Atmospheric aerosol has an important role in atmospheric physics and chemistry (Lohmann and Feichter, 2005; Pöschl, 2005) and significant impacts on climate (Buseck and Pósfai, 1999; Ramanatham et al., 2001) and human health (Pope, 2000; Brunekreef and Fosberg, 2005; Tobias et al., 2011).
Atmospheric aerosol is both the result of gas-to-particle transformation from natural or anthropogenically induced precursors and of direct emissions from the Earth's surface by the action of wind on soil and water. Gas-to-particle transformation gives fundamentally fine (submicrometre) particles, while wind-induced mechanic processes on the planet's surface produce mostly coarse particles as sea salt over the oceans or soil dust over deserts and other bare soil areas (Seinfeld and Pandis, 1998). On a global scale, sea spray and dust are highly dominant, in terms of suspended mass and regions affected, by comparison with other aerosol sources (Raes et al., 2000; Tanaka and Chiba, 2006). Although mostly natural, coarse aerosol particles associated with dust emissions are frequently affected by anthropogenic activities such as industry, transportation and agricultural practices at urban and regional/hemispheric scales (Almeida et al., 2006a; Ginoux et al., 2012).
It is important to know quantitatively the sources of atmospheric aerosols in order to correctly implement strategies and measures to control and reduce atmospheric particulate pollution and its effects on nature and humankind. There is an array of methodologies to evaluate the impact of sources and precursors on atmospheric particulate loading that range from emission, dispersion and transport modelling, to source apportionment techniques (Blanchard, 1999). Source apportionment techniques use information on aerosol atmospheric composition and concentrations, at one or several locations, to infer quantitatively the sources and processes responsible for the particulate loading at the receptor (Almeida et al., 2006b; Belis et al., 2013).
In source apportionment, when the number and composition of the sources are unknown, multivariate analysis, based on particulate composition variability at the receptor(s) as a function of time, is a very common and useful methodology to quantitatively evaluate the main sources of atmospheric particulate matter (Ashbaugh et al., 1984; Henry et al., 1984; Hopke, 1985). Presently, the most used multivariate analysis methodology is positive matrix factorisation (PMF), because it allows the discrimination of only positive values in both source profiles and contributions (Paatero and Tapper, 1994; Paatero, 1999; Reff et al., 2007; Amato et al., 2016).
Source apportionment multivariate methodologies permit frequently to identify the impact of the majority of direct sources and gas-to-particle conversion processes and their variability in time, during the aerosol measured period. If associated with statistical backward trajectory analysis, the method also permits the determination of source regions during regional and, principally, long-range transport (Salvador et al., 2016).
Multivariate methods, although very useful, are not perfect and have uncertainties resulting from collinearity of sources, the evolution of composition during transport, etc. that need to be detected and estimated (Belis et al., 2013). When a large number of compounds and elements is determined by chemical analysis of aerosol samples, an alternative methodology can be used to infer the total aerosol composition, which takes into account that the total aerosol mass is the sum of the mass of the individual components. Also from the chemical analysis, it is possible to intercompare analysed anions and cations, which have to obey the principle of electro-neutrality. From the mass and ionic composition, it is frequently viable to infer quantitatively the origin of the aerosol, because many of the analysed constituents are tracers of specific sources.
Ionic and mass balances (IMBs) rely mostly on the direct measurement of aerosol constituents and therefore are less affected by indemonstrable assumptions, as it happens in the assignment of the number of factors and their identities, in multivariate methods such as PMF (Belis et al., 2014). Mass balance has been frequently applied in the past but mostly as a screening tool (Watson et al., 2002). However, if properly applied, ionic and mass balances have the potential to correctly perform the source apportionment of atmospheric aerosol. We would like to emphasise that the European Guide on Air Pollution Source Apportionment with Receptor Models (Belis et al., 2014) exhorts to use receptor models in combination with independent methodologies to achieve more robust estimations by mutual validation of the outputs. Our objective in this paper is thus to develop and apply a detailed ionic and mass balance to aerosol particles in a dusty marine environment, demonstrating the capability of this methodology to determine the aerosol sources with an accuracy as good as that of the most developed multivariate methods, such as PMF.
Composition and mass balance is feasible when the main components of the aerosol sources, such as soil elements, sea-salt constituents, inorganic water soluble ions and carbonaceous mass, are measured and quantified (Sciare et al., 2005; Guinot et al., 2007; Grigoratos et al., 2014; Genga et al., 2017). Even when there is a thorough quantification of aerosol constituents, it is not often possible to apportion more than 70 % to 80 % of measured aerosol total mass, because important elemental constituents of particulate matter, such as oxygen, in water, soil and organic carbon, are not usually analysed (Malm et al., 1994; Andrews et al., 2000; Rees et al., 2004; Perrino et al., 2013; Grigoratos et al., 2014). Oxygen is the most abundant element in the Earth's crust, constituting on average 47 % of the continental crust mass (Wedepohl, 1995).
For mass balance purposes, the determination of soil contribution is,
usually, inferred from the analysis of the major soil elements measured in
the aerosol samples (Si, Al, Fe, Mn, Ti, etc.), taking into account the
presence of their oxides:
Depending on the completeness of the soil elemental analysis and the
composition knowledge of the source soils, it is possible to adapt the above
equation to better apportion the soil mass by mass balance (Formenti et al.,
2001; Eldred, 2003; Andrews et al., 2000, and references therein):
The sea-salt contribution is evaluated by considering that emitted sea-salt
spray has the composition of salt in seawater. A possible exception is
chloride that frequently appears in lower ratios due to sea-salt spray
interaction with atmospheric acids, such as
Soil carbonates are part of the carbonaceous aerosol. As they are infrequently analysed, in source apportionment they have to be approximately inferred from calcium and magnesium measurements. In dusty environments, the measurement of carbonates is important to permit a more correct composition/mass balance source apportionment.
Another component of the carbonaceous aerosol is the organic mass. This component is usually calculated from the measurement of organic carbon by applying a multiplication factor to take into account other unmeasured elements such as nitrogen, sulfur and, principally, oxygen. Factors ranging from 1.2 to 2.3 have been employed for this purpose (Countess et al., 1980; Japar et al., 1984; Rogge et al., 1993a, b; Sempere and Kawamura, 1994; Russel, 2003; Chen and Yu, 2007; El-Zanan et al., 2009). The highest values are commonly used in sites affected by biomass burning emissions, rich in sugars and organic acids, or away from emission sources, because, under these conditions, the precursor organic material had plenty of time to be strongly oxidised (Turpin and Lim, 2001; Sciare et al., 2005; Ervens et al., 2011). Genga et al. (2017) used variable values between 1.8 and 2.1, depending on the direction of the wind, to best fit the mass closure process in a Mediterranean port city.
Water is a common and important component of atmospheric aerosol that may constitute up to 20 % of the total PM mass (Canepari et al., 2013; Perrino et al., 2013). Model calculations estimate that particle-bound water constitutes 20 %–35 % of the annual mean European atmospheric PM concentrations (Tsyro, 2005). In spite of that, only in a few studies has aerosol particulate water been indirectly or directly estimated (Dick et al., 2000; Rees et al., 2004; Stanier et al., 2004; Speer et al., 1997, 2003; Kitamori et al., 2009).
Several attempts have been made and published to account for water in
sampled aerosols. Using a thermodynamic equilibrium ion modelling,
temperature, humidity and inorganic ions concentrations, Chen et
al. (2014)
estimated that water constituted up to 38 % of the
During laboratory studies with water and sea-salt particles, Tang et al. (1997) found the presence of a hysteresis supersaturation when decreasing relative humidity, with sudden efflorescence at 47 % RH. Depending on whether the particles were in a dry or wet state, the ratio of water to dry sea-salt masses observed at 50 % RH was 0.4, or 1.4, respectively.
Tang and Munkelwitz (1994) and Xu et al. (1998) determined the water content in ammonium sulfate. A water/salt ratio of 0.4 was obtained at 50 % RH in liquid meta-state equilibrium. A ratio of 0.45 was employed to calculate the water contribution to ammonium sulfate aerosols by Speer et al. (2003).
Speer et al. (2003) also estimated the water content in organic aerosol
particles. A relationship between the excess liquid water and the measured
organic carbon mass was found. Through modelling it was determined that, on
average, about 80 % of the liquid water in the
The present work uses data from the field campaign of the CV-DUST (Atmospheric aerosol
in the Cabo Verde region: carbon and soluble fractions of
Details of sampling and filter analysis are given elsewhere (Almeida-Silva,
2014; Salvador et al., 2016); here, we only provide a summary of published
information. Filters were weighted with semi-micro- or microbalances to
determine
Nuclepore filters were employed to determine particulate elemental content
using particle-induced X-ray emission (PIXE) and/or
The quartz filters were used to determine carbonates by acid evolution and
non-dispersive infrared analysis of evolved
Water-soluble anions and cations, sampled in Teflon filters, were measured by
ion chromatography. The method permitted the quantification of
As reported elsewhere (Almeida-Silva, 2013; Pio et al., 2014; Salvador et
al., 2016), Cabo Verde has two distinct atmospheric pollution seasons. During
winter months (December–February) the atmospheric boundary layer is impacted
by important dust intrusions from the Saharan region, with daily averaged
Levels of
Weighted average concentrations of
During May–September, there is no direct intrusion of dust plumes from
Africa, at lower atmospheric levels, in the boundary layer (represented by a
blue shade mask in Fig. 1), and we call it dust-free season. During the
dust-free period, the atmosphere contains still important quantities of
dust originating either from the island arid surface or from continuous
dust transport from Africa into the region across the free troposphere, which
partially sediments to lower atmospheric levels (Gama et al., 2015). The
months of March, April, October and November have intermittent direct
intrusions of Saharan dust, with
There is a handful of publications on soil composition in north Africa, which provide evidence of a wide composition variability across the Saharan and sub-Saharan regions (Guieu et al., 2002; Journet et al., 2014; Scheuvens et al., 2013, and references therein). One of the most complete Saharan soil data sets was given by the IDAEA-CSIC research group (Moreno et al., 2006). The publication provides the concentrations of 47 elements in the bulk soil of nine locations, across four regions, in north Africa (Hoggar massif, Chad Basin, Niger and western Sahara). Castillo et al. (2008) provides soil size distributed composition information for the same sites. We used this data set to infer the composition and mass balance of soil dust in our samples (Table S1, in the Supplement, adapted from Moreno et al., 2006, shows compound average contributions).
Saharan soil composition in Moreno et al. (2006) reveals some differences by comparison with the average crust/upper crust (Mason and Moore, 1986; Wedepohl, 1995), with a relative enrichment of Si and Ti, probably as a result of intense weathering of Sahara desert soils. Si and Ti form rather hard crystals (silica and rutile), resistant to physical weathering. The size chemical speciation of Saharan soils by Castillo et al. (2008) revealed Al, Mg and Fe moderate enrichments, in suspended finer particles, in contrast to K, which had increased concentrations at coarser sizes.
Aerosols generated by suspension from Sudan desert soil have also shown an
Al enrichment, while there has been an impoverishment in Si and Ti elements
in smaller particles (Eltayeb et al., 2001). The ratio of
Journet et al. (2014) concluded that, in desert soils, silica minerals
accumulate preferably in the silt fraction
(2
Taking into account the previous information, we speculate that, as a result of soil weathering, particles containing Si are heavier/larger than other soil particles and therefore are more difficultly suspended by the wind and exported to other regions, enriching local soils.
Consequently, it is expectable that Saharan suspended dust will be
impoverished in Si and Ti, by comparison with less hard minerals containing
Al, Fe, Mg, Na, etc. This is observed in our aerosol samples where there is a
quite constant
Published information concerning the
Because of the
In coastal, or marine, non-dusty environments, it is common, and correct, to
infer the mass contribution of particulate sea spray to the atmospheric
aerosol by using
To eliminate the soil interference in sea-salt estimation, we employed
Edge lines (in red) for Fe versus
A further refinement of sea-salt calculation can be implemented by looking at
the ratios (
Figure 1 presents the estimation of sea-salt contribution to the aerosol
(Sea-salt recalc), considering the methodology described above. The figure
shows that, with the modified methodology, there is no increase of sea-salt
aerosol loading during dust intrusions, in contrast with the standard
methodology. During the dust periods there is even a decrease in the
contribution of sea salt to the aerosol that may result either from excessive
calculation of soil
The correct determination of sea-salt ion concentrations permits the
estimation of the remaining common elements, supposedly from soil origin. In
this way, it is possible to calculate
Taking into account that we did not, or could not, measure with accuracy P
and Na, the calculation of soil contribution was done by adapting Eq. (2) to
The attribution of analysed water-soluble anions and cations to different
sources and formation processes can be done using the sequential ionic
balance proposed by Alastuey et al. (2005), adapted and developed by Mirante
et al. (2014) for Madrid urban aerosol. The present situation, with very
large inputs of dust and marine aerosols, imposes a further adaptation of the
ionic balance method, because gas-to-particle reactions involving precursor
pollutants and sea-salt spray, or dust, cannot be neglected, and from the
evaluation of dust and sea-salt composition, the amounts of soluble ions of
sea-salt and dust origin have to be initially calculated. Therefore, the
ionic balance applied to the present samples is the following:
Start by calculating soil Calculate sea-salt Recalculate sea-salt Calculate sea-salt mass concentration and composition from
Calculate non-sea-salt Associate, sequentially, free non-sea-salt From the differences between total and sea-salt cations, calculate soil
Associate free Associate, totally, Associate free Associate free Calculate the total masses of water-soluble soil sulfate, nitrate and
chloride. Edge line ratios between Fe and sulfate, nitrate, or chloride permit a
rough calculation of the fraction of these ionic compounds that is present
in the soil or that results from secondary reaction with atmospheric produced
acids (for visualisation, see Fig. S5 in the Supplement).
Using an Excel spreadsheet, the 13 steps were applied, sequentially, in order to attribute all measured cations and anions to sea salt, soil and secondary inorganic compounds. The first four steps were described in detail in the beginning of this section. Due to space limitations, only results for the remaining eight steps are presented.
Using a
With this IMB methodology, it is possible to account for the presence of seven source classes: sea-salt spray (SeaSalt), insoluble soil dust (SoilDust ins), soluble soil dust (SoilDust sol), secondary inorganic compounds from the reaction of atmospheric acids with ammonia (SIC am), sea salt (SIC ss) and dust (SIC du), and non-carbonate carbonaceous elemental and organic matter (Carbon).
The results of water-soluble compounds are presented in Table 2 for the dry-haze and dust-free seasons. Concentrations of secondary ammonium salts (SIC am) are more than doubled during the dust-free season, probably as a result of higher temperatures and transport of air masses from non-desert polluted areas, or the removal by co-sedimentation with dust during dust episodes. Soluble soil dust (SoilDust sol) are more than tripled during the dry-haze season, being formed mainly by calcium carbonates and sulfates, sodium nitrates and sulfates, and by sodium chloride.
Soil and secondary inorganic compounds resulting from the ionic balance.
Reaction of acid precursors with soil dust (SIC du) produces equivalent
amounts of secondary compounds during the two seasons, probably because of
limited availability of acidic precursors. Secondary processes resulting from
acidic reactions with sea salt (SIC ss) produce more sea-salt secondary
material during the dust-free season. As, in the present conditions, it is
difficult to clearly differentiate between the two processes, because it is
not possible to give a priority in the competitive acidic reaction with
sea salt or dust; in the rest of the publication, the two source processes
are treated together, as SIC ss
The fractional contribution of the six adapted source classes is given in
Fig. 3 for the two seasons and for the total campaign. The figure reveals
that the sum of the quantified sources only accounts for 76 % of the
measured
Contribution of different components to the
As, in our case, carbonates were directly measured, it is predictable that most
of the unaccounted mass results from the aerosol water content, in the form
of adsorbed/absorbed water (hydrates in soil constituents were already
considered with the application of factor
To estimate the amount of sorbed water in the aerosol, we consider that, by
approximation, there is a thermodynamic equilibrium between the controlled
room atmosphere, at 20
For sea salt, thermodynamic information from Tang et al. (1997) was used,
which, at 50 % RH and 20
For secondary inorganic water-soluble ammonium salts (mainly ammonium sulfate), thermodynamic information from Xu et al. (1998) and Tang and Munkelvitz (1994) was applied, which shows an equilibrium water/ammonium sulfate mass ratio of 0.4 at 50 % RH.
The information concerning the water content of organic polar matter was taken from Speer et al. (2003) who used a water/OC ratio of 0.2.
Suspended soil also sorbs water, principally the water-soluble ionic
component. We used ISORROPIA, version 2.1, which includes ions from crustal
origin, to estimate the water content in soluble dust. The ISORROPIA II
version was run for a liquid metastable assumption and applied to the soluble
soil dust (SoilDust sol) and to the sum of soluble soil dust and secondary
inorganic compounds formed from the attack of atmospheric acids on dust
particles (SoilDust sol
Various important components of the insoluble fraction of dust are
hygroscopic, such as clay minerals. Schuttlefield et al. (2007) measured
water adsorption by clay minerals having found a large variability in the
water uptake by different clay mineral species, with water/clay mass
ratios varying from 0.02 to 0.06 for kaolinite, going up to 0.17 for illite and
0.08–0.7 for montmorillonite, at 23
The estimation of total water content in the collected aerosols using the
above referred ratio assumptions is presented in Fig. 4. The figure shows
that, by using this methodology, there is almost a perfect account of the
Inclusion of estimated water in the IMB for the total sampling campaign and for the dust-free and dry-haze seasons.
The final ionic and mass balance calculations are presented in Fig. 5 for the total campaign and the two seasons, considering the components associated with the respective water uptake. The addition of sorbed water reinforces the impact of hygroscopic/soluble components, such as sea salt, in the atmospheric aerosol loading, which, for example, during the dust-free season increases its contribution from 25 % to 47 %.
The results of IMB can be evaluated and compared with PMF results applied to the same data set and already published by Salvador et al. (2016). Here, the published PMF results were reorganised, in order to make explicit the PMF contributions during the two dry-haze and dust-free seasons and to show unaccounted/excess calculated PM mass, as represented in Fig. 6.
IMB obtained by attributing the calculated water content to the
respective source classes for the entire campaign and the dust-free and
dry-haze seasons. Values are in
The PMF analysis could differentiate seven aerosol sources: three concerning soil
contamination and two considering secondary inorganic components, plus sea-salt
and combustion processes. The dust sources comprised “Mineral1”, associated
mainly with Al, Si and Fe; “Mineral2”, associated with
PMF source apportionment results for the total campaign and
during the two pollution seasons. Values are in
Figures 5–6 and Table 3 compare both methodologies for the two seasons
and the total campaign (for individual sampling events, consult Figs. S6 and
S7 in the Supplement). There is a good agreement between the two source
apportionment techniques. Both methodologies reproduce almost totally the
measured
Comparison between IMB and PMF results, grouped into four main
classes.
The figures and table show a good comparability between total soil dust
estimated by both methods in any season. In the IMB, the SoilDust sol fraction is
approximately equivalent to the Mineral2 component of PMF. The mass balance
method could not discriminate the Dust
Contribution of sea salt was also equally estimated by the two techniques, on average, during the dry-haze season, but during the dust-free period the IMB estimated somehow higher sea-salt levels than the PMF.
SIC values are similar in both source apportionment methodologies, but during the dust-free period PMF estimated higher SIC contributions. This was mainly due to higher estimations of ammonium secondary salts by PMF, which only can happen if other compounds are included in the source component, as evidenced by the PMF source profile.
There is also a higher contribution from carbonaceous/organic/combustion material in PMF, by comparison with IMB, during the dry season, although a high factor of 2 was applied to the OM/OC ratio in the mass balance approach. The inclusion by the PMF of other constituents from combustion processes in west Africa is, probably, the reason for the discrepancy.
Further insight into the capabilities and limitations of IMB versus PMF can
be attained by comparing source classes for each individual sample, as
presented in Fig. 7. From this figure it is possible to confirm the good
comparability between the soil source estimations by both methods, with a
linear ratio estimation of 1.04 and a correlation
For sea-salt estimation, the comparison is not so good with IMB/PMF ratio
estimation of only 0.68 and an
Both methodologies also compare reasonably well in what concerns secondary
inorganic compounds (SICs) contributions to the aerosol loading, with a
Comparison between the apportionment of individual samples using IMB and PMF for four source classes. Circles filled in white represent periods with intermittent dust intrusions. Linear best fits are presented for the total sampling campaign.
From Table 3 and Fig. 7 we may then conclude that the IMB solution compares well with PMF for dust and SIC, but the two methods show important discrepancies for individual samples, principally in the estimation of sea salt and carbonaceous aerosol contributions.
IMB and PMF are subject to a number of errors that affect the precision and accuracy of the sources' estimation. Fully accounting for all errors is difficult, because some used information (bibliographic or experimental) has no available accuracy or is subject to unknown and unexpected errors. PMF applies several statistical tests to evaluate the influence of random errors and the rotational ambiguity of the obtained solution. Although these tests indicated that the PMF solution was robust, they could not identify collinearity problems that resulted in the significant contamination of combustion and sea-salt sources with soil dust.
Both methodologies are equally affected by errors in the aerosol chemical analysis. Probably the higher relative analytical errors are related with elements and EC evaluation, in conditions of high dust concentrations, although care was taken to sample for shorter periods during dust episodes. EC is frequently near the detection limits and it is quite difficult to fully evaluate the interference of coloured dust during the thermo-optical analytical process. This affects, in an unknown value, principally, the evaluation of the PMF combustion source that uses EC as its principal tracer (see Fig. S8 for clarification).
In IMB, there are probably four estimations where the errors influencing the
source apportionment are higher: (a) calculation of water sorbed in sea salt;
(b) the estimation of total soil content based in factor
Estimation of water content in sea salt depends mainly on metastable
equilibrium considerations that give a water/dry sea-salt mass ratio
varying from 0.4 to 1.4. An average value of 0.9 was applied in our
calculations. Application of the two extreme values would vary the fractional
contribution of wet sea salt by approximately
The calculation of the total soil dust was based on Eq. (3) that uses an
average factor
The
The estimated value for non-carbonate carbonaceous matter depends strongly from the OM/OC ratio. In the literature, values in the range 1.2–2.3 have been proposed. We used a ratio of 2.0 in the high end of the range because the sampling was performed at a background location, away from primary combustion sources, with plenty of time for oxidation and secondary formation. However, during dust episodes, important fractions of the organic material have a soil origin and for that less reliable information exists. As the carbonaceous fraction in PM mass is only 2 %–6 %, errors in OM/OC ratio have only a small effect in total mass attribution, but important errors, of the order of 40 %, can result in the estimation of the carbonaceous mass if a OM/OC ratio of 1.6 is the correct assumption, instead of 2.0.
Atmospheric aerosol was collected during 1 year, as
The application of IMB to the collected aerosol permitted the determination of six to seven source classes: insoluble dust, soluble dust, sea salt, secondary inorganic compounds from the reaction of atmospheric acidic precursors with sea salt, dust and ammonia, and carbonaceous matter.
The sum of calculated components only partially closed the mass balance,
being 20 %–30 % of the measured
During the dry-haze season, dust contributed with around 80 % to the aerosol mass loading, while in the dust-free period the main aerosol component was sea salt that constituted approximately 50 % of the aerosol mass.
The IMB methodology was compared with PMF results applied to the same data set. In seasonal averaged terms, the outcomes of the two methodologies were comparable for the most important sources and formation processes. Comparison between individual samples showed, however, significant differences, principally for the sea-salt spray and the carbonaceous/combustion sources. Because of the overwhelming presence of dust in most samples, the PMF could not clearly separate dust from sea-salt sources. On the other hand, IMB could not discriminate soil organic matter from combustion emissions.
We can rely on the complementarity of both methods for the evaluation of sources contributing to atmospheric contamination, in circumstances of very high natural inputs of sea salt and desert dust particles, subject to atmospheric transformation during long-range transport. Utilisation of these two independent source apportionment methodologies adds confidence to the apportionment of an atmospheric aerosol with quite specific and uncommon characteristics.
Otherwise, source composition and contribution knowledge obtained with IMB can be used to complement the constrains already applied in the last versions of the PMF model (Amato and Hopke, 2012; Liu et al., 2015; Chen et al., 2018), in order to get more refined solutions than the original ones. Constraints can be created using specific ratios between two different species or mass balance equations derived from IMB techniques such as those performed in this study.
The data used in this study are available in the Supplement.
The supplement related to this article is available online at:
JC mounted the sampling station, collected the samples, analysed the carbonaceous and ionic fractions and participated in the treatment of data. SMA coordinated the analysis of elements and participated in the data treatment by PMF. TN coordinated the analysis of the mass and carbonaceous fraction, and participated in the treatment of the data. MA-S participated in the analysis of total mass and of elements by INAA. PC and MR were responsible for the analysis of elements by PIXE, and MR also participated in the paper discussion. MC coordinated the analysis of the ionic fraction and participated in data treatment and paper discussion. CA was responsible for the analysis of the organic fraction and participated in data treatment. FR was responsible for filter analysis by XRD. PS and BA coordinated the treatment of the data by PMF and participated in data treatment and paper elaboration. CP coordinated the CV-DUST project, was supervisor of JC PhD, developed the IMB methodology, treated the data and wrote the paper.
The authors declare that they have no conflict of interest.
The authors gratefully acknowledge the Portuguese Science Foundation through
the project CV-DUST – Atmospheric aerosol in the Cape Verde region: carbon
and soluble fractions of