Introduction
The Amazon Basin plays key role in atmospheric chemistry, biodiversity and
climate change (Keller et al., 2009; Andreae et al., 2015). The Amazon
rainforest is an important source of biogenic volatile organic compound
(BVOC) emissions to the atmosphere (Greenberg et al., 2004; Alves et al.,
2016), which give rise to secondary organic aerosol (SOA) through reaction
with atmospheric oxidants (i.e. O3, OH⚫ and
NO3⚫) (e.g. Martin et al., 2010). SOA particles scatter
and absorb solar and terrestrial radiation, influence cloud formation, and
participate in chemical reactions in the atmosphere and are thus suggested
to play an important role in climate change (Andreae and Crutzen, 1997;
Haywood and Boucher, 2000; Hallquist et al., 2009; Pöschl et al., 2010).
Aerosol optical properties, which govern the ability to absorb solar
radiation, strongly depend on SOA composition (Laskin et al., 2015). It has
been shown that organic nitrates, nitrooxy organosulfates and organic
sulfates may contribute to light absorption by SOA (e.g. Song et al., 2013;
Jacobson, 1999; Lu et al., 2011; Laskin et al., 2015). Chemical interactions
between anthropogenic and biogenic aerosol precursors can play a significant
role in the formation of SOA (Goldstein et al., 2009; Hoyle et al., 2011;
Kleinman et al., 2015). For example, anthropogenic nitrogen oxides
(NOx) and sulfur dioxide (SO2) are shown to react with a range of
BVOCs, leading to formation of organic nitrates (e.g. Roberts, 1990; Day et
al., 2010; Fry et al., 2014), nitroxy organosulfates and organosulfates
(Surratt et al., 2008; Budisulistiorini et al., 2015). Much remains to be
explored in terms of the molecular diversity of these compounds in the
atmosphere.
A comprehensive knowledge of aerosol molecular composition, which in turn
leads to a better understanding of aerosol sources, is required for the
development of effective air pollution mitigation strategies. However,
identification of the organic aerosol composition remains a major
analytical challenge (Nozière et al., 2015). Organic aerosol is composed
of thousands of organic compounds, which cover a wide range of physical and
chemical properties (Goldstein and Galbally, 2007), making it difficult to
find a single analytical technique for a detailed chemical analysis at the
molecular level. Methods based on ultra-high-resolution mass spectrometry
(UHRMS) have shown great potential in solving this longstanding problem. UHR
mass spectrometers (e.g. Fourier transform ion cyclotron resonance mass spectrometer and
Orbitrap mass spectrometer) have mass resolution power that is at least 1 order of
magnitude higher (≥ 100 000) than conventional MS and high mass accuracy
(< 5 ppm) and thus, when coupled with soft ionisation techniques
(e.g. electrospray ionisation, ESI), can provide a detailed molecular
composition of the organic aerosol (Nizkorodov et al., 2011; Nozière et
al., 2015). Direct infusion ESI-UHRMS has been applied successfully for the
analysis of aerosol samples from remote (e.g. boreal forest in Finland,
Pico Island of the Azores archipelago), rural (e.g. Millbrook, USA; Harcum,
USA; K-Puszta, Hungary) and urban (e.g. Cambridge, UK; Birmingham, UK;
Cork, Ireland; Shanghai, China; and Los Angeles, USA) locations (Wozniak et
al., 2008; Schmitt-Kopplin et al., 2010; Kourtchev et al., 2013, 2014; Tao
et al., 2014; Dzepina et al., 2015). UHRMS has proven to be extremely useful
in assessing chemical properties of the SOA.
The aim of this study was to investigate the detailed molecular composition
of organic aerosol from a site that received air masses from a wide range of
origins, including the background atmosphere of Amazonia, biomass burning
and urban pollution plumes. The measurements were performed as a part of the
Observations and Modeling of the Green Ocean Amazon
(GoAmazon2014/5) campaign (Martin et al., 2016). The location of the
research site where aerosol was collected for this study is ∼ 69 km downwind of Manaus (population 2 million), intersecting background and
polluted air with day-to-day variability in the position of the Manaus
plume. The study designed served as a laboratory for investigating
anthropogenic perturbations to biogenic processes and atmospheric chemistry.
Methods
Sampling site
Aerosol sampling was conducted at site “T3” of GoAmazon2014/5 located at
-3.2133∘ and -60.5987∘.
The T3 site is located in the pasture area, ∼ 2.5 km
from the rainforest. The air masses arriving to the sampling site often
passed over the single large city (Manaus) in the region. Detailed
descriptions of the site and instrumentation are provided in Martin et al. (2016).
PM2.5 aerosol samples were collected on 47 mm polycarbonate filters
(Nuclepore) using a Harvard impactor (Air Diagnostics, Harrison, ME, USA)
with flow rate of 10 L min-1 from 5 to 26 March 2014 and 5 September to 4 October 2014, which were during intensive
observation periods 1 and 2 (IOP1 and
IOP2) of GoAmazon2014/5, respectively, corresponding to the traditional
periods of wet and dry seasons of Amazonia. The sampling durations are shown
in Table S1 in the Supplement. The airflow through the sampler was approximately 10 L min-1. After collection, the aerosol samples were transferred into
Petri dishes and stored in the freezer at -4 ∘C until analysis.
Aerosol sample analysis
Fifteen samples, 5 from IOP2 and 10 from IOP1, were extracted and analysed
using a procedure described elsewhere (Kourtchev et al., 2014, 2015). Depending on the aerosol loading of the analysed samples, a part
(half to whole) of the filter was extracted in methanol (Optima TM LC/MS
grade, Fisher Scientific) in a chilled ice slurry, filtered through a Teflon
filter (0.2 µm, ISODiscTM, Supelco) and reduced by volume using a
nitrogen line to achieve approximately 0.3 µg of aerosol per
microlitre of methanol. Several samples with the highest aerosol loading were divided into
two parts for both direct infusion and LC/MS analyses, while the samples with
the lowest loading were only analysed using direct infusion analysis. The
LC/MS portion was further evaporated to 20 µL and diluted to 100 µL by aqueous solution of formic acid (0.1 %). The final extracts were
analysed as described in Kourtchev et al. (2013) using a high-resolution LTQ
Orbitrap Velos mass spectrometer (Thermo Fisher, Bremen, Germany) equipped
with an ESI and a TriVersa NanoMate robotic nanoflow chip-based ESI (Advion
Biosciences, Ithaca NY, USA) source. The Orbitrap MS was calibrated using
an Ultramark 1621 solution (Sigma-Aldrich, UK). The mass accuracy of the
instrument was below 1 ppm. The instrument mass resolution was 100 000 at
m/z 400. The ion transmission settings were optimised using a
mixture of camphor sulfonic acid (20 ng µL-1), glutaric acid (30 ng µL-1), and cis-pinonic acid (30 ng µL-1) in
methanol and Ultramark 1621 solution.
Direct infusion UHRMS analysis
The ionisation voltage and back pressure of the nanoESI direct infusion
source were set at -1.4 kV and 0.8 psi, respectively. The inlet temperature
was 200 ∘C and the sample flow rate was approximately 200–300 nL min-1. The negative ionisation mass spectra were collected in three
replicates at two mass ranges (m/z 100–650 and m/z 150–900) and processed using Xcalibur 3.1 software (Thermo Fisher
Scientific Inc.). Similar to our preceding studies (Kourtchev et al., 2015),
the average percentage of common peaks between analytical replicates was
∼ 80 %. This is also in agreement with literature reports
for similar data analysis (Sleighter et al., 2012). The identification of
IEPOX organosulfates was performed by comparing MS fragmentation patterns
and chromatographic elution time with a synthesised IEPOX-OS standard which
was provided by Dr Surratt from the University of North Carolina. It must be
noted that, due to competitive ionisation of analytes in the direct infusion
ESI analysis of the samples with a very complex matrix (i.e. aerosol
extracts), the ion intensities do not directly reflect the concentration of
the molecules in the sample (Oss et al., 2010); therefore, data shown in
this work are semi-quantitative.
LC/MS analysis
LC/MS ESI parameters were as follows: spray voltage, -3.6 kV; capillary
temperature, 300 ∘C; sheath gas flow, 10 arbitrary units, auxiliary gas
flow, 10 arbitrary units; sweep gas flow rate, 5 arbitrary units; and S-lens RF level, 58 %. LC/(-)ESI-MS
analysis was performed using an Accela system (Thermo Scientific, San Jose,
USA) coupled with LTQ Orbitrap Velos MS and a T3 Atlantis C18 column (3 µm; 2.1×150 mm; Waters, Milford, USA). The sample extracts were injected
at a flow rate of 200 µL min-1. The mobile phases consisted of
0.1 % formic acid (v/v) (A) and methanol (B). The applied gradient was as
follows: 0–3 min 3 % B, 3–25 min from 3 to 50 % B (linear), 25–43 min
from 50 to 90 % B (linear), 43–48 min from 90 to 3 % B (linear), and
then kept for 12 min at 3 % B. The collision-induced dissociation (CID)
settings for MS/MS analysis are reported in Kourtchev et al. (2015).
High-resolution MS data analysis
The direct infusion data analysis was performed using procedures described
in detail by Kourtchev et al. (2013). The calibration (as described in
Kourtchev et al., 2013) and ion transmission checks that include monitoring of
ion signal intensity were routinely performed. Briefly, for each sample
analysis, 60–90 mass spectral scans were averaged into one mass spectrum.
Molecular formulae assignments were made using Xcalibur 3.1 software using
the following constraints: 12C ≤ 100, 13C ≤ 1, 1H ≤ 200, 16O ≤ 50, 14N ≤ 5, 32S ≤ 2, 34S ≤ 1.
The data processing was performed using a Mathematica 8.0 (Wolfram Research
Inc., UK) code developed in-house that utilises a number of additional
constraints described in previous studies (Kourtchev et al., 2013, 2015). Only ions that appeared in all three replicates were kept for
evaluation. The background spectra obtained from the procedural blanks were
also processed using the rules mentioned above. The formulae lists of the
background spectra were subtracted from those of the ambient (or chamber)
sample and only formulae with a sample / blank peak intensity ratio ≥ 10
were retained.
The Kendrick mass defect (KMD) is calculated from the difference between the
nominal mass of the molecule and the exact KM (Kendrick, 1963). Kendrick
mass of the CH2 unit is calculated by renormalising the exact IUPAC
mass of CH2 (14.01565) to 14.00000.
Benzene and isoprene measurements
For benzene and isoprene analysis we used a high-resolution
selective-reagent-ionisation proton transfer reaction time-of-flight mass
spectrometer (SRI-PTR-TOF-MS 8000, Ionicon Analytik, Austria). The data reduction process used and a description
of the PTR-TOF-MS instrument are
provided elsewhere (Graus et al., 2010; Müller et al., 2013). Background
of the instrument was measured regularly by passing ambient air through a
platinum catalyst heated to 380 ∘C. Sensitivity
calibrations were performed by dynamic dilution of VOCs using several
multi-component gas standards (Apel Riemer Environmental Inc., Scott-Marrin,
and Air Liquide, USA). The calibration cylinders contained acetaldehyde,
acetone, benzene, isoprene, α-pinene, toluene and
trichlorobenzene, among other species During IOP1, the instrument was operated
with the H3O+ reagent ion and at a drift tube pressure of 2.3 mbar,
voltage of 600 V, and temperature of 60 ∘C, corresponding
to a field density ratio E / N ratio of 130 Td (E being the electric field
strength and N the gas number density; 1 Td = 10-17 V cm-2).
During IOP2, the reagent ion was NO+ and the drift tube settings were
2.3 mbar, 350 V, and 60 ∘C, resulting in an E/N ratio of 76 Td. The sampling was done with 1 min time resolution and the instrument
detection limit for benzene and isoprene were below 0.02 and 0.04 ppbv,
respectively.
Air mass history analysis
Air mass history analysis was done for the sampling period using the
Numerical Atmospheric-dispersion Modelling Environment (NAME) model,
developed by the UK Met Office (Maryon et al., 1991). NAME is a Lagrangian
model in which particles are released into 3-D wind fields from the
operational output of the UK Met Office Unified Model meteorology data
(Davies et al., 2005). These winds have a horizontal resolution of 17 km and
70 vertical levels, which reach ∼ 80 km. In addition, a
random walk technique was used to model the effects of turbulence on the
trajectories (Ryall and Maryon, 1998). To allow the calculation of air mass
history for the average sampling time (which varied between samples, 24, 36
or 48 h), 104 particles per hour were released continuously from
the T3 site. The trajectories travelled back in time for 3 days with the
position of the particles in the lowest 100 m of the model atmosphere
recorded every 15 min. The particle mass below 100 m was integrated over the
72 h travel time. The air mass history (“footprints”) for the periods of the
analysed filters are shown in Fig. S1 in the Supplement. The majority of the 3-day air
mass footprints originated from the east, although wind direction showed
variability nearer to the sampling site on some occasions, e.g. sample
MP14-17 (Fig. S1). Almost all air masses pass over Manaus and therefore
highlight this city as a potential source. Some air masses also pass
over Manacapuru, but this is rare and the corresponding time-integrated
concentrations are lower than the equivalent Manaus values.
Results and discussion
(-)-nanoESI-UHRMS of the representative PM2.5 samples during
(a) IOP1 and (b) IOP2. The line colours in the mass spectra
correspond to the CHO (black), blue (CHON), CHOS (red) and CHONS (green)
formulae assignments. The relative-intensity axis was split to make a large
number of ions with low intensities visible.
Figure 1 shows mass spectra from two typical samples collected during IOP1
and IOP2. The majority of the ions were associated with molecules below 500 Da, although the measured mass goes up to 900 Da. Although ESI is a
“soft” ionisation technique resulting in minimal fragmentation, we cannot exclude
the possibility that some of the detected ions correspond to fragments, also
in light of the many relative fragile compounds (e.g. thermally labile
compounds) that constitute OA. The largest group of identified molecular
formulae in all samples were attributed to molecules containing CHO atoms
only (1051 ± 141 formulae during IOP2 and 820 ± 139 during IOP1),
followed by CHON (537 ± 71 during IOP2 and 329 ± 71 during IOP1),
CHOS (183 ± 34 during IOP2 and 137 ± 31 during IOP1) and CHONS
(37 ± 11 during IOP2 and 28 ± 10 during IOP1) (Fig. 2). The number
of molecular formulae containing CHO and CHON subgroups increased by
∼ 20 % from IOP1 to IOP2; however, a rather
insignificant increase was observed for CHOS and CHONS subgroups. The
Student's t test showed that the observed difference for CHO (p=0.0092)
and CHON (p=0.00007) subgroups between two seasons is statistically
significant. This is consistent with the observed increase in odd reactive
nitrogen species (NOy) from IOP1 to IOP2 (Table S1). Organic nitrates
are believed to form in polluted air through reaction with nitrogen oxides
during daytime and from reaction of NO3⚫ with BVOCs during
nighttime (Day et al., 2010; Ayres et al., 2015). The average concentration
of NOy during IOP1 was found to be on almost 2 times higher, which
is possibly reflected in the increased number of organonitrates in the
aerosol samples from IOP2. Moreover, the increase in the number of
organonitrates during IOP2 is consistent with recent studies which
demonstrated that organonitrates groups in aerosol particles may hydrolyse
under high-RH conditions (Liu et al., 2012). In this respect, while
night-time maximum RH during both filter sampling periods was very similar
(∼ 90 %), daytime RH during IOP1 was higher (89 %)
compared to that from IOP2 (66 %) (Fig. S2).
Average number of molecular formulae during IOP1 and IOP2. Standard
deviation bars show variations between samples within individual season.
Carbon oxidation state (OSC) introduced by Kroll et al. (2011) can be
used to describe the composition of a complex mixture of organics undergoing
oxidation processes. OSC was calculated for each molecular formula
identified in the mass spectra using the following equation:
OSC=-∑iOSininC,
where OSi is the oxidation state associated with element i and
ni/nC is the molar ratio of element i to carbon within the
molecule (Kroll et al., 2011).
Figure 3 shows overlaid OSC plots for two samples from IOP1 and IOP2.
Consistent with previous studies, the majority of molecules in the sampled
organic aerosol had OSC between -1.5 and +1 with up to 30 (nC)
carbon atoms throughout the selected mass range (m/z 100–650)
(Kroll et al., 2011, and the references therein). The molecules with OSC
between -1 and +1 with 13 or fewer carbon atoms (nC) are suggested to be
associated with semivolatile and low-volatility oxidised organic aerosol
(SV-OOA and LV-OOA) produced by multistep oxidation reactions. The molecules
with OSC between -0.5 and -1.5 with seven or more carbon atoms are associated
with primary biomass burning organic aerosol (BBOA) directly emitted into
the atmosphere (Kroll et al., 2011). The cluster of molecules with OSC
between -1 and -1.5 and nC fewer than 10 could be possibly associated with
OH radical oxidation products of isoprene (Kourtchev et al., 2015), which is
an abundant VOC in Amazon rainforest (Rasmussen and Khalil, 1988; Chen et
al., 2015). The isoprene daytime average was above 1.5 ppbv during both
seasons, with hourly campaign averages reaching up to 2.3 and 3.4 ppbv for
IOP1 and IOP2, respectively. In general, aerosol samples from IOP1 contained
fewer oxidised molecules compared to those from IOP2. Wet deposition of aged
or processed aerosol during the wet (i.e. IOP 2) sampling period cannot be
the only reason for the observed differences in OSC. It has been shown that
different oxidation regimes to generate SOA (e.g. OH radical vs.
ozonolysis) can result in significantly different OSC of SOA (Kourtchev et
al., 2015). For example, the SOA component from the OH radical-initiated
oxidation of α-pinene as well as BVOC mixtures had a
molecular composition with higher OSC throughout the entire molecular mass
range compared to that obtained from the ozonolysis reaction (Kourtchev et
al., 2015).
Carbon oxidation state plot for CHO-containing formulae in organic
aerosol from IOP1 (red squares) and IOP2 (blue diamonds).
Ion intensity distributions (left axis) of selected tentatively
identified markers in individual samples using UHRMS analysis and averaged
benzene concentration (right axis) from PTR-TOF-MS analysis. Benzene
concentration was averaged for the aerosol filter sampling intervals. The
UHRMS data were corrected for organic carbon load in each individual filter
sample (see Methods section).
Figure 4 shows the distribution of ion signal intensities for selected
tentatively identified tracer compounds for anthropogenic, biogenic and
mixed sources in all 15 samples. The structural or isomeric information is
not directly obtained from the direct infusion analysis; therefore, the
identification of the tracer compounds was achieved by comparing MS/MS
fragmentation patterns from authentic standards and published literature.
The tracer compounds include anhydrosugars, structural isomers with a
molecular formula of C6H10O5 at m/z 161.0456
corresponding to levoglucosan, mannosan, galactosan and
1,6-anhydro-β-D-glucofuranose, which are regarded
as marker compounds for biomass burning (Simoneit et al., 1999; Pashynska et
al., 2002; Kourtchev et al., 2011). Nitrocatechols, with a molecular formula
of C6H5NO4 (m/z 154.01458), are attributed to mixed
anthropogenic sources, e.g. biomass and vehicular emissions, and
methyl-nitrocatechols (C7H7NO4, m/z 168.03023) are
important markers for biomass burning OA, formed from m-cresol
emitted during biomass burning (Iinuma et al., 2010) as well as diesel
exhaust. 3-Methyl-1,2,3-butanetricarboxylic acid (3-MBTCA), with a molecular
formula of C8H12O6 at m/z 203.05611, is an OH radical-initiated oxidation product of α- and β-pinene (Szmigielski et al., 2007), and regarded as a tracer for processed
or biogenic SOA. Finally, isoprene epoxydiol organosulfate ester (IEPOX-OS),
with a molecular formula of C5H12O7S at m/z 215.0231, has been
suggested to be formed through reactions between SOx and
isoprene oxidation products (Pye et al., 2013; Budisulistiorini et al.,
2015) and thus can be used to observe the extent of SO2 ageing effects
on the biogenic SOA. Direct infusion analysis suffers from competitive
ionisation in the complex matrices and thus comparing ion intensities across
samples must be done with caution. Moreover, other compounds with similar
molecular composition present in the aerosol matrix may also contribute to
the ion signal intensities of the above-discussed compounds. All selected
tracers showed very similar variations with benzene concentration that was
measured in the gas phase using PTR-MS (Fig. 3). Benzene, generally regarded
as an anthropogenic species, has various sources, including industrial
solvent production, vehicular emissions and biomass burning (Hsieh et al.,
1999; Seco et al., 2013; Friedli et al., 2001). Recent studies have indicated
that vegetation (leaves, flowers, and phytoplankton) emits a wide variety of
benzenoid compounds to the atmosphere at substantial rates (Misztal et al.,
2015). However, considering that benzene concentration correlated very well
with another anthropogenic tracer CO (R2=0.77, Fig. S3) during
IOP1 and IOP2, it is likely that the observed benzene
concentrations were mainly due to anthropogenic emissions. During the
sampling period, irrespective of the season, air masses passed over the
large city Manaus and small municipalities located near the T3 site (Fig. S1). It must be noted that, due to rather low sampling resolution time (≥ 24 h), the molecular composition of all analysed samples is likely to be
influenced by clean air masses and anthropogenic plumes from these urban
locations which usually last only a few hours per day, and thus individual
urban plume events cannot be identified with the data analysed here. In
Manaus natural gas is mainly used for heating and cooking and therefore the
contribution from these activities to biomass burning OA at our site is
highly unlikely. During IOP1 much lower incidents of forest fires were
observed compared to that during IOP2 (Martin et al., 2016). For example,
the number of forest fires in the radius of 200 km from the sampling site
varied between 0 and 340 fires (http://www.dpi.inpe.br/proarco/bdqueimadas/). This is reflected in the ion
signal intensities of the particle-phase biomass burning markers, i.e.
anhydrosugars (C6H10O5) and nitrocatechols
(C6H5NO4) and gas-phase benzene concentrations, which were
significantly lower during IOP1 compared to that from IOP2, when on average
more fires are observed.
It should be noted that ion signal intensities for anhydrosugars
(C6H10O5) and nitrocatechols (C6H5NO4) showed
a very good correlation (R2>0.7), suggesting that
nitrocatechols, observed at the sampling site, are mainly associated with
biomass burning sources. The highest ion signal intensities of these tracer
compounds were observed during two periods: 7–9 September 2014 (sample
MP14-128) and 27–28 September 2014 (sample MP14-148) with the latter one
coinciding with highest incident of fires (340 fires). Although during 7–9 September (sample MP14-128) a significantly lower number (22 fires) of fires
was observed compared to the period of 27–28 September 2014, lower wind
speed occurring during 7–9 September suggests that a high intensity of the
biomass burning markers could be due to the biomass burning emissions from
nearby sources. Between the T3 sampling site and Manaus (about 20 km east of
the site), there are a number of small brick factories, which use wood to
fire the kilns (Martin et al., 2016) and are thus an additional local
wood-burning source besides the forest and pasture fires.
Interestingly the sample MP14-148 had the highest ion intensity
corresponding to IEPOX-OS (Fig. 4), which also coincided with the strong
increase in the ion intensity at m/z 96.95987 corresponding to
[HSO4]-. This is consistent with the organosulfate formation
mechanism through reactive uptake of isoprene epoxydiols (IEPOX) in the
presence of acidic sulfate seed (Surratt et al., 2010; Lin et al., 2012,
2013). A similar relationship between sulfate and organosulfates
concentrations has been observed previously in field studies in the
southeastern US (Surratt et al., 2007, 2008, 2010; Lin et al., 2012, 2013).
This is also in agreement with previous studies from the Amazon, where the
highest levels of 2-methyltetrols were observed during the dry period, which
was characterised by biomass burning (and higher particle concentrations of
sulfuric acid) (Claeys et al., 2010). Considering that Claeys et al. (2010)
employed an alternative GC/MS procedure with prior trimethylsilylation,
2-methyltetrol sulfates were converted to 2-methyltetrols and not detectable
as separate organosulfate compounds. It should be noted that the 27–28 September period
(sample MP14-148) was marked by a very strong increase in the CO
concentration (Fig. S4). In mid-latitude environments it has been suggested
that the production of anthropogenic SOA in an air mass, as it travels from
an urban source region, can be estimated by using a relatively inert
pollution tracer, such as CO occurring in the air mass (De Gouw et al.,
2005; Hoyle et al., 2011). At the T3 sampling site, the highest CO
concentrations are observed in air masses affected by biomass burning.
Therefore, it is possible that organic aerosol in the sample MP14-148 has
experienced the highest contribution from biomass burning as well as other
anthropogenic activities.
To investigate the influence of anthropogenic activities (i.e. biomass
burning) on a detailed molecular composition of organic aerosol at the T3
site, we compared samples from the periods with the lowest (9 fires),
moderately high (254 fires) and the highest (340 fires) incidents of fires
occurring within 200 km around the site.
H / C vs. m/z plot for CHO-containing formulae in the samples from
the periods with (a) low, (b) moderately high and
(c) very high incidents of fires. The marker size reflects relative
ion abundance in the sample. The colour code shows double-bond equivalent
(DBE) in the individual molecular formula. Molecular formulae with
DBE < 6 are shown as grey markers. The largest grey circles correspond to
the ions at m/z 133.01425 (with a neutral molecular formula of
C4H6O5), m/z 187.0612 (C8H12O5),
m/z 201.07685 (C9H14O5), m/z 203.05611
(C8H12O6), and m/z 215.05611 (C9H12O6).
Figure 5a–c show H / C ratios of CHO-containing formulae as a function of
their molecular mass and double-bond equivalent (DBE), which shows a degree
of unsaturation of the molecule, for a sample with the lowest (a) moderately
high (b) and highest incidents (c) of fires. One of the obvious differences
between these samples is the abundance of ions with low H / C ratios
(< 1). The majority of these ions have a DBE value above 7,
indicating that they likely correspond to oxidised aromatic compounds, which
are mainly of anthropogenic origin (Kourtchev et al., 2014; Tong et al.,
2016). For example, the smallest polycyclic aromatic hydrocarbon (PAH),
naphthalene, with a molecular formulae of C10H8, has H / C = 0.8 and
DBE = 7. The number of CHO-containing formulae with high DBE equivalent and
low H / C increased dramatically during the days with moderately high and high
incidents of fires (Fig. 5a–c), suggesting that they are mainly associated
with biomass burning. The largest grey circles in Fig. 5a–c correspond to
the ions at m/z 133.01425 (with a neutral molecular formula of
C4H6O5), m/z 187.0612 (C8H12O5),
m/z 201.07685 (C9H14O5), m/z 203.05611
(C8H12O6), and m/z 215.05611
(C9H12O6) with DBE < 6.
Recent studies have indicated that different families of compounds with
heteroatoms (e.g. O, S) overlap in terms of DBE and thus may not accurately
indicate the level of unsaturation of organic compounds. For example, the
divalent atoms, such as oxygen and sulfur, do not influence the value of
DBE, yet they may contribute to the potential double bonds of that molecule
(Reemtsma, 2009; Yassine et al., 2014). Yassine et al. (2014) suggested using
aromaticity equivalent (Xc) to improve the identification and
characterisation of aromatic and condensed aromatic compounds in water-soluble organic carbon. The
aromaticity equivalent can be calculated as follows:
Xc=3DBE-(mNO+nNS)-2DBE-(mNO+nNS),
where “m” and “n” correspond to a fraction of oxygen and sulfur atoms
involved in π-bond structures of a compound which varies depending on
the compound class. For example, carboxylic acids, esters, and nitro
functional groups have m=n=0.5. For compounds containing functional
groups such as aldehydes, ketones, nitroso, cyanate, alcohol, or ethers “m”
and “n” are 1 or 0. Considering that ESI, in negative mode, is most
sensitive to compounds containing carboxylic groups we therefore used
m=n=0.5 for the calculation of the Xc. For molecular formulae with an
odd number of oxygen or sulfur, the sum (mNO+nNS) in Eq. (2) was
rounded down to the closest integer as detailed in Yassine et al. (2014). The
authors proposed that aromaticity equivalent with Xc≥ 2.50 and Xc≥2.71 as unambiguous minimum criteria for the presence of aromatics and
condensed aromatics.
Expressing our data using aromaticity equivalents confirmed that the
increase in the number of molecules with high DBE from the sample with the
lowest to the highest incidents of fires was due to the increase in the
number of aromatic and condensed aromatic compounds in the aerosol samples
(Fig. S5). Considering the Yassine et al. (2014) assignment criteria for
the aromatic-reach matrices, the highest number of the aromatic compounds in
the Amazon samples was observed for formulae with a benzene core structure
(Xc=2.50), followed by formulae with a pyrene core structure (Xc=2.83),
an ovalene core structure (Xc=2.92), and highly
condensed aromatic structures or highly unsaturated compounds (Xc>2.93). The largest grey circles in Fig. S5a correspond to
the ions at m/z 187.11357 with a neutral molecular formula of
C9H17NO3 and m/z 281.26459 with a neutral molecular
formula of C18H35NO. The largest grey circles in Fig. S5b and c
correspond to the ions at m/z 154.0146, m/z 168.03023 and
m/z 152.03532 with neutral molecular formulae of
C6H5NO4, C7H7NO4 and C7H7NO3,
respectively.
Overlaid Van Krevelen diagrams for CHON-containing formulae in the
samples from the periods with low (red markers) and very high incidents (blue
markers) of fires. The marker areas reflect relative ion abundance in the
sample. Areas “A” and “B” indicate differences in the number of ions
tentatively attributed to aliphatic and aromatic species, respectively.
Interestingly, a similar trend was observed for the molecules containing
CHON subgroups (Fig. S6). A number of CHON molecules with low H / C
(< 1) and high DBE (≥ 5) almost doubled from the days with 9 to
340 fires (Fig. S7). Nitro-aromatic compounds such as nitrophenols
(DBE = 5) and N-heterocyclic compounds, including 4-nitrocatechol and
isomeric methyl-nitrocatechols, are often observed in the OA from biomass
burning sources (Kitanovski et al., 2012a, b; Iinuma et al., 2010) and have
been suggested to be potential contributors to light absorption by brown carbon
(Laskin et al., 2015). It is worth mentioning that aerosol samples affected
by biomass burning contained another interesting ion at m/z 182.04588 with a neutral molecular formula of C8H9NO4,
possibly corresponding to other biomass burning OA markers, i.e. isomeric
dimethyl-nitrocatechols (Kahnt et al., 2013). The differences in the
increased number of nitro-aromatic compounds in aerosol samples affected by
biomass burning are also apparent in overlaid Van Krevelen diagrams (Fig. 6), which show H / C and O / C ratios for each formula in a sample. Van Krevelen
diagrams can be used to describe the overall composition or evolution of
organic mixtures (Van Krevelen, 1993; Nizkorodov et al., 2011; Nozière
et al., 2015). Organic aerosol affected by biomass burning contained a
significantly larger number of CHON formulae with O / C < 0.5 and H / C < 1 (Fig. 6a and b, area B) but a smaller number of formulae with
O / C < 0.5 and H / C > 1 (Fig. 6a and b, area A). While
molecules with H / C ratios (< 1.0) and O / C ratios (< 0.5)
(area A in Fig. 3) are generally associated with aliphatic compounds, molecules with high H / C
ratios (> 1.5) and low O / C ratios (< 0.5) typically belong to oxidised aromatic hydrocarbons (area B in Fig. 3)
(Mazzoleni et al., 2010, 2012). Although the smaller number of
nitro-aliphatic compounds in the samples affected by biomass burning
requires further investigation, it is possible that they were oxidised in
the polluted air by NOx and O3 (Zahardis et al., 2008; Malloy et
al., 2009), whose production is significantly enhanced during fire events
(e.g. Galanter et al., 2000). The majority (up to 80 %) of the CHON
molecules in the analysed samples have O / C ratios < 0.7 (Fig. 6).
The relatively low oxygen content suggests that these molecules include
decreased nitrogen-containing compounds (Zhao et al., 2013). Although biomass
burning material type is expected to result in a different molecular
composition, the presence of a large number of molecules with low O / C ratio
is consistent with the literature. For example, most of the CHON molecules
in OA from wheat straw burning in K-Puszta in the Great Hungarian Plain of Hungary
and biomass burning at Canadian rural sites (Saint Anicet, Quebec, and
Canterbury, New Brunswick) had O / C ratios below 0.7 (Schmitt-Kopplin et al.,
2010). In addition, the CHON molecules identified by LC/MS in biomass burning
OA from Amazonia showed O / C ratios below 0.7, i.e. 4-nitrocatechol
(C6H5NO4; O / C = 0.67), isomeric methyl-nitrocatechols
(C7H7NO4; O / C = 0.57), and isomeric dimethyl-nitrocatechols
(C8H9NO4; O / C = 0.50) (Claeys et al., 2012).
Overlaid carbon oxidation state (OSC) plots for CHO subgroups in the
samples from the periods with low (blue markers) and very high (red markers)
incidents of fires. The marker areas reflect relative ion abundance in the
sample. The area marked as SV-OOA, LV-OOA, BBOA and HOA correspond to the
molecules associated with semivolatile and low-volatility oxidised organic
aerosol, biomass burning organic aerosol and hydrocarbon-like organic aerosol
as outlined by Kroll et al. (2011).
Figure 7 shows overlaid OSC plots for OA from the days with low, moderately
high and high incidents of fires. During the days affected by high and
moderately high number of fires, OSC was shifted towards a more oxidised
state for the CHO molecules containing more than seven carbon atoms. The
difference in OSC becomes even more pronounced with the increased number of
carbons (e.g. > 7 carbon atoms) in the detected molecular
formulae. Interestingly, the affected ions with high OSC do not fall into
the category of the BBOA (encircled area in Fig. 7) which are associated
with primary particulate matter directly emitted into the atmosphere as
defined in Kroll et al. (2011).
Overlaid Van Krevelen diagram (a) and Kendrick mass defect
plot (b) for CHOS-containing formulae in the samples from the
periods with low (blue markers) and very high incidents of fires (red
markers). The marker areas reflect relative ion abundance in the sample. Red
markers correspond to the ions from the period with the lowest incidents of
fires. Note that IEPOX-OS is not a part of any homologous series in the
sample with very low incident of fires and it has only one additional homologue in
the sample that experienced very high incident of fires (see enlarged area of
Fig. 8a). Area “A” in Kendrick mass defect plot shows formulae
with KMD > 0.33 that are mainly present in the sample with high incident
of fires.
At a first glance, biomass burning seems to influence the number and intensity
of the CHOS-containing formulae; however, the effect was much lower compared
to that for the CHO and CHON molecules (see discussion above). A higher
number of CHOS-containing molecules was observed in the sample (MP14-148)
corresponding to the highest incident of fires (Fig. 8a). Interestingly,
IEPOX-OS was found to be very abundant in the sample that experienced the
highest incidents of fires (Fig. 8a). The significant IEPOX-OS mass was
previously observed during low-altitude flight campaigns in northern
California and southern Oregon under high-NO conditions (> 500 pptv) (Liao et al., 2015). The authors explained this observation by
the transport or formation of IEPOX from isoprene hydroxynitrate oxidation
(Jacobs et al., 2014) and higher sulfate aerosol concentrations occurring
during their sampling period (Nguyen et al., 2014). This explanation is also
consistent with our results. The ion at m/z 96.95987 corresponding
[HSO4]- in UHR mass spectra of the sample MP14-148 was 3 times
more abundant compared to that in the sample MP14-129, suggesting that particle
acidity may be one of the reasons for the high abundance of the IEPOX-OS in
this sample. Considering that the main sources of sulfate at T3 site are
industrial pollution (e.g. power plants), natural and long-range sources,
they could also be responsible for the high abundance of the sulfate and
IEPOX-OS in the samples besides the overlapping biomass burning event.
Noticeably, these samples contained not only a larger number of oxygenated
CHOS-containing molecules with O / C > 1.2 but also molecules with
O / C < 0.6 and H / C ranging from 0.4 to 2.2. Recent laboratory and
field studies indicated the presence of a large number of aromatic and
aliphatic organosulfates and sulfonates in OA and linked them to anthropogenic
precursors (Tao et al., 2014; Wang et al., 2016; Riva et al., 2015, 2016;
Kuang et al., 2016). Riva et al. (2015, 2016) demonstrated formation of organosulfates
and sulfonates in the laboratory smog chamber experiments from
photooxidation of alkanes and PAHs, respectively. The authors indicated
enhancement of organosulfates yields in the presence of acidified ammonium
sulfate seed and suggested that these organosulfates are mainly formed through
reactive uptake of gas-phase epoxides. It must be noted that the above-cited
field studies are based on measurements in the Northern Hemisphere and
thus organosulfate formation pathways and sources may differ from that of
Amazonia.
KMD plots are a useful visualisation technique for identification of
homologous series of compounds differing only by the number of a specific
base unit (e.g. a CH2 group). Anthropogenically affected aerosol
samples have longer homologous series of molecules containing CHOS subgroups
(Fig. 8b). One of these longer series includes a second most abundant ion
at m/z 213.0075 (C5H10O7S). The compound with a
molecular formula of C5H10O7S has been previously observed in
the laboratory and field studies and attributed to isoprene-derived
organosulfates (Surratt et al., 2008; Gómez-González, 2008;
Kristensen and Glassius, 2011; Nguyen et al., 2014; Hettiyadura et al.,
2015). This molecular formula could also be associated with organosulfates
(e.g. isomeric 3-sulfooxy-2-hydroxypentanoic acid and
2-sulfooxy-3-hydroxypentanoic acid) formed from the green leaf volatiles
2-E-pentenal, 2-E-hexenal, and 3-hexenal (Shalamzari et
al., 2016). The KMD plot (Fig. 8b) shows that OA from the
anthropogenically affected samples contained an additional series of CHOS
molecules with high KMD > 0.33 that were not present in the
sample from the less polluted period. Most of these ions are highly
oxygenated (containing > 10 oxygens) and are likely to be
associated with molecules produced through photochemical ageing reactions
(Hildebrandt et al., 2010).
It is worth noting that, in most of the samples, IEPOX-OS was not a part of
any homologous series in KMD plot (e.g. Fig. 8b). This observation confirms
that atmospheric oxidation reactions resulting in the incorporation of S and
N functional groups do not always conserve homologous series but could also
lead to a wide range of possible reaction products (Rincón et al., 2012;
Kourtchev et al., 2013).