Volatility and viscosity have substantial impacts on
gas–particle partitioning, formation and evolution of aerosol and hence the
predictions of aerosol-related air quality and climate effects. Here aerosol
volatility and viscosity at a rural site (Gucheng) and an urban site
(Beijing) in the North China Plain (NCP) in summer and winter were investigated
by using a thermodenuder coupled with a high-resolution aerosol mass
spectrometer. The effective saturation concentration (C*) of organic aerosol
(OA) in summer was smaller than that in winter (0.55 µg m-3 vs.
0.71–0.75 µg m-3), indicating that OA in winter in the NCP is more
volatile due to enhanced primary emissions from coal combustion and biomass
burning. The volatility distributions varied and were largely different among
different OA factors. In particular, we found that hydrocarbon-like OA (HOA)
contained more nonvolatile compounds compared to coal-combustion-related
OA. The more oxidized oxygenated OA (MO-OOA) showed overall lower volatility
than less oxidized OOA (LO-OOA) in both summer and winter, yet the
volatility of MO-OOA was found to be relative humidity (RH) dependent
showing more volatile properties at higher RH. Our results demonstrated the
different composition and chemical formation pathways of MO-OOA under
different RH levels. The glass transition temperature (Tg) and viscosity
of OA in summer and winter are estimated using the recently developed
parameterization formula. Our results showed that the Tg of OA in summer
in Beijing (291.5 K) was higher than that in winter (289.7–290.0 K), while
it varied greatly among different OA factors. The viscosity suggested that
OA existed mainly as solid in winter in Beijing (RH = 29 ± 17 %),
but as semisolids in Beijing in summer (RH = 48 ± 25 %) and
Gucheng in winter (RH = 68 ± 24 %). These results have the important
implication that kinetically limited gas–particle partitioning may need to
be considered when simulating secondary OA formation in the NCP.
Introduction
Organic aerosol (OA) accounts for a substantial mass fraction of atmospheric
fine particulate matter (Jimenez et al., 2009). However, the simulation
results (e.g., concentrations and oxidation states) from chemical transport
models often fail to agree with the observations to a certain degree
(Matsui et al., 2009; Chen et al., 2011), which is partly due to our
limited understanding of the chemical mechanisms, reaction rates and lifetime of OA. Volatility and viscosity are two important properties of OA. They
have substantial impacts on the gas–particle partitioning of oxidized
compounds (Shiraiwa and Seinfeld, 2012; Liu et al., 2018) and
consequently the formation and evolution of OA, which further contributes to
the uncertainty in predictions of aerosol-related air quality and climate
effects (Glasius and Goldstein, 2016; Shrivastava et al., 2017).
The OA volatility can be quantified by various approaches. Compared to the
estimations from elemental formulas and measured partitioning of
gas or particle species, thermogram analysis was found to be the most
reproducible (Stark et al., 2017). As a result, the thermodenuder (TD)
combined with a high-resolution time-of-flight aerosol mass spectrometer
(HR-AMS) has been widely used for the quantification of OA volatility.
Laboratory research has characterized the volatility of specific
secondary OA (SOA) and the effect of temperature and relative humidity (RH).
For example, the evaporation kinetics of limonene SOA particles at low RH
levels (< 5 % and 50 %) are nearly the same, while a slightly
larger fraction evaporates at higher RH (90 %) (Wilson et al., 2015;
Lee et al., 2011b). Zaveri et al. (2020) found that the aged α-pinene SOA had a higher volume fraction remaining (VFR) than fresh SOA
under a higher TD temperature. Compared with laboratory experiments, the
oxidation pathways and oxidants are far more complex in ambient air, and the
oxygenated products can be composed of hundreds or even thousands of species
with a wide range of volatilities. Previous field observations on volatility
distributions of OA have mainly been focused on Europe and the US under low NOx
levels (Xu et al., 2016; Louvaris et al., 2017a; Saha et al., 2017;
Kostenidou et al., 2018). Lee et al. (2011a) found that
NOx can have a large impact on the volatility of SOA in a chamber
experiment, suggesting that the OA volatilities in high-NOx and high
particulate-matter (PM) environments, e.g., the North China Plain (NCP), need to
be further investigated. The volatility of OA also presents strong seasonal
variations. For example, Huang et al. (2019) found
that OA in winter is less volatile than that in summer in Germany. The
volatility of cooking OA (COA) and hydrocarbon-like OA (HOA) at the same
sampling site varied substantially between summer and winter (Saha et
al., 2018; Paciga et al., 2016). These differences can be attributed to the
differences in source emissions, precursors and temperature
(Schervish and Donahue, 2020). To our knowledge, the volatility of
OA has only been characterized in summer in the NCP (Xu et al., 2019; Qiao et
al., 2020), and the observations in urban and rural areas during wintertime
are very limited. In addition, previous studies investigated the volatility
of primary OA (POA) including biomass burning, traffic and cooking emissions
(May et al., 2013a, b, c; Takhar et al., 2019); however,
the measurements of coal combustion OA (CCOA), a dominant factor of POA in
the NCP (Wang et al., 2019), are rare. Failing to consider the
contributions of intermediate-volatility organic compounds (IVOCs) and
semi-VOCs (SVOCs) from such POA factors may lead to the underestimation of SOA
concentration in models. In addition, many studies show that not all aerosols
evaporate even after heating to high temperatures (i.e., 230–300 ∘C)
(Massoli et al., 2015; Xu et al., 2016). Those nonvolatile compounds can
contribute to new particle formation and subsequent growth (Wehner et
al., 2004; Xu et al., 2016; Massoli et al., 2015; Wang et al., 2017).
Despite the increasing interest in nonvolatile particles, our understanding
of this type of nonvolatile particles is incomplete, especially in highly
polluted environments.
The OA volatility is intrinsically related to particle phase state, which plays an important role in affecting heterogeneous reactions and the
formation of cloud condensation nuclei. Particle phase states have been
measured by using a three-arm impactor (Liu et al., 2017, 2019)
and polarization lidar (Tan et al., 2020) in
China in recent years. The results showed that particles are generally in
liquid state throughout the year in south China, while there is a transition
from semisolid to liquid state as RH increases above 60 % in winter in
the NCP. However, these studies measured the phase state of bulk aerosol, which is
generally dominated by hygroscopic secondary inorganic species; our
knowledge of the OA phase state and viscosity remains limited. Some methods
have been developed to estimate OA phase state based on the molar mass, molecular
atomic oxygen-to-carbon ratio (O/C) of SOA components and the number of
carbon, hydrogen and oxygen atoms (Shiraiwa et al., 2017; DeRieux et
al., 2018). However, the effects of molecular structure and functional
groups on glass transition temperature (Tg), a parameter determining a
phase transition between amorphous solid and semisolid states, are not
considered in these studies. Recently, the close relation between volatility
and viscosity have been proved (Zhang et al., 2019; Champion et al.,
2019), and parameterizations have been developed to predict viscosity based on O/C
and volatility at 11 global sites (Li et al., 2020).
However, the simulation of the phase state and viscosity of OA in the NCP during
wintertime has not yet been made, impeding our understanding of the phase
states of OA and its potential impacts.
In this study, an HR-AMS coupled with a TD was deployed in summer and winter
at an urban site in Beijing and a rural site in winter in the NCP to
investigate the differences in OA volatilities in different seasons and
chemical environments. The volatility distributions of primary and secondary
OA factors are estimated, and the impacts of RH are elucidated. Further, the
glass transition temperature and viscosity of OA at urban and rural sites
are estimated by using the recently developed parameterization, and their
implications in phase state and gas–particle partitioning are demonstrated.
Experimental methodsMeasurements
The measurements were conducted at an urban site (Institute of Atmospheric
Physics, 39∘58′ N, 116∘22′ E), from 20 May to 23 June 2018 and from 20 November to 25 December 2018, and a rural site
(Gucheng in Hebei province, 39∘09′ N, 115∘44′ E) from 10 December 2019 to 13 January 2020. A detailed description of the two sampling
sites is given in Xu et al. (2015) and Sun et al. (2020).
Ambient particles passed through a PM2.5 cyclone and a Nafion dryer,
where aerosol particles larger than 2.5 µm were filtered and the
remaining particles were dried. After that, aerosol particles were sampled
into an HR-AMS by switching between the TD and bypass line every 15 min.
The settings of the TD heating temperature were 50, 120 (150) and 250 ∘C in the summer and winter of 2018 in Beijing. In addition, the data
during the temperature ramping were also included. In total, TD data
with seven temperature gradients were obtained in Beijing. By contrast,
the TD temperature was set to increase linearly in winter at the Gucheng site,
leading to more data points across different temperatures. The residence
time (RT) of aerosol particles in TD was 7.4 s in the summer of 2018 and 10 s
in the winters of 2018 and 2019 due to different plug flow rate. The TD loss
(90 %) was calibrated using aerosolized NaCl following the methods
described by Huffman et al. (2008).
AMS data analysis
The HR-AMS data were analyzed by PIKA (V 1.62F). The ionization efficiency
(IE) and relative ionization efficiencies (RIEs) of ammonium and sulfate
were calibrated following the standard protocols (Jayne
et al., 2000). The composition-dependent collection efficiency was applied
for the ambient data, while a constant value (0.5) was used for the TD data
(Huffman et al., 2009). All elemental
ratios of OA in this study were calculated by the “Improved-Ambient (I-A)”
method (Canagaratna et al., 2015) unless
specified. The combined data from bypass and TD lines (MSbypass+TD)
were analyzed with positive matrix factorization (PMF) to resolve potential
OA factors (Ulbrich et al., 2009). Four factors were
identified in the summer of 2018: HOA, COA, less oxidized oxygenated
OA (LO-OOA) and more oxidized OOA (MO-OOA). In the winter of 2018 in Beijing,
fossil-fuel-related OA (FFOA) and oxidized POA (OPOA) were also identified
in addition to the COA, LO-OOA and MO-OOA. Compared with Beijing, four OA
factors – HOA, coal combustion OA (CCOA), biomass burning OA (BBOA) and OOA – were
identified at the Gucheng site in winter. It should be noted that FFOA in winter
in Beijing refers to the mixed HOA and CCOA which cannot be separated by
PMF. A detailed description of the source apportionment of OA at the two
sites is given in Xu et al. (2019) and Chen et al. (2021).
Estimation of OA volatility distribution
A detailed description of the estimation of the atmospheric organic aerosol
volatility distribution is given in Karnezi et al. (2014). Briefly,
six logarithmically spaced effective saturation concentration (C*) bins with a maximum value of 100 µg m-3 are used to fit the measured
thermograms since there is little information on the partitioning of
compounds with C*≥ 1000 µgm-3 due to the average OA
concentration being 13–23 µg m-3 in this study. In addition, six discrete values of vaporization enthalpy and accommodation coefficient were
used, i.e., 20, 50, 80, 100, 150 and 200 kJ mol-1 and 0.01, 0.05,
0.1, 0.2, 0.5 and 1, respectively (Karnezi et al., 2014). The
choice of C* bins depends on the best fits between the measured and predicted
thermogram. In this study, the combinations of all properties with the
smallest error (top 1 %) were identified as “best estimate”. The
predicted and absolute thermograms are shown in Figs. S1–S2. The mass
fraction of each C* bin ranged from 0 to 1 with a step of 0.1.
Predictions of glass transition temperature and viscosity
A detailed description of predicting the glass transition temperature,
viscosity and some parameters of OA is given in Li et
al. (2020). Briefly, Tg,i for each volatility bin is predicted based on
volatility and O/C (Aiken ambient method) using Eq. (1).
Tg,i=289.10-16.50×log10Ci0-0.29×log10Ci02+3.23×log10Ci0(O/C)
The term C0 here refers to C∗ based on the assumption of ideal
thermodynamic mixing (Donahue et al., 2011). The
glass transition temperatures of organic aerosols under dry conditions
(Tg,org) are calculated by the Gordon–Taylor equation assuming a Gordon–Taylor constant (kGT) of 1 (Dette et al., 2014).
Tg,org=∑iωiTg,i,
where ωi is the mass fraction in the particle phase for each
volatility bin.
The Tg of organic–water mixtures (Tg(ωorg)) at a given RH
can be estimated using the Gordon–Taylor equation:
Tgωorg=1-ωorgTg,w+1kGTωorgTg,org1-ωorg+1kGTωorg,
where Tg,w is the glass transition temperature of pure water (136 K) and kGT is the Gordon–Taylor constant for organic–water mixtures, which
is suggested to be 2.5. ωorg is the mass fraction of organics in
particles of organic–water mixtures, and the water content in OA can be
estimated using the effective hygroscopicity parameter (κ), which is
calculated by the method in Lambe et
al. (2011) and Mei et al. (2013) marked as κ
(Lambe) and κ (f44), respectively.
Viscosity can then be estimated by applying the Vogel–Tammann–Fulcher
equation η=η∞eT0DT-T0, where η∞ is the viscosity at infinite temperature (10-5 Pa s), D is
the fragility parameter which is assumed to be 10 and T0 is the Vogel
temperature calculated as T0=39.17TgD+39.17. When
Tgωorg is larger than ambient T, particles are regarded as solid.
The characteristic timescale of mass transport and mixing by molecular
diffusion (τmix) is also calculated: τmix=dp2/(4π2Db), where dp is the particle diameter (assuming 200 nm here),
and the bulk diffusion coefficient Db is calculated from the predicted
viscosity by the fractional Stokes–Einstein relation: D=DC(ηCη)ξ, where ξ is an empirical fit parameter and ξ=0.93. ηC is the viscosity at which the Stokes–Einstein
relation and fractional Stokes–Einstein relation predict the same diffusion
coefficient.
Results and discussionVolatility of aerosol species
Figure 1 shows the thermograms of non-refractory submicron (NR-PM1) species at both urban and
rural sites. The remaining organics loading in Gucheng was lower than that
in Beijing under the same TD temperature during wintertime, particularly at
T>150∘C, suggesting that OA in Gucheng was overall more
volatile than those at urban sites. Such differences can be reasonably
attributed to the different OA composition at the two sites. For example, OA
at the rural site presented much higher contributions from coal combustion
and biomass burning emissions than that at the urban site (Sun et al.,
2020). Further, SOA composition could also be different. While photochemical
aqueous-phase reactions was found to play an important role in SOA formation
in Gucheng (Kuang et al., 2020), both photochemical and
aqueous-phase production were important in Beijing (Xu et al.,
2017). Despite a shorter TD residence time in summer, more remaining nitrate
was observed which was more likely caused by the less volatile nitrate,
(e.g., organic nitrate (ON) that cannot be distinguished from inorganic
nitrate with AMS). By using the NOx method
(Farmer et al., 2010), we estimated that ON can account for 11 %–27 % of total nitrate in summer
(Xu et al., 2020), while their contributions
were negligible during wintertime in both Gucheng and Beijing. Such seasonal
differences in ON are in good accordance with previous observations in China
(Yu et al., 2019), emphasizing the role of ON in
summer. There was 40 % of the residual mass of chloride in summer after
heating at T>200∘C, which is larger than that in winter
at both urban and rural sites (4 %–8 %). Such different residual loadings
are likely due to the different sources of chloride, which were mainly
associated with biomass burning and coal combustion emissions in winter,
while a considerable fraction existed in the form of less volatile chloride
salts (e.g., KCl) in summer.
Thermograms of non-refractory submicrometer aerosol species
including organics (Org), sulfate (SO4), nitrate
(NO3), ammonium (NH4) and
chloride (Chl). The mass fractions of size-resolved non-refractory submicrometer
aerosol (NR-PM1) species as a function of TD
temperature are also shown.
At T>150∘C, sulfate in Gucheng showed the lowest residual
mass compared to that in Beijing, while the behaviors are contrary at T< 150 ∘C. One explanation is that the different formation
mechanisms (gas-phase, heterogeneous or aqueous-phase chemistry) led to the
variations in mixed state which could affect the thermograms of sulfate
during three campaigns. The different contribution of organosulfate (OS)
compounds with a different volatility to total SO4 is another
possible reason, which is supported by the fact that the particles under a different TD temperature fell into different regions in a triangle-shaped
space (Fig. S3) defined by Chen et al. (2019) for organic and inorganic
sulfate species. All these differences in aerosol volatility between urban
and rural sites emphasize the influence of nonvolatile inorganic
components on species measured by HR-AMS.
Volatility of OA species
OA was dominated by SOA in summer in Beijing (72 %), and the contribution
was much higher than that in winter in Beijing (42 %) and Gucheng
(51 %), in agreement with previous studies (Zhou et al., 2020).
The C* of OA in summer was 0.55 µg m-3 in Beijing, which is smaller
than that in winter in Beijing (0.71 µg m-3) and Gucheng (0.75 µg m-3), indicating the more volatile nature of OA in winter. One
explanation was the higher contributions of POA in winter, which is
generally more volatile than SOA
(Huffman et al., 2009). This feature is
contrasts with Germany (Huang et al., 2019),
where organics are found to be more volatile in summer. Such a discrepancy is
likely due to the different OA compositions in different chemical
environments. Support for this possibility is a higher O/C of OA in Germany
but a lower O/C in Beijing during wintertime compared to summer. Enhanced
primary emission sources in winter with relatively high volatility are
another possible cause.
Despite HOA, CCOA and FFOA all being related to fossil fuel, they differ in
the order of volatility between urban and rural sites. HOA in Beijing showed a lower saturation concentration compared to that in Gucheng (C*= 0.75 ± 0.56 vs. 0.93 ± 0.69 µg m-3), while the C* of FFOA
in Beijing was relatively higher compared to that of CCOA in Gucheng
(1.41 ± 1.01 vs. 0.86 ± 0.75 µg m-3),
corresponding to the lower mass fraction remaining (MFR) at the same TD
temperature (Fig. 2). One reason was likely the different quality of
fuels used at urban and rural sites. Another reason could be the fact
that FFOA in Beijing was mainly from regional transport and was aged before
arriving in Beijing. FFOA and CCOA showed lower remaining loadings
(∼ 1 %) compared to HOA (8 %–10 %) at T>200∘C, implying that HOA contained more nonvolatile compounds.
Polycyclic aromatic hydrocarbons (PAHs, compounds dominantly from coal
combustion, which were determined with the approach recommended by
Dzepina et al., 2007) showed a contribution of SVOCs by
60 %–71 %, consistent with the high contributions of fossil sources to more
volatile organic aerosol based on a radiocarbon-based (14C) approach
(Ni et al., 2019). It should be noted that OA
factors related to fossil fuel combustion also showed a considerable
contribution of extremely low-volatility compounds (ELVOCs with C*≤10-4µg m-3) (5 %–13 %), which is comparable to that in
Paris (11 %) (Paciga et al., 2016).
Thermograms of OA factors at both urban and rural sites.
The mass fractions of OA factors as a function of TD temperature are also
shown.
The fraction of low-volatility compounds (LVOCs) in COA in winter (44 %) is
slightly higher than that in summer (40 %) in Beijing, which fell in the
range of unoxidized (54 %) and ozonolysis of canola oil (29 %)
(Takhar et al., 2019) and was comparable to that in previous
field studies (Paciga et al., 2016; Louvaris et al., 2017b). Higher C* of
COA in summer than that in winter (0.79 µg m-3 vs. 0.59 µg m-3) indicated the less volatile properties likely due to the different
cooking types. For example, barbecues are popular in summer but not in winter
due to low ambient temperature. The SVOC contributed 67 % to OPOA (C*= 1.3 µg m-3), which is in the range of C* of POA (0.6–1.4 µg m-3) at the urban site in winter. Overall, 33 % of BBOA in Gucheng
evaporated at T>200∘C, which is comparable to BBOA in
Xianghe, a rural site in the NCP (Qiao et al., 2020). The contribution of
SVOC (51 %) in BBOA in Gucheng is lower than that measured in combustion
chamber (80 %) (May et al., 2013a) but overall comparable with
that in Centreville, AL (47 %) (Kostenidou et al., 2018). The
differences in volatility distributions of BBOA were likely due to the
variations in biomass fuels, combustion conditions and the extent of
atmospheric aging (Ghadikolaei et al., 2020).
Similar to previous studies (Paciga et al., 2016; Kostenidou et al.,
2018), LO-OOA was more volatile than MO-OOA in summer and winter, consistent
with the fact that MO-OOA dominated OA at T>200∘C. SVOC
accounted for 64 % and 70 % of LO-OOA in winter and summer, respectively, with a lower C* in winter (0.78 µg m-3 vs. 1.58 µg m-3),
highlighting that LO-OOA was more volatile in summer. Such seasonal
differences can be explained by the different precursors and formation
conditions of LO-OOA in two seasons, which are further supported by the
differences in mass spectra. For example, the fC2H3O+/fCO2+ ratio of
LO-OOA in summer was higher than that in winter. It should be noted that the volatility of LO-OOA contradicted the results of thermograms which showed
higher evaporation loss in winter than in summer (Fig. 2). While the
longer RT in winter is one of the causes, higher effective vaporization
enthalpy (136 kJ mol-1 vs. 157 kJ mol-1) in winter is another
reason. MO-OOA had comparably effective vaporization enthalpy (56 kJ mol-1 vs. 58 kJ mol-1) in summer and winter, yet showed more
remaining loadings in winter at the same TD temperature with lower C* (0.49 µg m-3 vs. 0.69 µg m-3). Note that LVOCs with
C*=0.001µg m-3, 0.01 µg m-3 and 0.1 µg m-3 contributed similarly to MO-OOA in summer and winter (Fig. 3), indicating
that the LVOCs of more aged SOA are independent of seasons. One
reason is that the long-time aging process of OA in the atmosphere could lead to
similar chemical compounds in summer and winter. Compared with the urban site,
the remaining SOA after TD heating at the rural site fell into the range of
LO-OOA and MO-OOA in Beijing, which consisted of 32 % LVOC and 68 %
SVOC.
Predicted volatility distributions of OA, OA factors and
PAH. The error bars are the uncertainties derived using the approach of
Karnezi et al. (2014).
Average composition of total, volatile and nonvolatile PM
and OA in Beijing and Gucheng.
Thermograms of LO-OOA and MO-OOA during different RH
levels in summer and winter in Beijing.
Comparisons between volatile and nonvolatile compounds
We further compared aerosol composition at different TD temperatures during
three campaigns. Here we defined aerosol species remaining at T>200∘C as nonvolatile compounds, and those that evaporated at T<90∘C as volatile compounds. As shown in Fig. S4, signals for m/z 100–180 (a potential indicator for oligomers) (Denkenberger
et al., 2007) decreased with increasing TD temperature, suggesting that
nonvolatile organics are unlikely to be oligomers formed within the heated
TD. As indicated in Table S1, the mass loadings of nonvolatile sulfate and
nitrate were comparable in Beijing in both summer (∼ 0.39 µg m-3) and winter (0.15 vs. 0.1 µg m-3). Comparatively, the
ratio of nonvolatile SO4 to NO3 was 4.6 at the rural site during
wintertime, highlighting the dominant role of sulfate in nonvolatile
compounds. As shown in Fig. S5, sulfate between 100 and 300 nm accounted
for 56 % of total SO4 at T>200∘C, which is much
higher than that in ambient air (31 %). One explanation is that the
sulfate measured by HR-AMS has contributions from OSs, which showed a
prominent peak below 320 nm (Kuang et al., 2015) with lower
volatility than ammonium sulfate. Nonvolatile OA accounted for 51 % of
the total nonvolatile NR-PM1 in summer, which was lower than that in
winter (65 %–72 %). This result indicated that nonvolatile OA was more
important in winter than summer, which was likely related to tar balls
(Liu et al., 2021). Note that the contribution
of nonvolatile OA to the total OA was comparable between summer and winter
(6 %–8 %), yet lower than that observed in Athens (Gkatzelis et al.,
2016) and water-soluble nonvolatile OA in Kanpur (Chakraborty et al., 2016), likely
due to the different chemical compounds at various sites. The nonvolatile
OA correlated well with equivalent black carbon (eBC) measured by a seven-wavelength
Aethalometer (AE33) (R2=0.69–0.82), suggesting that it was well mixed
with eBC during the aging processes in the atmosphere, consistent with the
observations in Melpitz (Poulain et al., 2014) and
London (Xu et al., 2016). The nonvolatile OA was dominated by MO-OOA (a
factor related to aqueous processes; Xu et al., 2017), with a
contribution of up to 90 % in winter. This result suggests that the aqueous-phase processing played an important role in the formation of nonvolatile OA
(e.g., diacids and oligomers), particularly during severe haze episodes
with high RH in winter in the NCP (Yu et al., 2014; Ortiz-Montalvo et al.,
2012, 2014). Such results are also supported by the
large increase in nonvolatile OA as elevated RH (Fig. S6), and the
increases signal the fraction of CHO+ (m/z 29) in the mass spectra
(7.2 %–16.1 %), a tracer ion related to aqueous processes (Zhao et
al., 2019).
Different from nonvolatile components, the volatile compounds showed
overall comparable contributions to the total volatile NR-PM1 during
three campaigns. Chl was an exception with a higher fraction in winter
(7 %–8 %) than in summer (1 %). The volatile NR-PM1 was dominated by
NO3 (34 %–36 %) and OA (36 %–41 %) at both urban and rural sites, while
the contribution of SO4 was small (4 %–9 %). We noticed that the
composition of volatile OA was substantially different between summer and
winter. As shown in Fig. 4, the volatile OA was dominated by SOA (74 %,
mainly LO-OOA) in summer and POA in winter (61 %–62 %), indicating that
primary emissions played more important roles in volatile OA in winter. We
noticed that FFOA was a dominant contributor of volatile POA at rural site,
while COA made an important contribution to volatile POA at the urban site during wintertime.
Volatility of SOA under different RH levels
Figure 5 shows the thermograms of LO-OOA and MO-OOA at three different RH
levels in summer and winter in Beijing. We found that the MFR of MO-OOA as a
function of TD temperature was substantially different across different RH
levels. MO-OOA shows more evaporative loss at higher RH levels
(RH >70 %) in both summer and winter, suggesting that MO-OOA
compounds formed at high RH contained more relatively high-volatility
compounds compared to that formed at lower RH. This result is consistent
with the RH dependence of volatility for SOA in chamber experiments
(Wilson et al., 2015; Zaveri et al., 2020). By comparison, LO-OOA showed
similar changes in MFR at different RH levels, particularly in summer,
indicating that photochemical processing produced SOA with similar
volatility despite the different chemical environment. Overall, our results
highlight that the molecular composition of MO-OOA at different RH levels
could be very different, yet their similar AMS mass spectra make it a
challenge to separate them by PMF. For example, MO-OOA at low RH levels was
more likely from long-time aging in the atmosphere or aqueous-phase
processing on a regional scale that was transported to Beijing, while
it could be associated more with local aqueous-phase processing at high RH
levels with stagnant meteorological conditions. A recent study by
Chen et al. (2020) further supported
that the SOA factors identified by AMS-PMF can be further separated into
more factors with different chemical processing by using molecular
compositions from chemical ionization mass spectrometer with a filter inlet
for gases and aerosols (FIGAERO-CIMS) measurements. Another possibility for
the RH dependence of MO-OOA volatility is that the particle phase
diffusivity limited the evaporation under dry conditions (Li and
Shiraiwa, 2019; Yli-Juuti et al., 2017; Liu et al., 2016).
Predicted glass transition temperatures of organic
aerosols under dry conditions
(Tg,org). The fill color of the
markers represents Tg,org in (a) and O/C in (b). The marker edge color indicates the OA components identified
by PMF.
Diurnal variations in (a) viscosity of total OA and
ambient RH and T in Beijing in (b) summer and (c) winter. The diurnal cycles of ambient RH and T in
Gucheng during wintertime are shown in (d). Characteristic mixing timescales
of organic molecules with a radius of 10-10 m
within 200 nm particles are also shown on the right axis.
Viscosity of OA
Figure 6 shows the two-dimensional volatility basis set (2D VBS) framework of O/C vs. log10C* and the
correlation between Tg,org and log10C*. The averageTg,org of OA
varied from 289.7 to 291.5 K in the NCP in summer and winter, which is in the
range of the values estimated by chemical composition (Slade et
al., 2019; Ditto et al., 2019) and chemical transport model simulations
(Shiraiwa et al., 2017) in several field campaigns. In general,
Tg,org of OA in summer in Beijing (291.5 K) is larger than that in
winter (289.7–290.0 K), yet it is lower than that in Europe and the US
(Li et al., 2020). Such differences are caused by the
fact that highly volatile OA in China facilitates the partitioning of more
SVOC into the particle phase compared to megacities in Europe and the US
(Xu et al., 2019). The Tg,org of FFOA (or CCOA), a unique OA factor in
the NCP, is 285.8 K in Beijing and 288.9 K in Gucheng, which is overall slightly
lower compared to HOA (288.4–289.7 K) in China. Such differences in
Tg,org between HOA and FFOA (or CCOA) agree with the overall higher C* of
FFOA. Even for the same OA factor, the differences in Tg,org exist at
different sampling times and sites. For example, the Tg,org of BBOA in
Gucheng (294.4 K) is lower than that in Athens, yet comparable to that in
Mexico City (Li et al., 2020), which is partly
attributed to the different fuels, combustion conditions and oxidation
during transport leading to the differences in volatility and oxidation
degree. MO-OOA showed higher Tg,org than LO-OOA in both summer (290.2
vs. 285.5 K) and winter (292.5 vs. 289.9 K) in Beijing, consistent with previous urban observations, e.g., Paris and Mexico City
(Li et al., 2020).
Figure 7 shows diurnal variations in predicted viscosity of OA using
measured T and RH during three campaigns. The predicted viscosities using
different kappa (κ) values calculated by two methods correlate well
with each other. The diurnal variation in viscosity is significantly
affected by T and RH and thus water associated with organics. Overall, in
the winter of 2018 in Beijing, the OA occurred as a solid with the predicted
viscosity >1012 Pa s due to low ambient temperature and RH.
The mixing time is larger than 103 h; thus kinetically limited
gas–particle partitioning needs to be considered when simulating SOA
formation in winter in Beijing (Shiraiwa et al., 2011; Maclean et al.,
2017; Li and Shiraiwa, 2019). We further explored the viscosity of OA as a
function of RH in winter in Beijing and found that the viscosity of OA
varied from 102 Pa s to 1012 Pa s as RH was in the range of 40 %–85 %, and it was less than 102 Pa s at RH >85 %. These
results suggest that OA particles in winter in Beijing would exist mainly in a semisolid phase and mostly as liquid at RH = 40 %–85 % and
RH >85 %, respectively. The viscosity of OA varies from
102 to 106 Pa s in Beijing in summer and from 103 to
1011 Pa s in Gucheng in winter, suggesting a semisolid phase
throughout the day. The diurnal variations in predicted viscosity are
characterized by increases in the afternoon in summer in Beijing and in
winter in Gucheng, which are associated with diurnal variations in ambient RH
and T. However, such diurnal variations in predicted viscosity are different
from those in the Amazon region (Bateman et al.,
2017) and Michigan (Slade et al., 2019), where enhanced viscosity
at night due to the influence of biomass burning and the formation of high molar-mass organic compounds was observed. Note that the viscosity of OA in
Gucheng shows a large afternoon peak, while it is small in Beijing in
summer. Such differences are partly caused by the differences in diurnal
variations in RH that are negatively related to the rebound fraction, an
indicator of the viscosity (Liu et al., 2017).
For example, as shown in Fig. 7, the RH shows a considerable and rapid decrease
from ∼ 80 % at 10:00 to 44 % in the afternoon in Gucheng
in winter, while the decreases in RH in Beijing during the same period of
time are small (< 20 %). It should be noted that we did not
consider the mixing of OA and inorganic species in this work that can have
influences on Tg,org and viscosity due to the water absorbed by
inorganics (Pye et al., 2017).
Conclusions
A TD-AMS system was deployed at urban and rural sites in the NCP in summer and
winter to investigate the volatility and viscosity of OA. Our results showed
that the C* of OA in summer in Beijing (0.55 µg m-3) is lower than
that in winter (0.71–0.75 µg m-3), indicating that OA was more
volatile in winter. One reason was enhanced primary emissions from
coal combustion and biomass burning with high volatility. The volatility
distributions of OA varied differently among different OA factors and
seasons. We found that the volatile properties of fossil-fuel-related OA
were quite different between urban and rural sites, likely due to variations in oxidation during transport, different coal fuels and combustion
conditions. The compositional differences between volatile and nonvolatile
species were also evaluated. Our results showed that POA dominated volatile
OA in winter (61 %–62 %), while SOA contributed more to volatile OA in
summer (74 %). Nonvolatile OA that is dominated by MO-OOA was highly
correlated with BC and increased as a function of RH, highlighting the
potential formation of aqueous-phase SOA on BC. We also found that the
volatility of MO-OOA was RH dependent, with higher volatility at higher RH
levels. These results demonstrated that the composition and formation
mechanisms of MO-OOA can be significantly different under different RH
levels, yet such chemical information cannot be illustrated by PMF analysis
of bulk OA. Future studies combining AMS and molecular-level
characterization of OA can allow for deeper insights into the sources and
properties of MO-OOA (Qi et al., 2019; Chen et al., 2020). The glass
transition temperature and viscosity of OA were estimated using saturation
mass concentration and the atomic O/C ratio with the recently developed
parameterization formula (Li et al., 2020). Our
results showed that the Tg of OA in summer in Beijing (291.5 K) is
higher than that in winter (289.7–290.0 K), and both are overall lower than
those in Europe and the US. The viscosity analysis suggested that OA
occurred mainly as solid in winter in Beijing, and the mixing time can be as
long as 103 h because of low temperature and RH, while it
dominantly existed in a semisolid phase in Beijing in summer and Gucheng in
winter. Our results have the significant implication that kinetically limited
gas–particle partitioning needs to be considered in chemical transport
models when simulating SOA formation in the NCP.
Data availability
The data in this study are available
from the authors upon request (sunyele@mail.iap.ac.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-5463-2021-supplement.
Author contributions
YS and WX designed the research. WX,
CC, YQ, CX, ZL, JS, NM and WanX conducted the measurements. WX, CC, YQ, YL
and ZZ analyzed the data. YL and ZZ supported the viscosity analysis, and EK
and SNP supported the mass transfer model analysis. YL, PF, ZW, JZ, DRW and
NLN reviewed and commented on the paper. WX and YS wrote the paper.
Competing interests
The authors declare that they have no conflict of interest.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 41975170 and 91744207) and the Beijing Municipal Natural Science Foundation (grant no. 8202049).
Review statement
This paper was edited by Qiang Zhang and reviewed by two anonymous referees.
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