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Atmospheric Research

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Variability of the elemental composition of airborne mineral dust along thecoast of Central Tunisia

Mohamed Saada, Mohamed Masmoudia, Servanne Chevaillierb, Benoit Laurentb, Sandra Lafonb,Stephane C. Alfarob,⁎

a Physics Department, Faculty of Sciences of Sfax, Sfax University, Soukra Road, 3038 Sfax, Tunisiab LISA-UMR CNRS 7583, Universités Paris-Est Créteil et Paris-Diderot, IPSL, Créteil, France

A B S T R A C T

The first aim of this study is to document the variability of the elemental composition of the mineral dustcollected along the Mediterranean coast in Central Tunisia. The second aim is to correlate this composition withthe different source-areas from which the mineral dust (MD) originates. In all, 190 daily aerosol samples areanalyzed by X-Ray Fluorescence (XRF), and the elemental composition of the mineral dust is obtained by sub-tracting the non-crustal share from the bulk composition. On the basis of the MD concentration, 149 samples areclassified as corresponding to “moderate” (3 < MD < 25 μgm−3) dust-events, and 17 to “intense”(MD > 25 μgm−3) ones. By using a combination of MODIS satellite observations, HYSPLIT back-trajectoryanalysis, and dust emission simulations with the CHIMERE model, three geographical areas are identified asbeing at the origin of the intense events. From east to west, Area #1 corresponds to the south-east of Tunisia/west of Lybia region, Area #2 corresponds to the Algerian/Tunisian Border and contains the Chott El Jeriddepression. Finally, Area #3 is in Central Algeria. Elemental ratios are commonly used to discuss the nature andorigin of airborne particles. In good agreement with previous observations, the Ca/Al ratio is significantly largerin Area #1 than in Area #3 (2.4 ± 0.4 as compared to 1.3 ± 0.3). With values of 4.1 ± 0.8 and 2.8 ± 0.3 forAreas #1 and 3, respectively, a similar contrast is observed for the (Mg+Ca)/Fe ratio. In Area #2 that is at theorigin of 5 of the 9 intense events and thus appears as being the most influential source-region for CentralTunisia, the Ca/Al ratio (2.8 ± 0.9) compares to that of Area #1 but (Mg+Ca)/Fe (7.3 ± 0.7) is significantlylarger than in Areas #1 and 3. These differences of dust composition for the three source areas are confirmed bylaboratory experiments in which mineral aerosols are generated using natural soils collected in the source re-gions and subsequently analyzed by XRF. These results do not only emphasize the strength of the link existingbetween the composition of the source soil and that of the aerosol generated from it, they also document thevariability of the dust composition in the Central Mediterranean region and confirm the interest of using the Ca/Al and (Mg+Ca)/Fe ratios as tracers of the source areas.

1. Introduction

Because of the importance and diversity of their effects on the en-vironment at the local, regional and global scales (Alizadeh-Choobariet al., 2014), solid and liquid air-suspended particles (aerosols) havereceived increasing attention in the last decades. For instance, aerosolsaffect the Earth's climate directly by scattering and absorbing solar andterrestrial radiations or indirectly by favoring the formation of clouds orthe modification of their properties (Ramanathan et al., 2001; Andreaeand Rosenfeld, 2008). By altering the actinic flux density (e.g.,Dickerson et al. 1997; Palancar et al., 2013) and by being involved inheterogeneous reactions with atmospheric gases (Dentener and

Crutzen, 1993; Bauer et al., 2004; Ndour et al., 2008) primary andsecondary aerosols also play a major role in atmospheric chemistry.Depending on their composition, the transport and subsequent de-position of aerosols from the continent to the ocean can contributesignificantly to the delivery of either nutrients or contaminants to thecoastal and remote oceans (Duce and Tindale, 1991; Hutchins andBruland, 1998; Fung et al., 2000; Mahowald et al., 2005; Okin et al.,2011). The adverse effect of inhalable particles on human health is alsonow recognized (Dockery and Pope, 1994; Yang et al., 2005; Pope IIIand Dockery, 2009; Hoek et al., 2013), and they constitute a matter ofconcern for both the decision makers and the residents of pollutedareas. Quantifying all these effects requires that the aerosol

https://doi.org/10.1016/j.atmosres.2018.04.001Received 30 November 2017; Received in revised form 19 March 2018; Accepted 2 April 2018

⁎ Corresponding author.E-mail address: alfaro@lisa.u-pec.fr (S.C. Alfaro).

Atmospheric Research 209 (2018) 170–178

Available online 05 April 20180169-8095/ © 2018 Elsevier B.V. All rights reserved.

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concentration and compositional characteristics have been determinedat any point of the atmosphere, which is not an easy task because of thelarge temporal and spatial variability of the aerosol concentrationfields. Therefore, a number of models have been developed to providethis information at the regional scale (i.e. Heinold et al., 2007; Laurentet al., 2010; Schmechtig et al., 2011; Pérez et al., 2011; Menut et al.,2016) or global scales (i.e. Mahowald et al., 2005; Textor et al., 2006;Huneeus et al., 2011). For validating and improving these models,ground-truth field data collected in a variety of environments areneeded. This is particularly the case in areas such as the Middle Eastand North Africa (MENA) in which remote sensing observations showthat the aerosols concentrations can become quite large as a result ofthe sporadic but intense emissions of mineral dust from the surroundingarid and semi-arid areas (Tanré et al., 2001; El Metwally et al., 2008;Hatzianastassiou et al., 2009; Basart et al., 2009; Masmoudi et al.,2015). Unfortunately, despite the efforts of the scientific community tocarry out field campaigns and document the spatial variability of thecomposition of this mineral dust component (see the review byScheuvens et al., 2013), the sampling and direct characterization ofdesert dust remain rather rare in entire areas, such as the Mediterra-nean coast of North Africa.

In this work, we propose to contribute to the filling in of this gap bymaking the best of the 190 daily aerosol samples collected at two dif-ferent experimental stations of the coastal area of Central Tunisia(Fig. 1). The first station being located in Sfax, the second largest city ofTunisia, is on the continent and of the urban type. The second stationwas operated 20 km more to the east, at a cleaner island site of theKerkennah Archipelago (Trabelsi et al., 2015, 2016). In coastal areas,the aerosols of continental origin have generally already had the pos-sibility to interact and mix with aerosols of natural (marine) or an-thropogenic origins. Among several other possible techniques, X-Rayfluorescence analysis that does not require any special preparation ofthe samples appears as the most convenient for the determination of theelemental composition of aerosol collected on porous membranes.Therefore, we will apply it to our samples. After this determination ofits elemental composition, different methods based on the statisticalanalysis of the inter-elemental correlations can be used to apportion theaerosol between its desert-dust, marine, and anthropogenic components(Chiapello 1996; Marconi et al., 2014; Trabelsi et al., 2016). In thiswork, this apportionment will essentially be useful for removing thenon-crustal contributions from the bulk composition, which is neces-sary for determining the elemental composition of the mineral dust andassess its variability. In order to understand the reasons of this varia-bility, a combination of dust emission modeling and air-mass trajectoryreconstructions, will be used to determine the origin (local or longer

range transported) of at least the most intense dust episodes. The ele-mental composition of the mineral dust collected in Central Tunisia andsome elemental ratios such as (Mg+Ca)/Fe already identified as beinga potential source tracer (Scheuvens et al., 2013) will be compared notonly to those reported in the literature for other Saharan regions butalso to those of pure mineral aerosols generated from a variety of sourcesoils in the GAMEL generator specially designed for this purpose (Lafonet al., 2014).

2. Material and methods

2.1. Experimental sites and sampling

In 2010–2011, a temporary experimental site was implemented nearRemla, on the Kerkennah Archipelago. This site was selected for beinglocated relatively far (20 km) from the city of Sfax and thus assumedlyclean. In 2015, the measurements were resumed at the Sfax Universitysampling site (lat.: 34.73°N; long.: 10.72°E; alt.: 20m asl) located on theroof of the Physics Department of the Faculty of Sciences, in thesouthwestern part of the city and 4 km from the sea front. As moregenerally all the coast of central Tunisia, the two sites are expected tobe influenced by the presence of the sea but also to lie on the pathwayof the northward-bound atmospheric exports of Saharan dust.Therefore, there is no reason to expect important differences of mineraldust composition between the two sites. Conversely, atmosphericsampling is certainly more affected by anthropogenic emissions such asmotorized traffic and industrial activities at the urban sampling site ofSfax than in Kerkennah. In Sfax, the most important industries are lo-cated in the coastal area and just a few kilometers away from the citycenter. They mostly include a phosphate transformation unit, a leadfoundry, and agro-industrial activities. At least the first two activitiesare known to release heavy metals (Ni, Cu, Zn, Pb…) in the atmosphere(Azri et al., 2000).

At the two sites, the airborne particles were collected on poly-carbonate membranes (Nuclepore, Whatman™) with pore sizes of0.4 μm. On Kerkennah Archipelago, where the background aerosolconcentration is relatively low, the particles sampling lasted two daysand 50 samples were collected in 2010 and 2011. In Sfax, five samplingcampaigns were organized from March 2015 to April 2016 to cover thedifferent seasons of the year. In all, 140 samples were collected. At thissite, the filtration was performed at a flow rate of 1m3/h but only forthe first 15min of each hour and from 8:00 am of the first day to thesame hour of the following day. Thus, for each sample the air pumpingwas only active for 6 h and the volume sampled was measured with agas meter (model 2000, Gallus). This sequential procedure was adopted

Fig. 1. Map of North Africa with relief elevation indicated in meters, and location of the two experimental sites, Sfax (1) and the Kerkennah Archipelago (2), on theMediterranean coast of Central Tunisia (map adapted from a Google Earth 2018 view).

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to 1) collect particles representative of the diurnal variability of theaerosol composition, and 2) avoid collecting a mass of particles tooimportant for the XRF analysis to remain possible.

2.2. Chemical composition and apportionment

After collection, the samples were analyzed by wavelength-dis-persive X-ray fluorescence (WD-XRF) with a PW-2404 (Panalytical)spectrometer. In this fast, non-destructive, and multi elemental methodclassical for the analysis of atmospheric aerosols sampled on filters, themembranes are introduced in the spectrometer without any furtherpreparation. In our protocol, each element is analyzed three times invacuum for 8 to 10 s with specified conditions (voltage, tube filter,collimator, analyzing crystal and detector). The measurements arecorrected for blank membrane counts.

In order to avoid the absorption of the less energetic X-rays by theheavy elements and the resulting underestimation of the mass of thelight elements, the surface concentration of all the particles depositedon the membranes must not exceed 200 μg·cm−2. This corresponds toan approximate total mass of 1000 μg on each filter. For each detectedelement, the quantification limit (QL) is determined as being 10 timesthe standard deviation of the measurements performed on the blankmembrane and only the results above QL are considered for the furthercalculations.

In this study, sixteen elements were quantified: Na, Mg, Al, Si, P, S,Cl, K, Ca, Ti, Mn, Fe, Ni, Cu, Zn and Pb. Although we will seek toconfirm this point by the statistical analysis described below, this listcontains elements that are considered as being principally associatedwith the mineral (Al, Si…), marine (Na, Cl…) and anthropogenic (Ni,Cu, Pb…) components of the aerosol. Noteworthy, light (Z < 11) ele-ments cannot be analyzed by XRF, which means that among others Cand thus the biogenic and anthropogenic carbonaceous components ofthe aerosol are not quantified. However, this is not a limitation for ourstudy focusing on the mineral dust component of the aerosol.

The results of the XRF-analysis consist in 16 elemental concentra-tions for the 50 samples collected on Kerkennah Islands and the 140ones collected in Sfax. In a first step, we will use the XLSTAT® softwareto determine the correlation coefficient matrixes describing how theconcentrations of the analyzed elements are linked together. In asecond step, receptor models such as Chemical Mass Balance (CMB),Principal Component Analysis (PCA), and Positive Matrix Factorization(PMF), can use this chemical composition of particulate matter (PM) asinputs to determine the impact of the various PM sources on air quality.Recent inter-comparison studies have shown that PMF and PCA per-form equally well (Viana et al., 2008) but better than CMB (e.g., Cesariet al., 2016). In this study, we have chosen to apply the classical(Pearson) Principal Component Analysis separately to our two datasets.This statistical procedure (Wold et al., 1987) aims at unraveling theinternal structure of the data by grouping the variables in a way thatbest explains their observed variance. Thus, new variables, calledprincipal components (PC), are defined. The PCs are linearly in-dependent combinations of the original variables. They are classified bydecreasing order of importance, on the basis of the fraction of thevariance of the original data that they explain. Usually, only a few PCsare enough to explain at least 90% of the variance and the di-mensionality of the transformed data is thus dramatically reduced.

2.3. Concentration and composition of the mineral dust in the outdoorsamples

Some elements, as for instance Al, are commonly considered asbeing only carried by the dust component of the aerosols (Alfaro et al.,2003; Marconi et al., 2014; Scheuvens et al., 2013). This element is thusoften selected as a tracer of the mineral dust emitted in the atmosphere.Moreover, considering that the average mass fraction of Al in mineraldust is about 8% (Taylor, 1964), the concentration of mineral dust (MD)

can be estimated from the one of aluminum:

=MD Al/0.08 (1)

Note that in areas where carbonates constitute a significant fractionof the dust mass, Eq. (1) only provides a rough estimate of MD.

Similarly, Na can be considered as a tracer of the sea salt (SS)component after removal of the crustal share, which is done using theaverage value (0.1) of the Na/Al ratio in desert aerosols (Bowen, 1966):

= −Na Na 0.1Alss (2)

Thus, the sea salt concentration can be computed from the relativemass abundance (0.33) of Na in sea water (Brewer, 1975):

=SS 3 Nass (3)

For elements with a double marine/crustal origin, such as Mg or Ca,determining the composition of the mineral dust requires that the seasalt contribution has been previously subtracted from the bulk ele-mental concentration determined by XRF. If we call X the concentrationof such an element, and (X/Na)ss its ratio in sea-salt, the crustal share ofthe concentration is simply given by:

= −X X (X/Na) Nadust ss ss (4)

To ensure comparability with previous works, we will adopt for (X/Na)ss the values recommended by Henderson and Henderson (2009)and used in Marconi et al. (2014). For K, Mg, and Ca these ratios are0.037, 0.129, and 0.038, respectively.

Note that for elements such as K that in addition to the marine andcrustal sources can be produced by anthropogenic biomass burningactivities, the application of Eq. (4) only yields the non-marine con-centration, or in other words the sum of the crustal and anthropogeniccontributions. The same remark can be made about S that is presentunder the form of sulfate in marine aerosols, in anthropogenic pollu-tion, and also in a number of minerals of crustal origin such as gypsumfor instance.

2.4. Determination of the origin of the dust

Determining the exact location of the sources of at least the largestdust events sampled at the Kerkennah and Sfax experimental sites is notan easy task. However, this is important for ascribing the compositionalcharacteristics of transported mineral dust to its different potentialsource regions. Three independent methods can be combined forgathering information on this geographical origin. The maps of theaerosol optical depth (AOD) derived from satellite-borne instrumentssuch as MODIS aboard Aqua and Terra do not only help confirming thepresence of a dust plume over the experimental site, but the examina-tion of the day-to-day evolution of the shape of this plume can alsoprovide some hindsight on the position of the dust sources. By using theHYSPLIT model (Draxler and Hess, 1997; Draxler and Rolph, 2012;Rolph, 2013) in the backward mode, the trajectories of the dust-ladenair-masses reaching the experimental site can be studied. This does nottell from which sources the mineral dust was actually emitted and in-jected into the travelling air-masses, but this helps excluding the po-tential sources that lie outside the reconstructed trajectories. The thirdmethod consists in using regional models for simulating the emission ofdesert-dust in the area of interest for our study. In the present work, theCHIMERE CTM model (Menut et al., 2013) is used to simulate dustemissions at the 1°× 1° resolution from North African desert areas on adomain extending from 16°N to 38°N, and 18°W to 40°E. Dust emissionsare computed using the Dust Production Model configuration which isbased on the parameterizations developed by Marticorena andBergametti (1995) and Alfaro and Gomes (2001). The dust emissionprocesses, i.e. the erosion threshold, the saltation and sandblastingfluxes, are explicitly computed as a function of the desert soil propertiesand of the wind friction velocity (Laurent et al., 2008). The emissionmodel is forced by the surface wind fields from the European Centre for

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Medium Weather Forecast (ECMWF).

2.5. Comparison with aerosol generated in the laboratory

After documenting the variability of the elemental composition ofthe mineral dust sampled at the two Tunisian sites, this one will becompared with the values reported in the literature for other Saharanregions. These data were collected either in the immediate vicinity ofsome major Sahelian or Saharan source regions during expensive andtherefore short field campaigns (Kandler et al., 2008; Formenti et al.,2008; Scheuvens et al., 2011) or further downwind (Kandler et al. 2007;Guieu et al., 2010; Marconi et al., 2014) after dust particles transport inthe atmosphere. In order to supplement this rather lacunar data, we willuse the generator for mineral aerosol production from soil samples inthe laboratory (GAMEL) whose name is self-explanatory. The principlesand performances of the generator are fully described in Lafon et al.(2014). The gist of it is that the generator only requires a few grams ofnatural source soil to generate mineral aerosols whose composition andphysical characteristics (size-distribution) compare quite well to thoseof the aerosol collected on site in the source regions. As detailed furtherbelow in the results section, we have selected in the bank of soilsavailable at the laboratory the three ones that correspond to the source-areas identified as being at the origin of the major events recorded atthe Sfax and Kerkennah experimental sites. The generation parameters,as defined by Lafon et al. (2014), were chosen to collect aerosol filtersin the same conditions as on the field (with collected masses <200 μg·cm−2). These conditions were for the three soil source samples:a soil quantity of 1 g, a shaking duration of 9min at a frequency of500 cycle·min−1. One filter of each produced aerosol was analyzed byXRF following the same protocol applied to samples collected in-situ(cf.2.2). Then we will compare the range of variability of the compo-sition of aerosols generated from these soils with the characteristics ofthe aerosols collected in-situ.

3. Results and discussion

3.1. Statistical analysis

In Kerkennah, 4 elements (P, Ni, Zn, and Pb) were under thequantification limit of the XRF analysis in too many samples for them tobe included in the statistical analysis. In all, 50 samples have beenanalyzed for this station. Table 1 reports the values of the inter-ele-mental correlation coefficients (r2). As expected for these elements ofmarine origin, Na is strongly correlated to Cl. Similarly, Ca, Fe, Mg, Al,Si, K, Ti, and Mn are also strongly correlated which denotes theircommon crustal origin. Interestingly, Ca and S are also strongly linked,which suggests that the sulfur contained in the Kerkennah aerosolmight be of crustal rather than anthropogenic origin. Finally, Cu is notstrongly linked to any of the other analyzed elements.

The PCA allows identifying several principal components. The firstthree (PC1, PC2, and PC3) explain 67.2%, 16.4%, and 7.4%, of thevariance of the dataset, respectively, which amounts in all to> 90%. Inorder to understand the chemical meaning of these 3 PCs, it is possibleto examine the correlation linking each element to them (Table 2). Thecorrelation coefficients of Ca, Mg, Al, Si, K, and Ti with PC1 are alllarger than 0.9, which shows that this component can be interpreted asrepresenting the mineral dust fraction of the aerosol. As already statedabove, it is the temporal variability of this component that plays themost important role at the Kerkennah site. The two elements moststrongly correlated with PC2 are Na and Cl, which suggests that PC2can be essentially associated with sea-salts. It is not surprising that thiscomponent plays an important role in the marine environment of Ker-kennah, but interestingly it comes only second to mineral dust. Finally,PC3 is more difficult to interpret. The strongest correlation with PC3 areis that of Cu, which suggests a link with anthropogenic pollution.However, the fact that PC3 accounts for only 7.4% of the variability isin good agreement with the absence of major pollution source on theKerkennah Archipelago.

The same statistical analysis performed with the 140 Sfax samplesdata reveals again the strength of the correlation linking together onone hand, the crustal elements and on the other hand, the marine ones(see Table 1S in the supplementary material). Among the anthropogenicelements, Cu and Ni are strongly (r2= 0.8) correlated, which suggeststhat they are released in the atmosphere by the same sources.

Considered together, the first three PCs yielded by the PCA ex-plain> 80% of the observed variability. PC1 is again strongly corre-lated (Table 3) with the terrigenous elements (Ca, Fe, Mg, Al, Si, K, Ti,Mn) and accounts for as much as 55.2% of the variance. This confirmsthe major role played by mineral dust in Central Tunisia and probablymuch beyond. PC2 corresponds to the sea-salt component and explains

Table 1Strength of the correlation (r2) between the elemental concentrations measured in the 50 samples collected in Kerkennah. The values above 0.7 are reported in boldcharacters. The p-value is< 0.05.

Variables [Cl] [S] [Ca] [Fe] [Na] [Mg] [Al] [Si] [K] [Ti] [Mn] [Cu]

[Cl] 1 0.488 0.536 0.268 0.918 0.550 0.373 0.344 0.503 0.427 0.338 0.441[S] 0.488 1 0.823 0.432 0.531 0.594 0.601 0.567 0.656 0.635 0.410 0.698[Ca] 0.536 0.823 1 0.633 0.386 0.888 0.894 0.879 0.925 0.911 0.631 0.635[Fe] 0.268 0.432 0.633 1 0.183 0.706 0.769 0.757 0.786 0.737 0.563 0.136[Na] 0.918 0.531 0.386 0.183 1 0.322 0.164 0.115 0.298 0.214 0.074 0.422[Mg] 0.550 0.594 0.888 0.706 0.322 1 0.950 0.954 0.956 0.960 0.734 0.468[Al] 0.373 0.601 0.894 0.769 0.164 0.950 1 0.996 0.977 0.995 0.783 0.419[Si] 0.344 0.567 0.879 0.757 0.115 0.954 0.996 1 0.969 0.991 0.800 0.389[K] 0.503 0.656 0.925 0.786 0.298 0.956 0.977 0.969 1 0.983 0.772 0.488[Ti] 0.427 0.635 0.911 0.737 0.214 0.960 0.995 0.991 0.983 1 0.793 0.463[Mn] 0.338 0.410 0.631 0.563 0.074 0.734 0.783 0.800 0.772 0.793 1 0.193[Cu] 0.441 0.698 0.635 0.136 0.422 0.468 0.419 0.389 0.488 0.463 0.193 1

Table 2Strength of the correlation between the individual elemental concentrationsmeasured at Kerkennah and the first three Principal Components (PC) yieldedby the statistical analysis.

PC1 PC2 PC3

[Cl] 0.576 0.681 −0.410[S] 0.743 0.383 0.342[Ca] 0.952 0.088 0.178[Fe] 0.748 −0.282 −0.258[Na] 0.396 0.844 −0.336[Mg] 0.959 −0.083 −0.075[Al] 0.959 −0.255 0.016[Si] 0.947 −0.301 0.011[K] 0.985 −0.117 −0.027[Ti] 0.973 −0.193 0.023[Mn] 0.765 −0.317 −0.184[Cu] 0.559 0.488 0.589

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13.9% of the variance. Again PC3 is difficult to interpret but the factthat the elements most strongly correlated with it are S, P, Zn, and Cusuggests a contribution of the anthropogenic activities to this compo-nent. PC3 represents 11.5% of the variability of the concentration da-taset.

3.2. Concentration of the MD and SS aerosols

The characteristics of the statistical distributions of the MD and SSconcentrations obtained for the sites of Kerkennah and Sfax by appli-cation of Eqs. (1) and (3) are reported in Table 4. As expected, it clearlyappears that the SS concentrations are quite variable but also muchlarger at the island site of Kerkennah (12.7 ± 7.1 μgm−3) than at theurban site of Sfax (2.3 ± 1.8 μgm−3). Conversely, though slightlylarger at Sfax (11.7 ± 14.4 μgm−3) than at Kerkennah(9.3 ± 12.9 μgm−3), the MD concentrations are in the same order ofmagnitude at both sites. The fact that the MD maxima observed aremuch larger than the 90th percentile shows that relatively rare butintense dust peaks are observed at the two sites. In the following, wewill arbitrarily classify as “moderate events” the cases whose MD con-centrations are between the 10th (3 μgm−3) and 90th (25 μgm−3)percentiles of the MD distribution in Sfax, and as “intense events” thoseabove the 90th percentile. Because they can hardly be considered asrepresentative of dusty situations, the MD cases below 3 μgm−3 (ap-proximately the 10th percentile at Sfax) will not be retained in thisstudy focused on the characterization of mineral dust major situations.After this elimination, 132 samples among which 13 corresponding to“intense” dust cases remain available for the Sfax site and 34 cases, ofwhich 4 intense ones, for the Kerkennah station.

With these criteria, 9 intense dust events can be isolated in theKerkennah and Sfax datasets. Note that the number of events is lessthan the number of samples because some events lasted several days

and are pooled together. The temporal limits of the intense events arereported in Table 5, along with the maximum MD observed duringthem.

3.3. Origin of the intense dust events

As indicated in Section 2, localizing the source of a dust event ob-served at a given site is not straightforward. For illustrating the method,we have selected Event #4 of Table 5. Fig. 2 displays the MODIS AODobservations, the 72 h air-mass back-trajectories, and the dust emissionsmodelled by CHIMERE for the three days of the event. The analysis ofthe movements of the air masses indicates that they had travelled overAlgeria and southwest Tunisia before reaching the sampling site. TheCHIMERE runs show that the sources of central Algeria were active onthe three days, which is also consistent with the MODIS observationsrevealing high AODs over Algeria and in the vicinity of the border withTunisia. Therefore, all these elements strongly suggest that the origin ofevent #4 was located in an area centered on Central Algeria and ex-tending in direction of the border with Tunisia. Hereinafter, this areawill be referred to as Area #3. The same analysis allowed determiningthe origin of all the events but event#2 for which the modeling did notshow any emission upwind of the sampling site. Regarding the otherevents it was found (Table 5 and Fig. 3) that with the exceptions ofevent #4 already considered and events #1 and 3 that come from thevicinity of the Tunisian-Lybian border (Area #1), the majority of theintense events observed in Sfax and Kerkennah originate from an area(Area #2) including east Algeria and south-west Tunisia. Note thatthough not limited to it, the salty depression of Chott El Jerid is part ofArea #2. Two soil samples collected in areas # 1 and 2 are available atthe laboratory for the aerosol generation in GAMEL. The soil of Area #1was collected at the Dar Dhaoui (33°17′50″N; 10°46′51″E) experimentalsite of the Tunisian ‘Institut des Regions Arides’. The soil of Area #2 wascollected in Douz (33°25′N; 09°02′E) at the south-east side of Chott ElJerid. Because we did not have a soil sample from Area #3, we decidedto use instead the one collected more to the north at Kef Mokrane(33°47′36″N, 2°49′43″E) in the Laghouat (Algeria) governorate.

3.4. Composition of the mineral dust component

Analyzing finely the inter-elemental correlations is a powerful toolto study the variability of the composition of the mineral dust compo-nent of the aerosol. Si, Fe, Ti, and Mn are linearly correlated to Al. Forthese elements, the elemental ratios (X/Al)dust and associated r2 aregiven in Table 6. The strength of the correlations denoted by the largevalues of r2 confirms the lack of variability of the X/Al ratio for theseelements. This constancy implies that they cannot be used as tracers ofthe geographical origin of the mineral dust collected in Central Tunisia.

In the case of K, Mg, Ca, and P the correlation with Al is also positivebut not linear even after removal of the marine share of their con-centrations. In fact, for the four elements the (X/Al)dust ratios decreasewith Al, or equivalently with the MD concentration, and seem to tendtowards a constant limit (see Fig. 4 for the first three elements. A similarfigure obtained for P is not shown). As noted by Alfaro et al. (2003), thislimit corresponds to the elemental ratio of rare but intense desert dustevents. In comparison, the dust events of moderate intensity appear asbeing strongly enriched in Ca, K, Mg, and P. This is confirmed by thestatistical indicators of Table 7 showing that the mean of the (X/Al)dustratios during the most intense events compare to, and are even slightlylower than the 10th percentile of the observations in moderate loadingconditions.

The S/Al values reported in Table 7 show that the aerosol also seemsto be richer in S during the dust events of moderate intensity. Althoughthis could be due to a larger contribution of anthropogenic sulfates, thesimultaneity of the enrichments in Ca, K, Mg, and S rather suggests adifference of mineralogical composition of the dust that could itselfreflect the regional variability of the composition of the upper horizon

Table 3Same as Table 2 but for the site of Sfax.

PC1 PC2 PC3

[Cl] 0.130 0.843 −0.423[S] 0.185 0.208 0.560[Ca] 0.895 −0.096 0.139[Fe] 0.967 −0.194 −0.142[Na] 0.166 0.851 −0.336[Mg] 0.966 −0.008 −0.078[Al] 0.948 −0.212 −0.207[Si] 0.958 −0.205 −0.174[P] 0.401 0.140 0.631[K] 0.975 −0.083 −0.133[Ti] 0.955 −0.208 −0.183[Mn] 0.968 −0.174 −0.060[Zn] 0.524 0.274 0.677[Cu] 0.580 0.394 0.390[Ni] 0.726 0.323 0.097[Pb] 0.416 0.419 −0.205

Table 4Characteristics of the frequency distributions of the mineral dust (MD) and sea-salt (SS) concentrations (in μg m−3) measured at the two experimental sites:minimum, maximum concentrations, 10th, 50th and 90th percentiles.

MD SS

Kerkennah Sfax Kerkennah Sfax

Min. 0.6 1.2 0.4 0.3Max. 62.6 103.9 33.1 9.4Mean 9.3 11.7 12.7 2.310th percentile 1.1 3.4 4.2 0.850th percentile 4.8 7.2 11.9 1.890th percentile 20.6 24.5 21.3 5.2

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of the source-soils (Scheuvens et al., 2013; Lafon et al., 2014). Theseauthors also suggested that the (Mg+Ca)/Fe ratio of the mineral dustcould be used as a source tracer. The frequency distribution of this ratioshows that it is highly variable (Fig. 5). The majority (85%) of the all-

cases (moderate+ intense) observations lie in the 6–11 range. How-ever, with an average and standard deviation of 5.5 and 2.1, respec-tively, the (Mg+Ca)/Fe ratio tends to be lower during the intenseevents. As with the Ca/Al, K/Al, and Mg/Al ratios, this variability of

Table 5Summary of the periods of “intense” dust events detected during the measurements campaigns performed at the two experimental sites.

Event # Site Start date End Max. MD Dust sources

1 Kerkennah 13/04/2010 17/04/2010 62.6 West of Lybia2 9/11/2011 10/11/2011 53.4 Non determined3 Sfax 16/03/2015 16/03/2015 29.1 West of Lybia4 23/03/2015 25/03/2015 79.5 Central Algeria and South Tunisia5 27/04/2015 27/04/2015 25.3 Algerian/Tunisian border6 09/02/2016 10/02/2016 34.2 Algerian/Tunisian border7 23/02/2016 25/02/2016 103.9 East and central Algeria8 10/03/2016 10/03/2016 28.5 East and central Algeria9 12/04/2016 13/04/2016 57.4 South Tunisia

Fig. 2. Reconstruction of the trajectories of the air-masses reaching the Sfax experimental site on the three successive days (23 to 25 March 2015) of Event #4(central panels). MODIS observations of the AOD (top panels) and CHIMERE simulations of mineral dust emissions over North Africa (bottom panels) are alsorepresented for the same days.

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(Mg+Ca)/Fe could reflect a difference of composition of the parentsoils. In order to investigate this point further, the next section focuseson the influence of the geographical origin of the dust on its elementalcomposition.

3.5. Influence of the soil source on the composition of the mineral dust

The average and standard deviation of the Ca/Al, Mg/Al, and(Mg+Ca)/Fe ratios of the aerosols collected in situ are reported for thethree source areas (Table 7). The largest differences between the 3 areasare observed with the latter ratio that increases from a low 2.9 (± 0.3)in Area #3 to as much as 7.3 (± 0.7) in Area #2. Area #1 constitutesan intermediate case. These results compare well to the range of valuesreported by Scheuvens et al. (2013) for some Algerian (1.16–2.08) andLybian (4.29–8.40) sources and globally confirm the existence of aWest-East gradient reported by these authors. However, with its parti-cularly large values Area#2 clearly constitutes a hot-spot for the(Mg+Ca)/Fe ratio. The analysis of the aerosols generated in GAMELwith the soils of the three source areas (Table 8) confirms this classi-fication. Particularly, the (Mg+Ca)/Fe ratio (7.9) of the Douz aerosolis significantly larger than the one of Dar Dhaoui (5.6), itself beinglarger than the ratio (4.0) of the Algerian (Kef Mokrane) aerosol.

4. Summary and conclusion

In this work, we have used the results of the XRF analysis of 190aerosol samples collected in Kerkennah and Sfax to assess the varia-bility of the mineral dust concentration and composition in CentralTunisia. Among these, as many as 166 with MD concentration largerthan 3 μg/m3 were considered as exploitable. Noteworthy, this largeproportion emphasizes the importance of the mineral dust componentin the aerosol of the area. The 17 samples with MD concentrationslarger than 25 μg/m3 were classified as “intense” and those with MDbetween 3 and 25 μg/m3 as “moderate”. An original method based on acombination of reconstruction of air-masses back-trajectories, satelliteobservations, and dust-emission modeling allowed localizing the sourceareas of the intense events. Three different areas could be distinguished:Area #1 corresponds to the south-east of Tunisia and west of Lybia,Area #2 to the Chott el Jerid/eastern Algeria region, and Area #3 tocentral Algeria. Of all the elemental ratios studied in this work somesuch as Si/Al, Fe/Al, or Ti/Al present a too low variability to be used assource tracers. From this point of view, other more variable ratios suchas Ca/Al, Mg/Al, and particularly (Ca+Mg)/Fe appear as more in-teresting. This is in good agreement with the review of Scheuvens et al.(2013) who already noted the contrast between the compositions of themineral dust of Lybian and Algerian origins. In this work, the majorityof the intense events is shown to come from Area #2 that is char-acterized by values of the (Ca+Mg)/Fe ratio (7.28 ± 0.71) muchlarger than in Areas #1 and #3 (4.11 ± 0.82 and 2.87 ± 0.29, re-spectively). Though this should be confirmed by mineralogical analysisof the aerosol samples, this is consistent with the calco magnesiannature of the soils of the Chott el Jerid area. The large values of the

Fig. 3. Location of the three source areas from which the intense dust eventssampled at the two experimental sites (red square) originated. The trianglesindicate where the soil samples used in the laboratory experiments have beencollected. (For interpretation of the references to colour in this figure legend,the reader is referred to the web version of this article.)

Table 6Concentration ratios of the crustal elements of the aerosols linearly correlatedto Al and correlation coefficient (r2).

Element (X) Si Fe Ti Mn

(X/Al)dust 2.81 0.66 0.07 0.01r2 0.99 0.97 0.98 0.84

Fig. 4. Kdust/Al, Mgdust/Al, and Cadust/Al ratios as a function of the atmosphericconcentration in mineral dust (MD).

Table 7Comparison of the statistical indicators of the frequency distributions of theelemental ratios measured in Sfax during the moderate (132 samples) and in-tense (13 samples) mineral dust events. In the last case, the standard deviation(SD) is given between parentheses.

Type ofdustevent

K/Al Mg/Al Ca/Al P/Al S/Al (Mg+Ca)/Fe

Moderate 10thpercentile

0.36 0.42 3.37 0.030 0.54 5.58

Mean 0.54 0.62 5.90 0.058 1.60 8.1990thpercentile

0.77 0.93 8.73 0.241 3.68 10.10

Intense Mean(SD)

0.33(0.04)

0.38(0.08)

2.46(1.01)

0.023(0.01)

0.47(0.29)

5.5(2.1)

Fig. 5. Frequency distribution of the (Mg+Ca)/Fe ratios measured in the 166“moderate” or “intense” dust-event samples collected at Sfax and Kerkennah.

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(Ca+Mg)/Fe ratios of the dust samples collected not only during the“intense” but also during the “moderate” events indicates that thoughlong-range transport from more distant sources is possible, CentralTunisia is usually under the predominant influence of this particularsource-area. Finally, the fact that the range of variability of (Ca+Mg)/Fe reported by Marconi et al. (2014) for Lampedusa compares to theone of Sfax and Kerkennah suggests that the influence of Aera #2 is notlimited to the Tunisian coast but extends much further downwind.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.atmosres.2018.04.001.

Acknowlegment

The authors acknowledge the NOAA Air Resources Laboratory(ARL) for the provision of the HYSPLIT transport and dispersion modeland the READY website (http://www.arl.noaa.gov/ready.html) fromwhich the backtrajectories used in this publication have been down-loaded. They also thank NASA's Giovanni data system (http://giovanni.sci.gsfc.nasa.gov/giovanni/) for making the MODIS data available.

The Kef Mokrane soil sample used in this study was provided by theUniversity of Laghouat (Algeria) and those from Dar Dhaoui and Douzby the Institut des Régions Arides (Medenine, Tunisia).

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Table 8Elemental ratios of the mineral dust originating from the 3 source areas identified in this study. The values derived from the analysis of the samples collected in-situ inSfax and Kerkennah are compared with those of the dust generated in the laboratory with soils from Dar Dhaoui (D.D.), Douz (DZ), and Kef Mokrane (K·Mok.). Thevalues for Algeria and Lybia given by Scheuvens et al. (2013) are also reported.

Area #1 #2 #3

In situ D.D. In situ DZ In situ K·Mok. Algeria (Scheuvens) Lybia (Scheuvens)

Ca/Al 2.40 (0.40) 2.12 2.80 (0.93) 6.16 1.31 (0.32) 1.47 0.14–0.90 2.36–6.06Mg/Al 0.27 (0.16) 0.30 0.40 (0.09) 0.29 0.34 (0.04) 0.32 0.29–0.44 0.34–1.54(Mg+Ca)/Fe 4.11 (0.82) 5.64 7.28 (0.71) 7.91 2.87 (0.29) 4.11 1.16–2.08 4.29–8.40

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