Introduction
Atmospheric aerosol pollution has important impacts on climate change,
visibility and human health (Bohnenstengel et al., 2014; Baklanov
et al., 2015). Aerosols can be emitted, naturally or
anthropogenically, by primary sources or produced by secondary
chemical reactions from gaseous precursors (IPCC, 2013). Volatility is
one of the most important properties of aerosols, as it can determine
the gas–particle phase partitioning of aerosols directly. The
saturation vapor pressure, which is affected by temperature and
vaporization enthalpies as described by the Clausius–Clapyron
equation, is the main factor that dominates the gas–particle phase
partitioning of compounds. Volatility can be determined by the
saturation vapor pressure or saturation concentration, while the
deposition rates of aerosols for wet or dry deposition are greatly
influenced by the phase of aerosols (Bidleman, 1988). The chemical
mechanism and reaction rates of gas, liquid, and heterogeneous
reactions can also result in a great difference because of the phase,
the concentration, and the lifetime of aerosols can also be influenced
(Huffman et al., 2009a). The volatility of organic aerosol (OA) also
contributes greatly to the uncertainty in predicting the atmospheric
aerosol concentration (Donahue et al., 2006; Pankow and Barsanti,
2009). Since OAs contribute 30–80 % of total aerosol mass
according to previous studies (Hallquist et al., 2009; Zhang et al.,
2007a), further research regarding the volatility of atmospheric
aerosols, especially OAs, is very important in the
understanding of aerosol characterization and source
apportionment. Some previous studies examined several kinds of OA
emission sources, including traffic emissions, combustion sources, and
the oxidation of primary OA (POA), and showed differences in
volatility according to their different compositions (Huffman et al.,
2009a; Xu et al.,2016a; Paciga et al., 2016).
A thermodenuder (TD) is a device that is widely used to estimate
aerosol volatility distributions (Wehner et al., 2002; An et al.,
2007; Huffman et al., 2008; Xu et al., 2016a). The TDs designed by
Burtscher et al. (2001) and Wehner et al. (2002) are typically
operated under temperatures higher than 200 ∘C and have
average residence times from 0.3 to 9 s, focusing on very-low-volatility species. An et al. (2007) and Huffman et al. (2008)
developed TDs with longer residence times to make them more suitable
for measuring the volatility of semi-volatile OAs. The
combined TD and Aerodyne aerosol mass spectrometer (TD-AMS) system was
first applied in an ambient study by Huffman et al. (2008) to quickly
characterize the volatility of chemically resolved ambient aerosol in
a field campaign, and the temperature profiles, particle losses, and
key factors affecting the results were discussed. Huffman
et al. (2009a) then measured the volatility of OA from different
sources, including biomass-burning OA (BBOA), meat-cooking OA, trash-burning
OA, and chamber secondary organic aerosol (SOA) formed from α-pinene and gasoline vapors
and found semi-volatility for all the OAs, which is opposite from the
previous atmospheric models that only regarded POAs as nonvolatile
species. Huffman et al. (2009b) also analyzed the positive matrix
factorization (PMF) results based on the TD-AMS data and demonstrated
that all types of OA should be regarded as semi-volatile species in
the models. Lee et al. (2010) measured the volatility of aerosols with
two different residence time sets and suggested that longer residence
time was required to constrain the variation in OA volatility at lower
concentrations. Obviously, OA volatilities, especially for different
OA types, are still quite uncertain and need more ambient measurements
to constrain.
Aerosol pollution has been one of the most important air quality
problems in China. Many studies focusing on aerosol source
apportionment and chemical and physical properties have been carried
out in most of the regions in China, especially in the Yangtze River
Delta region, the Pearl River Delta region, the Beijing–Tianjin–Hebei
region, and northwestern China in the past few years (Huang et al., 2010,
2012, 2013; He et al., 2011; Xu et al., 2014; Li et al.,
2017). However, the volatility of aerosols is rarely researched in
China currently. Cheung et al. (2016) studied the aerosol volatility
in Guangzhou, China, based on the volatility tandem differential
mobility analyzer (VTDMA) measurement and found that nonvolatile
OA may contribute significantly to the nonvolatile
residuals. Also using a VTDMA, Nie et al. (2017) studied the
volatility of aerosol humic-like substances (HULISs) in Nanjing, China,
and figured out that the interaction between HULISs and ammonium
sulfate tended to decrease the volatility of OAs. In this
paper, the TD-AMS system was first deployed to determine the
volatilities of non-refractory PM1 species in China. With
the high-resolution mass spectra of organics, the volatilities of OAs
from different sources and their implications for OA
ageing were also explored.
Experimental methods
Sampling site description
Shenzhen (113.9∘ E, 22.6∘ N) is located on the
southeastern coast of China, in the southeastern corner of the Pearl River
Delta region with Hong Kong neighboring to the south and Dongguan
(a famous industrial city in China) to the north. The climate in
Shenzhen is a subtropical oceanic climate that is deeply influenced by the
monsoon. The sampling site was located on the campus of Shenzhen
Graduate School, Peking University in the western urban area of
Shenzhen. The area surrounding the sampling site was mostly covered by
subtropical plants, and there was only a local road that was
approximately 100 m away, which can be regarded as an
anthropogenic emission source. The measurement was taken from 31 December 2014 to 23 January 2015 in winter, which is the season with
the highest air pollution due to regional transportation from
northwestern and northeastern China. The average ambient temperature was
15.9±4.2 ∘C, and the relative humidity was 62.9±17.5 %. The wind was mostly from the northeast and northwest
with an average speed of 0.8±0.7 ms-1.
Instruments and methodology
The aerosol volatility measurements were conducted with a TD-AMS
system. The thermodenuder and the high-resolution time-of-flight
aerosol mass spectrometer (HR-ToF-AMS, referred to hereafter as AMS)
were both manufactured by Aerodyne Research Inc., US. The principle
theory of an AMS can be found in previous studies (DeCarlo et al.,
2006; Canagaratna et al., 2007). The AMS was set into two sampling ion
optical modes: the V mode with unit mass resolution (UMR) was used
for quantification of the UMR mass concentration and size distribution
of the non-refractory species (including organics, sulfate, nitrate,
ammonium, and chloride); the W mode was used to obtain the
high-resolution mass spectra (∼3000 mΔm-1),
and the ions with a m/z up to 170 could be separated properly in this
study. The calibrations were conducted at the beginning and end of
the campaign with the method described previously (Jayne et al., 2000;
Jimenez et al., 2003; Drewnick et al., 2005), including the inlet flow
rate, ionization efficiency calibration (IE), and particle size
calibrations. The relative ionization efficiencies (RIEs) used in the
study were 1.2 for sulfate, 1.1 for nitrate, 1.3 for chloride, 1.4 for
organics, and 4.0 for ammonium (Jimenez et al.,
2003). The TD used in this experiment is based on the design of
Huffman et al. (2008). It consists of two parts: the heating section
and the denuder section. The stainless steel heating section is 22.25
inches (56.5 cm) in length with a 1 inch OD (2.5 cm)
and a 0.875 inch ID (2.2 cm), wrapped with three
fiberglass-coated heating tapes. The heating section is then joined
to a 22 inch (56 cm) denuder section. The denuder section is
filled with activated charcoal at room temperature to adsorb the gas-phase species evaporated from particles. The temperature in the
heating section was set at 48, 95, 143, and 192 ∘C to make the
real temperature at the centerline, measured with a thermocouple,
reach 50, 100, 150, and 200 ∘C, respectively. The full
configuration of temperatures in the TD software was 35 min
at 50 ∘C, 5 min for the temperature increasing to
100 ∘C, 22 min at 100 ∘C, 5 min for
the temperature increasing to 150 ∘C, 24 min at
150 ∘C, 5 min for the temperature increasing to
200 ∘C, 25 min at 200 ∘C, and then
15 min for the temperature decreasing to 50 ∘C. The
complete temperature cycle lasted about 136 min. The TD was
placed upstream of the AMS. The aerosol flow can go through the
heating and denuder sections (TD path) before being sampled by the AMS
or flowing directly (bypass path) into the AMS. The residence time of
aerosols in the heating section was approximately 27.9 s with
a flow rate of 0.45 Lmin-1. The AMS was set with four menus:
bypass path in V mode, TD path in V mode, TD path in W mode and bypass
path in W mode, with 2 min in each menu. Only the data sampled
during the stable temperature plateau (1839 points for V mode and
1842 points for W mode in TD path) were selected for
the calculation of volatility.
The data analysis was performed with SQUIRREL 1.57 and PIKA 1.16
(http://cires1.colorado.edu/jimenez-group/ToFAMSResources/ToFSoftware/index.html)
with the method in DeCarlo et al. (2006). The mass concentration was
corrected with composition-dependent collection efficiency
(Middlebrook et al., 2012). All the elemental ratios calculated here
were based on the “Improved-Ambient” (I-A) method (Canagaratna et al.,
2015) while the previous “Aiken-Ambient” (A-A) method was also used
for comparison in Table S1 in the Supplement (Aiken et al., 2008).
An aethalometer (AE-31, Magee, US) coupled with a PM2.5
cyclone was used to measure the mass concentration of black carbon
(BC) with a time resolution of 5 min. The wavelength of
880 nm was used to calculate the BC mass concentration in the
data processing. The total BC (BCtotal) measured by
the aethalometer can be separated into BC emitted by traffic
(BCtr) and biomass burning (BCbb)
based on the aerosol absorption as described in the Supplement
(Sandradewi et al., 2008). A scanning mobility particle sizer (SMPS,
TSI, Inc.) was used to measure the particle number size distribution
(mobility diameter: 15–600 nm) with a time resolution of
5 min. By assuming the densities of the components obtained in
the literature (Kuwata et al., 2012; Poulain et al., 2014; Hu et al.,
2017), the corresponding mass concentration can be calculated from the
particle number size distribution. The AE-31 and SMPS were used only
for ambient sampling.
Particle loss correction
The particle losses through the TD should be of concern for
quantitative measurements with the TD, as they can decrease the
transmission efficiency through the TD. There are three mechanisms of
particle loss inside the TD: sedimentation, thermophoretic, and
diffusional processes (Burtscher et al., 2001; Wehner et al.,
2002). Burtscher et al. (2001) determined that the dominant effects
would be determined by the temperature and particle size:
sedimentation increases as the particle size increases and would be
negligible when the TD is vertical; diffusive losses increase with
decreasing particle size; thermophoresis is not strongly dependent
on particle size, is important in the denuder section, and will partly
compensate for diffusion in the heating section.
The transmission efficiency in this research was calculated via an
experiment. CsCl was chosen as the standard chemical species for the
test of the transmission efficiency of TD due to its thermal stability
as discussed by Huffman et al. (2008). The CsCl solution was atomized and
then measured using a differential mobility analyzer (DMA, TSI,
Inc.) and condensation particle counter (CPC, TSI, Inc.) before and after
the TD. The transmission efficiency of the bypass path was regarded as
1. The average transmission efficiency through the TD is approximately
90 % at 50, 100, 150, and 200 ∘C, as shown in
Fig. 1, which is similar to previous studies (Huffman
et al., 2008; Xu et al., 2016a). The transmission efficiency would be
applied in the calculation of the mass concentration of particles
flowed through the TD.
Temperature-related transmission efficiency through the TD.
Source apportionment method
PMF was applied to the high-resolution organic mass spectra using the
PMF evaluation tool developed by Ulbrich et al. (2009). The data and
error matrices were processed according to the signal-to-noise ratio
(SNR) as reported in previous papers (Ulbrich et al., 2009; Huang
et al., 2010; He et al., 2011). Weak ions (0.2 < SNR <2) were
downweighted by a factor of 2, bad ions (SNR <0.2) were removed
from the analysis (Paatero and Hopke, 2003), and CO2+-related ions (O+, HO+, H2O+, and
CO+) were also downweighted (Ulbrich et al., 2009).
The PMF analysis was based on the full high-resolution organic dataset
(including both data sampled under ambient temperature and data that were
thermally denuded) for 1 to 10
factors with fpeak varying from -1 to
1 (seed =0), increasing with a step of 0.1, and seed varying from 0
to 250 (fpeak =0) in steps of 10. The diagnostic plots of the
solutions are shown in Fig. S1 in the Supplement, including the
Q/Qexpected ratio, the characteristics of
the different mass spectra, the scaled residuals, and the correlation
of the component time series with the external tracers. The solutions
with more than five factors showed no distinct information but
splitting of the factors. The Q/Qexpected
showed the lowest value at fpeak =0; the varied fpeak did not
improve the results. The varied value of seed also made no
significant difference of the solution. Therefore, the solution of
five factors, fpeak =0, and seed =0 was determined as the
optimal solution for this experiment, and the five factors are
hydrocarbon-like OA (HOA), cooking OA (COA),
BBOA, less-oxidized oxygenated
OA (LO-OOA), and more-oxidized oxygenated OA
(MO-OOA).
In addition, the PMF results with the data obtained only under ambient
temperatures were also explored and the best solution was presented in
Fig. S2 in the Supplement. Compared to the results including the
thermally denuded data, the HOA and OOA were mixed to some extent,
with a signature of the high fraction of CO2+ in the HOA
mass spectrum. Therefore, the PMF solution with the inclusion of the
thermally denuded data was confirmed as the final result for later
discussion. Huffman et al. (2009b) also suggested that the PMF
solution of all data collected both with and without TD processing
could facilitate the separation of different OA factors by enhancing
the contrast of the time series of these factors.
Results and discussion
PM1 chemical compositions
Figure 2 shows the chemical compositions for only bypass
conditions. Figure 2a shows the time series of the mass concentration
of non-refractory species measured by the AMS and BC
measured with an aethalometer during the experiment. Sulfate showed
a relatively stable time series, with a relative SD (RSD) of
38.8 %, compared to the other species, such as organics
(RSD =56.1 %), nitrate (RSD =69.6 %), and black
carbon (RSD =70.2 %), indicating that sulfate was less
affected by local emission sources. However, all the species decreased
their concentrations largely during 12–13 January due to a heavy rain
event. The sum of the non-refractory species and BC was regarded as
PM1, which showed a high correlation (R2=0.94,
slope =1.1; in Fig. S3) with the mass concentration derived from
the particle number concentration measured by SMPS. As a result, the
average mass concentration of PM1 was 42.7±20.1 µgm-3, ranging from 3.9 to
134.1 µgm-3, while organics were the most abundant
PM1 component, contributing 43.2 % to the total
PM1 mass concentration, followed by sulfate (21.2 %),
BC (12.2 %), nitrate (11.4 %), ammonium
(10.4 %), and chloride (1.6 %). The measured and predicted
ammonium showed a high correlation (R2=0.97) with a slope of
0.85, implying that the aerosols showed some acidity (Zhang et al.,
2007b).
The diurnal variation in the PM1 species is shown in
Fig. 2b. The two peaks in the diurnal variation in BC obviously match
the traffic rush hours at approximately 08:00 in the morning and the
activities of heavy duty vehicles in the evening. When BC source
apportionment was applied for our BC dataset, the results indicated
that biomass-burning-emitted BC also made a small contribution to the
evening peak of BC (Fig. S4). Nitrate showed a significant peak about
2 h after the morning peak of BC, which was likely a result of
photochemical oxidization of NOx emitted from the morning
traffic. Then, the concentration of nitrate decreased because of both
the lifting of the planetary boundary layer (PBL) and its evaporation
at higher ambient temperatures (also shown in Fig. 2b). Nitrate
maintained a stable concentration level in the evening. Similar to
ammonium nitrate, ammonium chloride is also quite semi-volatile as
revealed in Sect. 3.3. Therefore, its diurnal variation was largely
influenced by the ambient temperature, as well as the height of the
PBL. Also, according to Sect. 3.3, sulfate is a less-volatile species
and thus would not lose significant particulate mass when the ambient
temperature increases. As a secondary species from SO2
oxidation, sulfate showed a slight diurnal variation, indicating that
it was less affected by the variation in the PBL. This implies that
sulfate was not a typical ground-emitted species and could be better
mixed in the PBL. Actually, aerosol sulfate in Shenzhen has been
proven to be a species mostly from regional air mass transport (He
et al., 2011; Huang et al., 2014). Since ammonium exists mostly in the
forms of (NH4)2SO4, NH4NO3, and NH4Cl,
its diurnal variation should be significantly affected by the
formation processes of all these inorganic salts, in addition to atmospheric
physical processes and semi-volatility. The measured and predicted
ammonium showed a similar correlation (R2=0.96–0.97) with
a similar slope of 0.84–0.85 for both the ambient temperatures and
50 ∘C (Fig. S5), implying that the aerosols showed some
acidity in the real ambient temperature range (Zhang et al.,
2007b). The diurnal variation in organics showed more fluctuation and
a few peaks, consistent with its complex origins, e.g., vehicles,
biomass burning, and secondary formation (He et al., 2011; Elser
et al., 2016), which will be discussed in detail in Sect. 3.2.
The average values of O/C and H/C of
OA were 0.52 and 1.61, respectively. The average
O/C value in this campaign is within the typical
O/C range of 0.28–0.56 previously observed under
polluted urban environments in China (Huang et al., 2011, 2012; He
et al., 2011; Xu et al., 2016b; Hu et al., 2016; Lee et al.,
2013). The diurnal variation in O/C plotted in Fig. 2c
shows elevated values during the daytime, which is a clear indicator
of the formation of secondary OA with more oxygen, while
H/C reasonably showed an opposite diurnal trend, with
decreased values during the daytime. The quick elevation of
H/C in the evening should be a combined result of
various primary emissions, e.g., traffic, cooking, and biomass
burning, which is supported by the source apportionment results
discussed in Sect. 3.2.
Figure 2d shows the average size distribution of the five
non-refractory species. The peaks of all the species were at
approximately 500–700 nm in the accumulation modes, while
organics apparently had more mass distribution at smaller sizes down
to ∼100 nm. The inorganic aerosol species showed
a similar average size distribution during the experiment as described
in previous studies in Shenzhen (He et al., 2011). Compared to other
species, the peak of organics was slightly smaller, which was a result
of the much broader size distribution of organics towards smaller
sizes. This character of organic size distribution implies that urban
fresh primary emissions contributed significantly to OA
(Canagaratna et al., 2004; He et al., 2011). The peak of sulfate was
slightly larger than the other species, suggesting that sulfate was
mostly associated with more aged particles that had grown during air
mass transport (Zhang et al., 2005; Huang et al., 2008).
Chemical composition for only bypass conditions: (a)
time series and the mass percentages of PM1 composition;
(b) diurnal variation in PM1 species and ambient
temperature; (c) diurnal variation in
H/C ratio and O/C ratio;
(d) average size distribution of non-refractory
PM1 species.
Source apportionment
Organics are one of the most important chemical species in aerosol
pollution in Shenzhen, contributing 43.2 % to the total
PM1 mass loading. As discussed in Sect. 2.4, PMF modeling
was applied to the high-resolution mass spectra of organics and five
factors were identified with their mass spectrometer profiles shown in Fig. 3a. Under
ambient temperatures, HOA, COA, BBOA, LO-OOA, and MO-OOA averagely
accounted for 13.5, 20.6, 8.9, 39.1, and 17.9 % of the total
organic mass, respectively (Fig. 3d).
HOA is most often dominated by long-chain hydrocarbon ion series of
CnH2n+1+ and
CnH2n-1+ in previous findings
(Canagaratna et al., 2004; Mohr et al., 2009; Ng et al., 2010), which
is also the case in this campaign. The average O/C of
HOA was 0.10 in this campaign, which was in its range (0.03 to 0.17)
reported in previous publications (e.g., Aiken et al., 2009; Huang
et al., 2010; Mohr et al., 2012). BC is regarded as a tracer of HOA
and can be significantly emitted from both fossil fuel combustion and
biomass burning (Zhang et al., 2007a; Lanz et al., 2007; Lan et al.,
2011). The good correlation (R2=0.82) of HOA and
BCtr (Fig. 3b) suggested that HOA was mainly from
traffic emissions. The diurnal variation in HOA was influenced by PBL
dynamics and also showed peaks that matched the rush hours, further
supporting the dominant role of traffic emissions in the HOA.
The O/C ratio of COA is 0.18, which is similar to the
previous results shown in Mohr et al. (2012). The mass spectral
signature of COA is dominated by the ion series of
CnH2n+1+ and
CmH2m+1CO+ (m/z 29,
43,57,71, 85…) and CnH2n-1+ and
CmH2m-1CO+ (m/z 41,
55, 69, 83…), which are mainly ionized from alkanes, alkenes, and,
possibly, long-chain fatty acids (He et al., 2010; Huang et al., 2010;
Mohr et al., 2009, 2012). Mohr et al. (2012) also identified COA from
HOA by comparing the signals of m/z 55 and m/z 57 and determined
that the differentiation between COA and HOA is mainly driven by the
oxygen-containing ions of C3H3O+ and
C3H5O+; especially if the signal ratio of m/z 55 to
m/z 57 is larger than 2, it can probably be recognized as COA. In
this study, COA showed much more C3H3O+ than HOA, and
the ratio of m/z 55 to m/z 57 showed values larger than 2,
indicating the origin of cooking emissions. The diurnal variation in
COA in Fig. 3c shows a small peak at approximately
8:00 UTC+8 (UTC+8 for all times following),
breakfast time, and the second peak at approximately 14:00 corresponds
with lunch time. The mass concentration of COA also rises after 17:00,
which is the time for dinner and partly because of the decreasing PBL
height. The good correlation of COA with the tracer ion
C6H10O (R2=0.91) (Sun et al., 2011; Crippa et al.,
2013; Elser et al., 2016) also demonstrated the presence of COA.
The most abundant signals in BBOA are m/z 29 (CHO+) and
m/z 43 (C2H3O+), and there are more fragments in the
range of m/z>100 for BBOA than for COA and HOA (He et al.,
2010). BBOA can be identified by the contribution of m/z 60 (mostly
C2H4O2+), which is a distinct fragment of ionized
levoglucosan, the molecular marker of biomass burning (Alfarra et al.,
2007; Aiken et al., 2009; Mohr et al., 2009). Some previous studies
have shown that the background level of m/z 60 / OA is approximately
0.3 % in urban areas without biomass-burning impacts (DeCarlo
et al., 2008; Docherty et al., 2008). The signal of m/z 60 in BBOA
is 1.36 % in this study, indicating the presence of BBOA during
this experiment, and the BBOA correlated well with m/z 60 (R2=0.83). The O/C ratios of BBOA varied a lot in
previous studies. Laboratory studies reported
O/C ratios of 0.18–0.26 for six types of biomass-burning emissions (He et al., 2010), O/C ratios of
0.31 for lodgepole pine burning, and 0.42 for sage/rabbitbrush burning
(Aiken et al., 2008). DeCarlo et al. (2010) reported an
O/C ratio of 0.42 for ambient biomass-burning aerosol.
The BBOA in this study showed an O/C ratio of 0.33,
which is within the range of previous studies. The diurnal trend in
BBOA showed a large peak in the evening, consistent with the
diurnal peak of BCbb in Fig. S4.
OOA is recognized by the most intense signal of m/z 44
(CO2+), and the signals at higher values of m/z are
lower relative to those of other OA factors (Ng et al.,
2010). Furthermore, the OOA can be divided into two factors, LO-OOA
(typically named semi-volatile OOA) and MO-OOA (typically named
less-volatile OOA), according to the O/C ratios and
f44 (Jimenez et al., 2009; Ng et al., 2010; Xu et al., 2015). The
factor with a relatively higher O/C ratio (0.95) of
OOA and higher f44 than f43 is identified as MO-OOA. It showed a good
correlation (R2=0.64) with sulfate, which was less volatile and
had been identified as a regional pollutant in Shenzhen (He et al.,
2011; Huang et al., 2014), implying MO-OOA could also be aged aerosol
from regional transport. The correlation of MO-OOA with nitrate
(R2=0.27) is much lower than with sulfate, which is a typical
result. Meanwhile, LO-OOA, which is less oxygenated
(O/C=0.76), showed a narrow gap between f43 and
f44. The correlation of LO-OOA with sulfate (R2=0.59) was indeed
a little bigger than with nitrate (R2=0.46), which could be
a combined result of the precursors, formation mechanisms, and
volatility of LO-OOA. Unlike the primary organic components, which had
lower concentrations during the daytime due to the elevated PBL, the
diurnal variations in both LO-OOA and MO-OOA showed higher
concentrations during the daytime, suggesting that photochemical
secondary production should be their main source. The diurnal
variation in MO-OOA was relatively stable compared to that in LO-OOA,
which is consistent with MO-OOA being a more aged and regional
component. The contribution of the two OOA to total OAs is
57.0 %, indicating that OOA contributes the majority of the OA
pollution in wintertime in Shenzhen.
(a) MS profiles of the five OA factors identified by
PMF; (b) time series of the five OA factors and the
correlation with the relevant species during the experiment;
(c) the diurnal variation in the five OA factors;
(d) the average contributions of the five OA factors to
total OA. The time series, diurnal variation, and pie chart display
the results only for ambient conditions.
Figure 4 showed the mass fractions of the five factors at different TD
temperatures. It is found that when the temperature increased, the
fraction of MO-OOA quickly increased up to 67.6 % at
200 ∘C, while LO-OOA showed a reverse trend, accounting for
only 2.9 % at 200 ∘C, indicating that they had quite
different volatilities. HOA, COA, and BBOA also exhibited
different volatilities, with HOA accounting for only 5–7 %
above 100 ∘C, while the fraction of COA did not change much
with the increasing temperature. The different volatilities of
different OA factors will be discussed in more detail in the following
section.
The average compositions of the total OA at different TD
temperatures.
Volatility of PM1 species and OA factors
Figure 5 shows the mass fraction remaining (MFR) of the non-refractory
species. The MFR is calculated as the ratio of the species mass
concentrations with and without TD processing. The narrow average
MFR ± SD regions show that the volatilities of these species were
stable during the measurement. The MFRs of the total non-refractory
species and organics both showed nearly linear correlations with the
TD temperature, which is consistent with the fact that they include
various compounds with a wide range of volatilities. For organics, the
MFR was 0.88 at 50 ∘C, 0.63 at 100 ∘C, 0.32 at
150 ∘C, and, finally, 0.16 at 200 ∘C. Based on the
linear relationship of the MFR of organics and the TD temperature, the
evaporation rate of organics is approximately
0.45 %∘C-1. The relationship of the
O/C, H/C, and
N/C ratios with the TD temperature is also shown in
Fig. 5. It can be seen that O/C kept increasing as the
temperature increased, especially after 150 ∘C, which is
consistent with previous studies (Xu et al., 2016a). When examining
the organic mass spectra at difference temperatures (in Fig. S6), the
elevation of O/C with increasing temperature was found
to be reasonably related to increasing of CO2+. Previous
PMF results usually correlated higher volatility with reduced species
and lower volatility with more oxygenated species (Ng et al., 2010;
Huang et al., 2012). In this study, the elevation of
O/C with increasing temperature was closely related to
the evaporation of more reduced organic components, as the PMF results
indicated. Conversely, H/C showed a reasonable
reverse trend relative to that of
O/C. N/C generally had an increasing
trend with the TD temperature increasing, but N/C
varied largely at the different TD temperatures, suggesting that the
volatilities of N-containing compounds are complex.
Figure 5 also shows the MFRs of inorganic species with different
temperature, and all the inorganic species showed trends similar to
the results shown by Huffman et al. (2008). Nitrate and chloride show
similar trends: they both decreased sharply from ambient temperature
to 50 ∘C, reaching approximately 0.57; then they decreased at
a lower rate from 50 to 150 ∘C. When the TD temperature
increased to 200 ∘C, the MFRs of nitrate and chloride were at
approximately 0.08, which is lower than those of all the other
species. Sulfate is the least volatile species among all PM1
species. The MFR of sulfate does not show a significant decreasing
trend with temperature below 100 ∘C (0.93 at 50 ∘C
and 0.89 at 100 ∘C); when the temperature reached
150 ∘C, the MFR decreased sharply to 0.43, with 11 %
left at 200 ∘C. The trend in MFR of sulfate is consistent
with the discussion in Burtscher et al. (2001): sulfuric acid would
evaporate under temperatures of 30–125 ∘C, while ammonium
sulfate and bisulfate would evaporate between 125 and 175 ∘C.
Variation in the average mass fraction remaining (MFR) of the
total of non-refractory species and the ratios of
O/C, H/C, and N/C
with the TD temperature. The shaded regions indicate the average ±SD.
Figure 6 shows the MFRs of the different OA factors identified by
PMF. The MFR of HOA was 0.56 at 50 ∘C and decreased by
1.26 %∘C-1 from the ambient temperature to
50 ∘C and reached only 17.8 % at 100 ∘C. Then,
the evaporation rate slowed down significantly, with a MFR of
7.6 % at 200 ∘C. BBOA showed significant volatility in
this study, with an evaporation rate of
0.37 %∘C-1 near the ambient temperature and an
MFR of 0.87 at 50 ∘C. In addition, it was noted that the
evaporation rate of BBOA increased when the TD temperature increased
from 100 to 150 ∘C, indicating that BBOA contained more
compounds that favor evaporation in this temperature range. COA
showed a similar volatility to that of BBOA, with an MFR of 0.85 at
50 ∘C, but it kept a stable evaporation rate throughout the
whole temperature range (∼0.44 %∘C-1). The volatility of LO-OOA
(MFR =0.70 at 50 ∘C) was higher than that of MO-OOA
(MFR =0.99 at 50 ∘C), COA, and BBOA but lower than that of
HOA. The MFR curve of MO-OOA remained relatively stable below
100 ∘C and then decreased significantly when the temperature
increased further. The MFR of MO-OOA was as high as 51.5 % even
at 200 ∘C. Thus, MO-OOA was the least volatile one among the
five factors. The OOA (LO-OOA+MO-OOA) in Fig. 6 presents the MFR
variation in the combination of LO-OOA and MO-OOA. The MFR of OOA,
which is regarded as a good surrogate of SOA, showed a good linear
decline at the temperatures under 150 ∘C, with an evaporation
rate of 0.54 %∘C-1. The evaporation rates of
the different OA factors identified by PMF in this study provide the
first-hand information of semi-volatility of OAs in
China, which will be helpful in constraining parameters in the
atmospheric chemical models.
Average mass fraction remaining (MFR) of the five OA factors
and OOA (calculated as the combination of MO-OOA and LO-OOA). The
shaded region is the average MFR ± SD.
Figure 7 compares both the volatilities (MFR at 50 ∘C) and
the O/C ratios of the five factors. The sequence of
the volatilities can be summarized as HOA > LO-OOA > COA
≈ BBOA > MO-OOA. It can be easily found that the sequence
of the volatilities of the OA factors does not completely follow the
sequence of the O/C ratios. For example, although
LO-OOA has a higher O/C ratio than BBOA and COA,
LO-OOA is also more volatile (or with a lower MFR) than BBOA and
COA. This clearly indicates the volatility of the OA factors depends on
not only the oxygenation of organic compounds but also other
factors, e.g., molecular weight and mixing state. HOA is identified as
the most volatile OA factor while MO-OOA is nearly nonvolatile near
the real atmospheric temperatures in Shenzhen, which is consistent
with the results observed in Mexico and Paris (Cappa and Jimenez,
2010; Paciga et al., 2016). However, LO-OOA is the second most volatile OA
factor after HOA in Shenzhen, which is different from that in Mexico,
where BBOA is more volatile than LO-OOA. Actually, the volatility of
the aerosols directly from biomass burning has been measured to be
quite variable, with an evaporation rate of
0.2–1.6 %∘C-1, depending on the kinds of wood
and combustion conditions (Huffman et al., 2009a). The relatively
lower volatility of COA was also identified in previous studies and
attributed to the abundant fatty acids of low volatility in COA (Mohr
et al., 2009; Paciga et al., 2016). Hong et al. (2017) recently
reported the estimation of the OA volatility in a boreal
forest in Finland using two independent methods, including using
a VTDMA with a kinetic evaporation model and applying PMF to HR-AMS
data. Semi-volatile and low-volatility organic mass fractions were
determined with both methods, similar to our study in China. This
implies that MO-OOA and LO-OOA, with different volatilities, could be
popular OA components across the world. Hong
et al. (2017) also pointed out that determining extremely low-volatility OAs from AMS data using the PMF analysis
should be explored in future studies.
Comparison of the volatilities (MFRs at 50 ∘C) and
O/C ratios of the five OA factors in this study. The
colorful regions indicate the SDs of the MFRs.
It should also be noted that as the most volatile species in this
study, HOA could be evaporated easily and thus have a larger potential
to experience the “evaporation – oxidation in gas
phase – condensation” process and then form SOA since previous
studies showed that semi-volatile hydrocarbons from diesel exhaust
(Robinson et al., 2007) and crude oil (de Gouw et al., 2011) can be
easily oxidized in the gas phase to form less-volatile SOA. Huang
et al. (2012) also pointed out that HOA could be oxidized to SOA based
on the analysis of their diurnal variations. This potential can be
further supported by the fact that it was difficult to resolve HOA in
downwind regions far from urban and industrial areas in China,
implying that HOA had been oxidized during the air mass transport
(Huang et al., 2011; Zhu et al., 2016). Therefore, the modeling work
needs to consider the process from HOA to SOA in future.
Conclusions
The source apportionment and volatility of the PM1 chemical
composition during winter in Shenzhen were investigated based on the
TD-AMS system. The mean PM1 mass concentration was 42.7±20.1 µgm-3 during the experiment, with organics
(accounting for 43.2 %) as the most abundant species. Sulfate,
BC, nitrate, ammonium, and chloride contributed 21.2, 12.2,
11.4, 10.4, and 1.6 % to the total PM1,
respectively. The chemical species in PM1 exhibited a range
of volatilities. Nitrate showed the highest volatility among the five
species measured, with the lowest MFR (0.57) at
50 ∘C. Organics exhibited a relatively linear MFR decrease,
with a rate of 0.45 %∘C-1, as the TD
temperature increased from ambient to 200 ∘C, which is mainly
due to the complex composition of organics in the atmosphere. The
organics were grouped into five subtypes by the PMF analysis,
including primarily emitted HOA, COA, BBOA, and two secondarily formed
oxygenated OAs: LO-OOA and MO-OOA, and they accounted for 13.5, 20.6,
8.9, 39.1, and 17.9 % of the total OA, respectively. Among all five OA factors, HOA was the most volatile species, whereas MO-OOA
had the lowest volatility with the slowest evaporation rate. According
to the MFR of different OA factors at 50 ∘C, the volatility
sequence of the five OA factors was HOA (MFR of 0.56 at
50 ∘C) > LO-OOA (0.70) > COA (0.85) ≈ BBOA
(0.87) > MO-OOA (0.99), which was not completely consistent with
the sequence of their O/C ratios. The most volatile
HOA had a high potential to be oxidized to secondary species in the
gas phase.