The present study offers the first chemical characterization of the
submicron (PM
Atmospheric aerosols play a key role in the Earth's radiative forcing, by
interacting with incoming solar and outgoing terrestrial radiations (direct
effect) and influencing cloud formation, growth and lifetime (indirect
effects). Important uncertainties related to such effects remain, as reported
in the IPCC reports (IPCC, 2007, 2013). Besides, several
epidemiological and toxicological studies (Brook et al., 2004; Kelly
and Fussell, 2012) also highlight the sanitary impacts of particulate matter
(PM) depending on its size, chemical composition and exposure time. In
2012, around 3.7 million deaths were attributed to cardiovascular and
respiratory diseases caused by outdoor PM exposure (Brauer et al.,
2012). As a response, in 2006 the World Health Organization (WHO) established
air quality thresholds for PM: a daily average of 25 (50)
Several studies in western Africa (mostly sub-Saharan regions) have focused on
the coarse fraction, since this area is strongly influenced by natural
sources, especially the Sahara, which injects high amounts of mineral
aerosols into the atmosphere (e.g., Chiapello et al., 1995). Among field
campaigns, a large effort was performed on aerosol measurements during the
AMMA (African Monsoon Multidisciplinary Analysis) program carried out between
2002 and 2010, with intensive observation periods in 2006 (Redelsperger et al., 2006).
During the months of January–February (dry season) the transport of desert dust (DD) and biomass
burning (BB) aerosols were observed to occur into two layers, one dominated
by DD close to the surface (< 1 km) and one containing BB aerosols
that is located at a higher altitude, between 2 and 4 km (Haywood
et al., 2008; Osborne et al., 2008). During one of the AMMA special observing
periods (SOPs), in February 2006, a chemical characterization of particles at
the ground level was performed near Mbour (Senegal) using filter sampling
and individual particle analysis (Deboudt et al., 2010; Flament et
al., 2011). The analysis evidenced both internal and external mixing of DD,
sea salt (SS) and carbonaceous aerosols (Deboudt et al., 2010) within
the surface layer. The compositions of the coarse (2–10
In this study we offer the first insight of a real-time, continuous and
long-term chemical characterization of PM
The sampling site is located within the
The main objectives of SHADOW are to better determine the physical and chemical
properties of particles in this region, largely influenced by high
concentrations, and to establish a link between them, the atmospheric
dynamics, and the aerosol load and optical properties. A large panel of
high-performance instruments has therefore been added to the AERONET station
(Holben et al., 1998) implemented in Mbour since 1996 (Derimian et al.,
2008). Optical and microphysical aerosol measurements (results not presented)
were also performed on-site by active and passive remote-sensing instruments,
such as the LiLAS lidar (Bovchaliuk et al., 2016; Veselovskii et al.,
2016) and a PLASMA airborne sun photometer (Karol et al., 2013).
PM
The online chemical composition measurements presented here were acquired
during IOP-1, which took place from 20 March to 22 June 2015. Results
discussed in this paper focus on the chemical characterization of surface
PM
The chemical characterization of nonrefractory submicron particles
(NR PM
Several calibrations were performed with ammonium nitrate, ammonium sulfate
and ammonium chloride individual solutions (at 0.005 mol L
Real-time measurements of aerosol absorption were performed every minute by a
seven-wavelength (370, 470, 520, 590, 660, 880 and 950 nm) Aethalometer
(AE33, Magee Scientific Inc.). The instrument was equipped with a PM
To achieve mass closure in the submicron fraction and account for the
expected refractory material (mineral dust and sea salt), ambient air was
sampled at 16.7 L min
Micrometeorological parameters (wind speed and direction) at the surface
(
The station of Mbour is under the influence of a typical Sahelian climatic
cycle composed of two contrasted dry and wet seasons observed around the
Equator, which originate from the closeness of the Intertropical Convergence
Zone (ITCZ), bringing moist air masses and heavy precipitation.
Kaly et al. (2015), based on 5 years of observations (2006–2010) at Mbour, defined the
dry season as the period during which no precipitation occurs from November
to April and the wet season from May to October, where significant
precipitation is measured, with a transition during April–May. In
the work of Mortier et al. (2016), who analyzed data from
2006 to 2012 at Mbour, the seasons are
defined based on RH levels: from December to March–April (RH < 40 %)
for the dry season and from June to September (RH
Therefore we based the definition of the dry and wet seasons in this work on
the observed weather parameters during the field campaign. Since absolutely
no precipitation was observed during the whole period, but differences in RH
levels – though not as pronounced as reported by Mortier et al. (2016) –
and wind patterns were clearly visible (Fig. 2a), we considered March (RH
Rose plots of wind direction divided into 15
During IOP-1 two main prevailing directions were found (Fig. 2a). The first
one corresponds to an oceanic influence characterized by surface winds coming
from west-southwest to northwest (210–300
Our measurements during IOP-1 are generally consistent with monthly average
frequencies of Mbour surface wind directions reported by Kaly et al. (2015)
between 2006 and 2010. Indeed, their climatology has shown that spring
months are generally influenced by winds coming predominantly from two main
sectors, north to east (0–90
Each sampling day of IOP-1 was classified according to the locally measured
surface wind directions. Three categories of days were indeed identified: (i) days
exclusively under northern trade wind influences, i.e., within
Mbour being largely under the influence of mineral dust, a possible
overestimation of the amount of BC derived from absorption coefficients
measured by the Aethalometer (due to DD absorbing properties at shorter
wavelengths) has to be considered (Bond and Bergstrom, 2006). These
interferences may be enhanced during the dry season by internal mixture of BB
and DD, as encountered and evidenced during AMMA by Deboudt et al. (2010) at
the surface and by Hand et al. (2010) and Paris et al. (2010) at higher altitudes.
Consequently, BC absorption coefficients have been
recalculated following the method developed by Fialho et al. (2005, 2006, 2014)
in order to obtain BC concentrations unbiased by DD
influence. It consists of a deconvolution of the wavelength-dependent aerosol
absorption coefficient over time,
Fialho et al. (2006) have replaced dust with iron in order to calculate an
iron concentration from dust absorption:
Combining Eq. (4) with Eqs. (1, 2 and 3) leads to the following expression:
Applying the propagation for uncertainties approach on the values of
The PMF model is a statistical source-receptor model developed by
Paatero and Tapper (1994), largely employed in source
apportionment of atmospheric pollutants when the source profiles and
contributions are not known a priori. In this study, PMF was applied
on the ACSM organic and chloride mass spectra by using the multilinear engine
(Paatero and Tapper, 1994) and the version 5.3 of the source finder (SoFi)
described in Canonaco et al. (2013) and operated with IgorPro 6.37
(Wavemetrics). PMF is based on a bilinear model described by the following
equations:
Air masses reaching the site were characterized through 48 h
back trajectories (every 3 h) retrieved from the computer version of the
Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT;
Draxler and Hess, 1998), for an altitude set at one half of the mixed-layer
depth and coupled with the GDAS (1
We also used pollution roses to identify local wind directions leading to high concentrations for each species or PMF factors, but also two additional tools provided by the ZeFir Igor-based package developed by Petit et al. (2017): (i) nonparametric regression (NWR, Henry et al., 2009) plots, which combine smoothed surface concentrations and local wind speed and direction, to discriminate between local and more distant or regional sources; (ii) potential source contribution function (PSCF, Polissar et al., 2001) maps for regional sources, which couple time series of one variable with air mass back trajectories to redistribute the concentrations observed at the site into geographical emission parcels.
As previously mentioned, Senegal is widely influenced by DD events
transported from arid and semiarid regions of Sahara and Sahel. Moreover,
Mbour being a coastal site, the influence of SS particles on the
measured aerosol mass concentrations may be significant. Thus, the
contributions of these two aerosol types, in both the coarse and fine
fractions of aerosol, have been investigated. PM
Figure 3a shows the mass concentration time series of NR PM
The temporal evolutions of the three aerosol fractions do not show any
particular correlations (the highest correlation coefficient is obtained
between the PM
Despite similar orders of magnitude between the values of NR PM
Fe and BC concentrations obtained after correction led to averaged values of
0.55
Time series of Fe and PM
Fe concentrations estimated from PM
From the only study in the literature focusing on iron concentrations in the
submicron fraction in western Africa (Val et al., 2013), we could infer an
elemental iron contribution of 7.8 % to PM
Table 1 reports ACSM measurements performed at Mbour between 20 March and
22 June 2015, as well as other field campaigns carried out worldwide. This
study shows an average value of 5.4
Averaged NR PM
The average NR PM
Figure 5 represents the average contributions of NR PM
Averaged contributions of NR PM
Comparable dynamics was already observed at Mbour during AMMA SOP-0. For
instance, Haywood et al. (2008) highlighted that BB aerosols
carried at high altitude (
Aerosol acidity can be considered as an indicator of the age of particles as
they will get neutralized during their stay in the atmosphere. In order to
estimate the degree of neutralization of ACSM inorganic species, NH
The slope obtained between measured and predicted NH
Figure 6 displays the 30 min temporal variability of NR PM
(Bottom) IOP-1 stacked time series of OM (green), SO
OM and SO
The daily profiles of all identified PM
Average daily profiles of
The marine averaged daily profiles show a very distinctive pattern, compared
to continental and sea breeze days (Fig. 7a), and are characterized by a sharp
decrease of OM, BC and Fe, with nitrate, ammonium and chloride presenting
rather constant profiles, while sulfate exhibits a higher and almost constant
concentration of 2.4
In Fig. 7b, Fe and BC profiles showed distinctive behaviors depending on
day types. As expected, both profiles for marine days are characterized by
very low concentrations of both BC and Fe in comparison to those measured
for sea breeze and continental days. In the latter cases, a major difference
can be observed for Fe concentrations which are almost twice as high as BC
ones. Fe exhibits an additional intense peak around 15:00 LT and concentrations
above 1.5
Pollution rose plots of OM, BC, Chl, Fe, SO
As observed previously, most of the winds reaching Mbour during IOP-1 were
associated with the north and west sectors, carrying air masses
from the continent and the ocean, respectively, with an averaged wind speed of 2.6 m s
Figure 8 also highlights an oceanic origin for some OM (with moderate
intensity), SO
Regarding the iron pollution rose plots and NWR plots reported in Figs. 8 and
S5b, maxima are measured when the site is under the influence of
northeastern
winds. The NWR plot evidences both local emissions possibly linked to
traffic resuspension of DD and a regional component, that the Fe PSCF map
clearly attributes to the Saharan region. For such directions we also
observe maximum values of the total PM
PMF was first applied without constraints on the whole IOP-1 organic database
and then separately on continental, sea breeze and marine extracted datasets
(see also Supplement S4). For each run, 3 to 10 factors were
tested and only solutions with a normalized
PMF constrained four-factor solution:
A final constrained solution of four factors is presented in Fig. 9 through
their respective mass spectra with associated pollution rose plots and daily
profiles for continental, sea breeze and marine days. The three primary factors linked to anthropogenic activities, that is to say HOA, COA and LCOA
corresponded to 18, 30 and 3 % of the organic fraction, respectively. HOA
and COA contributions to the OA fraction tend to be more important for
continental and sea breeze days with 21–24 and 24–31 %, respectively
than for marine days (11–20 %), while LCOA increases slightly from 2 to
7 %. The HOA profile is well correlated with BC (
As for LCOA, which is a very small fraction of the total OA, the fact that it
nonetheless consistently appears in the PMF analysis under both unconstrained
and constrained conditions suggests a specific behavior uncorrelated with
other sources. Its robustness has been tested under various starting
conditions (50 seed iterations) and through rotational ambiguity exploration
(
Additionally, the measured NH
OOA are often considered as SOA formed through
gas-conversion processes of volatile organic compounds or
photochemical oxidation of primary OA emitted by biogenic (such as plants or
algae) and/or anthropogenic sources. The hot temperatures and intense solar
irradiation encountered in the region enhance these processes and can explain
the major contribution (45 %) observed for the OOA factor during IOP-1,
and the predominance (
Previous observations of OM from daily filter measurements in the PM
The deployment of high-time-resolution instruments at the western African
coastal site of Mbour (Senegal) over 3 months, encompassing the end of
the dry season and the transition period toward the wet season of 2015,
allowed the investigation of the temporal variability and chemical composition of
the poorly characterized PM
Source apportionment of the organic fraction allowed the identification of four types
of OA. The organic fraction is composed of a highly oxidized OOA (45 %),
whose regional origin is underlined by its important contribution to the
organic fraction during marine days (62 %) but also by its increasing
concentration during daytime, with a maximum under sea breeze influence.
Nonetheless, its higher correlation with NO
Overall, our study suggests that natural sources strongly influence PM
As shown during this field campaign, at least half of the organic aerosols
measured in the submicron fraction are from anthropogenic origins
(HOA
Data from ACSM and Aethalometer instruments are available upon request to
the corresponding author, Véronique Riffault. PM
The authors declare that they have no conflict of interest.
Laura-Hélèna Rivellini's PhD grant and the SHADOW campaign are financially supported by the
CaPPA project (Chemical and Physical Properties of the Atmosphere), which is
funded by the French National Research Agency (ANR) through the PIA
(