Air pollution by particulate matter in China affects human health, the
ecosystem and the climate. However, the chemical composition of particulate
aerosol, especially of the organic fraction, is still not well understood.
In this study, particulate aerosol samples with a diameter of ≤2.5µm (PM2.5) were collected in January 2014 in three cities located
in northeast, east and southeast China, namely Changchun, Shanghai and
Guangzhou. Organic aerosol (OA) in the PM2.5 samples was analyzed by an
ultrahigh-performance liquid chromatograph (UHPLC) coupled to
a high-resolution Orbitrap mass spectrometer in both negative mode (ESI-)
and positive mode electrospray ionization (ESI+). After non-target
screening including the assignment of molecular formulas, the compounds were
classified into five groups based on their elemental composition, i.e., CHO,
CHON, CHN, CHOS and CHONS. The CHO, CHON and CHN groups present the dominant
signal abundances of 81 %–99.7 % in the mass spectra and the majority of
these compounds were assigned to mono- and polyaromatics, suggesting that
anthropogenic emissions are a major source of urban OA in all three cities.
However, the chemical characteristics of these compounds varied between the
different cities. The degree of aromaticity and the number of polyaromatic
compounds were substantially higher in samples from Changchun, which could
be attributed to the large emissions from residential heating (i.e., coal
combustion) during wintertime in northeast China. Moreover, the ESI-
analysis showed higher H/C and O/C ratios for organic compounds in Shanghai
and Guangzhou compared to samples from Changchun, indicating that OA
undergoes more intense photochemical oxidation processes in lower-latitude
regions of China and/or is affected to a larger degree by biogenic sources.
The majority of sulfur-containing compounds (CHOS and CHONS) in all cities
were assigned to aliphatic compounds with low degrees of unsaturation and
aromaticity. Here again, samples from Shanghai and Guangzhou show a greater
chemical similarity but differ largely from those from Changchun. It should
be noted that the conclusions drawn in this study are mainly based on
comparison of molecular formulas weighted by peak abundance and thus are
associated with inherent uncertainties due to different ionization
efficiencies for different organic species.
Introduction
In the last decades, China has experienced rapid industrialization and
urbanization accompanied by severe and persistent particulate air pollution
(Huang et al., 2014; Sun et al., 2014; Ding et al., 2016; Song et al.,
2018; Shi et al., 2019; Xu et al., 2019). These particulate air pollution
extremes can not only influence the regional air quality and human health in
China, but also lead to a global environmental problem due to long-distance
transport of pollutants. To better understand the effects of air pollution
on air quality and human health, chemical characterization of fine particles
(particulate matter with an aerodynamic diameter of less than 2.5 µm,
or PM2.5) is crucial. However, the chemical composition of PM2.5
in China is still poorly understood due to a wide variety of natural and
anthropogenic sources as well as complex multiphase chemical reactions
(Lin et al., 2012a; Huang et al., 2014; Ding et al., 2016; Wang et al.,
2017, 2018, 2019a, b; An et al., 2019; Tong et al., 2019). In particular, compared to the fairly well
understood nature of the inorganic fraction of aerosol, the organic
fraction, also named organic aerosol (OA), is considerably less understood
in terms of chemical composition, corresponding precursors, sources and
formation mechanisms (Huang et al., 2017).
During pollution events in China, OA accounts for as high as more than
50 % of the total mass of fine particles (An et al., 2019). Chemical
compounds in OA cover a large complexity of species including alcohols,
aldehydes, carboxylic acids, imidazoles, organosulfates, organonitrates and
polycyclic aromatic hydrocarbons (PAHs) (Lin et al., 2012a; Rincón et
al., 2012; Kourtchev et al., 2014; Wang et al., 2018, 2019a; Elzein et al., 2019). Thus, the capacity of traditional analytical techniques
is limited to identify the compounds in OA, and the majority (>70 %) of OA has not been identified yet as specific compounds
(Hoffmann et al., 2011). The insufficient knowledge of
chemical composition of OA hinders a better understanding of the sources,
formation and atmospheric processes of air pollution in China.
Recently, ultrahigh-resolution mass spectrometry (UHRMS), such as Fourier
transform ion cyclotron resonance mass spectrometry (FTICR-MS) and
the Orbitrap MS, coupled with soft ionization sources (e.g., electrospray
ionization, ESI, and atmospheric pressure chemical ionization, APCI), has
been introduced to elucidate the molecular composition of OA (Nizkorodov
et al., 2011; Lin et al., 2012a, b; Rincón et al., 2012;
Noziere et al., 2015; Kourtchev et al., 2016; Tong et al., 2016; Tu et al.,
2016; Brüggemann et al., 2017, 2019; Wang et al., 2017, 2018, 2019a; Fleming et al., 2018;
Laskin et al., 2018; Song et al., 2018; Daellenbach et al., 2019; Ning et al., 2019).
Due to the two outstanding features of high resolving power and high mass
accuracy, UHRMS can give precise elemental compositions of individual
organic compounds. However, UHRMS studies on Chinese urban OA are very
limited. Wang et al. (2017) characterized OA in
Shanghai and showed variations in chemical composition among different
months and between daytime and nighttime. Our recent Orbitrap MS study
(Wang et al., 2018) showed that wintertime OA in PM2.5
collected in Beijing, China, and Mainz, Germany, was very different in terms
of chemical composition. In contrast, for summertime OA from Germany and
China, Brüggemann et al. (2019) found similar compounds and
concentrations of terpenoid organosulfates in PM10, demonstrating that
biogenic emission can significantly affect OA composition at both locations.
Ning et al. (2019) analyzed the OA collected in a coastal Chinese city
(Dalian) and found that more organic compounds were identified on haze days
compared to non-haze days. Nonetheless, since severe particulate pollution
in China occurs on a large scale, more UHRMS studies are needed to fully
elucidate the chemical composition of OA in different Chinese cities.
In this study, PM2.5 aerosol samples were collected in three Chinese
cities, i.e., Changchun, Shanghai and Guangzhou, and their organic fraction
was analyzed using an ultra-high-performance liquid chromatograph (UHPLC)
coupled with an Orbitrap MS. The Chinese cities of Changchun, Shanghai and
Guangzhou are located in the northeast, east and southeast of China, which
are major populated regions in China with populations of 7.5, 24 and 15 million, respectively. The geographic locations of these three cities cover
a large latitude spanning from 23.12 to 43.53∘ N, resulting
in different meteorological conditions, including intensity and duration of
sunlight, average daily temperature, and monsoon climate. In addition, the
industrial structure, energy consumption and energy sources in these three
cities are different; for example there is much more heavy industry (e.g., coal
chemical industry and steelworks) in northeast China (Zhang,
2008), which can cause difference in anthropogenic emissions and can
therefore influence the chemical composition of urban OA. Moreover, OA is
strongly affected by residential coal combustion during winter in northeast
China (Huang et al., 2014; An et al., 2019). Therefore, this study
presents a comprehensive overview of chemical composition of OA in three
representative Chinese cities during pollution episodes, which eventually
can improve our understanding of OA effects on climate and public health and
also provide a chemical database for haze mitigation strategies in China.
Mass spectra of detected organic compounds reconstructed from
extracted ion chromatograms in ESI- and ESI+. The horizontal axis refers
to the molecular mass (Da) of the identified species. The vertical axis
refers to the relative peak abundance of each individual compound compared to the
compound with the greatest peak abundance. The pie charts show the
percentage of each organic compound subgroup (i.e., CHO, CHON, CHOS, CHONS
and CHN) in each sample in terms of peak abundance. The map in the lower
right corner shows the locations of these three megacities in China.
Three 24 h integrated urban PM2.5 samples were collected during
severe haze pollution events with daily average PM2.5 mass
concentration higher than 115 µg m-3 in each of the three
Chinese cities: Changchun (43.54∘ N, 125.13∘ E, 1.5 m above
the ground), Shanghai (31.30∘ N, 121.50∘ E, 20 m above the
ground) and Guangzhou (23.07∘ N, 113.21∘ E, 53 m above the
ground), which are located in the northeast, east and southeast regions of
China, respectively (see Fig. 1). Samples in Changchun were collected on 4,
24 and 29 of January 2014 with PM2.5 mass concentrations of 185–222 µg m-3; samples in Shanghai were collected on 1, 19 and 20 of
January 2014 with PM2.5 mass concentrations of 159–172 µg m-3; and samples in Guangzhou were collected on 5, 6 and 11 of January
2014 with PM2.5 mass concentrations of 138–152 µg m-3.
Further details (e.g., the daily average concentrations of PM2.5,
SO2, NO2, CO and O3, the average temperature, and the daily
solar radiation value during sampling dates) are presented in Table S1, and the
48 h back trajectories of air arriving at the three sampling sites
during the sampling periods are shown in Fig. S1 in the Supplement. All PM2.5 samples
were collected on prebaked quartz-fiber filters (20.3 cm × 25.4 cm)
using a high-volume PM2.5 sampler at a flow rate of 1.05 m3 min-1 (Tisch Environmental, USA) and at each sampling site field blanks
were taken. After sample collection, filters were stored at -20∘C until analysis.
Sample analysis
A detailed description of the filter sample extraction and UHPLC–Orbitrap MS
analysis can be found in our previous studies (Wang et al., 2018, 2019a). Briefly, a part of the filters (around 1.13 cm2,
corresponding to about 600 µg particle mass in each extracted filter)
was extracted three times with 1.0–1.5 mL of acetonitrile water (8/2, v/v)
in an ultrasonic bath. The extracts were combined, filtered through a 0.2 µm Teflon syringe filter and evaporated to almost dryness under a
gentle nitrogen stream. Finally, the residue was redissolved in 1000 µL
acetonitrile water (1/9, v/v) to reach the total particulate mass
concentration of around 600 µg mL-1 for the following analysis.
Compared to the direct infusion method applied in other UHRMS studies
(Lin et al., 2012a, b; Rincón et al., 2012; Kourtchev
et al., 2016; Fleming et al., 2018), the UHPLC technique was used in this
study, which could separate and concentrate the compounds before they
entered the ion source, reducing the ionization suppression and increasing
the sensitivity of the measurement. In addition, it can provide separation of
some compounds and information of retention time of the compounds, which is
useful for the identification of the compounds and the separation of
isomers. The analytes were separated using a Hypersil GOLD column (C18, 50×2.0 mm, 1.9 µm particle size) with mobile phases consisting of (A)
0.04 % formic acid and 2 % acetonitrile in Milli-Q water and (B) 2 %
water in acetonitrile. Gradient elution was applied with the A and B mixture
at a flow rate of 500 µL min-1 as follows: 0–1.5 min 2 % B,
1.5–2.5 min from 2 % to 20 % B (linear), 2.5–5.5 min 20 % B,
5.5–6.5 min from 20 % to 30 % B (linear), 6.5–7.5 min from 30 % to
50 % B (linear), 7.5–8.5 min from 50 % to 98 % B (linear),
8.5–11.0 min 98 % B, 11.0–11.05 min from 98 % to 2 % B (linear),
and 11.05–11.1 min 2 % B. The Q Exactive hybrid quadrupole-Orbitrap MS
was equipped with a heated ESI source at 120 ∘C, applying a spray
voltage of -3.3 kV and 4.0 kV for negative ESI mode (ESI-) and positive
ESI mode (ESI+), respectively. The mass scanning range was set from m/z 50
to 500 with a resolving power of 70 000 m/z 200. The Orbitrap MS was
externally calibrated before each measurement sequence using an Ultramark
1621 solution (Sigma-Aldrich, Germany) providing mass accuracy of the
instrument lower than 3 ppm. Each sample was measured in triplicate with an
injection volume of 10 µL.
Data processing
A non-target peak picking software (SIEVE®,
Thermo Fisher Scientific, Germany) was used to find significant peaks in the
LC–MS dataset and to calculate all mathematically possible chemical formulas
for ion signals with a sample-to-blank abundance ratio ≥10 using a mass
tolerance of ±2 ppm. The permitted maximum elemental number of atoms
was set as follows: 12C (39), 1H (72), 16O (20), 14N
(7), 32S (4), 35Cl (2) and 23Na (1) (Kind and Fiehn, 2007;
Lin et al., 2012a; Wang et al., 2018). To remove the chemically unreasonable
formulas, further constraint was applied by setting H/C, O/C, N/C, S/C and
Cl/C ratios in the ranges of 0.3–3, 0–3, 0–1.3, 0–0.8 and 0–0.8
(Kind and Fiehn, 2007; Lin et al., 2012a; Rincón et al., 2012; Wang
et al., 2018; Zielinski et al., 2018), respectively. For chemical formula
CcHhOoNnSsClx, the double bond equivalent
(DBE) was calculated by the equation DBE = (2c + 2 - h - x + n) / 2. The aromaticity equivalent (XC) as a modified index for aromatic
compounds was obtained using the equation:
XC= [3(DBE – (p×o+q×s)) – 2] / [DBE – (p×o+q×s)],
where p and q, respectively, refer to the fraction of oxygen and sulfur
atoms involved in the π-bond structure of a compound. As such the
values of p and q vary between compound categories
(Yassine et al., 2014). For example, carboxylic
acids and esters are characterized using p=q=0.5, while p=q=1 and p=q=0 are used for carbonyl and hydroxyl, respectively. Since
it is impossible to identify the structures of the hundreds of formulas
observed in this study, we cannot know the exact values of p and q in an
individual compound. Therefore, in this study, p=q=0.5 was applied
for compounds detected in ESI- as carboxylic compounds are preferably
ionized in negative mode. However, because of the high complexity of the
mass spectra in ESI+, p=q=1 was used in ESI+ to avoid an
overestimation of the amount of aromatics. Moreover, for DBE ≤ (p×o+q×s) or XC≤0, XC was defined as
zero. Furthermore, in ESI-, for odd numbers of oxygen or sulfur atoms in molecular
formulas, the value of (p×o+q×s) was rounded down
to the lower integer. XC≥2.50 and XC≥2.71 have been suggested
as unambiguous minimum criteria for the presence of monoaromatics and
polyaromatics, respectively (Yassine et al., 2014).
Comparing the peak abundance has been performed in recent UHRMS studies (Wang
et al., 2017; Fleming et al., 2018; Song et al., 2018; Ning et al., 2019) to
illustrate the relative importance of specific types of compounds. However,
it should be noted that different organic compounds have different signal
responses in the mass spectrometer due to the differences in ionization and
transmission efficiencies (Schmidt et al., 2006; Leito et al., 2008;
Perry et al., 2008; Kruve et al., 2014). Therefore, uncertainties may exist
when comparing the peak areas among compounds. In this work, we assume that
all organic compounds have the same peak abundance response in the mass
spectrometer. The peak abundance-weighted average molecular mass (MM),
elemental ratios, DBE and XC for the formula
CcHhOoNnSsClx were calculated using
following equations.
MMavg=∑(MMi×Ai)/∑AiO/Cavg=∑(O/Ci×Ai)/∑AiH/Cavg=∑(H/Ci×Ai)/∑ADBEavg=∑(DBEi×Ai)/∑AiXCavg=∑(XCi×Ai)/∑Ai
Here Ai is the peak abundance for each individual compound i.
Results and discussionGeneral characteristics
The main purpose of this study was to tentatively identify and compare the
chemical composition of organic compounds in the PM2.5 samples
collected in the three Chinese cities Changchun, Shanghai and Guangzhou
during pollution episodes. To reduce the uncertainty caused by the
variability between the samples collected at each location, only organic
compounds measured in all three samples of each city are used for intercity
comparison. The number of organic compounds and molecular formulas detected
in each city, the peak abundance-weighted average values (including the
standard deviations of peak abundance of the three samples from each city)
of molecular mass (MMavg), elemental ratios, DBE, XC, and the isomer
number fraction (meaning the percentage of formula numbers that have isomers
among all assigned formulas) for each subgroup are listed in Table 1. It
should be noted that in this study we focus solely on organic compounds with
elevated signal abundances and thus presumably rather high concentrations.
In contrast to our previous study (Wang et al., 2018),
compounds with low concentrations were excluded by increasing the
reconstitution volume from 500 to 1000 µL, reducing the sample
injection volume from 20 to 10 µL and increasing the
sample-to-blank ratio from 3 to 10 during data processing.
Overall, 416–769 (assigned to 272–415 molecular formulas) and 687–2943
(assigned to 383–679 molecular formulas) organic compounds in different
city samples were determined in ESI- and ESI+, respectively. The largest
number of organic compounds was observed in Changchun samples in both ESI-
and ESI+, indicating that OA collected during the winter season in northeast
China was more complex compared to urban OA in east and southeast China.
This increased number of compounds can possibly be explained by the large
residential coal combustion emissions in winter in north China (Huang et
al., 2014; Song et al., 2018; An et al., 2019), which is consistent with the
observation of higher average concentration (46±20µg m-3)
of organic carbon in Changchun than in Shanghai (24±8µg m-3) and Guangzhou (25±2µg m-3) as shown in Table S2.
In addition, ambient temperatures were lowest during the sampling period in
Changchun (i.e., -14 to -9∘, Table S1), which
likely led to a decreased boundary layer height and therefore enhanced
accumulation of pollutants and enhanced formation of secondary organic
aerosol through for example gas-to-particle partitioning.
Number of organic compounds and molecular formulas in each subgroup
and the peak abundance-weighted average values of molecular mass
(MMavg), elemental ratios, double bond equivalent (DBE), aromaticity
equivalent (XC) and isomer number fraction (meaning the percentage of
formula numbers that have isomers among all assigned formulas) for detected
organic compounds in ESI- and ESI+ in the three Chinese cities.
The standard uncertainty is the standard deviations of peak abundance of the
three samples from each city. a The values in brackets indicate the number of
unique molecular formulas. b The values in brackets indicate the (O-3S) / C
and (O-3S-2N) / C ratios for CHOS and CHONS compounds, respectively,
detected in ESI- mode.
As shown in Table 1, the abundance-weighted average values of MMavg and
O/C ratio of the total assigned formulas for Changchun samples detected in
negative mode (Changchun-) are 169 and 0.58, respectively, which are lower
than those for Shanghai- (MMavg=176 and O/C= 0.69) and for
Guangzhou- (MMavg=183 and O/C= 0.74). On the contrary, the
aromaticity equivalent XC for organics detected in Changchun-,
XC (Changchun-) = 2.13, is higher than that for Shanghai-,
XC (Shanghai-) = 1.92, and Guangzhou-, XC (Guangzhou-) = 1.65.
Furthermore, the relative peak abundance fraction of compounds with O/C≥0.6, which are considered to be highly oxidized compounds (Tu et al., 2016),
is 31 % in Changchun-, and higher in Shanghai- (46 %) and Guangzhou-
(51 %). These observations indicate that urban OA in northeast China
features a lower degree of oxidation and a higher degree of aromaticity
compared to urban OA in east and southeast China. The different chemical
composition of the samples is probably caused by the rather low ambient
temperatures and decreased photochemical processing of organic compounds in
northeast China (indicated by the lower solar radiation in northeast China;
see Table S1), slowing down oxidation processes and leading to a larger
number of PAHs, which are mainly emitted from coal burning (Huang et al.,
2014; Song et al., 2018) or by different biogenic/anthropogenic precursors.
Nitrate is mainly formed by photochemical oxidation, and the average
concentration of nitrate (see Table S2) was lower in particle samples from
Changchun (15.5±8.5µg m-3) compared to Shanghai
(28.2±9.4µg m-3) and Guangzhou (24.6±0.9µg m-3), again indicating less photochemical processing in northeast
China. In addition, long-range transport of air masses (see the 48 h
back trajectories in Fig. S1) may have a certain effect on the chemical
properties of aerosol samples collected in the three cities.
(a) Venn diagrams showing the number distribution of all molecular
formulas detected in ESI- and ESI+ for all sample locations. The
overlapping molecular formulas refer to the compounds detected in each city
with the same molecular formulas and with the same retention times
(retention time difference ≤0.1 min). (b) Peak abundance contribution
of each elemental formula category to the total common formulas.
Figure 1 shows the reconstructed mass spectra of organic compounds detected
in ESI- and ESI+. A major fraction of organic species detected in ESI-
are attributed to CHO- and CHON-, accounting for 30 %–42 % and
39 %–55 % in terms of peak abundance, respectively, and comprising
39 %–45 % and 23 %–33 % in terms of peak numbers, respectively. This is
consistent with previous studies on Chinese urban OA by Wang et al. (2017, 2018) and Brüggemann et al. (2019). Comparing the organic compounds
detected in ESI- for the three cities, 120 formulas were observed in all
cities as common formulas (which refer to the compounds detected in all
cities with the same molecular formulas and with the same retention times;
retention time difference ≤0.1 min) (Fig. 2a), accounting for
29 %–44 % and 57 %–71 % of all assigned formulas in terms of formula
numbers and peak abundance, respectively. Despite the abovementioned
differences in chemical composition for OA from Changchun compared to OA
from Shanghai and Guangzhou, these results demonstrate that still a large
number of common organic compounds exist in Chinese urban OAs collected in
different cities, in particular for organics with higher signal abundances.
Furthermore, as shown by the pie chart in Fig. 2b, these common formulas are
dominated by CHON- and CHO-, accounting for 62 % and 30 % of the
total common formulas in terms of peak abundance, respectively.
As is commonly known, ESI exhibits different ionization mechanisms in
negative and positive ionization modes. While ESI- is especially sensitive
to deprotonatable compounds (e.g., organic acids), ESI+ is more sensitive
to protonatable compounds (e.g., organic amines)
(Ho et al., 2003). Due to the different
ionization mechanisms, clear differences were observed in the mass spectra
(Fig. 1) and chemical characteristics (Table 1) from ESI- and ESI+
measurements. For example, CHO compounds were preferentially detected in
ESI-, accounting for a relatively larger fraction of 30 %–42 % of all
detected compounds in terms of peak abundance, compared to merely 4 %–13 %
for such CHO compounds in ESI+. In contrast, CHN compounds were only
observed in ESI+, yielding a rather large peak abundance fraction of
40 %–71 %. In particular, as can be seen in Fig. 1, several peaks of CHN+
compounds in Shanghai+ and Guangzhou+ have much higher abundance
compared to other organic species, probably due to their high concentrations
and/or high ionization efficiencies in the positive mode. This observation
indicates that most CHO compounds with high concentrations are probably
organic acids, whereas the majority of CHN compounds likely belong to the
group of organic amines, which is in good agreement with previous studies
(Lin et al., 2012a; Wang et al., 2017, 2018). Organic
compounds in ESI+ are dominated by CHN+ and CHON+ compounds in terms
of both peak numbers and peak abundance, and these compounds are
characterized by rather high H/C ratio and low O/C ratios (Table 1),
indicating a low degree of oxidation. The Venn diagram presented for ESI+
measurements in Fig. 2a shows that out of a total of 383–679 formulas, 129
formulas were found in samples from all three cities. Such common formulas,
thus, account for 19 %–34 % and 30 %–75 % of all assigned formulas in
terms of formula numbers and peak abundance, respectively. Among these
common formulas, CHN+ and CHON+ exhibit the highest abundance fractions
of 72 % and 26 %, respectively (Fig. 2b).
Double bond equivalent (DBE) versus carbon number for all CHO-
compounds for all sample locations. The molecular formula represents the
abundance-weighted average CHO- formula, and the area of the circles is
proportional to the fourth root of the peak abundance of an individual
compound (a diagram with circle areas related to the absolute peak
abundances is presented in Fig. S2). The color bar denotes the aromaticity
equivalent (gray with XC<2.50, purple with 2.50≤XC<2.70 and red with XC≥2.70). The pie charts show the
percentage of each XC category (i.e., gray color-coded compounds, purple
color-coded compounds and red color-coded compounds) in each sample in terms
of peak abundance.
In the following, we will compare and discuss the chemical properties in
detail for the three cities, including degrees of oxidation, unsaturation
and aromaticity of each organic compound class (i.e., CHO, CHON, CHN, CHOS
and CHONS). It should be noted that the chlorine-containing compounds were
not discussed in this study due to the very low MS signal abundance. In
addition, since peak abundances for the formula can vary by orders of
magnitude, the area of the circles presented in Figs. 3 and 5–7 is proportional to the fourth root of the peak abundance of each
formula to reduce the size difference of the circles. For a more detailed
comparison, figures with the circle size related to the absolute peak
abundances are presented in the Supplement.
CHO compounds
CHO compounds have been widely observed in urban OA, accounting for a
substantial fraction (8 %–67 %) of OA (Rincón et al., 2012; Tao et
al., 2014; Wang et al., 2017, 2018). Previous studies have
shown that a large fraction of CHO compounds in urban OA are composed of
organic acids, containing deprotonatable carboxyl functional groups, which
are detected preferentially in negative ionization mode when using ESI-MS.
As shown in Table 1, a total of 346, 164 and 196 CHO- compounds were
detected in ESI- in the OA samples collected in Changchun, Shanghai and
Guangzhou, accounting for 30 %, 40 % and 42 % of the overall peak
abundance in each sample, respectively. Out of all assigned formulas, 47
common CHO- formulas were observed for all cites, accounting for
35 %–52 % and 42 %–68 % of all identified CHO- formulas in terms of
formula numbers and peak abundance, respectively.
Despite this similarity, OA samples from Changchun- (i.e., in negative
ionization mode) exhibit certain differences compared to samples from
Shanghai- and Guangzhou-. The average H/C values for CHO- compounds
are in a similar range for the three locations (i.e., 0.96–1.10); however,
the average O/C values for O/C (Shanghai-) = 0.59 and O/C (Guangzhou-) = 0.65 are rather high compared to the average O/C ratio for Changchun-,
O/C (Changchun-) = 0.41. Furthermore, the relative peak abundance fraction
of CHO- compounds with O/C≥ 0.6, which are considered to be highly
oxidized compounds (Tu et al., 2016), is 14 % in Changchun and somewhat
higher in Shanghai- (34 %) and Guangzhou- (45 %). Altogether, these
results indicate that CHO- compounds in urban OA from east and southeast
China experienced more intense oxidation and aging processes and/or were
affected to a larger degree by biogenic sources.
Similarly, as shown in Fig. 3, the abundance-weighted average molecular
formulas for CHO- compounds in Changchun-, Shanghai- and Guangzhou-
are C8.58H7.86O3.22 (MMavg (Changchun-) = 162),
C8.01H7.27O4.22 (MMavg (Shanghai-) = 171) and
C7.70H8.04O4.48 (MMavg (Guangzhou-) = 172),
respectively. Again, these average formulas show that CHO- in Shanghai-
and Guangzhou- experienced more intense oxidation processes and/or were
affected to a larger degree by biogenic precursors, indicated by the larger
abundance-weighted MMavg with a higher degree of oxygenation. In
contrast, CHO- compounds from OA samples in Changchun- exhibit a lower
abundance-weighted MMavg with a decreased oxygen content.
Besides oxygenation, the aromaticity of the detected CHO- compounds
exhibits remarkable differences in these three cities. In all cities, the
CHO- compounds with high peak abundance were mainly assigned to
monoaromatics with 2.5≤XC<2.7 (purple circles in Fig. 3) in
the region of 7–12 carbon atoms per compound and DBE values of 5–7. The
relative peak abundance fraction of monoaromatics in total CHO- compounds
is 67 % in Changchun, which is higher compared to 64 % in Shanghai and
49 % in Guangzhou. In addition, 14 % of CHO- compounds in Changchun
were identified as polyaromatic compounds with XC≥2.7 (red circles in
Fig. 3), which is higher than the 8 % in Shanghai and 4 % in Guangzhou.
These observations indicate that CHO- compounds in the three Chinese
cities are highly affected by aromatic precursors (e.g., benzene, toluene
and naphthalene), in particular for the Changchun aerosol samples.
Besides the monoaromatics and polyaromatics, the rest of the detected CHO-
compounds were assigned to aliphatic compounds with an XC lower than 2.5
(gray circles in Fig. 3). Interestingly, these aliphatic compounds account
for about 47 % of all CHO- compounds for Guangzhou- samples in terms
of peak abundance, whereas samples from Changchun- and Shanghai- exhibit
only rather small fractions of such CHO- compounds, i.e., 19 % and
28 %, respectively. Such aliphatic compounds are commonly derived from
biogenic precursors (Kourtchev et al., 2016) and
vehicle emission (Tao et al., 2014; Wang et al., 2017) and/or generated
by intense oxidation processes of aromatic precursors, indicating the
different biogenic and anthropogenic emission sources and chemical reaction
processes for OAs in the three cities.
In addition, through the analysis of individual formulas, we find that for
the Changchun- samples, formulas of C8H6O4,
C7H6O2, C7H6O3, C7H6O3
and C8H8O3 with DBE values of 6, 5, 5, 5 and 5 dominate
the assigned CHO formulas with respect to peak abundance. According to
previous studies, C8H6O4, C7H6O2
and C7H6O3 are suggested to be phthalic acid, benzoic
acid and monohydroxy benzoic acid, respectively, which are derived from
naphthalene (Kautzman et al., 2010; Riva et al., 2015; Wang et al., 2017;
He et al., 2018; Huang et al., 2019). C7H6O3 is likely
4-hydroxy acetophenone, which could be derived from estragole
(Pereira et al., 2014), while
C8H8O3 is suggested to be either 4-methoxybenzoic acid
generated from estragole (Pereira et al., 2014) or vanillin emitted from
biomass burning (Li et al., 2014). For the Shanghai-
samples, besides C8H6O4, C7H6O3 and
C7H6O2, formulas of C6H8O7 and
C9H8O4 with DBE values of 3 and 6 were observed with high
peak abundances. C6H8O7 was identified as citric acid in
the pollen sample and mountain particle sample in previous studies (Fu et
al., 2008; Wang et al., 2009; Jung and Kawamura, 2011), and
C9H8O4 is probably homophthalic acid derived from
estragole (Pereira et al., 2014). For the
Guangzhou- samples, besides the formulas of C8H6O4 and
C6H8O7 discussed above, C4H6O4 and
C4H6O5 with low DBE values of 2 were detected with high
abundances and are suggested to be succinic acid and malic acid,
respectively (Claeys et al., 2004; Wang et al., 2017).
CHON compounds
A large number of nitrogen-containing organic compounds were detected in
these three cities, accounting for 39 %–55 % and 25 %–47 % of total peak
abundance detected in ESI- and ESI+, respectively. Out of all assigned
formulas, 45 common CHON- and 62 common CHON+ formulas were observed in
all cities, accounting for 65 %–82 % and 25 %–44 % of all CHON compounds
detected in ESI- and ESI+ in terms of peak abundance, respectively. This
indicates that a large number of CHON compounds in all three Chinese cities
show similar properties of chemical composition.
Classification of CHON- compounds into different subgroups
according to O/N ratios in their formulas. The y axis indicates the relative
contribution of each specific O/N ratio subgroup to the sum of peak
abundances of CHON- compounds.
The CHON compounds were further classified into different subgroups
according to their O/N ratios (Fig. 4 for CHON- and Fig. S3 for CHON+)
or according to the number of nitrogen atoms in their molecular formulas
(see Fig. S4 for CHON- and S5 for CHON+). As shown in Fig. 4, the
majority (84 %–96 % in terms of peak abundance) of CHON- compounds
exhibited O/N ratios ≥3, allowing the assignment of one nitro
(-NO2) or nitrooxy (-ONO2) group for these formulas, which are
preferentially ionized in ESI- mode (Lin et al., 2012b; Wang et al.,
2017, 2018; Song et al., 2018). CHON- formulas with O/N
ratios ≥4 suggest the presence of further oxygenated functional
groups, such as a hydroxyl group (-OH) or a carbonyl group (C = O). In
terms of peak abundance, 59 % of CHON- compounds observed in
Guangzhou- exhibited formulas with O/N ratios ≥4, which is higher
than 51 % in Changchun- and 45 % in Shanghai-, indicating that
CHON- compounds in southeast China show a higher degree of oxidation
compared to those in northeast and east China. Not surprisingly, CHON+
compounds generally exhibit lower O/N ratios (Fig. S3), as they probably
contain a reduced nitrogen functional group (e.g., amines), which is
preferably detected in ESI+. As shown in Fig. S3, CHON+ compounds with an
O/N ratio of 1 are dominant in Changchun+, whereas CHON+ compounds in
Shanghai+ and Guangzhou+ show a broader range of O/N ratios from 1 to 3.
Moreover, the average O/C ratios (0.27–0.45) in Shanghai+ and
Guangzhou+ (Table 1) are much greater than those (0.19) in Changchun+.
Consistent with the observations for CHO compounds, these results indicate
again that CHON+ compounds in the OA of east and southeast China
experienced more intensive photooxidation and/or were affected to a larger
degree by biogenic precursors.
Double bond equivalent (DBE) versus carbon number for all CHON-
compounds for all sample locations. The molecular formula represents the
abundance-weighted average CHON- formula, and the area of circles is
proportional to the fourth root of the peak abundance of an individual
compound (a diagram with circle areas related to absolute peak abundances is
presented in Fig. S6). The color bar denotes the aromaticity equivalent
(gray with XC<2.50, purple with 2.50≤XC<2.70 and
red with XC≥2.70). The pie charts show the percentage of each XC
category (i.e., gray color-coded compounds, purple color-coded compounds and
red color-coded compounds) in each sample in terms of peak abundance.
Figure 5 shows the DBE versus C number of CHON- compounds for the three
cities. The majority of CHON- compounds lie in the region of 5–15 C
atoms and 3–10 DBEs. A total of 67 % of CHON- compounds in terms of peak
abundance were assigned to mono or polyaromatics in Shanghai-, which is
higher than 52 % in Guangzhou- and 55 % in Changchun-. This indicates
that CHON- compounds are dominated by aromatic compounds in all cities,
while relatively higher peak abundance-weighted fraction of aromatic CHON-
compounds were observed in Shanghai. The peak abundance-weighted average
molecular formulas for CHON- compounds in Changchun-, Shanghai- and
Guangzhou- are C7.10H6.76O3.56N1.03,
C7.07H6.03O3.80N1.24 and
C7.12H6.36O3.99N1.24, respectively, showing that CHON-
formulas in Shanghai- and Guangzhou- contain more O and N atoms on
average than those for Changchun-. Formulas of
C6H5O3N1, C6H5O4N1,
C7H7O3N1, C7H7O4N1,
C8H9O3N1 and C8H9O4N1 were detected
with the highest abundance in all cities. These molecular formulas are in
line with nitrophenol or nitrocatechol analogs, which have been identified
in a previous urban OA study (Wang et al., 2017). Furthermore,
these nitrooxy-aromatic compounds were shown to enhance light-absorbing
properties of OA (Laskin et al., 2015; Lin et al., 2015). In addition, it
should be noted that the XC values for C6H5O4N1,
C7H7O4N1 and C7H7O4N1 were
calculated to be lower than 2.5, suggesting that the fraction of aromatics
in CHON- compounds was underestimated. This is because that for
nitrocatechol analogs with formulas of C6H5O4N1,
C7H7O4N1 and C8H9O4N1, only one
oxygen atom is involved in the π-bond structure corresponding to the p
value of 0.25 in the XC calculation equation, which is lower than the p
value of 0.5 applied for the XC calculation in this study. The diagram of
DBE versus C number for CHON+ compounds observed in the three locations
(presented in Fig. S7 in the Supplement) shows that more aromatic CHON+ compounds with
relatively lower degree of oxidation were assigned in Changchun+ samples
compared to Shanghai+ and Guangzhou+ samples.
CHN+ compounds
A total of 696 CHN+ compounds were detected in Changchun+ samples in ESI+, which
is higher than in Shanghai+ (253) and Guangzhou (205). These CHN+
compounds are likely assignable to amines according to previous studies
(Rincón et al., 2012; Wang et al., 2017, 2018). The
number of CHN+ compounds accounts for 24 %, 36 % and 30 % of the
total organic compounds in Changchun+, Shanghai+ and Guangzhou+,
respectively, whereas the peak abundance of these compounds accounts for
40 %, 71 % and 62 %, respectively. The majority (>97 %
in terms of peak abundance) of CHN+ compounds have one or two nitrogen
atoms in their molecular formulas (see Fig. S9). Comparing the CHN+
compounds for the three cities, 51 common CHN+ formulas were observed in
all cities, which contribute to as much as 43 %–89 % of the total
abundance of CHN+ formulas. This large percentage indicates that CHN+
compounds with presumably high concentrations in Changchun+, Shanghai+
and Guangzhou+ exhibit similar chemical composition. However, again OA
samples from Changchun show some distinct differences to samples from
Guangzhou and Shanghai.
Van Krevelen diagrams for CHN+ compounds in Changchun, Shanghai
and Guangzhou samples. The area of circles is proportional to the fourth
root of the peak abundance of an individual compound (a diagram with circle
areas related to absolute peak abundances is presented in Fig. S10) and the
color bar denotes the aromaticity equivalent (gray with XC<2.50,
purple with 2.50≤XC<2.70 and red with XC≥2.70). The
pie charts show the percentage of each XC category (i.e., gray color-coded
compounds, purple color-coded compounds and red color-coded compounds) in
each sample in terms of peak abundance.
A van Krevelen diagram of CHN+ compounds detected in the three samples is
shown in Fig. 6, illustrating H/C ratios as a function of N/C ratio. In this
plot, major parts of the CHN+ compounds are found in a region that is
constrained by H/C ratios between 0.5 and 2 and N/C ratios lower than 0.5.
Moreover, the pie charts show that the majority (83 %–87 % in terms of
peak abundance and 72 %–90 % in terms of peak numbers) of these CHN+
compounds can be assigned to mono- and polyaromatics with XC≥2.5. In
addition, as shown in Table 1, the average DBE and XC values of CHN+
compounds are the highest among all organic species. These observations
imply that CHN+ compounds exhibit the highest degree of aromaticity of all
organics in the Chinese urban OA samples, which is consistent with previous
studies (Lin et al., 2012b; Rincón et al., 2012; Wang et al., 2018).
Polyaromatic compounds with XC≥2.7 are displayed in the lower left
corner of the van Krevelen diagram, accounting for 41 % in terms of peak
abundance (48 % in terms of peak numbers) of CHN+ compounds detected in
Changchun+, but merely for 9 %–10 % in terms of peak abundance
(27 %–31 % in terms of peak numbers) in Shanghai+ and Guangzhou+. For
example, formulas of C11H11N1 (XC=2.7),
C10H9N1 (XC=2.7) and C12H13N1 (XC=2.7), which are assigned to be naphthalene core structure-containing
compounds, have relatively higher abundance in Changchun+ than in
Shanghai+ and Guangzhou+. Moreover, the average DBE and XC values of
CHN+ compounds (see Table 1) in Changchun+ are higher than those in
Shanghai+ and Guangzhou+, further indicating that CHN+ compounds in
Changchun+ show a higher degree of aromaticity, which can be caused by
large coal combustion emissions in the winter in Changchun. Remarkably, as
can be seen in Fig. 6, the abundance of CHN+ compounds in Changchun+
distributes evenly among different individual CHN+ compounds, while in
Shanghai+ and Guangzhou+ they are dominated by the formula of
C10H14N2 (the biggest purple circle in Fig. 6) with a DBE value of 5, which probably has a high concentration and/or high
ionization efficiency in the positive ESI mode. According to a previous smog
chamber study (Laskin et al., 2010), most CHN+ aromatics
are probably generated from biomass burning through the addition of reduced
nitrogen (e.g., NH3) to the organic molecules via imine formation
reaction, indicating that biomass burning probably made a certain
contribution to the formation of CHN+ compounds observed in the three
urban OA samples in our study.
CHOS- compounds
In this study, 75–155 CHOS- compounds were observed, accounting for
10 %, 12 % and 14 % of the total peak abundance of all organics in
Changchun-, Shanghai- and Guangzhou-, respectively. Around 89 %–96 %
of these CHOS- compounds were found to fulfill the O/S ≥ 4 criterion
allowing the assignment of at least one -OSO3H functional group and
thus a tentative classification to organosulfates (OSs) (Lin et al.,
2012a, b; Tao et al., 2014; Wang et al., 2016, 2017, 2018, 2019a). OSs were shown to affect the
surface activity and hygroscopic properties of the aerosol particles,
leading to potential impacts on climate (Hansen et al., 2015; Wang et
al., 2019a). Out of all formulas, 23 common CHOS- formulas were detected
for the three sample locations, accounting for 28 %, 58 % and 52 % of
the CHOS- peak abundance in Changchun-, Shanghai- and Guangzhou-,
respectively. However, 40 common CHOS- formulas were found between
Shanghai- and Guangzhou-, accounting for 60 %–65 % and 78 %–81 % in
terms of the CHOS- formula numbers and peak abundance, respectively. This
indicates that the chemical composition of the major CHOS- compounds of
Shanghai- and Guangzhou- are quite similar, while they show substantial
chemical differences for samples from Changchun-.
Double bond equivalent (DBE) versus carbon number for all CHOS-
compounds for all sample locations. The molecular formula represents the
abundance-weighted average CHOS- formula, and the area of circles is
proportional to the fourth root of the peak abundance of an individual
compound (a diagram with circle areas related to absolute peak abundances is
presented in Fig. S11). The color bar denotes the aromaticity equivalent
(gray with XC<2.50, purple with 2.50≤XC<2.70 and
red with XC≥2.70). The pie charts show the percentage of each XC
category (i.e., gray color-coded compounds, purple color-coded compounds and
red color-coded compounds) in each sample in terms of peak abundance.
Figure 7 shows the DBEs as a function of carbon number for all CHOS-
compounds detected for the three cities. The CHOS- compounds exhibit a DBE
range from 0 to 10 and carbon number range of 2–15. However, the majority
of CHOS- compounds with elevated peak abundances concentrate in a region
with rather low DBE values of 0–5. The average H/C ratios of CHOS-
compounds are in the range of 1.56–1.85 and thus higher than for any
other compound class, whereas the average DBE values of 1.71–2.55 are the
lowest among all classes. This indicates that CHOS- compounds in the OA
from the three Chinese cities are characterized by a low degree of
unsaturation. Moreover, the pie charts in Fig. 7 show that aliphatic
compounds with XC≤2.5 are dominant in CHOS- compounds with a
fraction of 96 %–99 % in terms of peak abundance, which is substantially
higher than that (13 %–48 %) for CHO, CHON and CHN species. Aliphatic
CHOS- compounds with C ≤ 10 can be formed from biogenic and/or
anthropogenic precursors (Hansen et al., 2014; Glasius et al., 2018; Wang
et al., 2019a), such as C2H4O6S1 (derived from
glyoxal) (Lim et al., 2010; McNeill et al., 2012),
C3H6O6S1 (derived from isoprene)
(Surratt et al., 2007) and
C8H16O4S1 (derived from α-pinene). However,
more CHOS- compounds with C > 10 and with DBEs lower than 1 are
observed in Changchun-, such as C14H28O5S1,
C13H26O5S1, C12H24O5S1,
C11H22O5S1 and C11H20O6S1. These
high-carbon-number-containing CHOS- compounds are likely formed from
long-alkyl-chain compounds with less oxygenated functional groups, which
were previously suggested to be emitted from traffic (Tao et
al., 2014) or derived from sesquiterpene emissions (Brüggemann et al.,
2019). However, as sesquiterpene emissions can be expected to be very low in
wintertime at Changchun, the presence of these compounds further underlines
the strong impact of anthropogenic emissions on CHOS- formation in
Changchun-. In this study, the (O-3S) / C ratio was used instead of the
traditional O/C ratio to present the oxidation state of CHOS- compounds,
since the sulfate functional group contains three more oxygen atoms than
common oxygen-containing groups (e.g., hydroxyl and carbonyl), which makes
no contribution to the oxidation state of the carbon backbone of the CHOS-
compounds. Comparing average values for H/C, (O-3S) / C and DBEs of CHOS-
for the three sample locations (see Table 1), we find that the H/C ratios
(1.85) and (O-3S) / C ratios (0.61–0.71) for Shanghai- and Guangzhou-
samples are larger than those for Changchun- samples (H/C= 1.56 and
(O-3S) / C = 0.52), whereas the DBE values (1.71–1.79) in Shanghai-
and Guangzhou- are lower than those for Changchun- (2.55). These
observations indicate that CHOS- compounds in urban OA from northeast
China are less oxidized but more unsaturated compared to those in east and
southeast China, likely due to enhanced emissions from residential heating
during winter in north China.
CHONS compounds
A total of 4 %–5 % of the total organics detected in ESI- were identified as CHONS-
compounds in terms of peak abundance. In contrast, CHONS+ compounds
account merely for 0.3 %–1 % of all organics detected in ESI+. The
average MMavg of the CHONS- compounds for the three sample locations
ranges from 214 to 293 Da, generally showing larger molecular masses than
compounds of any other class because of the likely presence of both nitrate
and sulfate functional groups. In total, only five common CHONS- formulas
were detected for all three sample locations, accounting for 4 %, 21 %
and 20 % of the CHONS- peak abundance in Changchun-, Shanghai- and
Guangzhou-, respectively. As already observed for other compound classes,
these percentages imply that the CHONS- compounds in urban OA of
Shanghai- and Guangzhou- exhibit a rather similar chemical composition,
whereas such compounds are different for Changchun-.
In the OA samples of Shanghai- and Guangzhou-, 78 %–87 % of CHONS-
compounds in terms of peak abundance have seven or more O atoms in their
formulas, allowing the assignment of one -OSO3H and one -NO3
functional group in the molecular structures, thus classifying them as
potential nitrooxy-organosulfates. In contrast to Shanghai- and
Guangzhou-, only 26 % of CHONS- compounds were assigned to such
nitrooxy organosulfates for Changchun-, indicating that most of the N
atoms in the CHONS- compounds are present in a reduced oxidation state,
e.g., in the form of amines. The average DBE and XC values of CHONS-
compounds in Shanghai- and Guangzhou- are 3.3–3.45 and 0.43–0.44,
respectively. Again these values differ for the Changchun- samples with an
increased average DBE of 3.75 and an average XC of 1.06, indicating that
CHONS- compounds in Changchun- possess on average a higher degree of
unsaturation and aromaticity compared to such compounds in Shanghai- and
Guangzhou- samples. Interestingly, the compound with the formula
C10H17O7NS has the highest relative peak abundance (32 %)
in Shanghai- and Guangzhou-, whereas in Changchun- the compound with the
formula C2H3O4NS is dominant. C10H17O7NS has
previously been identified as mononitrate organosulfate generated from
α/β-pinene (Iinuma et al., 2007; Surratt et al., 2008; Lin
et al., 2012b; Wang et al., 2017), while C2H3O4NS may be
assigned as a cyanogroup-containing sulfate. This observation is comparable
to our previous study (Wang et al., 2019a), which found
that C10H17O7NS was dominant for CHONS- compounds in
low-concentration aerosol samples collected in Beijing (China) and Mainz
(Germany). Consistently, a C2H3O4NS compound had the highest
abundance among CHONS- compounds in polluted Beijing aerosol samples. This
agreement can be explained by the adjacent locations of Beijing (39.99∘ N, 116.39∘ E) and Changchun (43.54∘ N, 125.13∘ E) and
similar residential heating patterns by coal combustion during wintertime.
In conclusion, these results further demonstrate that the precursors for
CHONS- compounds in Shanghai- and Guangzhou- are different from those
in Changchun-, which is probably due to differences in anthropogenic
emissions.
Limitations
In this study, we used the peak abundance-weighted method to illustrate the
difference in chemical formulas assigned by the Orbitrap MS. This comparison was
made based on the assumption that the measured organic compounds have the same
peak abundance response in the mass spectrometer. However, this assumption
can bring some uncertainties because the ionization efficiencies vary
between different compounds (Schmidt et al., 2006; Leito et al., 2008;
Perry et al., 2008; Kruve et al., 2014). For example, the ionization
efficiencies of nitrophenol species detected in negative ESI mode can vary
by a large degree depending on the position of the substituents at the
nitrobenzene ring (Schmidt et al., 2006; Kruve et al., 2014), and the
ionization efficiencies of carboxylic acids can also vary by several orders
of magnitude depending on the structures (Kruve et al.,
2014). Nonetheless, it is a challenging analytical task to identify and
quantify all compounds in ambient OA due to the high chemical complexity of
OA and the limits in authentic standards of OA. Despite the inherent
uncertainties, the peak abundance-weighted comparison of molecular formulas
provides an overview of the difference in chemical composition of OA in
these three representative Chinese cities. In particular, the chemical
formulas assigned in this study can be validated in future studies by
authentic standards, and the difference in ionization efficiencies can be
further evaluated.
Conclusions
The molecular composition of the organic fraction of PM2.5 samples
collected in three Chinese megacities (Changchun, Shanghai and Guangzhou)
was investigated using a UHPLC–Orbitrap mass spectrometer. In total,
416–769 (ESI-) and 687–2943 (ESI+) organic compounds were observed
and separated into five subgroups: CHO, CHN, CHON, CHOS and CHONS.
Specifically, 120 common formulas were detected in ESI- and 129 common
formulas in ESI+ for all sample locations, accounting for 57 %–71 % and
30 %–75 % in terms of peak abundance, respectively. Overall, we found that
urban OA in Changchun, Shanghai and Guangzhou shows a quite similar chemical
composition for organic compounds of high concentrations. The majority of
these organic species were assigned to monoaromatic or polyaromatic
compounds, indicating that anthropogenic emissions are the major source for
urban OA in all three cities.
Despite the chemical similarity of the three sample locations for organic
compounds in urban OA, remarkable differences were found in chemical
composition of the remaining particle constituents, in particular for OA
samples from Changchun. In general, a larger number of polyaromatics were
observed for Changchun samples, most likely due to emissions from coal
combustion during the wintertime residential heating period. Moreover, the peak
abundance-weighted average DBE and average XC values of the total organic
compounds in Changchun were found to be larger than those for Shanghai and
Guangzhou, showing that organic compounds in Changchun possess a higher
degree of unsaturation and aromaticity. For average H/C and O/C ratios a
similar trend was observed. While average H/C and O/C ratios detected in
ESI- were found to be highest for Guangzhou samples, relatively lower values were observed for Shanghai and Changchun samples, indicating that OA
collected in lower-latitude regions of China experiences more intense
photochemical oxidation processes and/or is affected to a larger degree by
biogenic sources.
Data availability
All relevant data have been included in this paper in
the form of tables and figures. Specific data requests can be addressed by
email to the corresponding authors.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-9089-2021-supplement.
Author contributions
RJH, TH and KW conducted the study design. LY, HN, JG and MW collected the PM2.5 filter samples. KW and YZ carried out the
experimental work and data analysis. KW wrote the manuscript. KW, TH, RJH,
MaB, YZ, JH, MB and MG interpreted data and edited the
manuscript. All authors commented on and discussed the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This study was supported by the National Natural Science
Foundation of China (NSFC, grant nos. 41925015, 91644219 and 41877408), the Chinese Academy of Sciences (nos. ZDBS-LY-DQC001 and
XDB40030202), the National Key Research and Development Program of China
(no. 2017YFC0212701), and the German Research Foundation (Deutsche
Forschungsgemeinschaft, DFG) under grant no. INST 247/664-1 FUGG. Kai Wang and Yun Zhang acknowledge the scholarship from the Chinese Scholarship Council (CSC). Kai Wang acknowledges the scholarship from the Max Plank Graduate Center with Johannes Gutenberg University of Mainz (MPGC) and thanks Ulrich Pöschl, Christopher J. Kampf and Yafang Cheng for their helpful suggestions for this study. Kai Wang
also thanks Huanfeng Dong from Zhejiang University for the great support
on the programming of data processing.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 41925015, 91644219 and 41877408), the Chinese Academy of Sciences (grant nos. ZDBS-LY-DQC001 and XDB40030202), the National Key Research and Development Program
of China (grant no. 2017YFC0212701), and the Deutsche Forschungsgemeinschaft (grant no. INST 247/664-1 FUGG).
Review statement
This paper was edited by Frank Keutsch and reviewed by three anonymous referees.
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