Introduction
Sulfur (S) in atmospheric aerosols has garnered significant interest because
of its influence on environmental processes (Bufalini, 1971; Galloway,
1995; Van Grieken et al., 1998) and human health (Burnett et
al., 1995). Sulfur-rich aerosol can serve as cloud condensation nuclei. On a
global scale, cloud condensation nuclei participate in cloud formation
processes to change atmospheric albedo (Charlson et al., 1987; Quinn and
Bates, 2011), which can ultimately influence global climate. Atmospheric
sulfur is responsible for the production of sulfuric acid, which has been
implicated in the solubilization of metals in aerosol (Wiederhold et al.,
2006; Nenes et al., 2011; Sullivan et al., 2007; Oakes et al., 2012a).
Solubilization of recalcitrant mineral phases during atmospheric transport
can release nutrient elements, such as iron, which impacts biological
productivity in many oceanic regions (Longo et al., 2014, 2016; Mahowald et al., 2005; Jickells et al., 2005). Additionally, soluble
metals present in the urban environment may cause adverse respiratory events
among affected populations (Fang et al., 2016; Pardo et al., 2016).
The range of sulfur oxidation states as well as organic and inorganic forms
present in typical samples confounds characterization of aerosol sulfur.
Characterization approaches, including ion chromatography, X-ray
fluorescence, and inductively coupled plasma mass spectrometry, have been
used to quantify bulk elemental or bulk concentrations, which can be used to
infer the composition of sulfur in aerosols. However, these techniques
cannot determine the oxidation state or directly identify the chemical form
of aerosol sulfur. Several studies have demonstrated the effectiveness of
synchrotron-based sulfur X-ray absorption near-edge structure (XANES) and
the closely related sulfur near-edge X-ray fluorescence spectroscopy (NEXFS)
to determine the oxidation state and chemical form of sulfur in
environmental samples (Solomon et al., 2003; Prietzel et al., 2011; Morra
et al., 1997; Farges et al., 2009); however, the application of these
techniques to ambient aerosol samples has largely been limited to studies
characterizing a few ambient aerosol samples that aim to explore the
feasibility and limits of applying XANES and NEXFS to aerosol sulfur studies
(Takahashi et al., 2006; Pongpiachan et al., 2012a, b; Higashi and Takahashi, 2009; Cozzi et al., 2009).
Consistent with traditional techniques, synchrotron-based XANES studies
indicate that sulfur occurs primarily in the S+VI oxidation state,
largely as inorganic sulfate compounds. Primary emission sources of
S+VI include both anthropogenic and biogenic sources, including
combustion of fossil fuels, biomass burning, volcanoes, and sea spray
(Querol et al., 2000). Sulfur in aerosol, ranging in oxidation state from
S-II to S+IV, has also been identified in smaller quantities
through the use of XANES techniques (Huggins et al., 2000; Eatough et al.,
1978; Cao et al., 2015; Higashi and Takahashi, 2009; Craig et al., 1974; Cozzi
et al., 2009). Primary sources of reduced sulfur to the atmosphere are
mainly attributed to gaseous emissions from volcanic gases, hot springs,
bacteria, vehicle exhaust, and oil refineries (Cozzi et al.,
2009; Andersson et al., 2006). While detected in the solid phase, reduced
sulfur in aerosols has not been extensively studied. Reduced aerosol sulfur
may reflect a contribution from primary emission sources or may result from
condensation of volatile reduced sulfur gaseous phases, but the ultimate
origin of reduced sulfur in aerosol particulates remains unresolved.
Here we use S-NEXFS to investigate the composition and oxidation state of
sulfur in ambient aerosols and common primary emission source samples.
Previous S-NEXFS studies characterized a limited number of ambient aerosol
samples in bulk, providing an average view of the sulfur oxidation state and
composition in particulate matter (Higashi and Takahashi, 2009; Takahashi
et al., 2006; Pongpiachan et al., 2012a, b; Long et
al., 2014). Recent studies of aerosol iron and selenium have demonstrated
that analyzing compositional differences between large particles
(> 1 µm) and the bulk sample can help elucidate aerosol
sources and transformations (Longo et al., 2016; Oakes et al., 2012b; De
Santiago et al., 2014). Following a similar approach, we used a combination
of bulk and individual particle S-NEXFS analyses to characterize aerosol
sulfur in rural and urban samples and from different emission sources. The
spatial distribution of sulfur oxidation states was also characterized in
individual particles by mapping particles at different incident energies to
gain further insights into the processes surrounding the formation of
reduced sulfur species in aerosol.
Methods
Ambient aerosol collection
PM2.5 samples were collected from urban and rural locations in and around
Atlanta, GA, USA. The five sampling sites, South DeKalb, Fire Station 8,
Jefferson Street, Yorkville, and Fort Yargo State Park, are associated with
two ongoing studies, the Southeastern Aerosol Research and Characterization
(SEARCH) study (Edgerton et al., 2005, 2006; Hansen et
al., 2003) and the Assessment of Spatial Aerosol and Composition in Atlanta
(Butler et al., 2003). Samples were collected on Zefluor filters
over 24 h periods at a flow rate of 16.7 L min-1 using cyclone inlet
samplers (URG, Chapel Hill, NC, USA). The multichannel particle samplers were
mounted approximately 2.5 m off the ground. Three samples were collected
from Yorkville (33∘55′42.3 N, 85∘2′43.68 W), a rural
background site for the Atlanta area. Two samples were collected from
Jefferson Street (33∘46′38.94 N, 84∘24′59.58 W), an
urban collection site that is not immediately influenced by any individual
source or roadside traffic. Five samples were collected from Fort Yargo
State Park (33∘58′4.18 N, 83∘43′29.16 W), a rural setting that is frequently impacted by biomass burning
plumes and power plant emissions. Four samples were collected in South
DeKalb, a mixed commercial–residential area approximately 1 km from a
major interstate (33∘41′16.48 N, 84∘17′25.26 W). Seven samples were collected from Fire Station 8, located in an
industrial area in close proximity to a rail yard, a fire station, and
an intersection with heavy diesel truck traffic (33∘48′6.01 N, 84∘26′8.75 W; Table S1 in the Supplement).
Emission source collection
PM2.5 samples were collected from gasoline and diesel exhaust, biomass
burning, and coal fly ash (Oakes et al., 2012a). Emissions from ultra-low
sulfur diesel fuel running a 10.8 L engine and conventional gasoline fuel
running a 3.3 L engine were collected according to US Environmental
Protection Agency protocols under typical urban driving conditions (Liu
et al., 2008; Oakes et al., 2012a). Polydisperse coal fly ash, provided by
Southern Company, from an electrostatic precipitator of a midsized
coal-fired power plant was aerosolized and collected with a cyclone inlet
sampler to separate the PM2.5 fraction (Oakes et al., 2012a). Smoke
produced from the burning of materials collected from coniferous and
deciduous trees native to Georgia, USA, was sampled during a controlled
biomass burning experiment using a cyclone inlet sampler placed 3.5 m above
the burn area at a flow rate of 16.7 L min-1 for approximately 30 min.
The ash produced from the biomass burning experiment was analyzed in the
same manner. The abovementioned primary emission source samples were collected on
polytetrafluoroethylene (Zefluor) filters (Longo et
al., 2014). In addition, two commercially available bacteria samples,
Azotobacter vinelandii (Sigma A2135) and Bacillus subtilis (Sigma B4006), were homogenized with agate mortar and
pestle and mounted on a cellulose acetate filter for analysis.
All samples were immediately stored after preparation in clean Petri dishes
at -20 ∘C until analysis. Preparation for synchrotron analyses
consisted of mounting an approximate 0.5 cm × 0.5 cm section of
ambient aerosol and primary emission source filters over a slot on an
aluminum support.
Sulfate standards
In order to create a database of inorganic sulfate standards necessary for
the data analysis described below, 10 compounds were analyzed using the
same experimental setup and synchrotron system as the ambient aerosols.
These standards included ammonium sulfate (CAS 7783-20-2), barite,
copper(II) sulfate (CAS 7758-98-7), gypsum, iron ammonium sulfate (CAS
7783-85-9), iron(III) sulfate (CAS 15244-10-7), jarosite
(KFe33+(OH)6(SO4)2, magnesium sulfate (CAS
7487-88-9), potassium sulfate (CAS 7778-80-5), and sodium sulfate (CAS
7757-82-6). The sulfur standards were ground using an agate mortar and
pestle to the consistency of a fine talcum powder (approximately 10 µm). A cellulose acetate filter was then gently dredged through a small
quantity (less than 1 mg) of powder placed on a microscope slide. This
procedure produced a thin and almost imperceptible coating on the filter in
order to limit the thickness and thus self-absorption. S-NEXFS spectra of
sulfate standards were collected in bulk mode. Self-absorption must be
carefully controlled when measuring fluorescent X-rays from thick specimens;
however, the effects of self-absorption are limited to the region of the
spectrum above the K-edge (Bajt et al., 1993; Iida and
Noma, 1993). In our repeated measurements, the post-edge features were
consistent and reproducible, which allows us to distinguish between sulfate
standards. A database of sulfate standards is provided in the Supplement.
Synchrotron-based spectromicroscopy
Samples were analyzed on the X-ray fluorescence microscope located at
beamline 2-ID-B at the Advanced Photon Source, Argonne National Laboratory.
The beamline is optimized to examine samples over a 1–4 keV energy range
using a focused X-ray beam with a spot size of approximately 60 nm2 (McNulty et al., 2003). The energy was
calibrated using an elemental sulfur standard (S0). The whiteline
energy of the elemental sulfur standard was aligned to 2472 eV (Cozzi et
al., 2009). Sulfur near-edge X-ray fluorescence spectroscopy (S-NEXFS) data
were collected in two modes that differ based on spatial resolution. In the
first mode, individual sulfur-rich particles with a diameter greater than
1 µm were identified in X-ray fluorescence maps; these particles were
then interrogated with micro-S-NEXFS. The number of individual particles
examined that provided usable spectra are provided in Table S1. The
individual sulfur-rich particles seen in X-ray fluorescence maps are obvious
contributors to the total sample sulfur. However, much of the total sulfur
on an aerosol filter can also be contained in particles that are smaller
than 1 µm or less sulfur-rich and therefore less apparent in X-ray
fluorescence maps. Consequently, in the second mode, large areas of the filters
were also examined with an unfocused beam (spot size = 0.25 mm).
In order to maximize the number of samples analyzed in the allotted time,
X-ray fluorescence (XRF) maps were created for a subset of samples by
rastering the sample through the focused beam in 0.5 µm steps with an
incident energy of 2535 eV. At this resolution, individual sulfur-rich
particles were clearly discernible. S-NEXFS spectra were scanned over a 50 eV range centered at 2485 eV in 0.33 eV steps, using a 1 s dwell time at
each step. Here, dwell time is used to refer to the time spent at each step
in the S-NEXFS spectrum or each pixel in an XRF map. Each S-NEXFS
measurement for both bulk and individual sulfur-rich particles were repeated
at least three times in a single location, creating a minimum effective dwell
time of 3 s. For these settings, the total scan time would be approximately
7.5 min. X-ray spectromicroscopy data were collected using an energy
dispersive silicon drift detector (Vortex with a 50 mm2 sensitive
area). A flow of helium was introduced between the X-ray optical hardware
and the sample to reduce elastically scattered X-ray background. An in-line
monitor stick coated with an aluminum sulfate standard was measured in
parallel with each sample in order to identify and correct for any potential
drift in monochromator energy calibration that can occur during analyses
(de Jonge et al., 2010), meaning that for every spectral
measurement two spectra are collected: the specimen and an aluminum sulfate
standard on the monitor stick. Because the energy was calibrated using
elemental sulfur S0, all the data use 2472 eV as the reference energy
for S0 during the data alignment to the monitor stick. Oxidation tests
were also done on a few samples where the same region or particle was
measured repeatedly, creating effective dwell times of more than 10 s. Even
with this longer dwell time, there was no noticeable shift in oxidation
state or the relative abundance of the oxidation states in the tested
samples. Clean areas of filter were examined as blanks and showed negligible
background signal.
S-NEXFS provides essentially the same information as another commonly cited
technique, S-XANES (sulfur X-ray absorption near-edge structure)
spectroscopy. The two techniques differ primarily in the method of signal
detection. S-NEXFS uses the X-ray fluorescence signal, which is inversely
proportional to the absorption signal used in a XANES measurement.
Additionally, XRF microscopy was conducted in two modes. First, to map the
distribution of sulfur regardless of oxidation state, an incident energy
above the S K-edge (2535 eV) was used, and samples were rastered through the
focused beam in 0.5 µm steps, as mentioned above. In the second mode,
the spatial distribution of S0 and S+VI oxidation states were
quickly mapped by selecting an incident X-ray energy tuned to their specific
whiteline energies. Because of the energy drift at this beamline, it is
important to identify the correct whiteline energies for the multi-energy
mapping; therefore, a S-NEXFS spectrum was collected for the particle of interest
immediately before mapping. The corresponding whiteline energies for S0
and S+VI were then taken directly off this spectrum. Individual maps
were then collected at the whiteline energies determined for S0 and
S+VI. Although it is possible that the energy drifted during mapping,
the interval between the two measurements is short enough that this is not a
problem. Due to the 6 eV difference between the whiteline energies of
S0 and S+VI, we do not expect significant overlap between the two
oxidation states in multi-energy mapping. Energy drift during mapping may
reduce signal intensities of S0 and S+VI, but the overall
distribution patterns of the sulfur oxidation states in the individual
particle should remain relatively unaffected. These multi-energy XRF maps
were completed in 0.3 µm spatial resolution and 0.5 s dwell time per
pixel. X-ray focusing was adjusted to maintain a consistent beam spot size
at both energies; however, some shift in the final image did occur. The
images were aligned using the stack registration function of the Fiji
software (Schindelin et al., 2012).
Data analysis
S-NEXFS data were normalized to create a relative intensity value of
approximately 1 for post-edge area of the spectra. The data were also
processed using a three-point smoothing algorithm built into the software
package Athena to remove high-frequency noise (Ravel and Newville,
2005). The relative contribution of each oxidation state can be determined
by assuming the entire area under the whiteline peaks is representative of
total sulfur, and the area under each whiteline peak represents the relative
contribution of each oxidation state (Huffman et al., 1991; Xia et al.,
1998). The area under the whiteline peaks was determined using the Gaussian
peak fitting function of Athena (Ravel and Newville, 2005) (Fig. S1 in the Supplement).
Because of the relatively low contribution of other sulfur oxidation
states, only sulfur species containing S+VI oxidation state could be
further characterized via linear combination fitting. Linear combination
fitting is an effective tool for the deconvolution of spectra of known
mixtures (Longo et al., 2014; Long et al., 2014; Huffman et al.,
1991; Solomon et al., 2003; Prietzel et al., 2011). Using Athena software,
individual particle and bulk S-NEXFS spectra were fit with previously
characterized sulfate standard materials using a linear combination approach
to determine both speciation and relative abundance of sulfate phases
(Ravel and Newville, 2005) (Fig. S2). Athena uses a nonlinear, least-squares minimization approach to fit spectra of unknown materials with
spectra of standard materials and computes an error term, R factor, to
quantify the goodness of fit produced by a particular linear combination of
standard S-NEXFS spectra. The linear combination of standards that yielded
the lowest R factor reflects the best fit (Ravel and Newville, 2005).
Oxidation state of sulfur in common emission sources. Common
emission sources were generally dominated by S+VI (grey). Only the
bacteria sample Azotobacter vinelandii showed a signal for S0 as well as S+VI.
Bulk oxidation state of ambient particulate matter samples. Ambient
particulate matter samples were characterized at the bulk level for
oxidation state. Here, the fractional relative abundance of S0 (black)
and S+VI (grey) are shown for each of the ambient particulate matter
samples. S0 is only present in two samples collected from South DeKalb.
The remaining samples contain only S+VI when examined in bulk.
Oxidation state of individual aerosol particles. The oxidation
state of sulfur was examined in individual particles from a subset of
samples. The fractional relative abundance of S0 (black) and S+VI
(grey) is shown for each particle interrogated with S-NEXFS. At the
individual particle level, sulfur is consistently seen at both the S0
and S+VI oxidation states.
Results
Oxidation state
Azotobacter vinelandii, Bacillus subtilis, gasoline and diesel exhaust, biomass burning, and coal fly ash were only
characterized at the bulk level (approximately 0.28 mm2 filter area).
These common primary emission sources, with the exception of one bacteria
sample, contained sulfur solely in the S+VI oxidation state.
Azotobacter vinelandii was the only emission source to contain both oxidized and reduced sulfur,
with approximately 44 % S+VI and 56 % S0 (Fig. 1).
At the bulk level, the oxidation state of sulfur in ambient PM2.5 samples is
dominated by S+VI; only two samples from South DeKalb contain S0
at quantities of less than 10 % total sulfur (Fig. 2). In contrast,
individual particles with aerodynamic diameters of greater than 1 µm
consistently contain both S+VI and S0 (Fig. 3). Only 1 out of
23 individual particles analyzed did not contain S0. At Fire
Station 8 and Jefferson Street, the individual particles sampled contain on
average 10 % S0. Individual particles from South DeKalb average only
4 % S0. The rural individual particles sampled from Fort Yargo and
Yorkville contain on average 5 and 9 % S0, respectively.
Multi-energy maps revealed that S+VI was present throughout the aerosol
particle. Often the highest concentration of S+VI was in the center of
the particle, where the most mass is present (Fig. S3). The spatial
distribution of S0 was more varied. In some cases, the S0 was
concentrated in the center of the particle with a less prominent ring on the
outside of the particle. In other cases, the S0 was most concentrated
in only one area of the particle (Fig. S3).
Composition of aerosol sulfate. Bulk (top) and individual particle
(bottom) composition of sulfur in ambient particulate matter. For bulk
samples, the composition of sulfate was consistent for all samples within a
particular sampling location, with the exception of South DeKalb. These
samples had three different compositional groups, and the month and year of
collection are provided next to the samples. For individual particles, the
sulfate composition was able to be determined for samples from Fire Station
8 (FS8), Fort Yargo (FY), and South DeKalb (SD). Different particles from
the same samples are numbered sequentially (P1–P5).
Sulfate composition
The relative contribution of different sulfur species can be determined by
the deconvolution of S-NEXFS spectra with linear combination fitting
(Prietzel et al., 2011; Ravel and Newville, 2005). In these samples, only
S+VI could be further characterized through linear combination fitting
because it was by far the most abundant species of sulfur present in the
samples. The regions of the S-NEXFS spectra represented by the lower sulfur
oxidation states did not possess sufficient detail to yield compositional
information. Because of spectral similarities between various metal
sulfates, linear combination fits using copper(II) sulfate or iron(III)
sulfate often yielded fits with similar R factors. This makes the absolute
determination of metal sulfate composition difficult; thus, iron(III)
sulfate, copper(II) sulfate, and jarosite are referred to collectively as
metal sulfates. The specific compositional information derived from the best
linear combination fits, i.e., iron(III) sulfate, copper(II) sulfate, or
jarosite, is presented in Tables S2 and S3, and suggests that a combination
of iron and copper sulfates is likely present in these samples. In common
primary emission sources and ambient PM2.5, S+VI corresponded to
sulfate aerosol. In emission sources, only biomass burning, coal fly ash,
and diesel exhaust had robust enough signals to characterize specific
sulfate composition, and each source had a unique sulfate composition (Table S3). Biomass burning contained potassium sulfate (100 ± 0.005 %).
Coal fly ash contained gypsum (100 ± 0 %), the mineral form of
calcium sulfate. Diesel exhaust contained ammonium sulfate (70 ± 11 %) and metal sulfate (30 ± 12 %).
The composition of sulfate was generally consistent within a sampling
location (Table S3 and Fig. 4). At the bulk level, rural sampling
locations contain a mix of ammonium sulfate and metal sulfate. Yorkville
samples are comprised of 61 ± 9 % ammonium sulfate and 39 ± 9 % metal sulfate, and Fort Yargo samples contain 84 ± 7 %
ammonium sulfate and 16 ± 7 % metal sulfate. Bulk urban samples all
contain a large fraction of ammonium sulfate. Samples from Fire Station 8
contain ammonium sulfate (84 ± 7 %) mixed with metal sulfate (14 ± 16 %), and Jefferson
Street contains ammonium sulfate (51 ± 8 %) combined with metal sulfate (50 ± 10 %). South DeKalb has
three different compositional groups at bulk level: (1) ammonium sulfate (63 ± 7 %), metal sulfate (29 ± 6 %),
and potassium sulfate (8 ± 13 %); (2) ammonium sulfate (42 ± 14 %) and metal sulfate
(58 ± 14 %); and (3) gypsum (43 ± 15 %) and metal sulfate
(58 ± 15 %).
Only individual particle S-NEXFS spectra from Fire Station 8, Fort Yargo,
and South DeKalb contained enough sulfur to determine the sulfate
composition (Table S3 and Fig. 4). In total, 17 individual particle
S-NEXFS spectra were used to gain insights on the sulfate composition of
ambient PM2.5. Consistent with the bulk results, individual particles
sampled from Fire Station 8 are largely ammonium sulfate and metal sulfate,
with the remainder made of gypsum. Individual particles from South DeKalb
contain either ammonium sulfate or metal sulfate. Individual particles from
Fort Yargo contain a mixture of mostly ammonium sulfate and potassium
sulfate with additional contributions from metal sulfate and gypsum.
Spectral signals of samples from Jefferson Street and Yorkville were not
strong enough to characterize the composition of sulfate in the ambient
PM2.5 samples.
Discussion
Ammonium sulfate and gypsum are commonly identified phases in studies of
aerosol sulfate composition (Long et al., 2014; Higashi and Takahashi,
2009; Takahashi et al., 2006). Consistent with these studies, ammonium
sulfate was found across all urban and rural sampling locations, and either
ammonium sulfate or gypsum was a major constituent of all bulk samples. At
the individual particle level, ammonium sulfate was also a primary
contributor to sulfate composition; however, potassium and metal sulfates
became more apparent. Our results showed that the sulfate in biomass burning
was dominated by potassium sulfate, which suggests biomass burning may be
the primary source of the potassium sulfate in ambient aerosol samples.
Potassium is often used as a tracer for biomass burning in source
apportionment studies (Viana et al., 2008), which show that biomass burning can contribute up to 40 % of total
PM2.5 in Georgia's spring months and 10 % of total PM2.5 in Georgia's
summer months (Tian et al., 2009). Organosulfates have
recently been identified as a significant component of sulfate aerosol
(Liao et al., 2015; Schmitt-Kopplin et al., 2010; Surratt et al., 2008; Xu
et al., 2015). In this study, organosulfates were not identified as a
constituent of sulfate aerosol. While organosulfates are not present in our
sulfate standard database, we were able to account for all the sulfate in
our samples without this class of compounds. However, the expected
concentration of organosulfates in ambient aerosol could be near or below
10 %, which is the detection limit of linear combination fitting techniques
(Longo et al., 2014; Oakes et al., 2012a, b). Therefore,
organosulfates may still be present in small quantities.
Metal sulfates were identified as a constituent of all the bulk ambient
particulate matter samples and 53 % of individual particle spectra. Linear
combination fitting of S-NEXFS spectra suggests that iron(III) sulfates are
an important group of metal sulfates in ambient Atlanta aerosol (Table S2 and S3). Furthermore, an Fe-XANES study of ambient Atlanta particulate
matter identified iron(III) sulfates, which accounted for approximately
20 % of the total iron (Oakes et al., 2012a). Iron sulfate represents a
very soluble, bioavailable form of iron that has been found in ambient
aerosol (Zhang et al., 2014; Moffet et al., 2012; Schroth et al.,
2009; Oakes et al., 2012a). Diesel exhaust was the only primary emission
source to contain metal sulfates in this study, and because source
apportionment studies show that diesel emissions account for approximately
5.3 % of PM2.5 in Atlanta (Hu et al., 2014), it is
unlikely that diesel exhaust alone can explain the quantities of metal
sulfates found in our ambient aerosol samples. This could suggest that
secondary processes play a role in the composition of S+VI in ambient
aerosol samples. Iron sulfate may be the end product of acidic reactions
hypothesized to occur in the atmosphere that are responsible for
solubilizing iron. Correlations between the sulfur content and iron
solubility have previously been used to suggest that this mechanism plays a role
in shaping the composition of iron in ambient Atlanta aerosol (Oakes et
al., 2012a). Briefly, sulfuric acid solubilizes more crystalline iron phases,
resulting in a solution rich in soluble iron and sulfate that can become
internally mixed and potentially precipitate out as iron(III) sulfate
(Moffet et al., 2012; Zhang et al., 2014; Oakes et al., 2012a). Evidence of
similar processes has been found for copper (Fang et al.,
2015), which is another metal that is likely present in these samples (Tables S2 and S3). The metal sulfates seen in ambient Atlanta aerosol are
likely the product of both primary emission sources as well as secondary
reactions.
In the multi-energy maps, S0 always occurs with S+VI (Fig. S3),
suggesting they are either co-emitted as a primary source or linked by a
secondary formation process that occurs in the atmosphere. Up to 20 % of
the sulfur in individual particles was in the reduced form as S0.
Previous examinations of ambient aerosol have reported less than 5 % of
total sulfur as reduced sulfur (Cozzi et al., 2009; Long et al., 2014) and
have suggested incomplete combustion or incinerator emissions as the likely
source (Andersson et al., 2006; Bao et al., 2009; Matsumoto et al., 2006).
Common primary emission sources, such as gasoline and diesel exhaust, coal
fly ash, and biomass burning, did not contain S0 as a readily
identifiable constituent in bulk S-NEXFS spectra, leaving the source of
reduced S in ambient Atlanta aerosol unresolved. While emission sources were
not analyzed at the individual particle level, bulk S-NEXFS of Azotobacter vinelandii revealed
this bacterium to be the only analyzed emission source to be enriched in
S0. Microbial cells are increasingly recognized as an important natural
component of aerosol (Burrows et al., 2009; Bauer et al., 2002). The
ubiquitous distribution of bacteria makes aerosolized soil bacteria, such as
Azotobacter vinelandii, another potential primary source of S0. Furthermore, S0 was
commonly found in individual particles, which constitute the largest
particle size fraction of these samples (> 1 µm). This could
further support the hypothesis that the S0 is from aerosolized soil
bacteria, which would reside in larger particles.
The reduced sulfur found in ambient aerosol particles could also be a result
of secondary formation processes that occur in the atmosphere. In a previous
study, S+IV found in ambient particle matter was attributed to the
absorption and incorporation of sulfur dioxide (S+IV) into atmospheric
particulate matter (Higashi and Takahashi, 2009). Theoretically, a
similar mechanism could help explain the finding of S0 in ambient
aerosols; however, gaseous phases of reduced sulfur compounds are unlikely
to absorb onto an aerosol surface or condense without undergoing oxidation
(Alexander et al., 2005; Liao et al., 2003).
Furthermore, reduced sulfur species on the surface of a particle would be
easily oxidized by ozone, oxygen, or the hydroxyl radical, suggesting that
only reduced sulfur inside of a heterogeneous particle would be likely to
survive (Long et al., 2014), further suggesting that the
S0 likely has a primary source.
The composition and oxidation state of sulfur in ambient aerosol provide
insights for atmospheric chemistry involving sulfur as well as metals. More
than 25 % of the bulk sulfate composition can be attributed to metal
sulfates, which cannot be accounted for by our primary sources alone
(Hu et al., 2014). The solubilization of metals with
sulfuric acid during atmospheric transport likely plays a role in
forming the metal sulfates observed in this study as well as others
(Oakes et al., 2012a). Reduced sulfur was also found to account for up to
20 % of the total sulfur in individual particles, which is a higher
fraction than typically observed in ambient aerosol samples (Long et al.,
2014; Cozzi et al., 2009). As is the case with previous studies that have
noted reduced sulfur in ambient aerosol samples, the S0 is likely from
a primary emission source. Incomplete combustion is the most commonly cited
source of reduced sulfur compounds found in aerosol (Long et al.,
2014; Cozzi et al., 2009; Bao et al., 2009; Matsumoto et al., 2006; Andersson et
al., 2006); however, in this study, a bacterium was the only potential
primary emission source to contain S0 at the bulk level. This suggests
that aerosolized bacteria may contribute to the S0 seen in ambient
Atlanta aerosol.