Oxygenated organic molecules (OOMs) are dominated by the N-containing
species in polluted urban environments. As N-containing OOMs, especially
those with more than one nitrogen atom, prevail in the high m/z (mass-to-charge) range
(m/z> 350 Th), unambiguous identification of N-containing OOMs is
highly desirable for understanding of their formation processes, precursors
and influencing factors. To achieve this, we applied an
ultra-high-resolution chemical-ionization Orbitrap (CI-Orbitrap) in a field
campaign and found that OOMs contain one (1N-OOMs), two (2N-OOMs) and three
(3N-OOMs) nitrogen atoms comprised 50 %, 26 % and 4 %, respectively, of
total OOMs. More interestingly, the fraction of 2N-OOMs increased with the
increase in carbon number (nC) and was dominated by the ones derived from
aliphatic precursors (2N-OOMAli, 64.2 %), indicating the importance
of multistep oxidation. Plausible precursors of 2N-OOMs were aliphatics
(2N-OOMAli, 64.2 %), aromatics (2N-OOMAro, 16 %) and
monoterpenes (2N-OOMMT, 15.4 %). The absolute concentrations of
2N-OOMs were greatly affected by the pollution level for most cases. The
2N-OOMAli was the most abundant 2N-OOM, and its fraction even increased
on the polluted day with an enhanced proportion of the ones with nC >10. While 2N-OOMAli and 2N-OOMAro were dominated by daytime
photochemical production, nighttime NO3-initiated oxidation played a
comparable role to the daytime photochemistry in the formation of
2N-OOMMT. The 2N-OOMAro species were of the highest oxygenation level, followed by
2N-OOMMT and 2N-OOMAli, which were affected by photochemistry and
NOx concentrations. These results highlight the significant formation
of 2N-OOMs and the influencing factors on their formation in polluted urban
environments, where various volatile organic compound (VOC) precursors and atmospheric oxidants are present.
National Key Research and Development Program of China2022YFC3700205China Postdoctoral Science Foundation2022T1504272022M712146Introduction
Secondary organic aerosol (SOA) accounts for a significant fraction of
particulate matters (Donahue
et al., 2009; Ehn et al., 2014; Hallquist et al., 2009; Jimenez et al.,
2009). Volatile organic compounds (VOCs) and their oxidation products,
i.e., OVOCs, are important precursors of SOA in the atmosphere (Atkinson
and Arey, 2003; Bianchi et al., 2019; Ehn et al., 2014; Nie et al., 2022). N-containing oxygenated organic molecules (OOMs) have been identified as important products upon VOC oxidation. Especially at high NOx
levels, these products become more dominant, while the others (i.e., alcohols,
hydroperoxides and RO2 cross-reaction products) are likely suppressed
(Bianchi et al.,
2019; Zhao et al., 2018). The nitrogen atoms in OOMs are assumed to
be mainly associated with the nitrate group (-ONO2) formed from
bimolecular reaction between RO2 radical and NO. Field measurements have
also shown that up to 77 % of molecules in organic aerosol (OA)
contain nitrate functional groups under different atmospheric conditions (Ditto
et al., 2020; Kenagy et al., 2021; Kiendler-Scharr et al., 2016; Lee et al.,
2016; Ng et al., 2017; Lin et al., 2021; Rollins et al., 2013; Xu et al.,
2015; Ye et al., 2021; Yu et al., 2019).
The N-containing OOMs can be classified into 1N-OOMs, 2N-OOMs and
3N-OOMs, according to the number of N atoms in the molecule. The chemical
composition of N-containing OOMs is determined by their precursors,
formation pathways and NOx level in the atmosphere (Bianchi
et al., 2019; Ehn et al., 2014; Nie et al., 2022; Pye et al., 2019; Riva,
2016; Yan et al., 2016). Recent observations in megacities of China
have indicated that 2N-OOMs account for significant fractions (about 30 %–33 %)
among total N-containing OOMs besides 1N-OOMs (66 %–70 %) due to the high
NOx concentrations in polluted urban environments (Nie et al., 2022; Yan et
al., 2021). Some laboratory studies have also proposed that the potential
formation pathways of 2N-OOMs, such as multiple-step OH oxidation (Garmash et al., 2020)
or NO3-initiated oxidation followed by NO termination (Kiendler-Scharr
et al., 2016; Liebmann et al., 2019), suggest the increased importance of
multistep bimolecular oxidation in the formation of 2N-OOMs. On the other
hand, it has also been found that the formation of 2N-OOMs shows a clear
preference for specific precursors compared to 1N-OOMs, i.e., a significantly
higher branch ratio of 2N-OOMs from aliphatic hydrocarbons compared to those from
aromatics (Nie et al., 2022), suggesting a considerable
difference from 1N-OOMs in terms of the formation pathway. Determining the
formation pathway of N-containing OOMs, especially those containing
two to three nitrogen atoms, in real atmosphere is challenging.
Identification of their chemical compositions at the molecular level is key
for advancing our understanding of the precursors, formation and sources of
N-containing OOMs in polluted atmosphere, where thousands of oxidation
products exist and evolve constantly.
Traditionally, a chemical-ionization atmospheric-pressure-interface
time-of-flight mass spectrometer (CI-APi-TOF) has been used to measure
gaseous OOMs (Berndt
et al., 2016; Ehn et al., 2014; Jokinen et al., 2014; Rissanen et al.,
2014). Using a CI-APi-TOF, an increasing number of studies have reported
the formation of OOMs through the oxidation of various VOC precursors in
chambers or flow tubes (Berndt
et al., 2016, 2018; Ehn et al., 2014; Garmash et al., 2020; Jokinen et al.,
2014, 2015; Rissanen et al., 2014; Wang et al., 2020; Zhao et al., 2018).
While 2N-OOMs in real ambient atmosphere are almost exclusively located in a high m/z
(mass-to-charge) range (i.e., 300–500 Th), a CI-APi-TOF with the highest mass resolving power of 12 000 (m/Δm, in full width at half maximum) at
m/z=200 Th and above can hardly identify the molecular compositions of
2N-OOMs unambiguously. This is because low mass resolving power imposes
significant uncertainties onto separating overlapping peaks, which increase
rapidly with increasing m/z and decreasing mass resolving power. Taking the
integer m/z of 342 as an example, multiple peaks overlap at this nominal
mass, i.e., C7H8O10N2(NO3)- (342.0057 Th),
C8H12O9N2(NO3)- (342.0421 Th),
C9H16O8N2(NO3)- (342.0785 Th) and
C10H20O7N2(NO3)- (342.1149 Th). The adjacent
peaks are of mass differences (m) of 0.0364, and a good peak separation of
these peaks (4σ) requires mass resolving power of at least 16 000.
Therefore, the development and application of mass spectrometry techniques with
extremely high performance in terms of the detection limit, time resolution and mass resolving power are highly desirable.
To achieve accurate identification of the molecular formula from the extremely
complex mass spectra, a CI (nitrate) inlet has also been coupled to an
Orbitrap mass spectrometer (CI-Orbitrap) to measure the OOMs at ultra-high
mass resolving power (m/Δm>100000 at m/z= 200 to 500 Th) (Riva et al.,
2019a; Zhang et al., 2022). The ultra-high mass resolving power of
CI-Orbitrap will undoubtedly provide significant improvements in molecular
identification, separation and quantification. Herein, we applied a
CI-Orbitrap in a field campaign for the measurements of OOMs, with a special
focus on 2N-OOMs, at the molecular level in urban Shanghai. The site represents
a typical eastern Chinese megacity characterized by intense human
activity, multiple anthropogenic emissions and high NOx
concentrations. Based on the measurement results as well as our current
knowledge on N-containing OOM formation, we classify the observed 2N-OOMs
into different precursor groups and explore the potential influencing
factors in their formation. Furthermore, supported by positive matrix
factorization (PMF), sources and gas-phase oxidation processes for 2N-OOM
formation in urban Shanghai are identified.
Ambient measurement and methodologyMeasurements
The field campaign was carried out from 31 October to 18 November 2020 on the top floor of an eight-story building in the Shanghai Academy
of Environmental Sciences
(31∘18′ N, 121∘43′ E;
Fig. S1), which sits in a densely populated region surrounded by
commercial properties and residential dwellings without significant
industrial sources nearby. The site can represent a typical urban area of
Shanghai affected by severe local emissions from vehicular traffic and
commercial and residential activities. Our campaign was carried out in
autumn, which represents a typical transition period from strong
photochemistry in summer to intense regional transport in winter. At times,
air masses transported from the neighboring provinces or even further from northern China can also affect the air quality of the site.
The 2N-OOMs as well other OOMs were measured in real time with a
nitrate Orbitrap. The operation of the nitrate Orbitrap has been detailed in
previous studies as well as in one of our companion studies
(Zhang et al., 2022); thus it is only briefly described
here. Ambient air was drawn into the ionization source through a 1 m
stainless-steel tube (3/4 in.). The reagent ion was produced by passing
nitric acid in sheath flow (20 L min-1) into a Photoionizer (Model L9491,
Hamamatsu, Japan) and was then introduced into a co-axial laminar flow
reactor, in which the reagent ions interact with the air samples. The
charged species were detected by an Orbitrap mass analyzer with a mass resolving power of about 140 000. Mass-dependent transmission calibrations
were also performed using a depletion method (Heinritzi et al., 2016). Other ancillary
measurements, including of the PM2.5 concentrations, trace gases
(SO2, O3 and NOx) and volatile organic compounds, as well as
meteorological parameters (wind direction and speed, solar radiation, etc.),
are detailed in the Supplement (Sect. S1). An overview of the
measurement data, illustrating the air quality as well as the meteorological
conditions during the campaign, is provided in Sect. S2 and Fig. S2.
Data analysis of nitrate CI-Orbitrap
The raw mass spectra were first extracted by Orbitool
(Cai et al., 2020), and the molecular information was
then achieved by applying a homemade toolkit based on MATLAB software.
The toolkit drew on the idea from the “tofTools” package, which is used for
analyzing the mass spectral data obtained from the TOF analyzer, such as
a nitrate CI-API-TOF (Junninen
et al., 2010). The concentrations of the detected species are then
determined as follows:
X=iX-NO3-⋅HNO30–2⋅C
where i[X-] is the transmission-corrected signal intensity of ion X in
units of counts per second (cps) and C represents the calibration factor. C is
determined from the collision frequency of target species with the nitrate
ions (cluster) during its residence in the charger, taking into account the losses onto the walls of the reactor and the tube (Eq. 2):
C=CH2SO4=1kion⋅RT⋅finlet,
where kions is the ion collision frequency in the range of 1.7×10-9 to 2.3×10-9 cm3 s-1 (Ehn et al., 2014), RT is the
residence time in the charger and finlet represents the fractions of
target species that passed through the inlet.
Herein, we apply the C determined for sulfuric acid (H2SO4) of
3.4×109 molecules cm-3 ncps-1 to semi-quantify the
concentrations of OOMs, which has been widely used in previous studies (Ehn
et al., 2014; Yan et al., 2021; Yao et al., 2018). Among the low-volatility
vapors, it has been demonstrated that nitrate ions exhibit the highest charging
efficiency toward H2SO4 (Ehn
et al., 2014; Hyttinen et al., 2015, 2018; Riva et al., 2019b). The
estimated concentrations of OOMs thus can be considered the lower limits
with an uncertainty of ±50 % according to error propagation
(Ehn et al., 2014). Positive
matrix factorization (PMF) was also performed for the measured species using
Source Finder (SoFi, v6.3) based on Igor and run by the multilinear engine
(ME-2) as detailed in Sect. S3 and Figs. S3–S6 (Canonaco et al.,
2013).
Results and discussionChemical characteristics of OOMs
In total, we have identified 562 OOMs, which were concentrated in the carbon number (nC) range of
5 to 10, taking up 84.6 % of total OOMs during the whole campaign (unless
otherwise stated, all the reported values hereafter correspond to
the average of the whole campaign). Possible precursors of C5–10 OOMs
include isoprenes (C5), benzene/alkylbenzenes (C6–10), aliphatic
VOCs (C5–10) and monoterpenes (C10) according to previous studies
(Bianchi et al., 2019; Nie et al., 2022).
C≤4 OOMs only took up a small fraction of 6.7 % among total OOMs
and were likely a result of the decomposition from OOMs with large carbon
numbers as suggested by one of our companion studies
(Zhang et al., 2022). The remaining 8.7 % were C>10
OOMs, which accounted for a dominating fraction (70 %, Fig. S7) among the
extremely low-volatility organic compounds (ELVOCs, C*<3×10-5µg m-3) based on a volatility parameterization proposed
by Donahue and co-workers (Donahue
et al., 2011, 2012; Schervish and Donahue, 2020) and potentially have
larger impacts on SOA formation owing to their lower volatility.
We further classified the detected OOMs into four groups based on the number
of N atoms they possessed, including non-nitrogen OOMs (0N-OOMs), 1N-OOMs,
2N-OOMs and 3N-OOMs. Their average fractional contributions to total OOM
concentrations as well as the carbon number (nC) distributions are shown in
Fig. 1. We found that 1N-OOMs dominated the total OOM concentration with an
average fraction of 50 %, followed by 2N-OOMs (26 %), demonstrating the
dominance of N-containing OOMs among total OOMs. The 3N-OOMs only took up a
small fraction (4 %) of total OOMs, and the remaining 20 % were 0N-OOMs.
(a) Average mass spectrum of the detected OOMs during the whole
campaign. The pie chart shows the fractions of OOMs with different numbers of
nitrogen and carbon atoms; (b) the fractions of 0N-OOMs, 1N-OOMs, 2N-OOMs
and 3N-OOMs among total OOMs as a function of the carbon number (nC).
More interestingly, we found that 1N-OOMs prevailed among the OOMs with nC ≤ 10, yet 2N-OOMs dominated the C>10 OOMs (41.8 %–84.2 %), suggesting
the increased importance of multistep bimolecular oxidation in the
formation of 2N-OOMs with nC > 10. We also note that the fraction
of 2N-OOMs increased stepwise with the increase in nC (Fig. 1b), while
3N-OOMs did not exhibit a similar dependence. The potential reason for this is that,
with the increase in nC on the one hand, more active sites are potentially
provided to promote the occurrence of multistep oxidation, but on the other
hand, the potentially larger steric effect can hinder multistep oxidation.
From our observation, these two factors lead to an overall positive coupling
for 2N-OOMs but result in a non-monotonic trend for 3N-OOMs. Furthermore,
these 2N-OOMs with nC > 10 had an average molecular composition of
C12.5H22.7O2.1(NO3)2. Assuming the nitrogen atoms
are only associated with the nitrate group (-ONO2), the mean double-bond
equivalent (DBE) value
(Nie
et al., 2022; Xu et al., 2021) was 1.15 on the carbon skeleton, suggesting
its origination from aliphatic compounds, such as alkanes or alkenes (Gong
et al., 2005; Mentel et al., 2015; Wang and Hildebrandt Ruiz, 2018).
We thus further classified the 2N-OOMs into their possible VOC precursors
following a recently developed workflow proposed by Nie and co-workers,
which is based on the up-to-date understanding of VOC oxidation and
molecular characters (i.e., number of different elements, DBE) as well as PMF
results (Nie et al., 2022), i.e., aromatics (2N-OOMAro),
aliphatics (2N-OOMAli) and monoterpenes (2N-OOMMT). Note that we
classify isoprene 2N-OOMs (2N-OOMIso) as 2N-OOMAli as well because
of the low concentration of isoprene in the cold season. As a result, the
average fractions of 2N-OOMAro, 2N-OOMAli and 2N-OOMMT among
total 2N-OOMs were 16.0 %, 64.2 % and 15.3 %, respectively (Fig. 2),
suggesting significant contribution of aliphatic compounds to 2N-OOM
formation. Taken together, the increased fraction of 2N-OOMs with the
increase in nC and the dominant fraction of 2N-OOMAli highlight the
significant contribution of high-molecular-weight aliphatic precursors
(i.e., intermediate-volatility or semi-volatile organic compounds, I/SVOCs) to
high-molecular-weight 2N-OOM formation and comprise potentially important SOA
material. We thus focus our attention on the formation of 2N-OOMs in the
following sections.
The time series of 2N-OOMs originating from different precursors.
Four sub-periods were selected to further investigate the fractional
distribution of different types of OOMs as shown in the pie charts,
including a clean daytime case (12:00 to 14:00 LT on 4 November,
PM2.5=7.5µg m-3, CLday), a clean nighttime case
(23:00 LT on 4 November to 01:00 LT on 5 November, PM2.5=9.5µg m-3, CLnight), a daytime case in a PM2.5 episode (12:00 to
14:00 LT on 7 November, PM2.5=44.0µg m-3, PLday)
and a nighttime case in a PM2.5 episode (23:00 LT on 7 November to 01:00 LT on
8 November, PM2.5=60.5µg m-3, PLnight). The
sizes of pie charts are scaled to the total concentrations of 2N-OOMs.
2N-OOM formation in PM2.5 episodes
To investigate the formation mechanisms and factors that may affect the
2N-OOM formation, 1 clean day (4–5 November) and 1 polluted day (7–8 November) based on the pollution levels,
i.e., PM2.5 concentrations, were selected for further analysis. Since OOM
formation is directly mediated by photochemistry or nighttime chemistry, the
clean and polluted cases were thus split into one clean daytime case
(CLday), one clean nighttime case (CLnight), one polluted daytime
case (PLday) and one polluted nighttime case (PLnight). Detailed
information on durations, pollution levels, meteorological conditions and
2N-OOM concentrations during these four cases is summarized in Table 1.
Summary of the four cases including the meteorological conditions
and concentrations of trace gases and 2N-OOMs. Note that “ppt” denotes parts per trillion throughout this paper.
CaseTime (LT)PM2.5SolarTRH[O3][NO][NO2][NO3][2N-OOMAro][2N-OOMAli][2N-OOMMT][2N-OOMTotal](µg m-3)(W m-2)(∘C)(%)(ppb)(ppb)(ppb)(ppt)(×106 cm-3)(×106 cm-3)(×106 cm-3)(×106 cm-3)CLday4 November 12:00–14:007.5635.618.935.241.93.28.20.16.311.72.320.8CLnight4 November 23:00–5 November 01:009.52.413.064.18.02.940.50.21.36.02.39.8PLday7 November 12:00–14:0044.0384.523.930.573.92.220.60.36.423.84.336.2PLnight7 November 23:00–8 November 01:0060.52.517.844.927.22.138.76.23.117.45.326.5
During the whole campaign, the concentrations of 2N-OOMs ranged from 1.1×106 to 42.0×106 molecules cm-3 as shown in
Fig. 2. We found the concentrations of 2N-OOMs in the polluted cases were
1.7–2.7 times higher than those in clean cases. Table 1 further indicates
that the absolute abundances of almost all 2N-OOM classes were higher during the
polluted cases as compared to the clean cases no matter whether it was the daytime or
nighttime, except for the daytime 2N-OOMAro. Specifically,
2N-OOMAli occupied the largest fractions, which were even higher in
polluted cases (66 %–66 %) than those in clean cases (56 %–61 %, Fig. 2).
Especially for the 2N-OOMAli with nC >10, its concentration
in polluted cases increased by a factor of 2.3–4.8 compared to the clean
cases (Fig. 3). From PMF analysis, we also identified a factor
characterized by a series of 2N-OOMAli (i.e., CnH2n-2O8N2, n= 5 to 11) as the fingerprint molecules
(Table S1). This factor tracks the PM2.5 concentration well, especially
during PM2.5 episodes (Fig. S8), likely due to the availability of adequate
aliphatic precursors during pollution episodes. Furthermore, 2N-OOMAli
with nC >10 presented both higher concentrations and higher fractions
during daytime cases compared to nighttime cases (Fig. 3), suggesting that the
photochemical formation of 2N-OOMAli prevailed compared to nighttime
formation. To compare CLnight and PLnight, it was also found that
the pollution case would lead to the enhanced importance of nighttime formation
pathways of 2N-OOMAli with nC >10.
The fractions of 2N-OOMAli with different carbon numbers in
the four cases.
We note that the fraction of 2N-OOMAli increased during CLnight
primarily due to the more evident decrease in 2N-OOMAro (Table 1),
whose formation is dominated by photochemistry. On the other hand, the
decrease in 2N-OOMAro concentrations during PLnight was not as
obvious as those during CLnight. Due to the significant increase in
2N-OOMAli concentration, the fraction of 2N-OOMAro species decreased in
pollution cases, but their absolute concentrations only underwent a few changes in
the daytime. The 2N-OOMMT species showed significantly higher concentrations but
similar fractions in polluted cases. On the other hand, equivalent or even
slightly higher concentrations during the nighttime compared to those in the daytime
suggest the comparable importance of nighttime chemistry in 2N-OOMMT
formation in contrast to 2N-OOMAli and 2N-OOMAro, which will be
discussed in later subsections.
To summarize, the absolute concentrations of 2N-OOM were greatly affected by
the pollution level for most cases. Both the concentrations and the
fractions of 2N-OOMAli were significantly promoted by pollution
conditions, whereas the 2N-OOMAro species were predominantly affected by
photochemical production, whose formation was less sensitive to pollution
levels compared to 2N-OOMAli in the daytime. In contrast, the absolute
concentrations of 2N-OOMMT were also significantly influenced by
pollution levels but seem not solely dependent on the daytime/nighttime
formation pathway. In addition, we note that both daytime photochemistry and
nighttime chemistry had profound effects on 2N-OOM formation at
different pollution levels, presumably depending on availability of the
precursors as well as the oxidants. We thus focus our attention on the
formation of 2N-OOMs during the daytime versus nighttime in the following
sections.
Daytime vs. nighttime formation of 2N-OOMs
We investigate the effects of photochemistry and nighttime chemistry on
the formation of individual 2N-OOMs. While the former is dominated by OH
radical oxidation, the latter involves NO3 radical oxidation as well as
reactions with ozone or other oxidants, e.g., halogen. Herein, we use solar
radiation as a proxy for photochemical reactivity, and the concentrations of
NO3 radicals were estimated assuming that NO3, NO2 and
N2O5 were under fast equilibration in the troposphere (Brown and Stutz, 2012). The correlation
coefficients (Spearman type) between individual 2N-OOMs and solar
radiation (R2N-OOMs-solar) or NO3 radicals (R2N-OOMs-NO3)
derived from different precursors during the whole campaign are shown in
Fig. 4a. It should be noted that the concentrations of 2N-OOMs and
NO3 radicals were scaled with the boundary layer height before
calculating the correlation coefficients here and below for correcting the
effects of meteorological dilution.
(a) Statistical distribution of the correlation coefficients
(Spearman type) between 2N-OOMs and solar radiation (R2N-OOMs-solar) in red and the correlation coefficients between 2N-OOMs and
[NO3] (R2N-OOMs-NO3) in blue for 2N-OOMs from different
precursors. The horizontal lines are the median values, boxes denote the
25th- and 75th-percentile values, and whiskers represent the 10th- and
90th-percentile values. (b) The diel patterns of 2N-OOMs from different
precursors.
Both 2N-OOMAro and 2N-OOMAli showed stronger correlations with
solar radiation compared to NO3 radicals, indicating their association with
daytime photochemistry, since benzene/alkylbenzenes and aliphatic VOCs
rapidly react with OH radicals compared to other oxidants, such as NO3
radicals. This is also supported by the observation that both 2N-OOMAro
and 2N-OOMAli peaked during noontime (12:00–14:00 LT) as shown in Fig. 4b. Similarly, the PMF analysis also distinguished two daytime factors. The
daytime factor 1 peaked at around 12:00–14:00 LT (Table S1) and highly
correlated with solar radiation (R=0.57). The fingerprint molecules of
daytime factor 1 are CnH2n-4O10N2 (n= 8 to 10) with
average DBE values of 2 on the carbon skeleton, suggesting the dominance of
2N-OOMs likely formed from aromatic precursors. Since each step of OH
oxidation of aromatics followed by RO2+NOx termination would
increase the hydrogen number (nH) by 1, this factor is likely dominated by 2N-OOMs formed
from two steps of OH-initiated oxidation from alkylbenzenes given that the carbon
numbers ranged from 8 to 10.
The key fingerprint molecule of daytime factor 2 is
CnH2nO8N2 (n= 4 to 5) (accounting for 30.8 % in the
factor profile), followed by CnH2n-2O8N2 (n= 5 to 6)
(accounting for 9.7 % in the factor profile), which is likely a result of
the decomposition from 2N-OOMAli species with large carbon numbers, according
to their DBE values of 0–1 on the carbon skeleton. This aliphatic factor
presented even higher correlation with solar radiation (R=0.65), peaking
at around 12:00–14:00 LT. Strong daytime peaks together with the good
correlations with irradiation suggest the dominance of photochemical
oxidation in the formation of 2N-OOMAli. For 2N-OOMAli, although
it showed strong a daytime peak, a weak nighttime peak was still observed. This
indicates that although daytime formation of 2N-OOMAli prevails, 2N-OOMAli
nighttime formation still existed. For example, we have obtained a nighttime
factor from PMF analysis (nighttime factor 2), whose fingerprint molecules
are C5H8O9N2 and CnH2nO7N2
(n= 5 to 8). C5H8O9N2 likely originated from isoprene,
and CnH2nO7N2 was likely from anthropogenic aliphatic
precursors.
Nighttime chemistry plays a more important role in the formation of
2N-OOMMT. This is further supported by the slightly stronger
correlation between 2N-OOMMT and NO3 radicals compared to solar
radiation. For some specific 2N-OOMMT species, the formation is likely
a result of NO3-radical-initiated oxidation. As shown in Fig. 5, we
have identified a series of 2N-OOMMT molecules with a molecular
composition of C10H16O7,9,11N2, which showed strong
positive correlations with NO3 radicals. The occurrence of the propagation
reaction from RO2 to RO was critical to the formation of odd oxygen as
proposed in previous chamber studies (Boyd
et al., 2015; Claflin and Ziemann, 2018). Furthermore, under the nighttime
conditions observed in urban Shanghai (Table 1), it is estimated that
monoterpenes primarily react with NO3, and the fate of nighttime
RO2's is dominated by NO, which is clearly different from rural
environments where NO levels likely drop to near zero after sunset (Romer et al., 2016) and
RO2's are likely terminated by NO3–RO2 cross-reactions (Bates et al., 2022).
Therefore, the formation of C10H16O7,9,11N2 likely started
with the reaction of monoterpenes with NO3 radicals forming a
NO3–C10H16 alkyl radical, followed by the formation of
organic peroxy radicals (RO2) upon addition of O2. The RO2 is
then converted to an alkoxy radical (RO) upon reaction with NO. The
autoxidation process would then start and introduce O2 into
the molecule stepwise, forming a series of more oxygenated RO2 radicals, i.e.,
NO3–C10H16(O)(OO)n. The NO termination reaction of these
RO2 radicals would finally result in ONs with a chemical composition of
NO3–C10H16(O)(OO)nO(NO)O (n= 0, 1, 2).
Scatterplot of R2N-OOMs-NO3 against R2N-OOMs-solar for specific 2N-OOM species.
On the other hand, the reaction rate between monoterpenes (i.e., α-pinene, β-pinene and limonene) and NO3 is about
60 000–140 000 times faster than that between monoterpenes and O3 at
293 K (Master Chemical Mechanism Version 3.3.1, MCMv3.3.1), but the averaged nighttime concentration of O3 (22.8 ppb) was only about 18 000 times higher than that of NO3 (1.3 ppt).
Therefore, the NO3-initiated oxidation process had significant impacts on
2N-OOMMT formation during the nighttime. The 2N-OOMMT resulting from
NO3 oxidation is also resolved as a nighttime factor (nighttime
factor 1) from PMF analysis, which tracked the NO3 concentrations well
(Fig. S9, R=0.46) and peaked at around 19:00–23:00 LT. The fingerprint molecule
of nighttime factor 1 mainly included C10H16O9N2
and C10H16O8N2, which are generated from
NO3-initiated oxidation followed by NO termination, and this process
will not change the nH of the parent monoterpene molecule.
Oxygenation level of 2N-OOMs
We then calculated the average effective oxygen number (nOeff= nO - 2nN) of 2N-OOMs, which is used to indicate the oxidation state of carbon
by excluding the oxygen atoms bonded with nitrogen atoms. Note that
calculation of nOeff assumes that the nitrogen atoms are only
associated with the nitrate group (-ONO2), which is reasonable after
excluding nitrophenol peaks. The average nOeff of 2N-OOMs from
different precursors in CLday, CLnight, PLday and
PLnight are shown in Fig. 6 and summarized in Table S2. The
2N-OOMAro had the highest nOeff (4.8–5.6), followed by
2N-OOMMT (4.5–4.9), and 2N-OOMAli had the lowest nOeff
(3.9–4.0). Difference in the oxygenation level of different types of OOMs
can be attributed to the difference in oxidation mechanisms of the
initiation reactions. For example, the OH-initiated oxidation of alkanes,
aromatics and monoterpenes/alkenes would form a CxHyO2
radical, CxHyO5 radical and CxHyO3 radical,
respectively, incorporating different numbers of oxygen atoms into the
original precursor molecules at the first step of oxidation (Master Chemical Mechanism Version 3.3.1, MCMv3.3.1). On
the other hand, during the multiple-step oxidation in the daytime, aromatics
could still provide more C=C bonds than other precursors after the initial
step, which are plausibly capable of further reacting with OH, O3 and other
oxidants.
The nOeff of 2N-OOMs derived from different precursors in the
four cases; the error bars represent the standard deviations.
Furthermore, we also found that regardless of the pollution level, the
nOeff was considerably higher in daytime cases than in nighttime
cases, particularly for 2N-OOMAro and 2N-OOMMT, suggesting a
profound effect of photochemistry on the formation of highly oxygenated
2N-OOMs. This is likely because of the high NOx concentrations during
the nighttime (Table 1), which could efficiently suppress the RO2
radicals from autoxidation reactions, forming overall less oxygenated OOMs. The effect of NOx on oxygenation levels will be discussed
in a subsequent paragraph. The average nOeff values of 2N-OOMAli in four
sub-periods were similar without significant daytime and nighttime
difference, ranging from 3.9–4.0. This could be partly explained by the fact
that reactions with oxidants such as OH and halogen radicals will similarly
result in the addition of oxygen atoms by 2 for alkanes during the first
step of oxidation. Thus, the oxygenation levels of 2N-OOMAli were
assumed to be insensitive to the oxidants in the daytime or nighttime.
It is known that NO is also critical in determining the fate of RO2
radicals during oxidation, forming RO radicals or organonitrates.
Formation of RO radicals and of organonitrates will have opposite effects on
the oxidation state of the termination products, since the former will
significantly increase the oxygenation state of carbon through initiating
propagation reactions before termination. We thus explore the effects of NO
as well as the total NOx concentrations on the average oxygenation
levels of 2N-OOMs from different precursors during the whole campaign
(Fig. 7). Consistently with previous studies in polluted urban environments (Qiao et al., 2021; Yan
et al., 2021), the detected 2N-OOMs were also of low oxygenation with
nOeff values of 3.9–5.4 (25th–75th percentile) compared to those measured in
forests or in laboratory studies (Berndt
et al., 2016; Ehn et al., 2014; Jokinen et al., 2014; Rissanen et al., 2014;
Yan et al., 2016). The nOeff of 2N-OOMAro and 2N-OOMMT increased
with the decrease in NO or NOx concentrations. This is likely due to the
prevalence of NO termination reactions because the maximum autoxidation rate
constant of alkylbenzenes with long-chain substituents (e.g., isopropylbenzene, ethylbenzene) and monoterpenes is comparable to the
bimolecular reaction rate between RO2 and NO (Bianchi et al., 2019). The oxygenation levels of
2N-OOMAli appear to be insensitive to the pollution levels and
NO or NOx concentrations, which should be further investigated in future
studies.
Effective oxygen number (nOeff) of 2N-OOMs as a function of
(a) NO concentration and (b) NOx concentration. The colored squares
represent the real measurements. The filled markers indicate the median
values in the range as horizontal error bars show; the vertical error bars
denote the 25th- and 75th-percentile values.
Conclusions
We report the unambiguous identification of 2N-OOMs as well as other OOMs
using an ultra-high-resolution Orbitrap coupled with a nitrate inlet. We
find that OOMs are distributed among a wide range of carbon numbers (nC = 4 to 16), among which the 2N-OOMs occupied a considerable fraction (26 %) of
the total observed OOMs. During the whole campaign, the 2N-OOM
concentrations ranged from 1.1×106 to 42.0×106 molecules cm-3 and were concentrated in the nC range of 5 to 10 with a high
molecular weight (m/z>350 Th), implying their low
volatilities and thus potentially high contribution to local SOA formation.
Aliphatic, aromatics and monoterpenes were plausible precursors of 2N-OOMs
with fractions of 64.2 %, 16 % and 15.4 %, respectively. The absolute
concentrations of 2N-OOMs were greatly affected by the pollution level for
most cases. The 2N-OOMAli was found to be the most abundant 2N-OOM,
and its fraction even increased on the polluted day with an enhanced proportion
of 2N-OOMAli species with nC >10, probably due to the high concentrations of
aliphatic precursors accompanied by PM2.5 episodes. A significant contribution
of long-chain aliphatic compounds (nC >10) to 2N-OOM formation
is also supported by the observation that the 2N-OOM fraction increased with the
increase in nC and the species had low DBE values, likely through multistep
bimolecular oxidation. The 2N-OOMAli and 2N-OOMAro mainly peaked in
the daytime and showed stronger correlations with solar radiation over NO3
radicals, indicating their association with daytime photochemistry, since
benzene/alkylbenzenes and aliphatic hydrocarbons rapidly react with OH
radicals compared with other oxidants, such as NO3 radicals. In
contrast, 2N-OOMMT prevailed in both the daytime and the nighttime; some
specific 2N-OOMMT species showed strong positive correlations with
NO3 radicals and were likely a result of NO3-radical-initiated
oxidation, suggesting the comparable importance of nighttime NO3
chemistry in 2N-OOMMT formation. In terms of oxygenation levels, we
found that 2N-OOMAro had the highest averaged nOeff followed by
2N-OOMMT. Daytime photochemistry and low NOx concentrations had
profound effects on the formation of more-oxygenated 2N-OOMs. The 2N-OOMAli
had the lowest nOeff and had negligible changes at different
pollution levels. These results demonstrate the preference of 2N-OOM
formation and the influencing factors in a Chinese megacity involving
various VOC precursors (biogenic VOCs such as monoterpenes and anthropogenic
VOCs such as aromatics and aliphatic hydrocarbons) and various atmospheric
oxidants (such as OH radicals and NO3 radicals) and highlight the
influence of PM2.5 episodes.
Data availability
Data presented in this paper are available upon request to the
corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-23-3233-2023-supplement.
Author contributions
CH designed this study. YL, YM, DDH, SL, SJ and YG conducted the field
campaign. YL analyzed data with contributions from CH and all the other
co-authors. YL wrote the manuscript with contributions from all the other
co-authors.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This study was financially supported by the National Key Research and Development Program of
China (2022YFC3700205) and China Postdoctoral Science Foundation
(2022T150427, 2022M712146).
Financial support
This research has been supported by the National Key Research and Development Program of China (grant no. 2022YFC3700205) and the China Postdoctoral Science Foundation (grant nos. 2022T150427, 2022M712146).
Review statement
This paper was edited by Nga Lee Ng and reviewed by three anonymous referees.
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