
	Sc	798
	Sn	211

Table 9 : Mass of Cu sampled on the cellulose part and on the quartz fiber part of the mixed filter and corresponding air volume passing through

 9

	Dates	Cu cell ( ng)	Cu QMA ( ng)	V tot ( m 3 )	Cu tot ( ng)	V cell ( m 3 )	V QMA ( m 3 )
	05/ 12/ 2010 (00-12)	56	122	333	178	105	228
	05/ 12/ 2010 (12-24)	37	77	333	114	109	224
	05/ 13/ 2010 (00-12)	246	246	333	492	166	167
	05/ 13/ 2010 (12-24)	181	244	329	425	140	189
	05/ 14/ 2010 (00-12)	145	136	331	281	171	161
	05/ 14/ 2010 (12-24)	101	101	327	201	164	163
	05/ 15/ 2010 (00-12)	194	206	329	400	160	170
	05/ 15/ 2010 (12-24)	100	116	330	215	153	177
	05/ 16/ 2010 (00-12)	58	73	332	131	147	186
	05/ 16/ 2010 (12-24)	183	167	330	350	173	157
	05/ 17/ 2010 (00-12)	180	198	332	378	158	174
	05/ 17/ 2010 (12-24)	209	293	326	502	136	190
	05/ 18/ 2010 (00-12)	211	236	326	447	154	172
	05/ 18/ 2010 (12-24)	124	147	325	271	149	176
	05/ 19/ 2010 (00-12)	118	72	327	190	204	123

Table 1 :

 1 Classification of the encountered wind speeds during our sampling campaigns ....................................

Table 2 :

 2 Average concentrations of ME, TE, SI, and TC found in PM 2.5 collected in Dunkerque and Boulognesur-Mer during the winter campaign (in ng/ m 3 ) ..................................................................................................

Table 3 :

 3 Average concentrations of ME, TE, SI, and TC found in PM 2.5 collected in Dunkerque and Saint-Omer during the spring campaign (in ng/ m 3 ) ................................................................................................................

Table 4 :

 4 Characteristics of the comparison sites chosen from different European locations ..............................

Table 5 :

 5 Arithmetic average concentrations (in ng/ m 3 ) comparison between our region and Europe ................

Table 6 :

 6 Percentage of average sea salt and non sea salt fractions within sea salt components in winter ..........

Table 7 :

 7 Percentage of average sea salt and non sea salt fractions within sea salt components in spring ...........

Table 8 :

 8 Example of concentration roses calculations ........................................................................................

  Maj or Elements (ME) including: Al, Ca, Fe, K, Mg and Na;-Trace Elements (TE) which group: Ag, As, Ba, Bi, Cd, Co, Cr, Cu, Mn, Nb, Ni, P, Pb, Rb, Sb, Sc, Sn, Sr, Te, Ti, Tl, V and Zn;

	-Soluble Ions (SI) which assemble: NH 4	+ , Cl -, NO 3	-and SO 4	2-

Table 1 : Classification of the encountered wind speeds during our sampling campaigns Wind speed Velocity ( in m/ s) Rank in Beaufort scale Description

 1

	High	> 8	5 to 8	Fresh breeze to fresh gale
	Moderate	4 to 8	3 to 4	Gentle to moderate breeze
	Low	2 to 4	2 to 3	Light to gentle breeze
	Very low	< 2	0 to 1	Calm to light air

  3.1.Winter campaign

	Sampling during the winter campaign was held in Dunkerque (from 11/18/2010 to
	12/31/2010) and Boulogne-Sur-Mer (from 11/16/2010 to 12/28/2010) simultaneously. A total

of 81 and 86 samples were taken at Dunkerque and Boulogne-sur-Mer respectively.

.Average concentrations and contributions to PM 2.5

  Ambient air PM 2.5 samples taken from Dunkerque, Boulogne-sur-Mer and Saint-Omer during winter and spring campaigns were analyzed for ME, SI and TC content following the analytical methodology described in Chapter II. After the gathering of analytical data, a comparison between the results of sites will be realized as a first step to understand the trends of the analyzed chemical species within the region. Furthermore, in order to understand the difference in the level of our ions and elements concentrations with those found in nearby countries, a comparison with other sites chosen from the European continent will take place in the forthcoming section.

	III.4
	°C
	Temperature in
	3/10/2011	3/11/2011	3/12/2011	3/13/2011	3/14/2011	3/15/2011	3/16/2011	3/17/2011	3/18/2011	3/19/2011	3/20/2011	3/21/2011	3/22/2011	3/23/2011	3/24/2011	3/25/2011	3/26/2011	3/27/2011	3/28/2011	3/29/2011	3/30/2011	3/31/2011	4/1/2011	4/2/2011	4/3/2011	4/4/2011	4/5/2011	4/6/2011	4/7/2011	4/8/2011	4/9/2011	4/10/2011	4/11/2011	4/12/2011	4/13/2011	4/14/2011	4/15/2011	4/16/2011	4/17/2011	4/18/2011	4/19/2011	4/20/2011	4/21/2011	4/22/2011	4/23/2011	4/24/2011	4/25/2011	4/26/2011	4/27/2011	4/28/2011	4/29/2011	4/30/2011
															T°C from 00h to 12h			T°C from 12h to 24h

III.4.1.Winter campaign averages comparison

The results of the chemical analysis of the collected samples in Dunkerque and Boulogne-sur-Mer are illustrated in Table

2

. We should note that between the TE group, Tl concentration in Dunkerque's winter campaign from the first sample until 12/13/2012 (44 samples) could not be acquired due to technical reasons.

: Average concentrations of M E, TE, SI, and TC f ound in PM 2.5 col l ected in Dunkerque and Boul ogne-sur-M er during the winter campaign ( in ng/ m 3 ) Dunkerque Boul ogne-sur-M er PM 2.5 (our study)

  2.5 monitoring stations are not installed atBoulogne-sur-Mer and Saint-Omer; instead Atmo monitors PM 10 at those sites.

				µ SD	%	µ SD	%
	Al			17.8 ± 12	0.1	13.1 ± 7.1	0.1
	Ca			49.5 ± 33.3	0.3	43.5 ± 26.8	0.3
	Fe			92.9 ± 96.1	0.5	52.6 ± 50.5	0.3
	K			132 ± 73.4	0.8	123 ± 75	0.7
	Mg			34.2 ± 38.2	0.2	24.5 ± 25.5	0.1
	Na			237 ± 267	1.3	202 ± 179	1.2
	TE			84.9 ± 102	0.5	50.3 ± 47	0.3
	NH 4	+	1914 ± 1008	10.9	1706 ± 892	10.2
	Cl -			580 ± 309	3.3	558 ± 279	3.3
	NO 3	-	5707 ± 4734	32.5	5872 ± 4338	35.1
	SO 4	2-	2521 ± 1779	14.3	2449 ± 1726	14.6
	TC			6198 ± 4175	35.3	5625 ± 3688	33.6
	Total anal yz ed	17568	100	16719	100
	PM 2.5 (Atmo)*	24860		-
	On the other hand, total PM 2.5 chemical species average concentration is found higher
	for Dunkerque than Boulogne-sur-Mer. ME average concentration in Dunkerque shows
	different levels, ranging from low values like 17.8 ng/m 3 found in the case of Al to very high

ones such as TC concentration of 6198 ng/m 3 . However, Na is found to have the highest average between maj or metals, followed by K, Fe, Ca, Mg and Al. Tabl e 2*Source: http://www.atmo-npdc.fr

: Average concentrations of M E, TE, SI, and TC f ound in PM 2.5 col l ected in Dunkerque and Saint-Omer during the spring campaign ( in ng/ m 3 )

  In addition, SI accounted for 74.6% of PM 2.5 load on average, which is comparable to the value found in Dunkerque (75.6%) for the same period. TC exhibited a different scenario in this campaign since its average contribution in fine PM rose to 23% in

				Dunkerque		Saint-Omer
	PM 2.5 (our study)	µ SD	%	µ SD	%
	Al			27.8 ± 18.0	0.1	21.8 ± 14.7	0.1
	Ca			65.1 ± 42.1	0.3	51.0 ± 29.3	0.2
	Fe			101 ± 174	0.5	52.1 ± 41.8	0.2
	K			98.9 ± 68.6	0.4	107 ± 67	0.5
	Mg			42.2 ± 56.9	0.2	25.2 ± 31.5	0.1
	Na			240 ± 362	1.1	186 ± 265	0.8
	TE			100 ± 160	0.5	76 ± 75	0.3
	NH 4	+	2306 ± 1261	10.4	4466 ± 2943	19.8
	Cl -			543 ± 351	2.4	508 ± 405	2.2
	NO 3	-	11064 ± 9470	49.9	9066 ± 7598	40.1
	SO 4	2-	2854 ± 1528	12.9	2826 ± 1751	12.5
	TC			4727 ± 2363	21.3	5205 ± 2733	23.0
	Total anal yz ed	22169	100.0 22591	100.0
	PM 2.5 (Atmo)*	33125		-
	Saint-Omer when compared to the 21.3% found in Dunkerque. It can be recalled that average
	contributions values for TC in the winter campaign were 33.6% (5625 ng/m 3 ) and 35.3%
	(6198 ng/m 3 ) in Boulogne-sur-Mer and Dunkerque respectively.

Tabl e 3

*Source: http://www.atmo-npdc.fr

Tabl e 4: Characteristics of the comparison sites chosen f rom dif f erent European l ocations

	Country	City	Site characteristics	Ref erences
	Finland	Helsinki	Urban	(Yli-Tuomi, et al., 2005)
	"	Raahe	Urban + Industrial (steelworks)	(Oravisj arvi, et al., 2003)
	"	Vallila	Urban

Table 5

 5 illustrates arithmetic average concentrations of ME, SI and TC found in our study as well as those found at the comparison sites. The values found in our study ranged between low, comparable and high when compared to those found all around Europe. In this section, we grouped the compared chemical species by aerosol type. Hence Na, Cl -and Mg will be grouped under "Marine maj or elements". Also, "SIA major ions" will group: NH 4

	+ ,

Tabl e 5: Arithmetic average concentrations ( in ng/ m 3 ) comparison between our region and Europe

			Other ME metals		Marine maj or elements		SIA maj or ions
	City	Al	Ca	Fe	K Mg Na	Cl -	NO 3	-	SO 4	2-	NH 4	+	TC	References
	Dunkerque winter	18	50	93	132	34	237	580	5707	2521	1914	6198	Our study
	Boul ogne-s/ M er winter	13	44	53	123	25	202	558	5872	2449	1706	5625	"
	Dunkerque spring	28	65	101	99	42	240	543	11064	2854	2306	4727	"
	St-Omer spring	22	51	52 107	25	186	508	9066	2826	4466	5205	"
	Vallila (PM 2.3 )	59	71	96	85	25	170	43	1600	2500	1100	-	(Pakkanen, et al., 2001)
	Rotterdam	36	83	206	130	51	251	400	3500	2600	1600	5700	(Mooibroek, et al., 2011)
	Zurich	48	54	124	223	17	96	72	3500	3500	2100	6500	(Hueglin, et al., 2005)
	Cantabria	-	600	30	100	100	800	800	800		1800	200		3100	(Arruti, et al., 2011)
	West Midlands	544	82	102	152	170 2474	470	1600	2200	-		4300	(Harrison & Yin, 2010)
	Onda	-	800	300	500	100	300	200	500		6200	1800	1200	(Querol, et al., 2001)
	Raahe	38	116	473	268	26	185	-	-		-		-		-	(Oravisj arvi, et al., 2003)
	Goteborg	-	41	77	120	-	-	-	-		-		-		-	(Moloi, et al., 2002)
	Ghent (cold)	-	-	-	-	-	-	-	5000	3000	2600	6400	(Viana, et al., 2007)
	Ghent (warm)	-	-	-	-	-	-	-	1500	2200	1000	3000	"
	Barcelona (cold period)	-	-	-	-	-	-	-	3800	3600	2300	7900	"
	Barcelona (warm period)	-	-	-	-	-	-	-	300		4200	1200	4500	"
	Athens	-	-	-	-	-	-	800	1330	5190	1300	6260	(Pateraki, et al., 2012)
	Helsinki	163	174	752	118	-	-	-	-		-		-		-	(Yli-Tuomi, et al., 2005)
	Basel	502	102	97	332	471 2437	-	-		-		-		-	(Ilacqua, et al., 2007)
	Budapest	-	-	214	-	-	-	110	2500	2300	1270	9180	(Szigeti, et al., 2012)
	Venice	634	-	98	229	-	1555	-	-		-		-		-	(Stortini, et al., 2009)
	Madrid	-	-	-	-	-	-	-	1490	2400	1480	9800	(Salvador, et al., 2012)
	Milan (cold)	-	-	-	-	-	-	600	20200	5800	5200	15000	(Lonati, et al., 2005)
	Milan (warm)	-	-	-	-	-	-	90	4600	4000	2200	6300	"
	Athens	789	471	13	368 1045 5413	-	-		-		-		-	(Ilacqua, et al., 2007)
	Salento	-	-	-	-	-	-	100	200		5590	2100	7100	(Perrone, et al., 2011)

.5.2.4.Other M E metal s

  The average concentrations for Al are found comparable to Rotterdam and Raahe values, and lower than in the rest of the comparison sites. Peaks of Al can be noticed in Athens, West Midlands, Basel and Venice in decreasing order. At these sites, Al levels can be associated to locally resuspended dust caused by high traffic density.However, Ca levels are comparable to those found in Vallila, Zurich and Goteborg.When in the same time, higher Ca values are found in the close Rotterdam study, West Midlands, Raahe, Helsinki and Basel. The Ca average concentration observed in Cantabria, Onda and Athens were the highest between the studies used for comparison including our own results. The reason behind these levels was linked to crustal sources in Onda, and to traffic resuspended road dust in Athens.

Furthermore, Fe levels are twice higher in Dunkerque when compared to the rest of our study sites during both winter and spring periods. But when compared to the other European sites, the high values encountered at Dunkerque are found comparable to those found in Vallila, Zurich, West Midlands, Goteborg, Basel and Venice. Much higher Fe concentrations were observed in northern Europe (Rotterdam, Raahe, and Helsinki) as well as in southern Europe (Onda and Budapest). Traffic sources are found to probably cause the Fe elevation in

Sea sal ts diurnal evol ution

  6.1.Winter campaignTable 6 compares the mean distributions of some selected species (Ca, Mg, K, Cl -, and

	SO 4	2-

III.6.1.1.

) between sea salt and non sea salt fractions for both Dunkerque qnd Boulogne-sur-Mer during the winter campaign.

Tabl e 6:

Percentage of average sea sal t and non sea sal t f ractions within sea sal t components in winter Dunkerque PM 2.5 ( winter) Boul ogne-sur-M er PM 2.5 ( winter)

	Sea salt	% Non sea salt	% Sea salt	% Non sea salt	%
	ss-Ca 2+	18.4 Nss-Ca		81.6 ss-Ca 2+	17.9 Nss-Ca		82.1
	ss-Mg 2+	79.5 Nss-Mg		20.5 ss-Mg 2+	94.1 Nss-Mg		5.9
	ss-K +		6.6 Nss-K		93.4 ss-K +		6.1 Nss-K		93.9
	ss-Cl -		73.4 Nss-Cl -		26.6 ss-Cl -		65.1 Nss-Cl -		34.9
	ss-SO 4	2-	2.4 Nss-SO 4	2-	97.6 ss-SO 4	2-	2.1 Nss-SO 4	2-

6.2.1.Sea sal ts diurnal evol ution

	III.6.2.Spring campaign
																																				NH 4 +			NO 3 -			SO 4 2-
																											SIA (Boulogne-sur-Mer)			NH4 NH 4 +			NO3 NO 3 -		SO4 SO 4 2-
	Concentration (in ng/m 3 )
	11/16/2010	11/17/2010	11/18/2010	11/19/2010	11/20/2010	11/21/2010	11/22/2010	11/23/2010	11/24/2010	11/25/2010	11/26/2010	11/27/2010	11/28/2010	11/29/2010	11/30/2010	12/1/2010	12/2/2010	12/3/2010	12/4/2010	12/5/2010	12/6/2010	12/7/2010	12/8/2010	12/9/2010	12/10/2010	12/11/2010	12/12/2010	12/13/2010	12/14/2010	12/15/2010	12/16/2010	12/17/2010	12/18/2010	12/19/2010	12/20/2010	12/21/2010	12/22/2010	12/23/2010	12/24/2010	12/25/2010	12/26/2010	12/27/2010	12/28/2010	12/29/2010	12/30/2010	12/31/2010

III.

Tabl e 7: Percentage of average sea sal t and non sea sal t f ractions within sea sal t components in spring Dunkerque PM 2.5 ( spring) Saint-Omer PM 2.5 ( spring)

	Sea salt	% Non sea salt	% Sea salt	% Non sea salt	%
	ss-Ca 2+	14.2 Nss-Ca		85.8 ss-Ca 2+	14.1 Nss-Ca		85.9
	ss-Mg 2+	67.3 Nss-Mg		32.7 ss-Mg 2+	86.3 Nss-Mg		13.7
	ss-K +		9.0 Nss-K		91.0 ss-K +		6.5 Nss-K		93.5
	ss-Cl -		75.2 Nss-Cl -		24.8 ss-Cl -		66.0 Nss-Cl -		34.0
	ss-SO 4	2-	2.1 Nss-SO 4	2-	97.9 ss-SO 4	2-	1.7 Nss-SO 4	2-

  .7.2.1.2.iv.Episodes from December 19 th till 21

st

An increase in PM concentrations can be observed also synchronized with a decrease of temperatures to negative values for a couple of days. The difference with previous observations resides within wind directions that are under the S-E sector, which is a rare event in our studied region. Same as in the case of N-E winds, the NPdC region is subj ected to continental air masses, which probably crossed over European neighboring countries as well as other French regions.

The episode ends in the evening of December 21 st , after an increase in wind speed (>10 m/s) coupled to an increase in temperature. III.

7.2.1.2.v

.Episodes from December 26 th till 30 th

Chemical composition of PM 2.5 above l imit val ue sampl es in Dunkerque and Saint-Omer during the spring campaign

	.7.2.2.PM 2.5 spring campaign episodes III.7.2.2.1.General observations Figure 34 resumes the chemical composition of the above limit selected samples for Dunkerque and Saint-Omer. The illustration shows that NO 3 -and NH 4 + are the main contributors in PM 2.5 load during peak episodes. On the other hand, TC concentrations followed a pattern that is not related to the fluctuations in total analyzed PM 2.5 : for example, when comparing 03/12 to 03/20/2010 samples taken from Dunkerque, we find that total 147 analyzed PM 2.Chapter III: Concentrations trends and statistical analysis of ME, SI and TC Figure 34: 0 10000 20000 30000 40000 50000 60000

5 increases while TC remains relatively constant. However, we can notice that the contribution of TC in the load of fine PM in spring is smaller when compared to the one found in winter, which is probably due to a decrease in heating related combustion emissions. Furthermore, SO

4 2-

did not fluctuate significantly between the selected samples. Finally, TE, ME and Cl -had relatively negligible contribution in the total analyzed PM 2.5 concentration during these episodes.

2.5 l evel , TC, SIA and Al concentrations ( ng/ m 3 ) in PM 2.5 during spring

	Concentration in ng/m 3	Concentration in ng/m 3				Concentration in ng/m 3						Temperature in	°C
	0 The increase of fine PM above limit value is found associated to a dry climate and low 2000 4000 6000 8000 10000 0 10000 20000 30000 40000 50000 III.7.2.2.2.i. Episodes succession between 12 and 28 March 2011 60000 0 10 20 30 40 50 60 70 80	12000	0	10000	20000	30000	40000	50000	60000	70000	80000	90000	0	4	8	12	16	20	24	28
	average daily temperatures which did not increase above 10°C. Succeeding high PM 2.5
	concentrations (till March 22 nd ) were observed under N-E winds. The decrease of PM levels
	detected in 18 March is synchronized to a sudden shift in wind direction towards the S-W
	sector. During the first episode of this period we can observe that air masses have crossed
	over the continent (East of France and Germany) resulting in an important increase in PM 2.5
	records. Meanwhile, and as illustrated for 03/15/2010 in Figure 36, we had a comeback of the
	air mass with N-E wind flux towards the NPdC region. However, on the 18 th of March, a
	break was noticed during which a temporary shift of S-W winds had occurred.
	The situation is found different between the 24 th and the 28 th of the same month, since
	wind activities were almost absent.
	SIA (Saint-Omer)	SIA (Dunkerque)	TC (Saint-Omer)	TC (Dunkerque)									Atmo-PM2.5							T°C from 12h to 24h	T°C from 00h to 12h

The episode terminates after a shift of wind directions to the S-W sector. The chemical composition of the samples during this episode shows that the elevations in PM loads are due primarily to SIA and TC. The remaining maj or elements, especially Al, Fe and Ca concentrations do not increase remarkably during this period. III.7.2.2.2.ii.Episode starting the 15 th to 25 th of April 2011

Table 1 :

 1 TE average concentrations (ng/m 3 ) and contributions (in %) in PM 2.5 collected in Dunkerque and Boulogne-sur-Mer during the winter campaign ..................................................................................................

Table 2 :

 2 TE average concentrations (ng/m3) and contributions (in %) in PM 2.5 collected in Dunkerque and Saint-Omer during the spring campaign .......................................................................................................................

Table 3 :

 3 Characteristics of the comparison sites chosen from different European locations ..............................

Table 4 :

 4 Arithmetic average concentrations (ng/m 3 ) comparison between our region and Europe ....................

Table 5 :

 5 Results of the Industrial Impact calculation in Dunkerque during the winter campaign ......................

Table 6 :

 6 Summary of highly correlated TE elements coefficients and slope values for Dunkerque and Boulogne-

sur-Mer winter campaign .................................................................................................................................... Table 7: Summary of highly correlated TE elements coefficients and slope values for Dunkerque and Saint-Omer spring campaign .................................................................................................................................................. Table 8: Main anthropogenic influences, corresponding wind sector and TE elements in Dunkerque during winter .................................................................................................................................................................. Table 9: Main anthropogenic influences, corresponding wind sector and TE elements in Boulogne-sur-Mer during winter ....................................................................................................................................................... Table 10: Main anthropogenic influences, corresponding wind sector and TE elements in Dunkerque during spring .................................................................................................................................................................. Table 11: Main anthropogenic influences, corresponding wind sector and TE elements in Saint-Omer during spring .................................................................................................................................................................. Table 12: Average elemental concentrations, minimum and maximum values (ng/m 3 ) in Dunkerque's samples under W-NNW industrial sector influence (n=20) .............................................................................................. Table 13: Average elemental concentrations, minimum and maximum values (ng/m 3 ) in Dunkerque's samples under NE-E industrial sector influence (n=12) ...................................................................................................

Table 14 :

 14 Selection of elemental ratios under industrial sources influence in Dunkerque compared to total average ratios found with all sectors included. ...................................................................................................

Table 15 :

 15 Average elemental concentrations, minimum and maximum values (ng/m 3 ) in Saint-Omer under ESE-SSE industrial sector influence (n=5) .................................................................................................................

Table 16 :

 16 Average elemental concentrations, minimum and maximum values (ng/m 3 ) in Saint-Omer under the N-NE industrial sector influence (n=8) ...............................................................................................................

Table 17 :

 17 Selection of elemental ratios under industrial influences in Saint-Omer compared to total average ratios found with all sectors included. ................................................................................................................. LIST OF EQUATIONS Equation 1 ...........................................................................................................................................................

Equation 2 ...........................................................................................................................................................

Tabl e 1: TE average concentrations ( ng/ m 3 ) and contributions ( in % ) in PM 2.5 col l ected in Dunkerque and Boul ogne-sur-M er during the winter campaign

		Dunkerque		Boul ogne-sur-M er
	PM 2.5	µ SD	%	µ SD	%
	Ag	0.05 ± 0.06	0.06	0.03 ± 0.03	0.06
	As	0.91 ± 0.68	1.06	0.46 ± 0.48	0.91
	Ba	0.90 ± 0.68	1.04	0.89 ± 0.49	1.77
	Bi	0.14 ± 0.17	0.16	0.13 ± 0.09	0.25
	Cd	0.30 ± 0.63	0.35	0.18 ± 0.14	0.36
	Co	0.09 ± 0.06	0.10	0.09 ± 0.06	0.18
	Cr	1.43 ± 3.11	1.65	0.46 ± 0.39	0.92
	Cu	2.74 ± 3.77	3.18	1.66 ± 1.22	3.30
	Mn	7.19 ± 12.66	8.34	4.84 ± 12.54	9.60
	Nb	0.02 ± 0.04	0.03	0.03 ± 0.15	0.06
	Ni	3.23 ± 4.93	3.74	1.62 ± 0.78	3.22
	P	10.54 ± 11.63 12.22	6.70 ± 5.33	13.31
	Pb	13.90 ± 15.59 16.13	9.61 ± 5.96	19.09
	Rb	0.45 ± 0.42	0.52	0.35 ± 0.24	0.70
	Sb	1.07 ± 1.74	1.24	1.17 ± 0.73	2.33
	Sc	0.03 ± 0.05	0.03	0.04 ± 0.07	0.08
	Sn	1.37 ± 1.36	1.59	1.03 ± 0.71	2.05
	Sr	0.36 ± 0.22	0.42	0.32 ± 0.21	0.64
	Te	0.03 ± 0.03	0.03	0.02 ± 0.02	0.03
	Ti	1.00 ± 1.63	1.16	1.28 ± 1.52	2.54
	Tl*	0.06 ± 0.06	0.07	0.04 ± 0.05	0.09
	V	1.71 ± 1.20	1.98	1.19 ± 0.83	2.37
	Zn	38.70 ± 46.48 44.89	18.20 ± 14.94	36.14
	Total	86.21	100	50.34	100

*Dunkerque winter campaign concentrations for Tl could not be obtained between the first sample until 12/13/2010 (44 samples), due to technical reasons.

Tab l e 2: TE average concentrations ( ng/ m3) and contributions ( in % ) in PM 2. 5 collected in Dunkerque and Saint-Omer during the spring campaign

		Dunkerque		Saint-Omer
	PM 2. 5	µ SD	%	µ SD	%
	Ag	0.04 ± 0.06	0.04	0.06 ± 0.08	0.08
	As	0.77 ± 1.02	0.77	1.36 ± 1.28	1.79
	Ba	0.77 ± 0.40	0.77	0.67 ± 0.38	0.88
	Bi	0.11 ± 0.10	0.11	0.19 ± 0.14	0.25
	Cd	0.35 ± 0.84	0.35	0.51 ± 0.53	0.67
	Co	0.19 ± 0.15	0.19	0.18 ± 0.14	0.24
	Cr	1.23 ± 2.27	1.23	0.74 ± 0.53	0.97
	Cu	2.26 ± 1.89	2.26	3.11 ± 4.79	4.10
	Mn	8.11 ± 14.56	8.12	4.19 ± 4.94	5.52
	Nb	0.01 ± 0.02	0.01	0.014 ± 0.03	0.02
	Ni	4.63 ± 3.68	4.63	2.91 ± 2.18	3.83
	P	16.43 ± 28.11	16.44	9.61 ± 13.58	12.65
	Pb	9.77 ± 8.19	9.78	16.71 ± 12.59	22.00
	Rb	0.42 ± 0.55	0.42	0.67 ± 0.67	0.88
	Sb	0.79 ± 0.64	0.79	2.61 ± 2.48	3.44
	Sc	0.01 ± 0.03	0.01	0.04 ± 0.04	0.05
	Sn	1.01 ± 0.77	1.01	3.10 ± 6.01	4.08
	Sr	0.37 ± 0.28	0.37	0.29 ± 0.20	0.38
	Te	0.03 ± 0.04	0.03	0.04 ± 0.07	0.05
	Ti	2.22 ± 2.25	2.22	3.73 ± 4.37	4.91
	Tl	0.09 ± 0.22	0.09	0.095 ± 0.13	0.13
	V	6.35 ± 5.06	6.35	3.98 ± 3.44	5.24
	Zn	43.99 ± 88.45	44.01	21.15 ± 16.40	27.84
	Total	99.95	100	75.95	100

Table 4

 4

	(continued)
	Country	City	Pb Rb Sb Sc Sn Sr Te	Ti	Tl	V	Zn	References
	Our Study	DK winter	13. 9 0. 45 1. 07 0. 03 1. 37 0. 36 0. 03 1. 00 0. 06 1. 71	38. 7	Our Study
	"	Blg winter	9. 61 0. 35 1. 17 0. 04 1. 03 0. 32 0. 02 1. 28 0. 04 1. 19 18. 2	"
	"	DK spring	9. 77 0. 42 0. 79 0. 01 1. 01 0. 37 0. 03 2. 22 0. 09 6. 35 43. 99	"
	"	St-O spring	16. 7 0. 67 2. 61 0. 04 3. 1 0. 29 0. 04 3. 73 0. 1	3. 98 21. 15	"
	Finland	Raahe	7.96 2.37 0.17	-	-	0.33	-	0.49 0.09 3.63	29.3	(Oravisj arvi, et al., 2003)
	"	Vallila	5.8	0.24 0.77	-	-	0.46	-	0.83 0.02	5	14	(Pakkanen, et al., 2001)
	"	Helsinki (22-24 October, 2003)	7	-	-	-	-	-	-	15	-	2	33	(Yli-Tuomi, et al., 2005)
	"	Helsinki (Fall, 1996 -Winter, 1997)	4.9	-	-	-	-	-	-	3.2	-	4.9	10.6	(Ilacqua, et al., 2007)
	Spain	Onda	330	2	1	-	1	2	-	28	-	5	186	(Querol, et al., 2001)
	"	Tarragona	17.5	-	2.6	-	0.9	1.2	-	6.1	-	5.3	19.3	(Moreno, et al., 2006)
	"	Barcelona	40.3	-	4.3	-	3.7	1.9	-	26.3	-	9.5	55.5	"
	"	Huelva	26.9	-	1	-	1.9	1	-	21.8	-	3.6	42.6	"
	"	Llodio	76.5	-	1.2	-	37	0.9	-	8.6	-	7	239	"
	"	Cantabria region	3.5	-	-	-	-	-	-	1.3	-	0.8	-	(Arruti, et al., 2011)
	Switzerland	Zurich -Kaserne	21	0.5 0.57	-	-	-	-	-	0.04	1.1	-	(Hueglin, et al., 2005)
	"	Basel (Spring, 1998 -Spring 1999)	19	0.58 0.41	-	-	-	-	-	0.03	1.2	-	"
	"	Basel (Winter, 1997 -Spring, 1998)	21	-	-	-	-	-	-	6.5	-	3.5	39	(Ilacqua, et al., 2007)
	Netherlands	Rotterdam	2.9	-	1.6	1.9	3	1.6	-	1.9	-	6.5	94.6	(Mooibroek, et al., 2011)
	Hungary	Budapest	11	0.44	2	-	1.5	-	0.01	-	0.02 0.51	28	(Szigeti, et al., 2012)
	Italy	Venice	18.08	-	-	-	-	-	-	186.1	-	14.06 84.51	(Stortini, et al., 2009)
	Greece	Athens	151	-	-	-	-	-	-	10.8	-	14	46	(Ilacqua, et al., 2007)
	United Kingdom West Midlands	18.1	-	-	-	-	-	-	8.3	-	-

Table 5 .

 5 It must be noted that I.I. values for elements which are present in very low atmospheric concentrations should be carefully considered. Indeed, negative values are observed within the table for Co, Nb, Sb, Sc and Ti. In the same manner, the I.I. values obtained for Ag, Te and Tl are probably

Table 5 : Results of the I ndustrial I mpact calculation in Dunkerque during the winter campaign

 5

	Elements Ag	As	Ba	Bi	Cd	Co	Cr	Cu	Mn	Nb	Ni	P
	I.I. (%)	44	50	0	8	40	-3	67	39	33	-27	50	36
	Elements Pb	Rb	Sb	Sc	Sn	Sr	Te	Ti	Tl	V	Zn
	I.I. (%)	31	22	-10	-37	25	10	39	-28	27	30	53

  Ag As Ba Bi Cd Co Cr Cu Mn Nb Ni P Pb Rb Sb Sc Sn Sr Ti Tl V Zn Howe ve r, SPLOM calculat ions we re divide d int o t wo st e ps. The f irst one was accomplishe d using all dat a in orde r t o ide nt if y possible e xt re me value s f ound in some sample s whe n compare d t o t he ot he rs at a single sampling sit e . Following it s ide nt if icat ion, and in orde r t o avoid any biase d corre lat ions, t he se corre sponding sample s we re discarde d f rom t he corre lat ion st udy. SPLOM and R 2 pre se nt at ion are summariz e d in APPENDI X-I and -I I re spe ct ive ly.

	10000
	1000
	100
	10
	1
	Dunkerque (spring)	Saint-Omer

Table 6 : Summary of highly correlated TE elements coefficients and slope values for Dunkerque and Boulogne-sur-Mer winter campaign

 6 The re sult s show t hat t he re are more corre lat e d spe cie s and highe r corre lat ions coe f f icie nt s in Dunke rque t han in Boulogne -sur-Me r. The re sult s be longing t o t he f irst sit e showe d a t ot al of 19 good corre lat ions wit h value s ranging f rom 0. 60 t o 0. 78. Howe ve r, in Boulogne -sur-Me r t he re sult s show some ave rage and more low corre lat ions. The value s f or R 2 at t his last sit e show t hat t he numbe r of combinat ions having ave rage and re asonably we ak corre lat ions was limit e d t o 6. W he n compare d t o e ach ot he r, t he slope re sult s show t hat close value s e xist be t we e n t he t wo sit e s only in t he case s of Ag/ As, Cd/ Pb, Pb/ Rb and As/ Pb.The corre lat ion re sult s obt aine d using t he spring dat ase t summariz e d in Table7shows t e n R 2 > 0. 60 coe f f icie nt value s for bot h sit e s, but not all corre sponding t o t he same e le me nt combinat ions. Share d high coe f f icie nt s are f ound f or Ni, V, Co, As, Bi, Cd and Rb corre lat ions (va lue s in bold f or bot h sit e s columns) . I n addit ion, t he se combinat ions have ide nt ical slope s, e xce pt f or As/ Bi, which e xhibit e d a highe r slope( 6. 73) in Saint -Ome r. This ge ne ral obse rvat ion point s at t he possibilit y of a similar source of e missions pre se nt at bot h sit e s, or t he possibilit y of having t he inf lue nce f rom a spe cif ic source at one sit e t ransport e d wit h winds t o t he ot he r.

	TE ratios	Dunkerque winter R 2 Slope	Boulogne-sur-Mer R 2 Slope
	V/ Ni	0.78	1.05	0.43	2.08
	Bi/ Pb	0.77	0.01	0.11	0.004
	Ag/ As	0.73	0.03	0.55	0.04
	Cd/ Pb	0.73	0.01	0.55	0.01
	Cu/ Pb	0.72	0.16	0.45	0.12
	As/ Cd	0.72	2.09	0.25	1.51
	As/ Bi	0.69	3.92	0.06	1.42
	Ag/ Rb	0.68	0.11	0.10	0.03
	Pb/ Rb	0.67	25.99	0.56	20.12
	Ag/ Ba	0.66	0.04	0.004	0.003
	As/ Rb	0.66	1.25	0.31	0.90
	Mn/ Ni	0.65	2.08	0.01	0.23
	Co/ Nb	0.65	0.02	0.004	0.50
	As/ Pb	0.65	0.06	0.36	0.04
	Co/ Ni	0.64	0.02	0.34	0.05
	Cd/ Rb	0.64	0.59	0.48	0.37
	Mn/ V	0.63	2.39	0.001	-0.05
	Ag/ Cd	0.61	0.12	0.07	0.04
	Cu/ Sb	0.60	1.60	0.09	0.56
	The list ing of t he e le me nt was chose n t o re spe ct t he de cre asing orde r of t he corre lat ion

coefficient values for Dunkerque's samples. Values in bold represent high correlations (R 2 ≥ 0.6 0), whe re as light gre y shade d value s corre spond t o t he highe st corre lat ions f ound in Boulogne -sur-Me r, which range d be t we e n ave rage and we ak corre lat ions.

Table 7 : Summary of highly correlated TE elements coefficients and slope values for Dunkerque and Saint-Omer spring campaign

 7 Ome r. Some of t he se corre lat ions have close or e ve n ide nt ical slope value s be t we e n t he t wo sit e s like Ag: Rb and Cr: Zn re spe ct ive ly. This last obse rvat ion occurring in our inland sit e along wit h t he re maining corre lat ion case s ide nt if ie d at t he be ginning re quire prof ound analy sis in orde r t o obt ain some e xplanat ions about t he possible origins of t he se e le me nt s. I t should be not e d t hat t he maj orit y of corre lat e d TE we re ide nt if ie d as e nriche d e le me nt s or as e le me nt s e xhibit ing high I ndust rial I mpact value s.

	TE ratios	Dunkerque spring R 2 Slope	R 2	Saint-Omer Slope
	V/ Ni	0.98	1. 51	0.97	1.58
	Co/ Ni	0.95	0.04	0.86	0.05
	Co/ V	0.93	0.03	0.82	0.03
	As/ Pb	0.71	0.07	0.34	0.05
	As/ Bi	0.65	4.78	0.61	6.37
	Cr/ Cu	0.65	0.27	0.40	0.18
	Pb/ Rb	0.63	20.18	0.27	11.90
	As/ Zn	0.61	0.01	0.36	0.03
	Ag/ Bi	0.61	0.33	0.25	0.27
	Cd/ Rb	0.60	0.68	0.65	0.63
	Ag/ Rb	0.58	0.11	0.70	0.10
	Ag/ Tl	0.53	0.34	0.62	0.43
	Cu/ Sn	0.52	1.69	0.63	0.81
	Cr/ Zn	0.49	0.02	0.64	0.02
	Rb/ Tl	0.48	2.24	0.68	3.63
	In addition to the good correlations, Dunkerque's re sult s include 3 ave rage and 2

re asonably we ak coe f f icie nt s. The se corre lat ions be long t o Ag: Rb, Ag: Tl and Cu: Sn on one hand, and Cr: Zn and Rb: Tl on t he ot he r re spe ct ive ly, all of which are f ound we ll corre lat e d in Saint -

  Howe ve r, t he re sult s in Dunke rque de monst rat e d good Ni/ V and Co/ Ni coe f f icie nt s during wint e r and spring, wit h an addit ional Co/ V corre lat ion. The se good corre lat ions we re also obse rve d in Saint -Ome r. The slope s we re ve ry similar t o t he one s in Dunke rque , le ading us t o conside r a possible re gional background source of e missions. This sugge st ion can be backe d up by t he f act t hat all sample s wit hout any spe cif ic se le ct ion we re conside re d in e st ablishing t he se corre lat ions. I n all sit e s, t he calculat e d slope s corre sponding t o conce nt rat ion rat ios show value s t hat are close r t o t he pe t roche mical rat ios ide nt if ie d in t he lit e rat ure e xce pt f or Dunke rque V/ Ni wint e r slope ( 1. 05) . I t is f ound close r t o t he re side nt ial combust ion signat ure source . All t he se obse rvat ions can be e xplaine d by t he pre se nce of a pe t roche mical indust rial inf lue nce at t he sit e s, in addit ion t o signif icant re side nt ial he at ing e missions inf lue nce t hat af f e ct t he rat ios, like in t he case of Dunke rque during wint e r. W e cannot e xclude possible local indust rial cont ribut ions in Ni during t he f irst campaign in Dunke rque . I V. 6. 2. Traf f ic non e xhaust TE Traf f ic e missions t race rs include an e xclusive list of t race e le me nt s t hat are wide ly use d in t he lit e rat ure . From t he se non e xhaust e missions, brake linings, whe e l rubbe r de bris and road pave me nt s abrasion e missions made t he subj e ct s of many int e re st ing st udie s. Ele me nt s like Cu and Sb are mainly use d t o t race brake linings e missions f rom urban t raf f ic. The origin of Sb is usually ide nt if ie d as almost e xclusive ly originat ing f rom t he e xce ssive use of ve hicle brake s in urban are as. The rat io of Cu/ Sb varie s in t he lit e rat ure f rom one st udy t o anot he r de pe nding on t he loads of Cu or Sb f rom ne arby source s ot he r t han t raf f ic. I n 2001, W e ckwe rt h adopt e d t he rat io value of 4.6±0.5 for Cu/ Sb as an indicat or of brake linings in Cologne , Ge rmany ( W e ckwe rt h, 2001) . I n 2004, anot he r st udy f ound a Cu/ Sb rat io value of 1.3 for brake dust and re port e d t his low value f or t he Cu f re e brake pads ( Adachi

7 ( Le e , e t al. , 1999)

. On t he ot he r hand, Co can also be conside re d wit h t he se e le me nt s in corre lat ions t hat point s at oil combust ion in pe t roche mical indust rie s ( Que rol, e t al. , 2002) . I n addit ion t o oil combust ion, ship e missions has be e n conside re d t o have spe cif ic apport ionme nt of Ni and V. Highe r rat ios t hat range be t we e n 3. 5 t o 4 f or V/ Ni is f ound whe n sample s we re dire ct ly t ake n f rom t he point e missions source of ships

[START_REF] Nigam | Ef f e ct of f ue l sulphur cont e nt and cont rol t e chnology on PM e mission f rom ship' s auxiliary e ngine[END_REF]

and f rom a re ce pt or cit e unde r ship e missions inf lue nce ( Maz z e i, e t al. , 2008) .

  Ome r ( spring) sample s only. This obse rvat ion dismisse s t he possibilit y of isolat ing a t ire t re ad e missions signat ure in our st udy . Finally, road pave me nt we ar can also e nrich PM wit h some e le me nt s t hat are most ly of a crust al charact e r like : Al, F e , Mg, Ca and Ti. I V. 6. 3. Ot he r TE Be t we e n t he re st of TE, As, Ag, Bi, Cd, Cu, Pb and Rb we re we ll corre lat e d t o e ach ot he r in Dunke rque during bot h sampling pe riods. Saint -Ome r corre lat ion coe f f icie nt s value s also showe d int e re st ing re sult s in t he f orm of good corre lat ions f or t he same pre viously discusse d list e xce pt f or Pb. I n addit ion, Boulogne -sur-Me r share d t he se charact e rist ic f e at ure s of t he t wo ot he r sit e s f or As, Ag, Cd, Cu, Pb and Rb. The combinat ions might be slight ly dif f e re nt f rom one sit e t o t he ot he r, but t he common f act or is t he good corre lat ion sit uat ion of t he se e le me nt s. Howe ve r, we re mind t hat corre lat ion coe f f icie nt s in Boulognesur-Me r we re not high. This obse rvat ion j oins t he one s f ound in t he pre vious se ct ions for t he combinat ions of e le me nt s t hat re quire f urt he r inve st igat ions in orde r t o e lucidat e all t he possible de t ails conce rning t he ir origins.

. Up t o our knowle dge , t he re are no ide nt if ie d rat ios t hat allow t racing t he se t ype s of e missions in t he lit e rat ure . Howe ve r, our corre lat ion re sult s show t hat Zn corre lat e d we ll wit h As and Cr in Dunke rque ( wint e r) and

Saint -

The high corre lat ions f ound in t he case of Dunke rque can be linke d t o st e e lmaking e missions, and spe cif ically f rom iron ore sint e ring act ivit ie s. I n f act , it is known t hat t his proce ss is be hind point and dif f use source s of PM e missions charact e riz e d by high conce nt rat ions of : Pb, Cd, As, Ag and Rb. This was conf irme d by a dire ct che mical analysis of a re f e re nce sample pre se nt e d by t he indust ry. The se obse rvat ions are also in accordance wit h t he che mical charact e rist ics e vide nce d in source sample s ( Hle is, e t al. , 2013) on one hand, and in PM colle ct e d dire ct ly unde r t he inf lue nce s of indust rial inst allat ions

( Lamaison, 2006)

on t he ot he r.

Table 8 : Main anthropogenic influences, corresponding wind sector and TE elements in Dunkerque during winter

 8

  prove s t hat t he large se ct or st re t ching f rom t he S t o t he N and passing by t he E (0° -180°) is re sponsible of almost all t he analy z e d TE maximum conce nt rat ions ( Ag, As, Ba, Bi, Cd, Co, Cr, Cu, Mn, Ni, Pb, Rb, Sb, Sn, V and Zn) . The se se ct ors e nclose all e missions originat ing f rom t he urban se ct or of Boulogne -sur-Me r, but might also include part icle s e mit t e d f rom ot he r dist ant source s. At t his le ve l, we should re call t hat E winds are ge ne rally blowing slowly and f ound associat e d wit h dry we at he r condit ions. The se lat t e r induce pollut ant s accumulat ion and could e xplain why maximum conce nt rat ions are re corde d f or t he se wind dire ct ions. He nce , common point s

re ve ale d in t he conce nt rat ion-rose analysis f or Co, Ni and V allow us t o propose a possible cont ribut ion f rom a f ossil f ue l combust ion t ype of e missions. I n paralle l, t he rose s of Ag, Bi, Cd, Mn, Pb, Rb, Tl and Zn we re marke d by a NE-E se ct or orie nt at ion, which re ve al a re gional cont ribut ion unde r t his wind se ct or. I t is highly import ant t o e xamine if t he indust rial sit e locat e d in Dunke rque can af f e ct Boulogne -sur-Mer's coastal relatively distant site, under ce rt ain wind dire ct ions. On t he opposit e , ave rage conce nt rat ions of TE are ge ne rally f ound low whe n t he sit e is unde r t he marine se ct or inf lue nce . A conce nt rat ion rose analysis summary can be f ound illust rat e d he re af t e r in Table

9

, and include s t he most import ant obse rvat ions le ading t o t he classif icat ion of TE me t als by groups of mixe d or spe cif ic source s.

Table 9 : Main anthropogenic influences, corresponding wind sector and TE elements in Boulogne-sur- Mer during winter

 9 V. 7. 2. Spring campaign conce nt rat ion rose s

	Sources of emissions	W ind sector Corresponding elements
	Urban	SE-SW	Bi-Ba-Sn-Cu
	Ant hropoge nic cont ine nt al NE-SSE	Ag-As-Cd-Cr-Cu-Pb-Rb-Tl-V-Ni-Co-Sr-Mn-Zn
	Fue l oil combust ion	N-E	V-Ni-Co
	I n addit ion, t he part icipat ion f rom t he ne arby se aport in Boulogne -sur-Me r was cle arly

pre se nt and was cont ribut ing part ially in at mosphe ric conce nt rat ion of t he f ollowing e le me nt s (Cd, Cr, Nb, Rb and Sb) which can be re lat e d t o act ivit ie s like ship cargo unloading and/ or maint e nance . I I V.7.2.1.Dunkerque spring concentration roses

Table 10 : Main anthropogenic influences, corresponding wind sector and TE elements in Dunkerque during spring Sources of emissions W ind sector Corresponding elements

 10 The analy sis of t he summary illust rat e d above re ve als t hat t he st e e lworks cont ribut e t o t he highe st conce nt rat e d mixt ure of t race e le me nt s, f ollowe d by t he SW se ct or t hat include s t he urban inf lue nce s. Fue l oil combust ion re groupe d t he be st corre lat e d e le me nt s ( Ni, V and Co) in addit ion t o a part ial load of Nb. The e le ct ric st e e l plant in Le f f rinckoucke cont ribut e d along wit h t he urban act ivit ie s in t he loads of Cr, Cu, Pb, Sn and Zn. Finally, Sr rose re pre se nt s an isolat e d case in which t he inf lue nce is ge ne rat ing almost e qually f rom all t he above me nt ione d se ct ors.

	I nt e grat e d st e e lworks	W -NW	Ag-As-Cd-Cr-Cu-Mn-Pb-Rb-Tl-V-Ni-Co-Nb-Ti-Bi
	Fossil f ue l combust ion	All	Co-Ni-V-Nb
	Urban + Ele ct ric st e e l plant NE-E	Cr-Zn-Cu

Table 11 : Main anthropogenic influences, corresponding wind sector and TE elements in Saint-Omer during spring

 11 he re sult s of t his analysis showe d t hat t he dif f e re nt source s cont ribut e in t he e missions of similar e le me nt s combinat ions. The e le me nt al rat io ( slope ) analysis allowe d us t o de t e rmine t hat de spit e t he mixt ure of source s, we we re able t o ide nt if y which se ct or cont ribut e d t he most in ce rt ain me t als conce nt rat ions. For e xample , Cu and Sb we re most ly ge ne rat ing f rom t he t raf f ic act ivit ie s in t he urban se ct or. Fue l oil combust ion is anot he r good

	Sources of emissions	W ind sector Corresponding elements
	Urban	All	Cd-Cr-Zn-Cu-Ni-Pb-Sb-Sr-V-Zn
	Urban + I ndust rie s	N-E	Ag-As-Cd -Cr-Mn-Pb-Rb-Tl-V-Ni-Co-Sr
	Urban + Glassmaking	SE	As-Bi-Ba-Sn-Cu-Cr-Nb-Sb-Sr-Ti
	Fue l oil combust ion	SW -E	Ni-V-Co
	Finally, t

e xample t hat conce rns Ni, V and Co t hat could be e mit t e d by t he e missions f rom t he marine t ransport at ion se ct or which is import ant in t he St raight of Dove r and t he Nort h Se a.

7.3.1.The case of Dunkerque Table

  Howe ve r, t he air mass back t raj e ct orie s did not conf irm t his hy pot he sis, which was f inally discarde d. 12 and Table13gat he r st at ist ical dat a ( ave rage conce nt rat ion, minimum and maximum value ) about t he se le ct e d PM 2. 5 sample s colle ct e d unde r W -NNW ( int e grat e d st e e lworks) and NE-E ( e le ct ric st e e l plant ) inf lue nce s in Dunke rque . W he n unde r t he st e e lworks inf lue nce , t he ave rage conce nt rat ions are cle arly highe r t han t he t ot al ave rage of t he whole campaign in Dunke rque ( Table13) , e spe cially f or Fe , Mn, Ni, As, Rb, Ag, Cd, Tl, Pb and Zn. Furt he rmore , we have obse rve d dif f e re nt conce nt rat ion f luct uat ions wit hin t he 20 se le ct e d sample s, wit hin which, t wo e xt re me case s are illust rat e d in Table12( st e e lworks sint e ring st ack and f ugit ive e missions) . I n orde r t o e xplain t he se dif f e re nce s, it is import ant t o re mind t hat t he int e grat e d steelworks complex is a "multi-sources" site, with a large stock of iron ore , coke making plant , iron ore sint e ring, blast f urnace , st e e l plant and slag t re at me nt . we not ice d t hat t he conce nt rat ions le ve ls are re lat ive ly high f or Fe , f ollowe d by lowe r le ve ls of Zn, Mn, Cr, Ni, Cu, As and Pb. W he n comparing t he se obse rvat ions t o t he PM source s charact e rist ics as we ll as t o t he lit e rat ure ( ( Mache me r, 2004) and ( Hle is, e t al. , 2013) ) , we f ind t hat t his sample t ype e xhibit s st e e l making inf lue nce .

) . I n t he case of Boulogne -sur-Me r sit e , t he st udy of t he sample s colle ct e d unde r t he NE t o E se ct or re ve ale d a possible indust rial e missions inf lue nce at t hat sit e .

I V.

The se inst allat ions e mit part icle s which physicoche mical charact e rist ics are dif f e re nt f rom one source t o anot he r ( Hle is, e t al. , 2013) . I n addit ion, it has be e n de monst rat e d in t he work of D. Hle is ( Hle is, 2010) t hat t he se inst allat ions have dif f e re nt impact s in t e rms of t he ir cont ribut ions in t he le ve l of suspe nde d part icle s on t he local le ve l in t he cit y of Dunke rque .

He nce , conside ring t he knowle dge on t he che mical composit ion of t he PM e mit t e d f rom e ach of t he se inst allat ions, we we re able t o sugge st t he inf lue nce of sint e ring st ack and dif f use e missions of t he st e e l plant . I n t he f irst case , we not ice d re lat ive ly highe r K conce nt rat ions whe n compare d t o Fe as we ll as t o Rb, Ag, Tl and Pb. The se obse rvat ions are in accordance wit h t he che mical charact e rist ics of PM source sample s provide d by t he indust ry and analy z e d in t he laborat ory. Furt he rmore , t he se charact e rist ics we re also me nt ione d in ot he r st udie s (

( Lamaison, 2006) , ( Oravisj arvi, e t al. , 2003) and ( Dall' Ost o, e t al. , 2008)

) . As f or t he dif f use e missions,

Table 12 : Average elemental concentrations, minimum and maximum values ( ng/ m 3 ) in Dunkerque's samples under W -NNW industrial sector influence ( n=20) Element Average Minimum Maximum Sinter stack* Fugitive emissions*

 12 These samples are selected between the first 20 samples under the sector, and constitute a signature of the two different industrial sources

	Fe	312.28 103.33 1542.44 438.14	1542.44
	K	127.31 48.69 357.84 357.84	122.27
	Zn	49.08 8.38	174.14	26.7	111.31
	Cr	1.3	0.39	3.79	2.13	2.6
	Mn	29.7	7.82	92.42	54.91	60.5
	Ni	6	2.03	16.85	16.41	16.85
	Cu	2.79	0.76	9.57	5.73	9.57
	As	1.66	0.31	9.45	2.07	9.45
	Rb	1.15	0.23	4.86	4.86	1.05
	Ag	0.14	0.02	0.42	0.42	0.13
	Cd	0.99	0.14	7.98	1.89	0.71
	Sn	0.85	0.2	2.54	1.09	2.54
	Sb	0.66	0.22	1.85	0.83	0.99
	Tl	0.32	0.05	2.04	2.04	0.2
	Pb	16.26 5.97	36.76	34.6	21.02

*

Table 13 : Average elemental concentrations, minimum and maximum values ( ng/ m 3 ) in Dunkerque's samples under NE-E industrial sector influence ( n=12) Element Average Minimum Maximum Total average ( Dk2)

 13 The obse rvat ions in t he case of Dunke rque show t hat many at mosphe ric e le me nt al conce nt rat ions are impact e d by t wo ide nt if ie d indust rial act ivit ie s. I n orde r t o dist inguish t he se e mission source s, we sugge st spe cif ic e le me nt al rat ios ( Table14) . I n orde r t o prove t he source spe cif icit y of t he se rat ios, we compare d e le me nt al rat ios value s calculat e d unde r t he inf lue nce of t he indust rial se ct or wit h t hose obt aine d t hrough wind inde pe nde nt calculat ions using all t he sample s.-ISW and Electric steel plant specific ratios: we prove he re in t hat Zn/ Fe and Zn/ Mn rat ios allow a cle ar se parat ion be t we e n integrated steelworks "ISW" ( Zn/ Fe = 0. 16) and e le ct ric st e e l plant "ESP" ( Zn/ Fe = 1. 15) e missions t racing re spe ct ive ly. The same conclusion can be also use d f or Cr/ Cd(1.3 for I SW and 16 fo r ESP) and Pb/ Cd (16 f or I SW and 77 f or ESP) rat ios re spe ct ive ly. -Steelmaking source specific ratios: t he rat ios sugge st e d he re t o se parat e be t we e n t he t race of sint e ring st ack and dif f use e missions is highly import ant , and allow t o ide nt if y if one t ype be t we e n bot h source s ( f ugit ive ve rsus point source ) has more impact on t he PM 2. 5 e le me nt al composit ion.The comparison be t we e n t he dif f e re nt inf lue nce s allows us t o se le ct t he fol lowing rat ios

	Total average
	( Dk1)

Table 14 : Selection of elemental ratios under industrial sources influence in Dunkerque compared to total average ratios found with all sectors included. Ratios I SW I SW ( sintering stack) I SW ( fugitive) Electric steel plant

 14

	Total Average
	Dunkerque

The case of Saint-Omer's site

  Table 15 and Table 16 re group st at ist ical dat a ( ave rage conce nt rat ion, minimum and maximum value s) on se le ct e d PM 2. 5 sample s, which we re colle ct e d unde r ESE-SSE ( glassmaking inf lue nce ) and N-NE ( dist ant Dunkerque's industrial activities inf lue nce ) wind se ct ors in Saint -Ome r. W he n unde r t he glassmaking inf lue nce , t he calculat e d ave rage conce nt rat ions f or Fe , K, Zn, Cr, Cu, As, Ag, Cd, Sn, Sb and Pb are highe r whe n compare d t o t he t ot al ave rage conce nt rat ions calculat e d f or t he whole campaign in Saint -Ome r.

Table 15 : Average elemental concentrations, minimum and maximum values ( ng/ m 3 ) in Saint-Omer under ESE-SSE industrial sector influence ( n=5)

 15

	Elements Average Minimum Maximum	Total average ( Saint-Omer)
	Fe	72.29 25.83 98.51	52.1
	K	142.43 120.3 175.92	107
	Zn	38.9	16.53	78.9	21.15
	Cr	2.15	1.03	2.97	0.74
	Mn	2.92	0.83	4.43	4.19
	Ni	1.29	0.82	1.81	2.91
	Cu	12.24	7.81	20.23	3.11
	As	4.79	2.26	7.2	1.36
	Rb	0.58	0.39	0.8	0.67
	Ag	0.13	0.04	0.2	0.06
	Cd	0.7	0.48	1.26	0.51
	Sn	27.11	23	31.46	3.1
	Sb	5.25	4.33	5.74	2.61
	Tl	0.07	0.05	0.14	0.1
	Pb	25.45 14.92	51	16.71
	The e missions of some of t he se e le me nt s are de clare d by t he indust ry ( E-PRTR, 2012) .
	I n addit ion, dif f e re nt works on t he e missions of glassmaking indust ry me nt ione d many
	e mission possibilit ie s f rom t his act ivit y. I n f act , a f irst t ype of e missions can be linke d t o t he
	ove n use d t o me lt raw ore . I t can be loade d by maj or e le me nt s t hat are include d wit hin t he
	composit ion of glass, but also ot he r adde d e le me nt s use d t o adj ust t he composit ion and/ or
	use d f or de sign purpose s: like re f ine me nt addit ive s and colorant s ( Rampaz z o, 2008) . On t he
	ot he r hand, some product s have t o unde rgo addit ional comple me nt ary t re at me nt s be f ore
	f inaliz at ion. Some of t he se st e ps re quire a se cond round of baking, which e ve nt ually le ads t o
	addit ional e missions.

Table 16 : Average elemental concentrations, minimum and maximum values ( ng/ m 3 ) in Saint-Omer under the N-NE industrial sector influence ( n=8)

 16 On t he ot he r hand, as prove d in t he conce nt rat ion rose s of Saint -Ome r whe n subj e ct e d t o t he inf lue nce of t his indust ry ( Figure9), t he spe cif ic t race r f or t he se glassmaking act ivit ie s are Sn, Cr, As and Cu. Unde r t his inf lue nce , t he principal spe cif ic rat ios ( Table17) be t we e n

	Elements	Average	Minimum Maximum
	Fe	132.01	34.38 251.08
	K	202.82	93.08 473.96
	Zn	36.69	7.46	57.44
	Cr	1.11	0.52	1.92
	Mn	10.79	3.47	21.68
	Ni	4.38	1.93	6.97
	Cu	2.82	1.02	4.76
	As	2.22	0.83	3.73
	Rb	2.26	1.39	4.76
	Ag	0.21	0.07	0.44
	Cd	1.56	0.74	3.32
	Sn	1.98	0.52	4.42
	Sb	2.57	1.14	4.51
	Tl	0.34	0.09	0.74
	Pb	26.37	13.37 41.97
	e le me nt s are : Sn/ Cr (12.6 ) , Sn/ Sb (5.1) , Cu/ Cd (17) and As/ Ag (37 ) . The obt aine d value s f or
	t he se rat ios are cle arly dif f e re nt t han t hose obt aine d by wind inde pe nde nt calculat ions using
	all t he sample s.

Table 17 : Selection of elemental ratios under industrial influences in Saint-Omer compared to total average ratios found with all sectors included.

 17 Finally, it is important to search for a possible impact of Dunkerque's e mission on t he e le me nt al at mosphe ric conce nt rat ions me asure d in Saint -Ome r whe n unde r t he N-NE wind dire ct ion inf lue nce . I t se e ms t o be a f air que ry since t his e f f e ct is obse rve d f or e le me nt s like Fe , K, Zn, Ag, As, Cd, Mn, Pb, Rb and Tl ( Figure9and Table16).To be gin wit h, we e xamine d if t he local wind dire ct ions at Saint -Ome r can be e xt e nde d t o a large r re gional le ve l. The re f ore , air mass backward t raj e ct orie s we re calculat e d f or some se le ct e d sample s pre se nt ing re lat ive ly high conce nt rat ion le ve ls of e le me nt s usually e mit t e d by Dunkerque's industrial activities (Figure10) . This lat t e r prove s in many case s t hat t he air mass passes over Dunkerque's agglomeration before settling in Saint-Ome r. I n ot he r case s, t he phe nome na se e m t o occur whe n t he air mass circulat e s t he re gion and re t urns inland t owards our sampling sit e .I n a se cond phase , and in orde r t o grasp addit ional inf ormat ion on t he origin of part icle s, we compare d e le me nt al rat ios value s whe n unde r t he N-NE inf lue nce at Saint -Ome r wit h t he spe cif ic rat ios obt aine d in t he vicinity of Dunkerque's industrial sources (Table14and Table17). The comparison showe d t hat t he calculat e d e le me nt al rat ios at Saint -Ome r unde r t he N-NE inf lue nce mat ch t he I SW one s: Pb/ Cd ~ 16, Cu/ Cr ~ 2. 5 and As/ Ag ~ 10.The se rat ios are linke d t o ave rage value s obt aine d unde r st e e lmaking se ct or. The se re sult s t e nd t o show t hat t he st e e lworks act ivit ie s in Dunke rque can impact t he at mosphe ric load of PM 2. 5 in Saint -Omer's atmosphere, spe cif ically whe n unde r N-NE f lux. On t he ot he r hand, t he calculat e d rat ios do not re ve al any e le ct ric st e e lmaking act ivit ie s inf lue nce . The se lat t e r cannot be e xclude d since Zn/ Fe and Zn/ Mn cohe re nt rat ios we re ide nt if ie d in rare sample s.

	Ratios	Glassmaking N-NE sector	Total average ( Saint-Omer)
	Zn/ Fe	0.54	0.28	0.4
	Zn/ Mn	13.3	3.4	5.04
	Rb/ Cr	0.27	2.04	0.91
	Pb/ Cr	11.8	23.77	22.58
	Cr/ Cd	3.06	0.71	1.45
	Cu/ Cr	5.69	2.54	4.2
	Cu/ Cd	17.4	1.8	6.1
	Pb/ Cd	36	16.9	32
	Sn/ Cr	12.6	1.78	4.19
	Sn/ Sb	5.16	0.77	1.19
	As/ Ag	37.07	10.54	22.6

  Ele me nt s like Cu, Ni, Sb, Sn, Ti and V e xhibit e d conce nt rat ions globally highe r t han 1 ng/ m 3 , whe re as t he re maining e le me nt s Ag, As, Ba, Bi, Cd, Co, Cr, Nb, Rb, Sc, Sr, Te and Tl are le ss conce nt rat e d. I n t he t hre e sampling sit e s, during t he conside re d pe riods, Pb, Ni, Mn, Cd and As conce nt rat ions we re lowe r t han t he le gislat e d limit value s f or ambie nt air.The le ve l of conce nt rat ion in t race e le me nt s was f ound comparable t o t hose obse rve d in ot he r urban and indust rial sit e s around Europe . The comparison of t he se conce nt rat ions be t we e n our sampling sit e s showe d t hat Dunke rque and Saint -Ome r e xhibit e d ce rt ain e nrichme nt s t hat might be at t ribut e d t o e missions f rom local indust rie s:-The impact of t he e missions f rom an int e grat e d st e e lworks and an e le ct ric st e e l plant in Dunke rque . The se t wo t ype s of e missions are charact e riz e d by a combinat ion of dif f e re nt e le me nt al rat ios like Zn/ Fe , Zn/ Mn, Cr/ Cd and Pb/ Cd, which allows t he dist inct ion be t we e n t he se source s. Classif icat ion base d on a-priori knowle dge re quire me nt s of me t hods use d t o de t e rmine source s apport ionme nt using re ce pt or mode ls ( e xt ract e d f rom( Viana, e t al. , 2008) which re produce d it f rom ( Schaue r, e t al. , 2006) ) ................................ ................................ .......................... ................ 226 Figure 2: A se ct ion of a mat rix X including spe cie s conce nt rat ions in ng/ m 3 ....................... .... 231 Figure 3: A section from a matrix Σ of uncertainties expressed in % ........................... ................ 231 Figure 4: I llust rat ion of a se ct ion of F init mat rix ................................ ........................... ................. 232 Figure 5: Equalit y const raint s f irst mat rix showing t he locat ion of some const raint s in t he prof ile s . 234 Figure 6: Equalit y const raint s se cond mat rix showing t he value s of some const raine d e le me nt s in some source prof ile s ................................ ................................ ................................ ....................... ..... 235 Figure 7: I ne qualit y const raint s mat rix showing t he orde r of abundance of some e le me nt s .............. 236 Figure 8: Comparison be t we e n t ot al NMF calculat e d and t ot al obse rve d PM 2. 5 spe cie s conce nt rat ions ( ng/ m 3 ) in Boulogne -sur-Me r during t he wint e r campaign ................................ ...................... . 239 Figure 9: Comparison be t we e n NMF calculat e d ( Y axis) and obse rve d ( X axis) spe cie s conce nt rat ions ( ng/ m 3 ) in Boulogne -sur-Me r during t he wint e r campaign ................................ ...................... . 240 Figure 10: Comparison be t we e n t ot al NMF calculat e d and t ot al obse rve d PM 2. 5 spe cie s conce nt rat ions ( ng/ m 3 ) in Dunke rque during bot h wint e r and spring campaigns ............................. .................. 241 Figure 11: Comparison be t we e n NMF calculat e d ( Y axis) and obse rve d ( X axis) spe cie s conce nt rat ions ( ng/ m 3 ) in Dunke rque during bot h wint e r and spring campaigns ..................... 242 Figure 12: Comparison be t we e n t ot al NMF calculat e d and t ot al obse rve d PM 2. 5 spe cie s conce nt rat ions ( ng/ m 3 ) in Saint -Ome r during t he spring campaign ................................ ......................... ............ 243 Figure 13: Comparison be t we e n NMF calculat e d ( Y axis) and obse rve d ( X axis) spe cie s conce nt rat ions ( ng/ m 3 ) in Saint -Ome r during t he spring campaign ............................ ................. 244 Figure 14: Eight common NMF prof ile s ide nt if ie d in t he t hre e sampling sit e s ...................... . . 247 Figure 15: I ndust rial NMF prof ile s ide nt if ie d in Dunke rque during bot h wint e r and spring campaigns ................................ ................................ ................................ ............................... .................... 248 Figure 16: I ndust rial NMF prof ile s ide nt if ie d in Saint -Ome r during t he spring campaign ................ 249 Figure 17: Ave rage cont ribut ions of source s in t he PM 2. 5 load in Boulogne -sur-Me r during wint e r . 251 Figure 18: Ave rage cont ribut ions of source s in t he PM 2. 5 load in Dunke rque during wint e r ............. 252 Figure 19: Ave rage cont ribut ions of source s in t he PM 2. 5 load in Dunke rque during spring .............. 253 Figure 20: Ave rage cont ribut ions of source s in t he PM 2. 5 load in Saint -Ome r during spring ............. 254 Figure 21: Diurnal t re nds of source cont ribut ions in Boulogne -sur-Me r during wint e r ( sampling e nde d on De ce mbe r 28 t h , 2010) ................................ ................................ ............................ .................. 256 Figure 22: Diurnal t re nds of source s cont ribut ions in Dunke rque during wint e r ( missing dat a be t we e n De c.7 t h and 9 t h are cause by sampling proble ms) ................................ .......................... ............... 257 Figure 23: Diurnal t re nds of source s cont ribut ions in Dunke rque during spring ( March 15 t h and 16 t h missing dat a are cause d by sampling proble ms; t he gap be t we e n April 24 t h and 26 t h re pre se nt s out lie rs discarde d due t o une xplainable high K conce nt rat ions) ................................ ....................... ...... 259 Figure 24: Diurnal t re nds of source s cont ribut ions in Saint -Ome r during t he spring campaign ( t he missing sample s of April 16 t h , a nd be t we e n t he 24 t h and t he 26 t h are out lie rs discarde d due t o une xpl ainable high

	Figure 1:
	ng/ m 3 ) , P ( 6. 7 -
	16.4 ng / m 3 ), a nd fi nally Mn ( 4. 1 -8.1 ng / m 3 ) .
	CHAPTER V: I de nt if icat ion of source s prof ile s and cont ribut ions using W NMF re ce pt or --The impact of glassmaking indust ry in Saint -Ome r t hat can be ide nt if ie d by mode ling
	fol lowing t he rat ios of Sn/ Cr, Sn/ Sb, Cu/ Cd and As/ Ag.
	Finally, we have f ound t hat unde r t he N-NE wind se ct or, e missions f rom t he int e grat e d
	st e e lworks sit e locat e d in Dunke rque have an impact , whe n t ransport e d along a 30 km
	dist ance , in Saint -Ome r.

  ne gat ive mat rix f act oriz at ion me t hod ( W NMF) unde r const raint s( ( De lmaire , e t al. , 2010a) and( De lmaire , e t al. , 2010b) ) . Mat rix f act oriz at ion me t hods are wide ly use d in t he f ie lds of signal t re at me nt and analy sis, but also f or t he manipulat ion of e nvironme nt al dat a. Be t we e n t he se me t hods, t he Principal Compone nt Analysis ( PCA) re sume s a group of variable s in a small numbe r of synt he t ic variable s calle d principal compone nt s. Howe ve r, t he PCA might e xhibit ne gat ive f act ors which int e rpre t at ion is ve ry dif f icult . Posit ive f act oriz at ion algorit hms such as Posit ive Mat rix Fact oriz at ion ( PMF) we re succe ssf ully use d in many e nvironme nt al st udie s. One inconve nie nce whe n using t he comme rcializ e d ve rsion of t he PMF mode l ( PMF v. 3.3) de ve lope d by t he Unit e d St at e s Envi ronme nt al Prot e ct ion Age ncy ( EPA: ht t p: / / www. e pa. gov/ he asd/ product s/ pmf / pmf . ht ml) is in t he collapse d t ime re quire d in orde r t o pe rf orm t he calculat ions. I n t his t y pe of st udie s, it is known t hat t he large r is t he mat rix use d as input dat a, t he more re liable are t he re sult s.

3. Comparison between receptor models

  ). Mode ling improvements kept on being searched and applied during this period until the early 80's, during which a workshop on re ce pt or mode ling came t o e nhance t he use of t he se algorit hms: t he 1982 M at he mat ical and Empirical Re ce pt or Mode ls W orkshop. I n t his workshop, t he most import ant st e p t ake n was in t he int e r-comparison of t he pe rf ormance of re ce pt or mode ling approache s using pre viously acquire d dat a t o ve rif y t he f unct ioning of t he mode ls. The comparison also was conduct e d be t we e n dif f e re nt mode ls using t he same dat a se t s. Howe ve r, Europe an scale , many re ce pt or and dispe rsion mode ls are st ill be ing use d in dif f e re nt count rie s f or source apport ionme nt st udie s. An asse ssme nt on t he Europe an use of mode ling t e chnique s was re port e d in a re vie w on t he source apport ionme nt st udie s conduct e d in EU count rie s ( Frangkou, e t al. , 2011) . The st udy showe d t hat dispe rsion mode ls are t he most commonly use d in Europe , f ollowe d by re ce pt or mode ls. On t he usage of re ce pt or mode ling, and be t we e n t he dif f e re nt st udie d t ype s, PMF t oppe d PCA, f ollowe d by CMB which is f ound rare ly use d whe n compare d t o t he f irst t wo. The re vie w conclude d wit h a list of improve me nt s t o be conside re d in f ut ure similar st udie s, such as t he inclusion of unce rt aint y dat a in t he input and t he out put , t he se arch f or spe cif ic source s t race rs, t he use of large dat a mat rice s or incre ase t he t ime cove rage of t he me asure me nt campaigns, and work on solving t he dif f icult y of se parat ing che mically close PM ( like mine ral dust and re -suspe nde d t raf f ic dust ) . All t he se re comme ndat ions will be t ake n in conside rat ion in our mode ling manipulat ions. Figure 1 illust rat e s t he comparison be t we e n dif f e re nt re ce pt or mode ling me t hods st art ing f rom t he le f t wit h t he one s re quiring lit t le knowle dge on t he source s of pollut ion (PCA, PMF, UNMIX…etc), t o t he one s on t he right side t hat use s a priori inf ormat ion on t he source s as e sse nt ial re quire me nt s f or succe ssful calculat ions. Howe ve r, as discusse d e arlie r on t he e volut ion of mode ling me t hods, we not ice t hat most re comme ndat ions pushe d in t he dire ct ion of t he use of a-priori know le dge on t he source s of e missions in orde r t o have a basis f or mode l e valuat ion. This proposal was also me nt ione d in a re ce nt import ant re vie w on t he me t hods and re sult s of dif f e re nt source apport ionme nt t e chnique s use d in Europe

	Positive Matrix Factorization (PMF) created in the 90's (( Paat e ro & Tappe r, 1994) and
	( Paat e ro, 1997) ) was use d as an alt e rnat ive t o CMB, giving re liable re sult s while using large r
	dat a se t s in t he calculat ions.
	On a

V.

  An import ant se ct ion of t he re vie w de als wit h a comparison of dif f e re nt source apport ionme nt me t hods. Many complicat ions e me rge d while comparing be t we e n dif f e re nt Europe an st udie s, which are not only linke d t o t he ge ographical dif f e re nce s but most import ant ly t o t he mode l it se lf : t he applicat ion of dif f e re nt re ce pt or mode ls using t he same dat a se t gave dif f e re nt re sult s. I n de t ails, scie nt ist s conf ront e d many dif f icult ie s whe n t ry ing t o obt ain coinciding re sult s while using dif f e re nt mode ls wit h t he same dat a se t . This obse rvat ion was also not ice d e ve n whe n using const raine d mode ls, and he nce t he re comme ndat ions came t o push t owards f inding robust source apport ionme nt me t hods, t o e valuat e and st andardiz e t he se me t hods.I n addit ion, t he re vie w came out wit h a crucial list of import ant re comme ndat ions t o f ill t he hole s wit hin t he curre nt ly use d mode ls. Be t we e n t he se re comme ndat ions we cit e :-The use of unce rt aint ie s -The se arch f or nat ural source s cont ribut ions -The ide nt if icat ion of biomass combust ion source s -The se parat ion be t we e n re gional "se condary sulf at e " and "secondary nit rat e "The mode l t hat we de ve lope d come s at t his st age in t he f orm of a we ight e d nonne gat ive mat rix f act oriz at ion mode l, which re spe ct s t he point s re comme nde d by t he scie nt if ic re vie w. I n addit ion, t he a-priori dat a is also conside re d in t he mode l calculat ion and can also be use d for mode ling re sult s validat ion. Thus we will be re spe ct ing t o a ce rt ain e xt e nt t he maximum numbe r of re comme ndat ions on t he use of mode ling in source apport ionme nt st udie s. A de t aile d e xplanat ion on t he principle s of mat rix f act oriz at ion me t hods can be f ound he re af t e r. . 4. 1. Basics of Mat rix Fact oriz at ion As pre viously me nt ione d, mat rix f act oriz at ion is use d in many re se arch domains, and whe n use d in e nvironme nt al scie nce s, it be come s known as re ce pt or mode l. This lat t e r cre at e s a link be t we e n a dat a mat rix and dif f e re nt source s.For a give n obse rvat ion mat rix X ( conce nt rat ions of che mical spe cie s in our case ) having a siz e e qual t o "n*m": whe re "n" is t he numbe r of sample s and "m" is t he numbe r of analy z e d che mical spe cie s, t he f act oriz at ion f ollows t his e quat ion:

	V.4. Matrix factoriz ation methods
	Equation 1
	prof ile s
	-The use of hybrid mode ls

V

  4. 2. Posit ive Mat rix Fact oriz at ion ( PMF)The f irst one be t we e n t he curre nt available f act oriz at ion me t hods is t he Posit ive Mat rix Fact oriz at ion ( PMF) , init ially cre at e d by Paat e ro ( Paat e ro & Tappe r, 1994) , and f ollows t he calculat ion principle de scribe d in Equat ion 1. The obj e ct ive of t he mode l is t o minimiz e t he quadrat ic e rror "e ij " re sult ing f rom dat a re const ruct ion. Equat ion 1 can also be writ t e n in t his for m:

	Equation 2

M atr ix of initial iz ation ( F init )

	V. 5. 1. 3.
						.7 …
	…	…	…	…	…	…
	Al Ca Fe	K M g Na …
	15 % 15 % 14% 15 % 14% 15 % …
	14% 14% 14% 15 % 14% 15 % …
	15 % 15 % 14% 15 % 100% 15 % …
	15 % 15 % 15 % 15 % 100% 15 % …
	14% 15 % 100% 14% 100% 14% …
	15 % 15 % 14% 14% 15 % 14% …
	14% 14% 14% 14% 15 % 15 % …
	…	…	…	…	…	…

Table 1

 1

	illustrates all

Tabl e 2: Char ac te r is tic s of the datas e ts us e d f or NM F c al c ul ations

	Site	Campaign	Sampl e s ( n)	Spe c ie s ( m)	Sour c e s ( p)
	Boulogne-sur-M er W inter	86	28	8
	Dunkerque	W inter + Spring	173 ( 80+ 93)	28	11
	Saint-Omer	Spring	98	28	10

Tr af f ic non-e xhaus t

	V.8.2. Additional calculated profiles in Dunkerque
															Combus tion 2
	1000
	100
	ng/ µg	0.1 1 10
	0.01
	0.00 1	Al	Ca	Fe	K	M g	Na Boulogne-sur-M er Ag As Ba Cd Co Cr	Cu	M n Dunkerque Ni Pb Rb	Sb	Sn Saint-Omer Sr Ti V	Zn	NH4+	Cl-	NO3-	SO42-	TC

  3. Additional calculated profiles in Saint-OmerSimilar to the calculated profiles of Dunkerque, Saint-Omer's data modeling gave two additional calculated profiles, which raised the total number of identified profiles for this site to 10 sources profiles in total.The first profile resembles to the emissions from the steel works industrial site of Dunkerque. A deeper look on this profile composition shows that it cannot be classified neither as fugitive or sintering stack profile. I n fact, it represents a mixture of both: high Ca,

	Fe, M g, M n and SO 4	2-

emphasize the I SW -fugitive face, whereas Pb, Cd, Rb, Cl -, K and NH 4 + represent the I SW -sintering stack side of the profile.

Sour c e s c ontr ibutions in Dunke r que ( winte r )

	Sea-salts	Aged sea-salts
	Crustal	Secondary Sulfates
	Secondary Nitrates	I SW -fugitive
	I SW -sintering stack	Combustion 1+2
	Traffic non-exhaust	Electric Steel Plant

  origin. I n fact, the total contributions of I SW and Glassmaking in PM 2.5 concentrations at Saint-Omer were estimated to 0.16% and 0.03% respectively.

	100%
	90%
	80%
	70%
	60%
	50%
	40%
	30%
	20%
	10%
	0%	Al	Ca	Fe	K	Mg Sea-salts Na Ag	As	Ba	Cd	Co	Cr Aged sea-salts Cu Mn Ni	Pb	Rb	Sb	Sn Crustal Sr Ti	V	Zn	NH4+	Cl-	NO3-	SO42-	TC
						Secondary Sulfates			Secondary Nitrates			I SW-fugitive
						I SW-sintering stack		Combustion 1				Traffic non-exhaust
						Combustion 2				Electric steel plant

their

  The coefficient represented in this matrix is equal to the square of Pearson correlation coefficient Les obj ectifs principaux de cette é tude é taient d'acquérir une meilleure connaissance des niveaux d'exposition aux particules fines PM 2.5 , de leur composition chimique et de leurs sources, dans trois villes situé es sur la faç ade littorale de la ré gion du Nord-Pas-de-Calais. Les particules fines ont é té collecté es dans le cadre de deux campagnes d'échantillonnage menées entre novembre 2010 et avril 2011 (campagne 'hiver' à Dunkerque et Boulogne-sur-M er ; campagne « printemps » à Dunkerque et Saint-Omer. La composition chimique des PM 2.5 a é té dé terminé e suite à la quantification d'éléments majeurs, d'éléments traces, d'ions hydrosolubles et du carbone total. Pour les deux pé riodes considé ré es, les concentrations et la composition chimique en PM 2.5 é voluent en suivant les mê mes tendances sur chacun des sites, L'influence de sources locales a été mise en évidence en comparant l'évolution temporelle et les roses de concentration de certains éléments majeurs et éléments traces, d'un site à l'autre. Cette exploitation a permis de proposer des rapports spé cifiques entre é lé ments, qui peuvent ê tre utilisé s comme traceurs de certaines sources anthropiques. Enfin, l'application d'un modè le source-ré cepteur, basé sur la factorisation matricielle non-né gative (NM F), a permis d'identifier les sources principales des PM 2.5 , d'évaluer leur contribution et leur part relative au niveau de chacun des sites é tudié s.

	Ré sumé :
	APPENDI X-I I APPENDI X-I I
	Dunkerque winter TE correlation coefficient matrix 2
	Dunkerque spring TE correlation coefficient matrix Saint-Omer spring TE correlation coefficient matrix
	R²	Ag	As	Ba	Bi	Cd	Co	Cr	Cu	M n	Nb	Ni	Pb	Rb	Sb	Sn	Sr	Ti	Tl	V	Zn
	Ag As Ba Bi Cd Co Cr Cu M n Nb Ni Pb Rb Sb Sn Sr Ti Tl V Zn Ag As Ba Bi Cd Co Cr Cu M n Nb Ni Pb Rb Sb Sn Sr Ti Tl V R² Ag As Ba Bi Cd Co Cr Cu M n Nb Ni Pb Rb Sb Sn Sr Ti Tl V R² Ag As Ba Bi Cd Co Cr Cu M n Nb Ni Pb Rb Sb Sn Sr Ti Tl V 2 R² Zn Zn Zn	1 0.7266 0.6616 0.4384 1 0.4603 0.6853 0.2685 1 0.6124 0.7167 0.4453 0.5926 1 0.4270 0.4268 0.0971 0.3379 0.2780 1 0.0817 0.2262 0.0550 0.3201 0.2517 0.0765 1 0.2707 0.3588 0.3044 0.3996 0.5277 0.1916 0.2358 1 0.3420 0.2308 0.0164 0.1282 0.0655 0.5800 0.0161 0.0031 1 0.4625 0.3665 0.2444 0.3235 0.4039 0.6481 0.0542 0.3297 0.2455 1 0.2526 0.2141 0.0260 0.2435 0.0529 0.6356 0.0865 0.0012 0.6504 0.2695 1 0.4634 0.6456 0.3476 0.7654 0.7261 0.3003 0.4744 0.7247 0.0463 0.3139 0.0952 1 0.6759 0.6553 0.5089 0.5362 0.6351 0.4126 0.1735 0.5439 0.1895 0.5270 0.1003 0.6667 1 0.3242 0.2698 0.3865 0.2924 0.4789 0.1081 0.1455 0.6031 0.0056 0.2623 0.0015 0.4831 0.4232 1 0.1086 0.2127 0.2047 0.1893 0.2370 0.1313 0.0567 0.5167 0.0300 0.2093 0.0002 0.2791 0.2036 0.3860 1 0.0075 0.0916 0.0275 0.1496 0.1057 0.0468 0.1404 0.1580 0.0002 0.0040 0.0021 0.2150 0.0890 0.0347 0.1645 1 0.3061 0.3299 0.3788 0.2062 0.2015 0.1191 0.0017 0.2311 0.0069 0.2904 0.0359 0.1782 0.2356 0.2451 0.3916 0.0109 1 0.5624 0.4001 0.1758 0.1796 0.2079 0.3800 0.0125 0.0113 0.6528 0.1989 0.3289 0.1416 0.4019 0.0224 0.0151 0.0014 0.0668 1 0.2219 0.0861 0.0259 0.0406 0.0112 0.4748 0.0004 0.0363 0.6267 0.1862 0.7848 0.0019 0.0281 0.0116 0.0377 0.0078 0.0086 0.3808 1 0.4091 0.3428 0.2768 0.5230 0.4485 0.2844 0.2597 0.3952 0.0917 0.3928 0.2124 0.5513 0.4627 0.5488 0.0750 0.0414 0.0958 0.1150 0.0730 1 Ag As Ba Bi Cd Co Cr Cu M n Nb Ni Pb Rb Sb Sn Sr Ti Tl V 1 0.5488 1 0.0041 0.0410 1 0.0578 0.0647 0.1598 1 0.0726 0.2486 0.0651 0.1110 1 0.0105 0.0011 0.0216 0.0030 0.0160 1 0.0391 0.0864 0.0534 0.2857 0.2621 0.0311 1 0.1297 0.2422 0.1646 0.0627 0.2981 0.0032 0.0733 1 0.0256 0.0519 0.0201 0.1426 0.1468 0.0017 0.1000 0.0259 1 0.0376 0.0524 0.0395 0.0088 0.0092 0.0041 0.1689 0.0044 0.0068 1 0.0035 0.0477 0.1159 0.0862 0.0545 0.3438 0.0307 0.0165 0.0132 0.0085 1 0.1758 0.3581 0.1520 0.1074 0.5493 0.0120 0.2669 0.4518 0.0444 0.0151 0.0373 1 0.1034 0.3073 0.0239 0.0876 0.4839 0.0805 0.3542 0.3574 0.0190 0.0546 0.0781 0.5645 1 0.0162 0.1167 0.0094 0.0029 0.0904 0.1707 0.1499 0.0882 0.0058 0.2238 0.0446 0.2077 0.2695 1 0.0434 0.1746 0.0851 0.1312 0.3646 0.2053 0.1586 0.2168 0.0519 0.0179 0.0948 0.2827 0.4682 0.1289 1 0.0179 0.0000 0.0515 0.0110 0.0320 0.0122 0.0552 0.0265 0.0007 0.0250 0.0467 0.0452 0.1040 0.0051 0.0599 1 0.0035 0.0047 0.0347 0.0856 0.0019 0.0076 0.1529 0.0054 0.0345 0.0432 0.0537 0.0027 0.0008 0.0015 0.0032 0.0010 1 0.1218 0.2085 0.0429 0.2049 0.2701 0.0004 0.2142 0.1604 0.1378 0.0040 0.0389 0.2515 0.3265 0.0126 0.2479 0.0080 0.0078 1 0.0566 0.1838 0.0212 0.0029 0.0052 0.0824 0.0219 0.0290 0.0012 0.0013 0.4283 0.0420 0.0499 0.0628 0.0322 0.1469 0.0006 0.0067 1 Ag As Ba Bi Cd Co Cr Cu M n Nb Ni Pb Rb Sb Sn Sr Ti Tl V 1 0.5231 1 0.5143 0.4304 1 0.6065 0.6536 0.5707 1 0.3719 0.5722 0.2525 0.4103 1 0.0916 0.2212 0.1078 0.1682 0.0888 1 0.5678 0.4346 0.5282 0.5551 0.2075 0.0386 1 0.4792 0.4055 0.5420 0.5393 0.2321 0.0115 0.6532 1 0.4314 0.3447 0.2219 0.3847 0.3447 0.1005 0.4150 0.2790 1 0.0154 0.0119 0.0562 0.1146 0.0417 0.0129 0.0002 0.0165 0.0007 1 0.0622 0.1772 0.0716 0.1065 0.0689 0.9504 0.0267 0.0040 0.0874 0.0413 1 0.4861 0.7131 0.4992 0.5216 0.4863 0.1190 0.4275 0.4735 0.2507 0.0014 0.0858 1 0.5752 0.5685 0.4032 0.4318 0.6029 0.1027 0.2555 0.2331 0.2467 0.0481 0.0702 0.6256 1 0.1929 0.3610 0.3420 0.3367 0.3014 0.0181 0.2517 0.5074 0.0915 0.0446 0.0087 0.4253 0.1885 1 0.2051 0.3065 0.3828 0.3591 0.1786 0.0494 0.2453 0.5169 0.1114 0.0384 0.0238 0.4214 0.2313 0.4678 1 0.0000 0.0289 0.0043 0.0014 0.0045 0.0158 0.0004 0.0092 0.0016 0.1728 0.0245 0.0202 0.0019 0.0264 0.0191 1 0.1156 0.1981 0.3105 0.2300 0.0591 0.1033 0.2389 0.1904 0.1730 0.0080 0.0791 0.1992 0.0980 0.1615 0.2691 0.0042 1 0.5328 0.2855 0.2120 0.3897 0.3504 0.1690 0.1839 0.1110 0.1948 0.0112 0.1247 0.2391 0.4794 0.0391 0.0899 0.0063 0.0420 1 0.0454 0.1474 0.0522 0.0814 0.0540 0.9298 0.0121 0.0001 0.0614 0.0396 0.9793 0.0545 0.0558 0.0035 0.0101 0.0164 0.0553 0.1128 1 Ag As Ba Bi Cd Co Cr Cu M n Nb Ni Pb Rb Sb Sn Sr Ti Tl V 1 0.2395 1 0.0746 0.2709 1 0.2497 0.6112 0.2193 1 0.3997 0.4792 0.1532 0.4489 1 0.1257 0.3546 0.1188 0.2998 0.2184 1 0.2655 0.3705 0.2964 0.4369 0.2649 0.1263 1 0.1646 0.2937 0.5651 0.2951 0.2002 0.0993 0.3952 1 0.2858 0.3565 0.0924 0.5253 0.4282 0.2317 0.3305 0.1679 1 0.0348 0.0082 0.0061 0.0017 0.0012 0.0103 0.0151 0.0017 0.0008 1 0.0591 0.2647 0.0703 0.2142 0.1450 0.8556 0.0808 0.0579 0.2062 0.0299 1 0.1506 0.3358 0.1132 0.3379 0.3832 0.1252 0.1871 0.1373 0.1619 0.0037 0.0713 1 0.7038 0.3824 0.0979 0.4206 0.6525 0.2786 0.3133 0.1804 0.4512 0.0037 0.1721 0.2680 1 0.0958 0.4157 0.2026 0.2782 0.2413 0.1836 0.1742 0.2010 0.0916 0.0004 0.0696 0.2217 0.1434 1 0.1202 0.4217 0.4627 0.2423 0.1518 0.0793 0.3863 0.6340 0.0863 0.0322 0.0331 0.1575 0.1051 0.2981 1 0.0001 0.0098 0.0131 0.0033 0.0000 0.0002 0.0012 0.0155 0.0026 0.0004 0.0023 0.0165 0.0047 0.0000 0.0131 1 0.0559 0.1356 0.2834 0.1826 0.1215 0.1856 0.1277 0.2265 0.0648 0.0061 0.0530 0.0299 0.1127 0.2450 0.2460 0.0159 1 0.6224 0.2802 0.0873 0.3094 0.4180 0.3140 0.2281 0.1269 0.3157 0.0023 0.1867 0.1852 0.6759 0.0362 0.0626 0.0005 0.0613 1 0.0370 0.2224 0.0374 0.1567 0.0903 0.8215 0.0434 0.0311 0.1497 0.0358 0.9723 0.0494 0.1237 0.0597 0.0197 0.0044 0.0386 0.1435 1 0.1275 0.1620 0.1349 0.3715 0.3213 0.0091 0.3238 0.2481 0.1473 0.0026 0.0387 0.4401 0.3215 0.0102 0.1840 0.0114 0.0053 0.5127 0.0104 0.3636 0.6069 0.3631 0.4728 0.2673 0.1567 0.4853 0.3931 0.3340 0.0019 0.1181 0.5256 0.2899 0.2369 0.2596 0.0022 0.2035 0.1542 0.0985 0.1785 0.3595 0.2065 0.4619 0.3713 0.1365 0.6385 0.2794 0.3486 0.0009 0.1119 0.4525 0.2844 0.1395 0.2357 0.0130 0.0249 0.1970 0.0707	1 Zn Zn Zn	1 1 1
												287 288

Al (Dunkerque)Al(Boulogne-sur-Mer)

Al (Dunkerque)Al(Saint-Omer)

Figure 36: Air mass backward traj ectories drew using HYSPLIT ( Draxl er & Rol ph, 2012) f or three sel ected sampl es ( M arch 15 th , April 16 th and 24 th 2011) in Dunkerque ( above three) and Saint-Omer ( bel ow) ; Height: 100m AGL, Duration: 48 hrs, Start: 1100 UTC, Vertical motion: isobaric, New traj ectory every: 2 hrs, M ax traj ectories: 6

(Harrison & Yin, 2010)

Acknowledgments

I would like to address my gratitude to Pr. Dominique COURCOT and Dr. Frédéric LEDOUX. Thanks to both of you I was able to accomplish this work in the best possible conditions.

Pr. COURCOT you were an excellent director during this project, starting with your scientific critique all the way to the human character that you showed towards me during this thesis. I will not forget how much you stood beside me, simplifying the difficulties that we encountered along the way, and helping me to overcome them.

3/10/2011 3/11/2011 3/12/2011 3/13/2011 3/14/2011 3/15/2011 3/16/2011 3/17/2011 3/18/2011 3/19/2011 3/20/2011 3/21/2011 3/22/2011 3/23/2011 3/24/2011 3/25/2011 3/26/2011 3/27/2011 3/28/2011 3/29/2011 3/30/2011 3/31/2011 4/1/2011 4/2/2011 4/3/2011 4/4/2011 4/5/2011 4/6/2011 4/7/2011 4/8/2011 4/9/2011 4/10/2011 4/11/2011 4/12/2011 4/13/2011 4/14/2011 4/15/2011 4/16/2011 4/17/2011 4/18/2011 4/19/2011 4/20/2011 4/21/2011 4/22/2011 4/23/2011 4/24/2011 4/25/2011 4/26/2011 4/27/2011 4/28/2011 4/29/2011 4/30/2011 Average concentration in µg/m 3 12/03/2011-DK 12/03/2011-StO 14/03/2011-DK 14/03/2011-StO 15/03/2011-DK 15/03/2011-StO 16/03/2011-DK 16/03/2011-StO 17/03/2011-DK 17/03/2011-StO 19/03/2011-DK 19/03/2011-StO 20/03/2011-DK 20/03/2011-StO 21/03/2011-DK 21/03/2011-StO 22/03/2011-DK 22/03/2011-StO 24/03/2011-DK 24/03/2011-StO 25/03/2011-DK 25/03/2011-StO 26/03/2011-DK 26/03/2011-StO 27/03/2011-DK 27/03/2011-StO 28/03/2011-DK 28/03/2011-StO 07/04/2011-DK 07/04/2011-StO 15/04/2011-DK 15/04/2011-StO 16/04/2011-DK 16/04/2011-StO 17/04/2011-DK 17/04/2011-StO 18/04/2011-DK 18/04/2011-StO 19/04/2011-DK 19/04/2011-StO 20/04/2011-DK 20/04/2011-StO 21/04/2011-DK 21/04/2011-StO 22/04/2011-DK 22/04/2011-StO 23/04/2011-DK 23/04/2011-StO 24/04/2011-DK 24/04/2011-StO 25/04/2011-DK 25/04/2011-StO 28/04/2011-DK 28/04/2011-StO 29/04/2011-DK 29/04/2011-StO 30/04/2011-DK 30/04/2011-StO 3/12/2011 3/13/2011 3/14/2011 3/15/2011 3/16/2011 3/17/2011 3/18/2011 3/19/2011 3/20/2011 3/21/2011 3/22/2011 3/23/2011 3/24/2011 3/25/2011 3/26/2011 3/27/2011 3/28/2011 3/29/2011 3/30/2011 3/31/2011 4/1/2011 4/2/2011 4/3/2011 4/4/2011 4/5/2011 4/6/2011 4/7/2011 4/8/2011 4/9/2011 4/10/2011 4/11/2011 4/12/2011 4/13/2011 4/14/2011 4/15/2011 4/16/2011 4/17/2011 4/18/2011 4/19/2011 4/20/2011 4/21/2011 4/22/2011 4/23/2011 4/24/2011 4/25/2011 4/26/2011 4/27/2011 4/28/2011 4/29/2011 4/30/2011 Concentration in ng/m 3

The characteristics of the comparison sites range from typical urban sites to urban influenced by industries. As for the latter, it fluctuates between heavy and light industries that can be found combined in some sites. (Pakkanen, et al., 2001) "

Helsinki (22-24 October, 2003) Urban (Yli-Tuomi, et al., 2005) "

Helsinki (Fall, 1996-Winter, 1997) Urban (Ilacqua, et al., 2007) Spain Onda Urban + Ceramic industry [START_REF] Querol | Monitoring of PM10 and PM2.5 around primary particulate anthropogenic emission sources[END_REF]) " Tarragona Urban + Petrochemical (Moreno, et al., 2006) " Barcelona Urban " " Huelva Urban + Heavy industry " " Llodio Urban + Heavy industry " " Cantabria region Urban + Industry (Arruti, et al., 2011) Switzerland Zurich -Kaserne Urban [START_REF] Hueglin | Chemical characterisation of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland[END_REF]) "

Basel (Spring, 1998-Spring 1999) Urban " "

Basel (Winter, 1997-Spring, 1998) Urban (Ilacqua, et al., 2007) Netherlands Rotterdam Urban [START_REF] Mooibroek | Source apportionment and spatial variability of PM2.5 using measurements at five sites in the Netherlands[END_REF]) Hungary Budapest Urban [START_REF] Szigeti | Chemical characterisation of PM2.5 fractions of urban aerosol collected in Budapest and Istanbul[END_REF]) Italy Venice Urban + Light industry [START_REF] Stortini | An evaluation of the PM2.5 trace elemental composition in the Venice Lagoon area and an analysis of the possible sources[END_REF]) Greece Athens Urban (Ilacqua, et al., 2007) United Kingdom West Midlands Urban background (Harrison & Yin, 2010) IV.

3.2.Comparison of TE average concentrations

The approach that we will use in this section will be purely descriptive in order to rank TE elements concentrations of the NPdC region between other European sites that are influenced by similar types of emissions as our sites. These emissions can differ especially in the scales of the contribution from anthropogenic sectors from one site to the other.

The interpretation of the TE average concentrations summarized in Table 4 will take in consideration three groups depending on their characteristics in the periodic table of elements:

• Alkali and Alkaline-earth metals: Rb, Sr and Ba.

• d-block elements: Ag, Cd, Co, Cr, Cu, Mn, Nb, Ni, Sc, Ti, V and Zn.

• Other metals, metalloid and non-metals: Sn, Sb, Tl, Pb, Bi, P, As and Te.

Chapter IV: Concentrations trends, statistical analysis and source identification of Trace Elements (TE) 182 , M g, K and Ca (Vallius, et al., 2005(Vallius, et al., ) (W u, et al., 2007(Vallius, et al., ) (M ooibroek, et al., 2011(Vallius, et al., ) (M oreno, et al., 2013) ) Aged Sea-salt Anthropogenic NO 3 -, Na, SO 4

, Cl -, M g, K, TC, Ca and Sr (W u, et al., 2007) [START_REF] Amato | Quantifying road dust resuspension in urban environment by M ultilinear Engine: comparison with PM F2[END_REF] Crustal Natural

Al, Ca, Fe, K, TC, M g, SO

, Ti, NO 3 -, Cl -, Na and Ba (Vallius, et al., 2005 Based on the composition of some samples selected under the corresponding industrial sector

Between the profiles of Table 1 we define the following profiles:

-Traffic non-exhaust: including road resuspended crustal dust mixed with emissions from brake linings and tyre wear abrasion.

previously discussed in Chapter I V (Table 17), as well as with the values found in similar studies ((M antovan, et al., 2003) and (Rampazzo, et al., 2008)).

V. 9. Sour c e s c ontr ibutions ( matr ix G)

The NM F calculations can also determine the contributions of each identified source profile in the samples. The result of these contributions is given in matrix G, which will be discussed hereafter. For this next step, we combined the contributions of the two combustion profiles in one representing the combustion contribution in general.

I n fact, maj or elements in both profiles are similar, which makes the distinction between the two sources very difficult as discussed by Frangkou [START_REF] Frangkou | Review of the current evaluation methodologies used for source apportionment applications in EU countries[END_REF]. W hile running the NM F calculations, we noticed in many occasions a decrease in Combustion 2 contributions matched by an increase in Combustion 1. However, the sum of both contributions did not significantly vary. Finally, having these results in hand, the choice fell on the consideration of a single global contribution for combustion sources.

I n the case of Dunkerque, sources contributions for each PM 2.5 sample have been determined using one NM F calculation using the winter and spring samples species concentrations data combined. Thus, we were able to extract the contributions data corresponding to each season, and analyze the results separately.

V.9.1. Average contributions load during the winter campaign, and consequently discuss the evolution of these contributions between the sites.

The average contribution calculated for Boulogne-sur-M er (see Figure 17) shows that secondary nitrates and combustion profiles participated in 39.8 % and 35.9% of the PM 2.5 load in that site during winter. The secondary sulfates profile came third (15.9%). I t must be noted that these three sources contributed in about 91.6% of the total PM 2.5 atmospheric load. On the other hand, the marine source contributed in 8% divided between aged and freshly emitted