This study investigates the chemical composition of
PM2.5 collected at a central location in Beijing, China, during winter
2016 and summer 2017. The samples were characterised using direct-infusion
negative-nano-electrospray-ionisation ultrahigh-resolution mass spectrometry
to elucidate the composition and the potential primary and secondary sources
of the organic fraction. The samples from the two seasons were compared with
those from a road-tunnel site and an urban background site in Birmingham,
UK, analysed in the course of an earlier study using the same method. There
were strong differences in aerosol particle composition between the seasons,
particularly regarding (poly-)aromatic compounds, which were strongly
enhanced in winter, likely due to increased fossil fuel and biomass burning
for heating. In addition to the seasonal differences, compositional
differences between high- and low-pollution conditions were observed, with
the contribution of sulfur-containing organic compounds strongly enhanced
under high-pollution conditions. There was a correlation of the number of
sulfur-containing molecular formulae with the concentration of particulate
sulfate, consistent with a particle-phase formation process.
Introduction
Ambient air pollution is of growing concern regarding its negative effect on
public health, especially in low- and middle-income countries (Cohen et
al., 2017; Hoek et al., 2013; Lelieveld et al., 2015). One of these
countries is China, where rapid development has led to air pollution
becoming a major environmental issue (Guan et
al., 2016). Areas of strong industrial activity and rapid urbanisation are
particularly impacted (Chan
and Yao, 2008; Hu et al., 2014). Beijing, China's capital, has faced severe
issues with air pollution in recent decades, with a particular impact of
particulate pollution (Zhang et al., 2016).
As a response to this problem, an international research collaboration, the
Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing)
project, was launched in an effort to understand the emissions, processes
and health effects of air pollution in Beijing (Shi et
al., 2019). This improved scientific understanding will then feed into the
development of efficient mitigation measures to improve air quality and
reduce health impacts. As a central part of the project, two 1-month-long
coordinated field campaigns were conducted at two sites, central Beijing and
rural Pinggu, in November–December 2016 and May–June 2017.
Fine ambient particulate matter (PM) is the major air pollutant
(Dockery et al., 1993; Pope III et
al., 2002), estimated to cause about 3 million deaths per year
(World Health Organization, 2016). About 20 %–90 % of the fine
particle mass is organic (Jimenez et
al., 2009; Kanakidou et al., 2005). Organic material in the atmosphere is
highly complex, consisting of thousands of different compounds
(Goldstein and Galbally, 2007). Ultrahigh-resolution mass
spectrometry
(UHRMS) analysers such as the Fourier transform ion cyclotron (FTICR) or the
Orbitrap have a high mass resolving power and high mass accuracy. This
allows for separation of 100s to 1000s of compounds within a relatively
small mass range, usually 100–500 Da, even without further chromatographic
separation (Nozière et al., 2015). Additionally, it is
possible to assign molecular formulae to unknown compounds. Direct-infusion
UHRMS has proven to be highly successful in assessing the chemical
properties of ambient aerosols from a large variety of sampling locations,
ranging from remote (e.g. Dzepina et al.,
2015; Kourtchev et al., 2013) to rural
(e.g. Schmitt-Kopplin et al., 2010; Wozniak
et al., 2008) and urban (e.g.
Giorio et al., 2019; Tao et al., 2014; Tong et al., 2016). Since it is
possible to assign elemental compositions to unknown compounds, compounds
containing heteroatoms such as sulfur and nitrogen can be unambiguously
detected. This has for example been used for the study of organosulfates
(Lin et al., 2012; Schmitt-Kopplin et al.,
2010; Tao et al., 2014).
In this paper we investigate the chemical composition of the polar organic
fraction of PM2.5 collected in central Beijing during the
APHH-Beijing campaign. While the average annual contribution of organic
matter (OM) to PM2.5 in Beijing has decreased since the 2000s, it
remains a major contributor (>20 %) (Lang et
al., 2017). The contribution of organic carbon to PM2.5 is usually
highest during winter (He
et al., 2001; Lin et al., 2009; Wang et al., 2015). There are numerous
studies investigating the sources of PM2.5 in Beijing. Biomass burning,
dust, coal combustion, vehicle emissions, cooking and the secondary
products have all been identified as important sources of PM2.5 in
Beijing (Lv et al., 2016),
with varying importance for the different seasons
(Yu et al., 2013; Zhang et al., 2013). Coal
combustion for residential heating was often found to be the dominant source
of PM2.5 during winter (Song et
al., 2007; Zhang et al., 2017b), though some recent studies highlight
traffic emissions as the winter main source (Gao
et al., 2018; Zíková et al., 2016).
In our study, we are focussing on the differences in aerosol composition
between summer and winter and the influence of high- vs. low-pollution
conditions. The Beijing data are also compared with data from Birmingham, UK,
to investigate differences in composition in comparison with European urban
background samples as well as with samples which are strongly influenced by
traffic emissions.
Materials and methodParticle sampling and sample preparation
As part of the APHH-Beijing campaign (Shi et al.,
2019), a total of 67 PM2.5 aerosol samples were collected over 23 h
each on quartz microfiber filters (20.3×25.4 cm, Whatman™,
GE, USA) at the Institute of Atmospheric Physics (IAP), Chinese Academy of
Sciences in Beijing, China, using a high volume sampler (HVS) (TE-6070VFC,
Tisch Environmental, USA). This field site is an urban site located in a
residential area between the fourth and third north ring roads of Beijing.
In addition to the samples, several procedural blank filters were collected.
After sampling, all filters were stored at <-22 ∘C until
analysis. Of these filters, 33 were sampled during winter from 9 November
to 11 December 2016, and 34 were sampled during summer from 22 May to 24 June 2017. For the study presented here, we selected the five filters with the
highest mass loading and the five filters with the lowest mass loading for
winter and summer each. All of the filters with high mass loadings in winter
were impacted by haze events, while this was only the case for two of the
five filters with high mass loadings in summer (Shi et
al., 2019). An overview of the selected filters and their respective mass
loadings is given in Table S1 in the Supplement. From these 20 filters, filter punches (1.8 or
3.5 cm2) were extracted three times in 5 mL methanol
(Optima® LC/MS grade) by sonication. The samples were cooled
in an ice water bath during the extraction process to prevent heating of the
solvent. The combined extracts from the three rounds of sonication were
first filtered using 0.45 µm pore size filters (Supelco
Iso-Disc™ filters PTFE-4-4, 4 mm × 0.45 µm).
After gentle evaporation under nitrogen (N2) to about 1–2 mL, the
extracts were filtered a second time using 0.2 µm pore size filters
(Supelco Iso-Disc™ filters PTFE-4-2, 4 mm × 0.2 µm) and evaporated further under N2 to volumes between 120 µL and
to 1.8 mL. The volumes were adjusted so that the concentration of total
particle mass in the extracts is approximately the same (∼1.0µg µL-1 assuming complete solvation). In
addition, one of the procedural blank filters was extracted using the same
method as described above for each of the two seasons. The extracts were
stored in the freezer for 1–2 d until mass spectrometry analysis. For the
Birmingham samples, five 24 h HVS quartz fibre filters were collected form
an urban background site and a road tunnel each and processed in a similar
way as described above, although with only one filtration step. A more
detailed description of these samples and their collection, analysis, and
data processing can be found in Tong
et al. (2016).
Mass spectrometry measurement and data processing
All mass spectrometry measurements were performed in direct-infusion,
negative-ionisation mode using an ultrahigh-resolution LTQ Orbitrap Velos
mass spectrometer (Thermo Fisher, Bremen, Germany) with a TriVersa
NanoMate® chip-based electrospray ionisation source (Advion
Biosciences, Ithaca NY, USA). The source parameters were set to an injection
volume of 5.0 µL, an ionisation voltage of -1.4 kV and a back
pressure of 0.7 psi. The capillary temperature was 200 ∘C. The
mass spectrometer was routinely calibrated using a Pierce LTQ Velos ESI
Positive Ion Calibration Solution and a Pierce ESI Negative Ion Calibration
Solution (Thermo Scientific, Waltham, MA, USA). The mass accuracy of the
instrument was below 1.5 ppm, which was regularly checked before the
analysis. Mass spectra were collected in full scan mode over two different
mass ranges: m/z 100–650 and m/z 150–900, with a resolution of 100 000 at m/z 400.
For each sample and blank, three replicate measurements of 1 min each were
carried out for the two different mass ranges.
Initial assignments of molecular composition were made using Xcalibur 2.2
software (Thermo Scientific). The following constraints were applied to both
sample and blank mass spectra: the maximum number of molecular formula
assignments per peak in the mass spectrum was 40, and the mass tolerance was ±5 ppm; the molecular formulae were assumed to contain only the following
elements with the given number of atoms: 1≤12C≤100, 0≤13C≤1, 1≤1H≤200, 0≤16O≤50, 0≤14N≤5, 0≤32S≤2 and 0≤34S≤1. The three
repeat measurements of the blank filters for both high and low mass range
were manually merged to yield four final blank files: low mass range winter,
high mass range winter, low mass range summer and high mass range summer.
Each of these merged blank files contains all masses from the three repeat
measurements as separate data points. Data filtering was performed using
Mathematica 11.2 (Wolfram Research Inc., UK) with a code package developed
in-house (Zielinski et al., 2018). In the first
instance, all ions below the noise level, which was estimated based on
fitting a normal distribution to a histogram of intensities, were removed
from the spectrum. As a second step, peaks in the sample that have a
corresponding match in the blank with an intensity above a minimum
sample-to-blank ratio of 10 were removed (blank subtraction). Based on the
mass drift of 12–15 reference compounds present in the sample, the maximum
acceptable mass drift was set to the highest/lowest reference mass drift
±0.5 ppm. All assignments based on mass drifts outside this range were
discarded. Several additional rules were employed to remove chemically
non-meaningful assignments: all molecular formulae where O/C≥2.0, 0.3≤H/C≥2.5, N/C≥1.3 and S/C≥0.8 were eliminated with
the aim to remove compounds that are not likely to be observed in nature, as
well as assignments without carbon, hydrogen or oxygen. Neutral formulae
that had either a noninteger or a negative value of the double bond
equivalent (DBE) were also removed from the list of possible molecules.
Double bond equivalents were calculated using the following equation
(McLafferty and Tureček, 1993):
DBE=x-12y+12z+1,
with IyIInIIIzIVx, where I represents monovalent elements,
II represents bivalent elements, III represents trivalent elements and IV represents tetravalent
elements. Sulfur was assumed to be bivalent and nitrogen trivalent for this
calculation. Assignments which fail the nitrogen rule
(McLafferty and Tureček, 1993) were similarly removed.
Elemental formulae containing 13C or 34S were checked for the
presence of their 12C or 32S counterparts respectively. If there
was no peak with a matching composition containing only the lighter isotope
or if the intensity ratio of heavier-to-lighter isotope was greater than the
natural isotopic abundance, the formula with the next larger mass error was
used instead. The three repeated measurements for each sample and mass range
were then combined into one file, keeping only ions present in all three
replicates. The two mass ranges were then merged, resulting in one combined
mass spectrum per sample. As a final step, the five samples taken for each
of the four measurement conditions (winter high, WH; winter low, WL;
summer high, SH; and summer low, SL) were combined into a single mass
spectrum for each of the four conditions. These mass spectra contain only
ions present in all five samples analysed for the respective atmospheric
condition, thus representing a typical chemical composition for WH, WL, SH
and SL, respectively. With the exception of Sect. 3.3, the following
discussion will only compare these four common ion spectra and not the 20
individual samples.
Ion chromatography analysis
A cut piece of 3 cm2 of theses filters and 10 mL deionised water
(18Ω) were added into a 15 mL polypropylene centrifuge tube. They
were then sonicated using an ultrasonic bath with ice water for 1 h at a
controlled temperature (<20∘C). Subsequently, the
extract solution was shaken on a mechanical shaker for 3 h at
approximately 60 cycles per minute. Water anion soluble ions
(SO42-, NO3-) in the filtered extract solution were
analysed by ion chromatography (Dionex model ICS-1100).
Back trajectory modelling
The Numerical Atmospheric-dispersion Modelling Environment (NAME, UK Met Office)
(Jones et al., 2007) was used to track the
pathways of air masses arriving in Beijing. A large number of hypothetical
inert particles are released and their pathways are tracked backwards in
time using meteorological fields from the UK Met Office's Unified Model with
a horizontal grid resolution of 0.23∘ longitude by 0.16∘
latitude and 59 vertical levels up to an approximate height of 30 km
(Brown et al., 2012). For this study, we modelled 3 d backward footprints with release periods that were the same time as the
measurements (i.e 22/23 h). The output has a resolution of 0.25∘× 0.25∘ and represents the hypothetical inert particles passing
through the surface layer (here defined as 0–100 m above ground) during
their travel to the IAP meteorological tower in Beijing. The residence time
the air masses spent over a specific location (Fig. 3) or region (Fig. S2 in the Supplement) during the four measurement conditions (WH, WL, SH,
SL) was calculated by producing a summed plot of the NAME footprints from
the five individual samples analysed for each measurement condition. More
in-depth information about the origin of different air masses in Beijing,
including time periods not covered by the APHH campaign, can be found in
Panagi et al. (2020). They investigated the different
origins of the air masses during a 4-year period and how this is
correlated with CO levels and CO transportation to Beijing and calculated
the residence times in air masses from four quadrants around the IAP tower
in Beijing, finding many more north-westerly air masses in winter and southern air masses in summer.
Results and discussionCompositional overview
The common ion mass spectra for the four atmospheric conditions are shown in
Fig. 1, indicating that the vast majority of
compounds above the detection limit are below m/z 500. While in winter
almost no peaks are present with m/z>450, a significant number
of peaks up to m/z 500 are detected in the summer samples. The overall
number of assigned formulae per sample ranged from 918 in the SL sample to
1586 in the WH sample. This is a lower limit on the total number of ionised
compounds in the samples, as the technique cannot distinguish between
structural isomers. Figure 2 shows the relative
number contribution of the four different compound groups (CHO, CHON, CHOS,
CHONS) to the total number of assigned formulae. ESI, like all ionisation
methods for mass spectrometry, is not equally efficient in ionising
different compounds, and the ionisation efficiency for one compound will
differ between positive and negative mode. ESI works best for polar
molecules, which is why pure hydrocarbons are usually not detected
efficiently. A trend that can be seen in the detected formulae is the
presence of significantly more CHON formulae in summer compared to winter.
The opposite trend was observed in Shanghai by Wang et al. (2017a), who found higher numbers and relative contribution of CHON in
winter. Further information would be needed to explain this discrepancy.
Another seasonal difference is that significantly more sulfur-containing
formulae (CHOS and CHONS) were observed for high-pollution conditions in
both winter and summer. Overall, the highest percentage of
sulfur-containing formulae was found under high-pollution conditions in
winter. Figure S1 shows the overlap of assigned formulae, divided into CHO,
CHON, CHOS and CHONS, between the different samples. From this graph, it can
be seen that the low mass loading samples (i.e. WL and SL) contain hardly
any unique CHOS and CONS formulae above our detection limit. These results
regarding the sulfur-containing compounds are in good agreement with
Jiang et al. (2016), who
compared wintertime Beijing samples collected under hazy and clean
conditions. A more detailed discussion of the sulfur- and nitrogen-containing compounds and their sources can be found in Sect. 3.3.
Negative-ionisation mass spectra for the four composite samples WH (a), WL (b), SH (c) and SL (d). Colour-coding differentiated formulae with
differing molecular compositions: CHO (red), CHON (blue), CHOS (yellow) and
CHONS (green).
Bar charts showing the relative contribution of different compound
groups (CHO, CHON, CHOS, CHONS) to the total number of formulae for each of
the composite samples: WH (dark blue), WL (light blue), SH (dark yellow) and
SL (light yellow). The numbers on the bars indicate the absolute number of
formulae detected for the respective sample and compound class.
The strongly varying particle composition in winter and summer and during
high- and low-pollution conditions is reflected by changes of the source
regions during these four conditions. Back trajectories show that during
strongly polluted days (WH, SH), air masses originate from south of Beijing,
which is widely industrialised (Fig. 3). In
contrast, days with low-pollution conditions in both seasons are
characterised by more varied air mass histories. Especially during the
collection of the WL samples, the air masses originated predominantly from
the north-west (Fig. S2), a region including a less developed part of China
as well as the only sparsely populated country Mongolia.
Sum of the 72 h back trajectories for (a) winter, high-pollution
days (WH), (b) summer, high-pollution days (SH), (c) winter, low-pollution
days (WL), and (d) summer, low-pollution days (SL). The colours denote the
relative (on a logarithmic scale) residence time of the air masses in each
0.25∘× 0.25∘ grid box (up to 100 m from the surface)
during the last 72 h before arriving at the monitoring station (the
model calculates the concentration of theoretical air mass particles
integrated over time per volume). The black cross denotes the sampling
location.
Average oxygen-to-carbon (O/C) and hydrogen-to-carbon (H/C) ratios for each
sample set were calculated by dividing the total number of oxygen respective
hydrogen atoms in all formulae in the data set by the total number of carbon
atoms. The calculated ratios are shown in Table 1.
The average H/C ratios are lower in winter and in the Birmingham tunnel (BT)
sample. This points towards the presence of more aromatic formulae, while
the summer and the Birmingham background (BB) sample are more aliphatic.
Aromatic compounds are predominantly produced from anthropogenic sources
such as traffic, industry and heating (Baek et al., 1991; Hamilton
and Lewis, 2003), whereas aliphatic compounds can be of both anthropogenic
and biogenic origins, the latter of which are more prevalent in the summer
(Gelencsér
et al., 2007; Hu et al., 2017; Kleindienst et al., 2007). One type of source
that will be present in both seasons and which contributes compounds of both
high and low H/C to particulate matter is vehicle emissions, as these are
usually a mix of low carbon number (<24) polycyclic aromatic hydrocarbons (PAHs) and single-ring
aromatics with low H/C and alkenes and cyclic, branched and straight-chain
alkanes with high H/C (Gentner
et al., 2012, 2017; Huang et al., 2015; May et al., 2014; Worton et al.,
2014). The SL H/C ratio is particularly high, which may be due to a larger
proportion of primary biogenic organic aerosol components from plant sources
with a high H/C, such as plant waxes, and a smaller influence of industrial
sources or vehicle emissions, which is more pronounced in the high-pollution
sample. Increased contribution of biogenic plant waxes to PM2.5 during
summer in Beijing has been observed previously
(Feng et al., 2005).
Average O/C and H/C ratios for each sample set.
Sample setAverage O/CAverage H/CWinter low (WL)0.411.19Winter high (WH)0.481.25Summer low (SL)0.541.53Summer high (SH)0.621.44Birmingham background (BB)0.541.44Birmingham tunnel (BT)0.421.16
While the overall H/C ratio shows whether the sample is more aromatic or
aliphatic, the O/C ratio gives an indication of how strongly oxidised a
sample is. Table 1 shows that there are
more highly oxidised formulae in the summer and the BB sample sets, which
could be due to higher levels of photochemistry in the summer in Beijing,
and at the Birmingham background site. The O/C ratio is much lower in the
winter and in the Birmingham tunnel sample. In Beijing, this could be due to
reduced photochemistry in the winter months in Beijing, so that the
particles are sampled before they can undergo atmospheric ageing processes,
for example reacting with OH radicals and ozone. The average ozone
concentrations at the IAP site during sample collection were 13 ppb (WL),
6 ppb (WH), 39 ppb (SL) and 63 ppb (SH). In addition to ozone, the
concentrations of other gas pollutants as well as temperature and humidity
data for the different samples can be found in the Supplement in Table S2.
For the BT sample, the lower O/C ratio likely reflects a largely primary
particle composition.
Van Krevelen diagrams, in which H/C is plotted against O/C for each assigned
formula (Kim et al., 2003), are widely used as a means to
categorise aerosol samples since they provide a clear representation of the
range of O/C ratios found in the organic aerosol sample
(Nizkorodov et al., 2011). Van Krevelen plots
for both the Beijing samples and the samples from Birmingham are shown in
Fig. 4. It can be seen that for the winter samples,
a lot of formulae are located in the aromatic region (H/C<1,
O/C<0.5) (Mazzoleni et al., 2012) and that there is
a significant reduction of formulae in this region in the summer data. This
aromatic region is also strongly populated in the BT sample. The SL period
is nearly devoid of formulae in the aromatic region, while the aromatic
region of SH is similar to the BB sample, which was recorded in early
autumn. These observations about aromaticity are in agreement with the
conclusions drawn from the total H/C ratio. The formulae that can be
identified as aromatic tend to be CHO and CHON. Conversely, CHOS and CHONS
formulae are primarily located outside of this aromatic region, with much
higher H/C ratios, indicating a lower level of aromaticity.
Figure 4 shows that there is a visible increase in
not just the percentage of sulfur-containing formulae (see
Fig. 2), but also the absolute number of sulfurous
formulae in WH over WL. A similar trend can be seen for the summer.
Van Krevelen plot for the WH, WL, SH, SL, BT and BB sample sets.
The colours denote the CHO (red), CHON (blue), CHOS (yellow) and CHONS
(green) formulae detected for each sample set. The aromatic region is shaded
in grey.
Aromatic compounds
The low H/C ratios for several of the samples suggest the presence of a
large number of aromatic compounds. Figure 5 shows
a Van Krevelen plot for the different samples in which the different
formulae are colour mapped according to their double bond equivalent (DBE),
which was calculated according to Eq. (1). The DBE gives the number of double
bonds plus rings in a molecule. For DBE calculations, it is usually assumed
that all atoms involved (except hydrogen) obey the octet rule. While this
assumption tends to hold true in strictly reducing environments, under oxic
conditions, such as in the atmosphere, it is likely that both sulfur and
nitrogen compounds with higher valency are present. Such compounds for
example include organosulfates or organonitrates and nitro compounds, which are
frequently detected in ambient particles. The DBE values calculated here
therefore represent a lower limit. A high DBE indicates likely aromaticity
of a compound. It can be seen that the region identified in
Fig. 4 as containing most likely aromatic
compounds contains the formulae with the highest DBE. The smallest PAH,
naphthalene, has a DBE of 7; thus all formulae in
Fig. 5 shown in yellow, red or brown colours are
probably polycyclic aromatics, likely oxidised PAHs. This assumption is
corroborated by the findings of Elzein
et al. (2019), who used gas chromatography–time-of-flight mass
spectrometry to quantify the concentration of 10 oxygenated PAHs (OPAHs) and
9 nitrated PAHs (NPAHs) during the APHH winter campaign and found the total
concentration of OPAHs to range from 1.8 to 95.5 ng m-3 and
that of NPAHs from 0.13 to 6.43 ng m-3.
Van Krevelen plot for the WH, WL, SH, SL, BT and BB sample sets.
Colour indicates the double bond equivalent (DBE), calculated under the
assumption that sulfur is bivalent. The aromatic region is shaded in grey.
The DBE values of the molecules found in the aromatic region for WH and WL
exceed that of the sample from BT. This suggests that larger polycyclic
aromatics are found in Beijing air in the winter than in aerosol collected
in a road tunnel in a European city, likely due to an increase in solid fuel
burning for residential heating. A general increase in the concentration of
PAHs and oxidised PAHs in China during winter, generally attributed to
residential heating, has been observed in multiple studies
(Bandowe et al.,
2014). In addition to this, Zhang et al. (2017)
found a sharp increase in the concentration of higher-ring-number (≥4)
PAHs at the start of the heating season and Huang et al. (2014)
observed a similar increase in emission of higher-ring-number PAHs for coal
combustion compared to gasoline and diesel, lending weight to our hypothesis
about the origin of the larger polycyclic aromatics.
While the DBE can be used to determine the number of C–C double bonds in
pure hydrocarbons, heteroatoms present in a compound can form double bonds
not contributing to aromaticity, ring formation or condensation
(Koch and Dittmar, 2006). To
overcome this problem associated with DBE another parameter has been
developed, the aromaticity equivalent (Xc). For organic molecules containing
only oxygen and/or nitrogen, the aromaticity equivalent can be calculated as
follows (Yassine et al.,
2014):
Xc=2C+N-H-2mODBE-mO+1,
where C, N, H and O are the number of carbon, nitrogen, hydrogen and oxygen
atoms respectively, and m is the fraction of oxygen atoms involved in the
π-bond structure of the compound which differs for different functional
groups. For carboxylic acids, esters and nitro functional groups m=0.5.
Since the measurements in this study were performed with electrospray
ionisation in negative mode, carboxylic acids likely dominate the signal so
m=0.5 is presumed for the calculation of Xc. Organic peroxy compounds
can account for a significant fraction of organic particulate matter. For
these compounds m is <0.5, as less than half of the oxygen atoms
are involved in a π-bond structure. If our samples were to contain a large number of peroxy compounds, we would be underestimating the degree of
unsaturation in the sample. The same is true if most of the of nitrogen-containing compounds are organonitrates rather than nitro compounds. The
values should therefore be considered a conservative estimate. If DBE≤mO, then Xc = 0. Compounds with Xc ≤ 2.5000 are non-aromatic, for
monocyclic aromatics 2.5000 ≤ Xc ≤ 2.7143 and for polycyclic
aromatics Xc ≥ 2.7143. There are too many possible oxidation states of
sulfur to correctly assign the aromaticity of the CHOS and CHONS compounds, and so this characteristic was not investigated further for S-containing
formulae.
The distribution of polycyclic, monocyclic aromatics and aliphatic formulae
on a Van Krevelen plot for all samples is shown in
Fig. 6. The vast majority of polycyclic aromatics
can be found in the region outlined before as aromatic (H/C<1,
O/C<0.5); however, some have slightly higher H/C ratios, which may
be due to extended alkyl chains as the H/C cut-off does not account for
this. The majority of monocyclic aromatics are found outside the aromatic
region, which indicates that the majority of them contain long alkyl chains.
Molecules classified as non-aromatic are found entirely outside of the
previously defined aromatic region, as expected. The calculated Xc values
confirm that the summer samples have many fewer aromatic formulae than the
winter samples. The SL sample is particularly low in aromatic formulae (132
aromatic formulae vs. 801 in total), especially regarding polycyclic
aromatics (26 formulae). In contrast, there are a reasonable number of
polycyclic (85) and monocyclic (159) aromatics in SH. The two winter samples
both show high contributions of aromatic formulae (76 % in WH vs. 77 %
in WL of all detected formulae in these samples). However, the WH sample is
strongly dominated by polycyclic aromatics with 403 polycyclic vs. 260
monocyclic aromatic formulae, while monocyclic and polycyclic aromatics are
present in nearly equal numbers in the WL sample (320 monocyclic and 297
polycyclic aromatic formulae). The high fraction of aromatics present in the
winter samples is consistent with the results from Wang et al. (2018), who also compared filter samples collected in Beijing wintertime air
under high- and low-pollution conditions. As mentioned previously, a lot of
the polycyclic aromatic compounds are likely oxidised PAHs. Oxidised PAHs
such as nitro-PAH, oxy-PAH and hydroxy-PAH can be produced either directly
from incomplete combustion or pyrolysis of fossil fuel and biomass or
through oxidation of PAHs in the atmosphere (Albinet
et al., 2007; Andreou and Rapsomanikis, 2009; Atkinson and Arey, 1994;
Bandowe and Meusel, 2017; Walgraeve et al., 2010). Bandowe et al. (2014) found higher concentrations of oxidised PAHs in winter in Xi'an, a
Chinese megacity. They partially attribute this increase to heating
activities. A similar increase in the concentration of oxidised PAHs during
the heating season was observed in Beijing by Lin et al. (2015), who also
state that this increase might be linked to winter heating activities. Their
source apportionment showed that oxy- and hydroxy-PAHs in particular are
dominated by biomass burning emissions during the heating season. They state
that during the non-heating period, secondary sources become more relevant
due to increased photochemical activity, dominating as a source for oxy-and
nitro-PAHs. This is consistent with our observations of increased O/C during
the summer (Table 1). Studies by
Liu et al. (2019) and Lyu et al. (2019) confirm that coal combustion and biomass burning where significant
sources of particulate matter during the APHH winter campaign, while a
quantitative comparison with the summer is still pending.
Van Krevelen plot for the WH, WL, SH, SL, BT and BB sample sets.
Polycyclic aromatic (2.7143 ≤ Xc), monocyclic-aromatic (2.5000 ≤ Xc ≤ 2.7143) and non-aromatic (Xc ≤ 2.5000) formulae for all CHO
and CHON ions present in the samples are indicated in magenta, cyan and
black, respectively. The aromatic region is shaded in grey.
To further investigate the different aromatic compounds, the approximate
average carbon oxidation state (OS‾C) for each formula was plotted
against the number of carbon atoms (nC), with all formulae
classified as polycyclic aromatics, monocyclic aromatics or non-aromatic.
Results for CHO compounds are shown in Fig. 7, while
results for the CHON compounds can be found in the Supplement (Fig. S3).
OS‾C was calculated for each molecule as follows (Kroll et al., 2011):
OS‾C≈2O/C-H/C,
where O, C and H are the number of oxygen, carbon and hydrogen atoms
respectively . OS‾C is used as an alternative metric to O/C for
assessing the degree of oxidation since the O/C ratio of organic compounds
can also change when a compound undergoes non-oxidative reactions. As
different formulae can have the same combination of OS‾C and
carbon number, Figs. 7 and S3 have a lot of
overlapping points. In Fig. S4, an offset was applied to the overlapping
points to make more data points visible. Figure 7 shows that the summer samples contain large numbers of aliphatic
CHO formulae with a huge range of different carbon numbers, whereas the
non-aromatic formulae in the winter samples only rarely have more than 15
carbons. This is possibly due to increased biogenic emissions of long-chain
fatty acids, alkenes and similar compounds during summer.
Plot of carbon oxidation state against carbon number for all CHO
formulae in the WH, WL SH, SL, BT and BB samples. Polycyclic aromatic
(2.7143 ≤ Xc), monocyclic aromatic (2.5000 ≤ Xc ≤ 2.7143) and
non-aromatic (Xc ≤ 2.5000) formulae are depicted as magenta circles,
cyan diamonds and open black diamonds respectively.
The majority of the polycyclic aromatics and monocyclic aromatics in the
summer are below a carbon number of 20 (C20). Similarly, the BT sample, where
road traffic is the main source, contains hardly any polycyclic aromatics
above C18 and only few monocyclic aromatics above C20. This is in strong
contrast with the winter samples from Beijing, which show a strong presence
of polycyclic aromatics with up to 25 carbon atoms. The winter samples
contain large numbers of aromatics, especially polycyclic aromatics above
C15. The likely source for these is heating, as they are present in large
numbers in both WH and WL yet not in the summer. This link between
higher-ring-number polyaromatics and residential heating is supported by
studies showing a sharp increase in the concentration of higher-ring-number
PAHs at the start of the heating season in Beijing (Zhang et al., 2017a)
and increased emissions of higher-ring-number PAHs for coal combustion
compared to gasoline and diesel (Huang et al., 2014).
Similar trends can be observed for the CHON compounds in Fig. S3.
Sulfur- and nitrogen-containing compounds
The mass spectra of all four investigated conditions show that the majority
of assigned formulae contain sulfur and/or nitrogen atoms. Organic compounds
containing sulfur and nitrogen such as organosulfates, organosulfonates,
nitrooxyorganosulfates, amines, organonitrates and nitro compounds are
prevalent in the atmosphere (Bandowe
et al., 2014; Canales et al., 2018; Huang et al., 2012b; Iinuma et al.,
2007; Kiendler-Scharr et al., 2016; Kristensen and Glasius, 2011; Lee et
al., 2016; Riva et al., 2015; Valle-Hernández et al., 2010). As
mentioned previously, all ionisation methods for mass spectrometry differ in
their ionisation efficiency towards different compounds. Nonpolar compounds
are generally not ionised well in ESI, and the negative-mode ESI is not sensitive
towards reduced nitrogen compounds such as amines and imines, while
organosulfates, organosulfonates, organonitrates and nitro compounds should
ionise well.
Organosulfates are estimated to contribute as much as 30 % to organic fine
particle mass (Surratt et al., 2008). Secondary
organosulfate formation likely proceeds through particle-phase chemistry via
a variety of proposed mechanisms such as esterification of hydroxyl and keto
groups (Liggio and Li, 2006), acid-catalysed ring-opening
of epoxides (Iinuma et al., 2009;
Minerath and Elrod, 2009), radical-initiated processes
(Galloway et al., 2009; Nozière
et al., 2010) and the direct reaction of SO2 with unsaturated compounds (Passananti et al., 2016). Inorganic
SO42- in particles is formed through oxidation of sulfur dioxide,
which is primarily emitted from anthropogenic sources, especially over land
(Smith et al., 2001). In Beijing, SO2
concentrations tend to be particularly high in winter
(Zhang et al., 2011; Zhou et al.,
2015), with a large contribution from heating (Huang
et al., 2012a). However, the resulting concentration of SO42- also
strongly depends on the rate of SO2 oxidation. In Beijing, average
SO42- concentrations in fine particles are usually higher in
summer than in winter (Chen
et al., 2017; Hu et al., 2016; Huang et al., 2016; Liu et al., 2017), which
is attributed to increased photooxidation during summer. In our study, the
average sulfate concentration was however slightly lower in summer
(6.9 µg m-3) than in winter (8.5 µg m-3). This
unusually low concentration compared to the cited studies might be due to
the fact that our campaign was conducted earlier in summer than the others.
Studies have also shown that the formation rate of SO42- is
particularly high during haze episodes, with a strong contribution from
heterogeneous oxidation within haze droplets (Ma
et al., 2018; Wang et al., 2006), leading to high maximum concentrations of
sulfate during haze events, of which several strong ones occurred in winter
during our campaign. While organosulfate formation has been primarily
understood to be a secondary process, a recent direct-infusion UHRMS study
of coal combustion by Song et al. (2019) found 5 %–25 % of formulae in the methanol-extracted fraction to
contain sulfur, indicating the potential importance of direct organosulfate
emissions. A study by Wang et
al. (2019) showed that organosulfates in wintertime PM2.5 in Beijing
originated from multiple types of biogenic and anthropogenic precursors.
The majority of S-containing formulae in our samples contain only one sulfur
atom. Out of those, most (≥99 % for WH and WL, >92 %
for SH and SL) also contain at least four oxygen atoms, marking them as
potential organosulfates. This is in line with previous studies (Jiang
et al., 2016; Tao et al., 2014; Wang et al., 2019). For the CHONS formulae,
77 % in WH, 94 % in WL, 86 % in SH and 91 % in SL have at least
seven oxygen atoms, indicating potential nitrooxy organosulfates.
Unlike organosulfates, formation of organonitrates is thought to occur
mostly in the gas phase through for example the reaction of peroxy radicals with NO or
oxidation of volatile organic compounds through the NO3 radical
(Ng et al., 2017; Zhang et al., 2004). Inorganic
NO3- in particles is predominantly present in the form of
NH4NO3, which is formed through reaction of NH3 with
HNO3. The dominant pathways for HNO3 formation are the reaction of
NO2 with OH radical during daytime and N2O5 hydrolysis during
the night (Bauer
et al., 2007; Khoder, 2002). In Beijing, ammonium-poor particles were found
to still have very high NO3- content. It has been suggested that
hydrolysis of N2O5 in particles is responsible for this phenomenon
(Pathak et al., 2009). Just like for SO42-,
NO3- concentrations in Beijing are enhanced during haze episodes (Huang
et al., 2016; Wang et al., 2006). Apart from organonitrates, nitroaromatics
are another important class of N-containing organics in the atmosphere which
can be detected in negative-mode ESI. They can either be emitted directly via
combustion of biomass and fossil fuels (Heeb
et al., 2008; Karavalakis et al., 2010; Wang et al., 2017b) or formed in the
atmosphere through reaction of aromatics (Keyte
et al., 2013). Apart from being both a primary and secondary source of
nitroaromatics, biomass burning can also lead to formation of other
nitrogen-containing organics, such as alkaloids
(Laskin et al., 2009). However, these
compounds are unlikely to be detected in negative-mode ESI.
Number of molecular formula with the elemental composition CHOS
(yellow diamonds), CHONS (green square) and CHON (blue triangles) plotted
against the concentration of SO42-(a, b) or NO3-(c, d) in the particle phase, analysed via ion chromatography. An
overview of the correlation parameters can be found in the Supplement (Table S4).
The majority of detected N-containing formulae (97 % WH, 89 % WL,
98 % SH and 97 % SL) also contained at least three oxygen atoms, marking
them as potential organonitrates. Nitro compounds on the other hand have a
minimum of two oxygens per molecule, which is the case for >99 % of N-containing formulae in the WH, SH and SL samples and 96 % in
the WL sample.
To obtain further information about the potential sources of the sulfur-
and nitrogen-containing compounds in our study, the total number of
molecular formulae containing sulfur and/or nitrogen were plotted against
the concentration of inorganic sulfate (SO42-) and nitrate
(NO3-) (Fig. 8) for each measured sample
respectively. The direct-infusion MS analyses done here do not allow us to determine compound concentrations accurately; thus we use the number of CHOS
compounds as an indicator for the importance of the formation processes for
S-containing organic compounds. An overview of the correlation parameters
can be found in the Supplement (Table S4). For the winter samples, there was
a statistically significant positive correlation between the number of CHOS
formulae and the concentration of inorganic SO42- on that day
(Fig. 8a), corroborating results of other studies
that S-containing organics are formed in the particle phase. The sulfate
data did not correlate as well for summer (Fig. 8b), which might be explained by the lower maximum concentrations of
SO42- and the on average slightly lower SO42-
concentrations during our summer campaign, where particle-phase formation
reactions of S-containing organics might become less important. Very similar
trends are observed for CHONS, suggesting common formation routes for these
compounds as for CHOS. The ions that are found only in the WH and not the WL
sample are likely to be representative of winter haze events. Over half of
these components are sulfur containing (Fig. S1) – which suggests high
formation rates for organic sulfurous compounds during winter haze events.
Wang et al. (2019) found that
organosulfates with a high carbon number were significantly more abundant in
polluted Beijing winter samples than in samples taken under low-pollution
conditions, which indicates that in polluted air, more organosulfates are generated
from long-chain alkanes.
It can be seen in panel c and d of Fig. 8 that the
number of CHON formulae does not correlate significantly with the
NO3- concentration, while CHONS does, though this correlation is
only statistically significant for the winter samples. This suggests that in
contrast to the S-containing organics, most N-containing organics detected
in our samples are not formed in the particle phase. As stated earlier, the
detection mode we used is biased towards oxidised N-containing organics such
as organic nitrates, which are known to form predominantly in the gas phase
and nitro compounds, which can be of either primary or secondary origin,
with a strong contribution of gas-phase oxidation in the second case. A
correlation with particle-phase nitrate is therefore not expected.
Conclusions
PM2.5 filter samples from Beijing from the winter and
summer APHH-Beijing campaign were analysed with direct injection ultrahigh-resolution mass spectrometry. A strong variation in the composition of
organic particles was observed between high- and low-pollution
conditions as well as between the two seasons. In summer significantly more
CHON formulae were detected compared to winter, likely due to the increased
NOx-driven photochemistry in summer. S-containing formulae were more
dominant during high-pollution events in both seasons and highest during
winter haze events. The contribution of aromatic compounds was strongly
increased in winter, likely due to heating as an additional source. The
relative contribution of S-containing formulae increased under high-pollution conditions, and the number of S-containing formulae showed
correlation with inorganic SO42- on the filters, consistent with a
particle-phase formation process, while no such correlation was found for
N-containing formulae and inorganic NO3-.
Data availability
The direct-infusion ultrahigh-resolution mass spectrometry data are
available through the CEDA Archive (https://catalogue.ceda.ac.uk/uuid/680ebdcb83c244fdb9d069e2f8952812), last access: 20 July 2020, Steimer and Kalberer, 2020.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-13303-2020-supplement.
Author contributions
MK and SSS designed the research. DJP and SSS performed the mass
spectrometry measurements and data analysis. TVV measured the ion
chromatography data. MP ran the NAME model. SSS wrote the manuscript with
contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We acknowledge the support from Pingqing Fu, Zifa Wang, Jie Li and Yele Sun
from IAP for hosting the APHH-Beijing campaign at IAP. We thank Di Liu and
Bill Bloss from the University of Birmingham; Siyao Yue, Liangfang Wei, Hong Ren, Qiaorong Xie, Wanyu Zhao, Linjie Li, Ping Li, Shengjie Hou and Qingqing Wang from IAP; Rachel Dunmore, Ally Lewis and James Lee from the University
of York; Kebin He and Xiaoting Cheng from Tsinghua University; and James Allan and Hugh Coe from the University of Manchester for providing logistic
and scientific support for the field campaigns.
Financial support
This research has been supported by the Natural Environment Research Council (grant nos. NE/N007190/1 and NE/N007158/1) as part of the APHH-Beijing study, the Swiss National Science Foundation (project no. P2EZP2_162258) and the AXA Research Fund (through a 2017-LIFE-PostDoc fellowship).
Review statement
This paper was edited by Mei Zheng and reviewed by three anonymous referees.
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