Introduction
Organic aerosol (OA) is ubiquitous in the atmosphere and constitutes a large
fraction of submicron aerosol worldwide (Zhang et al., 2007; Jimenez et
al., 2009). Organic aerosol has two different sources. One is direct
emissions from combustion processes (e.g., burning of fossil fuel and
biomass), which is known as primary organic aerosol (POA). The other is
secondary formation from the oxidation of volatile organic compounds (VOCs)
termed secondary organic aerosol (SOA) (Hallquist et al.,
2009). Recent studies have found that SOA is the major component of OA not
only in rural and remote regions but also in the highly polluted urban
regions (Zhang et al., 2007; Jimenez et al., 2009). Although model
simulations of SOA have been improved during the last decade contributing to
significant improvements in understanding the formation mechanisms and
volatility of OA, the discrepancy between model simulations and ambient
observations can still be substantial (Shrivastava et al., 2011; Fu et
al., 2012; Fast et al., 2014). As a result, SOA contributes one of the
largest uncertainties in evaluating climate radiation forcing of aerosol
particles (Boucher et al., 2013). While a better understanding of
SOA formation and evolutionary mechanisms is essential to improve model
performances (Shrivastava et al., 2017), constraining the models with
observations, particularly long-term measurements would be one of the most
effective ways to reduce the radiative forcing uncertainties.
Aerodyne aerosol mass spectrometer (Jayne et al., 2000; Canagaratna et al.,
2007) is one of the state-of-the-art instruments by providing quantitive
measurement of organic aerosol in real time. Subsequent analysis of OA mass
spectra using receptor models, e.g., multiple component analysis (Zhang et
al., 2005), positive matrix factorization (PMF; Ulbrich et al., 2009) and a
multilinear engine (Canonaco et al., 2013) can further resolve various OA
factors that correspond to different sources and processes. Since 2006,
aerosol mass spectrometer (AMS) has been widely deployed in various regions
in China for real-time characterization of non-refractory submicron aerosol
(NR-PM1) species (Li et al., 2017b, and references therein). The sources
of OA in different seasons were analyzed using PMF. While hydrocarbon-like
OA (HOA) and oxygenated OA (OOA) are ubiquitously identified, cooking
OA (COA) in urban areas, biomass burning OA (BBOA) and coal combustion
OA (CCOA) in specific seasons are also resolved. PMF analysis of
high-resolution OA mass spectra can further differentiate between different
types of SOA, for instance, less oxidized OOA (LO-OOA) and more oxygenated
OOA (MO-OOA). However, most of these studies are focused on intensive
measurements in relatively short periods (e.g., 1–2 months) in a single
season (mostly in either summer or winter), long-term measurements and
characterization of OA in China are still rather limited. Zhang et al. (2013)
reported the season variations in NR-PM1 species in Beijing in 2008. The
results showed the dominance of OA during all seasons. Similar conclusions
were also found from the high-resolution AMS (HR-AMS) measurements from 2012
to 2013 in Beijing (Hu et al., 2017). While both studies showed the dominant
role of OOA in summer, the contribution of POA was quite different (76 vs.
50 %). However, most measurements in these two studies lasted
approximately 1 month in each season, and our understanding of the sources
and variations in OA is far from complete, particularly in a city with
frequent changes in different air masses and largely different emissions
sources (Guo et al., 2014; Sun et al., 2015; Zheng et al., 2016). Sun et
al. (2015) conducted 1-year real-time measurements of NR-PM1 species
from 2011 to 2012 using an aerosol chemical speciation monitor (ACSM). While
OA showed a similar seasonal variation to those in Zhang et al. (2013) and Hu
et al. (2017), substantial differences in monthly averaged mass
concentrations were also observed, highlighting the importance of continuous
real-time measurements for understanding seasonal characteristics. However,
Sun et al. (2015) only present the characterization of the total OA; the
sources and seasonal variations in different OA factors remain less
understood.
In this work, we present an analysis of OA measurements lasting nearly
2 years by an ACSM in the megacity of Beijing. Although year-round
measurements of NR-PM1 species and source characterization of OA have
been reported at many sites throughout the globe (Minguillón et al.,
2015; Parworth et al., 2015; Petit et al., 2015; Ripoll et al., 2015; Bressi
et al., 2016; Schlag et al., 2016; Rattanavaraha et al., 2017), real-time
source characterization of OA in Beijing for more than 1 year is never
reported. Here the sources of OA in each season are determined by the
bilinear model with a multilinear engine (ME-2) (Paatero, 1999). The seasonal
variations, diurnal cycles, and relative humidity and temperature dependence
of OA source factors are elucidated. The roles of different OA factors and
POA and SOA in haze pollution are discussed, and the potential source regions
are also investigated with potential source contribution function analysis.
To our knowledge, this study presents the longest continuous characterization
of OA in Beijing, which is of great importance for validating and
constraining the chemical transport models.
Experimental methods
Sampling
An Aerodyne aerosol chemical speciation monitor (Ng et al., 2011b) was
deployed at an urban site, the tower site of the Institute of
Atmospheric Physics (39∘58′ N, 116∘22′ E; 49 m a.s.l.), in Beijing for long-term
real-time measurements of NR-PM1 species including organics (Org),
sulfate (SO4), nitrate NO3), ammonium (NH4),
and chloride (Chl). The measurements were conducted for nearly 2 years from
July 2011 to May 2013 with a time resolution of approximately 15 min. In
addition, gaseous species of NOy, NO, and O3 were
simultaneously measured with a suite of gas analyzers during the same period,
while CO and SO2 were measured during the period of December
2011–May 2013. Black carbon (BC), light extinction of dry particles at
630 nm, and gaseous NO2 were also measured from August 2012 to May
2013 using an aethalometer (AE22, Magee Scientific), a cavity attenuated
phase shift (CAPS) extinction monitor, and a CAPS NO2 monitor
(Kebabian et al., 2008), respectively. The meteorological parameters,
temperature (T), and relative humidity (RH) at the ground site and wind
direction (WD) and wind speed (WS) at 240 m were obtained from the
measurements at the Beijing 325 m meteorological tower. More detailed
descriptions of the sampling site, the operations of the ACSM, and the
collocated measurements are given elsewhere (Sun et al., 2012, 2015; Ge et
al., 2013; Wang et al., 2015). All data in this study are reported in Beijing local time.
Data analysis
The ACSM data were analyzed using the standard software (v.1.5.3.0) written
in Igor Pro (WaveMetrics, Inc., Oregon USA). The mass concentrations of
NR-PM1 species and the mass spectra of OA (m/z 12–140) were
determined using a composition-dependent collection efficiency recommended by
Middlebrook et al. (2012) and default relative ionization efficiencies (RIEs)
were used, except for ammonium whose RIE was determined from measurements of
pure ammonium nitrate. More detailed evaluations on the mass quantifications
are given elsewhere (Sun et al., 2012, 2013b).
Positive matrix factorization was first performed for the ACSM OA mass
spectra (m/z 12–120) in each season. The detailed procedures for
pretreatment of data and error matrices have been given in Ng et al. (2011b)
and Sun et al. (2012). As shown in Fig. S1 in the Supplement, two factors, i.e., a hydrocarbon-like OA (HOA) and an oxygenated
OA (OOA), can be relatively well resolved during all seasons. Although
extending the PMF solution to more factors can help to reduce uncertainties
in the separation of POA and SOA, e.g., four factors in winter 2011–2012
(Sun et al., 2013b), this often generates unrealistic factors due to the low
sensitivity, low mass resolution, and limited m/z values of the ACSM
measurements. Moreover, it is very challenging for PMF-ACSM to separate BBOA
from other POA factors, traffic-related HOA from COA in summer, and different
types of SOA in winter according to our previous studies (Sun et al.,
2012, 2013b; Zhang et al., 2016). Therefore, using
the prior known source information as constraints, the multilinear engine
algorithm (ME-2) (Paatero, 1999) was used for source apportionment of OA.
In this work, the a-value approach was used for
ME-2 analysis (Canonaco et al., 2013). The mass spectral profiles of three
primary OA factors, i.e., standard HOA and BBOA from Ng et al. (2011a) and
COA from Sun et al. (2016b), were constrained by varying a-values from 0 to
1, while the other factors were left free. ME-2 analysis was first performed
to the entire dataset assuming that all OA factors have similar spectral
profiles in different seasons. Such an assumption could introduce large
uncertainties for secondary aerosol factors considering the large differences
in meteorological conditions and precursors of VOCs in different seasons. We
then performed ME-2 analysis on the seasonal datasets of ACSM, which include
summer 2011 (S11), fall 2011 (F11), winter 2011 (W11), spring 2012 (Sp12),
summer 2012 (S12), fall 2012 (F12), winter 2012 (W12), and spring
2013 (Sp13). Indeed, the mass spectral profiles of LO-OOA from the seasonal
ME2-ACSM analysis show large differences in different seasons (Fig. 1).
Therefore, the results from the seasonal ME2-ACSM analysis are used for the
discussions. To better compare the variations in primary and secondary OA in
different seasons and to also allow for some degrees of freedom for model
runs, the five-factor solution, i.e., fossil-fuel-related OA (FFOA), COA,
BBOA, and a less oxidized OOA (LO-OOA) and a more oxidized OOA (MO-OOA), from
the average of three model runs with a-values of 0, 0.1, and 0.2 were
selected. The ME-2 results with an a-value of 0.2 are also presented for
comparisons. It should be noted that the traffic-related HOA shows a
remarkably similar spectral pattern as CCOA at m/z < 120 (Sun et
al., 2016b), which cannot be separated with PMF-ACSM. Therefore, FFOA here
represents a combined factor of traffic-related HOA and CCOA. We also noticed
that the diurnal cycle of FFOA showed similar pronounced mealtime peaks as
that of COA in summer although these two factors were forced to separate in
the ME-2 analysis. To better understand the uncertainties in quantification
of FFOA and COA, we estimated COA concentrations using BC as a tracer for the
traffic-related FFOA in August 2012. Indeed, POA from the two-factor solution
of PMF-ACSM was highly correlated (r2 > 0.75) with BC
between 04:00 and 10:00 LT when cooking emissions are
small, suggesting the dominant contribution of traffic emissions on BC. The
average ratio of POA / BC during this period of time (0.96) was then used
to derive the traffic-related FFOA, and COA was estimated as the difference
between POA and FFOA. Our results showed that COA estimated using the
BC-tracer method contributed 13 %, on average, to OA in August, which
agrees well with the ME-2 analysis (Table 1), while the value of FFOA
(12 %) was lower than the sum of FFOA and BBOA (17 %). These results
together suggest that the results of the ME analysis are quite reasonable
even in summer when FFOA and COA are difficult to separate.
Mass spectral profiles of five OA factors from ME2-ACSM
analysis in (a1) S11, summer 2011, (a2) S12, summer 2012; (b1) F11, fall
2011; (b2) F12, fall 2012; (c1) W11, winter 2011; (c2) W12, winter 2012; (d1) Sp12, spring 2012; and (d2) Sp13, spring 2013.
Figures S2 and S3 show a comparison of source apportionment results from
three different approaches. While the monthly average SOA
(= LO-OOA + MO-OOA) and POA (= HOA + COA + BBOA) are highly
correlated (r2 = 0.76 and 0.89, respectively; Fig. S3), the seasonal
ME2-ACSM analysis reports an overall 16 % higher SOA concentrations than
the conventional PMF-ACSM analysis. The largest differences occur mainly in
cold season, for example November–March, which can be partially explained by
the changes in LO-OOA. Comparatively, the contributions of POA and SOA are
close in warm season, for example July–October. We also compared the results
between the seasonal and entire dataset from ME-2 analysis. As shown in
Fig. S2, the POA and SOA contributions present differences of 5–14 % on
average during the first 8 months, while they are very consistent during the
other months with the differences of less than 3 %. Figure S4 presents a
comparison of POA and SOA contributions from ME2-ACSM in this study with
those reported previously from PMF-ACSM (Sun et al., 2012, 2013b,
2014; Jiang et al., 2013, 2015). The results are
overall consistent except for winter 2011–2012, which shows a higher SOA
contribution (54 %) in this study than that (31 %) reported in Sun et
al. (2013b). We found that such a difference was mainly caused by the
contribution of LO-OOA (23.6 %), which was not resolved in the Sun et
al. (2013b) study with PMF-ACSM.
A summary of monthly average mass concentrations and mass
fractions of five OA factors, POA (= FFOA + COA + BBOA), and SOA (= LO-OOA + MO-OOA).
Month-
Mass concentrations (µg m-3)
Mass fractions of OA
year
FFOA
COA
BBOA
LO-OOA
MO-OOA
POA
SOA
FFOA
COA
BBOA
LO-OOA
MO-OOA
POA
SOA
Jul-11
1.5
2.6
1.4
3.7
8.4
5.5
12.1
0.08
0.15
0.08
0.21
0.47
0.31
0.69
Aug-11
1.4
2.6
1.7
4.9
10.4
5.7
15.3
0.07
0.12
0.08
0.23
0.49
0.27
0.73
Sep-11
1.8
3.3
1.8
3.5
11.7
7.0
15.2
0.08
0.15
0.08
0.16
0.53
0.31
0.69
Oct-11
3.4
4.3
3.1
7.3
12.3
10.8
19.7
0.11
0.14
0.10
0.24
0.41
0.35
0.65
Nov-11
4.4
3.8
4.7
8.6
8.2
12.9
16.7
0.15
0.13
0.16
0.29
0.28
0.44
0.56
Dec-11
6.9
4.0
4.4
5.7
10.6
15.3
16.3
0.22
0.13
0.14
0.18
0.33
0.48
0.52
Jan-12
4.7
4.2
3.3
6.2
11.3
12.3
17.5
0.16
0.14
0.11
0.21
0.38
0.41
0.59
Feb-12
5.2
3.8
1.8
4.6
12.3
10.8
16.9
0.19
0.14
0.07
0.17
0.44
0.39
0.61
Mar-12
3.7
2.6
3.0
3.9
10.3
9.4
14.1
0.16
0.11
0.13
0.16
0.44
0.40
0.60
Apr-12
1.1
2.9
1.3
2.8
9.2
5.3
12.1
0.06
0.17
0.08
0.16
0.53
0.30
0.70
May-12
1.8
3.4
1.8
3.7
15.2
7.1
18.9
0.07
0.13
0.07
0.14
0.59
0.27
0.73
Jun-12
2.6
3.8
3.1
10.9
17.4
9.5
28.2
0.07
0.10
0.08
0.29
0.46
0.25
0.75
Jul-12
1.2
3.5
1.7
2.3
15.2
6.4
17.5
0.05
0.15
0.07
0.10
0.64
0.27
0.73
Aug-12
1.1
2.7
2.2
2.0
12.1
6.0
14.2
0.06
0.13
0.11
0.10
0.60
0.30
0.70
Sep-12
1.3
2.5
1.3
1.6
10.7
5.1
12.3
0.08
0.14
0.07
0.09
0.61
0.29
0.71
Oct-12
2.7
3.8
2.4
3.3
13.4
8.9
16.6
0.10
0.15
0.10
0.13
0.52
0.35
0.65
Nov-12
7.6
4.8
3.6
11.2
10.2
16.0
21.4
0.20
0.13
0.10
0.30
0.27
0.43
0.57
Dec-12
6.3
5.7
4.4
10.8
11.8
16.4
22.6
0.16
0.15
0.11
0.28
0.30
0.42
0.58
Jan-13
8.5
4.8
5.6
9.2
17.2
18.9
26.4
0.19
0.11
0.12
0.20
0.38
0.42
0.58
Feb-13
4.4
4.7
3.1
6.5
14.8
12.1
21.2
0.13
0.14
0.09
0.19
0.44
0.36
0.64
Mar-13
6.5
3.6
3.4
9.9
13.9
13.5
23.7
0.17
0.10
0.09
0.26
0.37
0.36
0.64
Apr-13
1.5
3.3
1.2
3.4
9.0
6.0
12.5
0.08
0.18
0.06
0.18
0.49
0.33
0.67
May-13
1.2
1.6
0.8
1.8
7.6
3.7
9.3
0.09
0.13
0.06
0.14
0.58
0.28
0.72
Potential source contribution function analysis
The potential source regions of five OA factors in each season were
determined using potential source contribution function (PSCF) analysis
(Polissar, 1999) with 72 h back trajectories that were calculated hourly
using the HYSPLIT model (Draxler and Rolph, 2013) at a releasing height of
100 m. The back trajectories are counted in gridded cells (i,j), and the
PSCF is calculated as the ratio of the number of points above a threshold
value (75th percentile in this study, mij) to the total points
(nij) in each grid cell. A weighting function that is the same as Sun et
al. (2015) was further applied to the calculation to downweight cells
associated with low values of nij. The regions with high PSCF values
indicate the potential sources for high concentrations of OA
factors.
Time series of meteorological parameters (a) WS and (b) RH and T and hourly average mass concentrations of (c) organics (Org), and
(d–h) five OA factors, i.e., FFOA, COA, BBOA, LO-OOA, and MO-OOA. The period
of heating season (15 November–15 March) is also marked as shaded areas.
Results and discussion
Mass concentrations, composition, and seasonal variations
Figure 2 shows the time series of organics, five OA factors, and
meteorological parameters for the entire study. OA presents dynamic
variations during all seasons with hourly average concentrations ranging from
0.45 to 301 µg m-3 and daily average values from 3.0 to
120 µg m-3. The variations in OA are much larger in the
heating season than in summertime, mainly due to the more frequent changes
between clean periods and polluted episodes (Sun et al., 2013b, 2015).
Indeed, our previous studies showed a much higher frequency of clean periods
(NR-PM1 < 10 µg m-3) in winter than summer (23
vs. 5 %) (Sun et al., 2015). As shown in Fig. 3, OA presents a strong
seasonal variation with monthly average concentrations ranging from 13.6 to
46.7 µg m-3. The concentrations in wintertime are nearly
twice those in summertime mainly due to the largely enhanced coal combustion
emissions in the heating season (Sun et al., 2015), and the highest
concentration occurred in January 2013, a month with severe haze pollution
(Sun et al., 2014; Wang et al., 2014). High OA concentration (monthly average
of 37.2 µg m-3) was also observed in June 2012, mainly due to
the impacts of agricultural burning.
Monthly average OA mass concentrations and composition.
The error bars represent 1 standard deviations of monthly averages.
Monthly average mass concentrations of five OA factors.
The bars are the average of ME-2 results from three model runs, i.e., a = 0, 0.1, and 0.2. The results with a = 0.2 are also shown for a comparison.
The error bars represent 1 standard deviations of monthly averages.
Five OA factors vary differently across different seasons. FFOA that is
mainly associated with traffic and coal combustion emissions presents the
strongest seasonal variation among the OA factors. The monthly average FFOA
concentrations are 3.7–6.9 and 4.4–8.5 µg m-3 in the
heating seasons of 2011–2012 and 2012–2013, respectively, which are much
higher than 1.1–1.5 µg m-3 in summer (Fig. 4a).
Consistently, the contribution of FFOA to OA is significantly increased from
5–8 % in summer to 13–22 % in the heating season (Fig. 3). The time
series of FFOA (Fig. 2d) also shows a substantial increase after the heating
season starts on 15 November in both 2011 and 2012, supporting the large
impacts of coal combustion emissions on FFOA. Comparatively, FFOA in summer
is expected to be mainly from traffic emissions considering that residential
coal combustion emissions could not be significant. Assuming that
traffic-related FFOA is relatively constant throughout the year, we then
estimate an upper limit of ∼ 70 % of FFOA from coal combustion
emissions in the heating season (∼ 30 % from traffic emissions).
This result is consistent with our previous HR-AMS analysis in winter
according to which HOA and CCOA contributed 10 and 20 %, on average, to
OA, respectively (Sun et al., 2016b).
The temporal variations in COA are characterized by pronounced daily peaks
that are associated with cooking emissions (Fig. 2e). However, the seasonal
difference of COA is much smaller than that of FFOA, for example, the COA
concentration is 3.8–4.2 µg m-3 in winter 2011–2012, which
is only ∼ 50–60 % higher than that in summer 2011 (Fig. 4b). This
is overall consistent with the facts that cooking emissions are expected to
be relatively constant throughout the year. The seasonal differences in COA
concentrations can be explained by the different mixing layer heights (MLH)
in different seasons, for example, a ∼ 40 % higher MLH during
daytime in summer than winter (Tang et al., 2016). Despite the seasonal
concentration differences, the contributions of COA to OA are relatively
stable, varying from 10 to 15 % except for slightly higher values in
April (17–18 %). These results indicate that COA is an important source
of OA during all seasons, consistent with previous results observed in
Beijing (Huang et al., 2010; Elser et al., 2016; Hu et al., 2016). In fact,
the contributions of COA to OA are higher than those of FFOA by 3–10 %
in non-heating seasons, indicating that cooking emission is a more important
primary source than traffic emissions in non-heating seasons in the megacity
of Beijing.
BBOA shows a similarly pronounced seasonal variation to FFOA. As shown in
Fig. 4c, the BBOA concentration increases gradually from summer
(< ∼ 2 µg m-3) to winter (mostly
> 4 µg m-3) with an enhancement by a factor of
more than 2. Consistently, the contribution of BBOA to OA shows an increase
from 7–8 % in summer to 11–14 % in winter (Fig. 3). These results
indicate that BBOA is a more important source of OA in winter than summer,
which is overall consistent with the fact that biomass is also an important
fuel for residential heating in northern China (Chen et al., 2017). However,
we also observed high BBOA concentrations in June and October, 2 months with
significant impacts from agricultural burning. Our subsequent measurements in
June 2013 at a suburban site, Xianghe, which is approximately 50 km
southeast of Beijing, further support the significant agricultural burning
impacts on OA, and the contribution of BBOA was increased from 11 to 21 %
during the periods with biomass burning (Sun et al., 2016c). However,
compared with winter, the biomass burning (BB) impacts in June and October are relatively short and usually last only a few
days.
LO-OOA also presents a pronounced seasonal variation pattern, yet the highest
concentration occurs in the heating season (Fig. 4d). This is not expected as
the photochemical processing is more significant in summer. As indicated in
Fig. 1, the mass spectral profiles of LO-OOA have many differences in
different seasons in terms of m/z 43 / 44 ratios and the fractions of
large m/z values. For example, the LO-OOA spectrum in winter presents much
higher signals at m/z > 60, and the time series of LO-OOA is
even correlated with primary aerosol species, e.g., FFOA
(r2= 0.58–0.75), BC (r2= 0.73), and Chl (r2= 0.58–0.65)
(Fig. S5). LO-OOA is also correlated with FFOA and Chl in spring and fall
seasons, and high concentrations mainly occur during the periods with
residential heating, i.e., 15–30 November and 1–15 March. These results
suggest that LO-OOA in the heating season is more like a combustion-related
SOA that is formed under low temperature. Another possibility is that LO-OOA
is still mixed with part of primary coal combustion OA, yet cannot be
completely separated using the standard traffic-related HOA spectrum as a
constrain. For example, previous HR-AMS studies in Beijing showed higher
signals of large m/z values in CCOA than HOA (Hu et al., 2016; Sun et al.,
2016b). In comparison, LO-OOA is weakly correlated with other species in
summer, supporting its different characteristics in winter. Note
that LO-OOA shows high concentrations during late June 2012 when there
are significant biomass burning impacts, and the LO-OOA spectrum is
remarkably similar to that of oxygenated OA influenced by biomass
burning (OOA-BB) observed in Nanjing during harvest seasons (Zhang et al.,
2015). These results suggest that a larger fraction of LO-OOA in June is
likely from the oxidized BBOA. Indeed, a recent study of transported wildfire
plumes showed that BBOA becomes significantly oxidized through atmospheric
aging and that the mass spectra of aged BBOA can appear highly similar to
low-volatility OOA (LV-OOA) (Zhou et al., 2017). In addition, our previous
studies at the suburban site (Xianghe) near Beijing also found that biomass
burning aerosols can be rapidly oxidized to form a considerable fraction of
LO-OOA (Sun et al., 2016c). LO-OOA represents a large fraction of OA, on
average accounting for 16–29 and 19–30 % in the heating seasons of
2011–2012 and 2012–2013, respectively, and also as much as
∼ 15–20 % during other seasons except for the summer of 2012
(∼ 10 %).
variations in OA composition as a function of OA mass
loadings during four seasons. The data are grouped in OA bins (5 µg m-3 increment). The white circles indicate the frequency of each OA
bin, and the total number of points (N) for each season is also shown.
Average diurnal cycles of five OA factors during four
seasons.
The monthly average concentration of MO-OOA varies from 7.6 to 17.2 µg m-3 and does not present a strong seasonal variation in the 2 years (Fig. 4e). Also, the highest concentrations of MO-OOA occur in
different months in different years, for example, May–July in 2012 (15.2–17.4 µg m-3) and January–March in
2013 (13.9–17.2 µg m-3). MO-OOA is tightly correlated with secondary inorganic
aerosol species including nitrate, sulfate, and ammonium during all seasons
(r2 > 0.60 for most of the time) and has slightly better
correlations with nitrate particularly in winter (Fig. S5). Although the
MO-OOA spectrum is similar during all seasons and also resembles that of low-volatility OOA (LV-OOA) (Ng et al., 2011a), MO-OOA is
still likely a mixed factor with different types of SOA. It cannot be
separated with ME-2 in this study due to the limited chemical resolution of
the ACSM measurements, for instance, the aqueous-phase SOA factor observed
in winter (Sun et al., 2016b). Our results appear to be
different from previous findings that LO-OOA tends to represent
semi-volatile SOA that is generally correlated with nitrate, while MO-OOA
usually represents low-volatility OOA that is generally correlated with
sulfate (Lanz et al., 2007; Zhang et al., 2011). In fact, previous studies
in Beijing show that LO-OOA is often weakly correlated with nitrate, for
example, r2 < 0.12 in fall 2015 (Zhao
et al., 2017), likely indicating the different chemical processing between
LO-OOA and nitrate. For example, the nighttime heterogeneous formation of
nitrate was recently found to be similarly important to that of gas-particle
partitioning of nitric acid (Wang et al., 2017), while
the nighttime formation of LO-OOA is not clear yet. In addition, the
similarly tight correlations between MO-OOA and secondary inorganic species
highlight another fact that secondary aerosols in Beijing can have large
contributions from regional transport (Sun et al., 2014). This is
also consistent with the simultaneous increases during the formation stage
of most polluted episodes (Zhao et al., 2013; Sun et al., 2016a). Overall,
MO-OOA constitutes the largest fraction of OA mass among five OA factors
during all seasons, and the contributions present a pronounced seasonal
variation with the highest values in summer (47–64 %) and the lowest
values in winter (30–34 %).
Table 1 presents a summary of monthly average concentrations and fractions
of five OA factors. It is clear that the contributions of POA and SOA show
strong seasonal differences. SOA (= LO-OOA + MO-OOA) shows the largest
contribution to OA during the non-heating season by accounting for 65–75 %. Although the contributions decrease to 52–64 % in the heating season, they are still higher than those of POA, indicating that SOA plays a
more important role controlling OA chemistry than POA during all seasons.
Consistently, the dominance of SOA in OA mass has been widely reported at
various urban sites (Zhang et al., 2007; Jimenez et al., 2009). We also
noticed the seasonal changes in SOA composition. For example, the
contributions of LO-OOA generally exceed those of MO-OOA in the heating season.
Compared with SOA, POA (= FFOA + BBOA + COA) shows a large increase
from ∼ 30 % in summer to ∼ 40–50 % in
winter, and such increases are mainly driven by FFOA and BBOA from coal
combustion and biomass burning emissions, respectively, supporting the large
impacts of these two sources on POA in the heating season.
Loading-dependent OA composition
Figure 5 shows chemically resolved OA composition as a function of OA mass
loadings during four seasons. In summer, the SOA contribution first shows an
increase at low OA mass loadings (< 20 µg m-3) and
then remains at relatively high values (∼ 70–74 %) at high mass
loadings. These results indicate that SOA plays a dominant role in OA across
different OA mass loadings in summer. However, we observe a significant
change in SOA composition as a function of OA mass loadings. In particular,
LO-OOA shows large increases from ∼ 10 % to nearly 30 %
associated with corresponding decreases in MO-OOA as OA mass loadings
increase to > 40 µg m-3. These results suggest
that the periods with high OA loadings facilitate the formation of less
oxidized SOA. A change in POA composition was also observed in summer 2011,
which is characterized by an increase in FFOA and a corresponding decrease in
COA as a function of OA mass loadings. As discussed in Sect. 3.3, FFOA in
summer presents a similarly pronounced diurnal profile as COA suggesting that
FFOA and COA might not be well separated even with the constrained mass
spectral profiles. In fact, the contributions of the sum of FFOA and COA are
relatively stable across different OA mass loadings, which are
∼ 20–25 % except for the period of
OA > 65 µg m-3 in the summer of 2012 with
significant BB impacts.
Similar mass-loading-dependent OA composition is also observed in the fall
season, yet with some differences between 2011 and 2012 (Fig. 5b). The SOA
contribution in fall 2011 first shows a large increase from ∼ 50 to
65 % at OA < 30 µg m-3, then remains at
relatively constant levels (67–69 %) at high OA mass loadings. In
comparison, it is consistently high (65–70 %) at
OA < 50 µg m-3 with a slight decrease to
∼ 60 % as OA mass loading increases in the fall of 2012. We also
noticed large differences in BBOA and LO-OOA contributions at low mass
loadings (OA < 15 µg m-3) between 2011 and 2012,
yet the total contribution of BBOA and LO-OOA was close. One of the reasons
is the uncertainties in splitting BBOA and LO-OOA with the ME-2 analysis.
Indeed, the mass spectrum of LO-OOA resembles that of BBOA during the two
fall seasons (Fig. 1). Although the contributions of the sum of FFOA and COA
are relatively constant across different mass loadings, which are 21–25 and
23–27 % in the fall of 2011 and 2012, respectively, we observed a clear
increase in FFOA contribution by nearly a factor of 2 as OA mass loading
increases. One reason is due to the half-month period of the fall season
(15–30 November) when FFOA shows a large increase because of residential
heating (Wang et al., 2015). This is particularly clear for the bins with the
largest OA mass loading (80–85 and 90–95 µg m-3 in F11 and
F12, respectively), which occurred mainly during the half-month period of the
heating season (Fig. 2). As a result, the contribution of FFOA increased to
23 and 27 % in 2011 and 2012, respectively, which was associated with a
large decrease in MO-OOA (15 and 22 %, respectively).
The POA composition changes significantly as a function of OA mass loading in
winter (Fig. 5c). The contribution of FFOA gradually increases from 3–7 to
22–28 % when OA mass loading increases from less than 20 to
> 60 µg m-3, highlighting a largely enhanced role
of FFOA during highly polluted periods. In comparison, the COA contribution
shows a large decrease from ∼ 30 to < 10 % although the
mass concentration is relatively stable at
∼ 5–6 µg m-3, consistent with our previous conclusion
that COA is a more important POA during clean periods with low OA mass
loadings. The total contribution of SOA shows a small decrease of 5–10 %
at high OA mass loading periods mainly due to the decreases in MO-OOA, yet it
is still as high as 52–58 %. Such a result indicates that SOA is still
important during the highly polluted periods in winter (Huang et al., 2014).
The changes in OA composition as a function of OA mass loading in spring are
similar to those in fall. The SOA contribution shows a decrease during more
polluted periods, yet it is still higher than that of POA (56–64 vs.
36–44 %). Again, FFOA shows a large increase at high OA mass loading.
For example, the FFOA contribution increases from 6–9 to ∼ 20 % as
OA increases from < 20 to > 50 µg m-3.
This is consistent with the fact that the spring season also consisted of a
half month of heating season when OA presents the highest mass loading
(Fig. 2)
Diurnal variations in OA factors
The diurnal profiles of five OA factors in four seasons are shown in Fig. 6.
Similar to previous studies (Sun et al., 2011b; Ge et al., 2012), COA
presents similar and pronounced diurnal cycles during all seasons, which are
characterized by two peaks during lunch and dinner times, respectively. The
nighttime COA peak is nearly twice that of noontime during all seasons except
for summer indicating higher cooking emissions at nighttime coupled with
shallower boundary layer height compared to the middle of the day (Tang et
al., 2016). The contribution of COA to OA is correspondingly high at
nighttime (∼ 20–25 %), supporting the significant impact of
cooking activities on OA loading in urban areas. FFOA presents the lowest
concentrations throughout the day in summer, yet two small peaks
corresponding to mealtimes are also observed. Such a diurnal profile is not
expected as the traffic-related HOA typically peaks during rush hours. One of
the reasons is that the FFOA factor was not fully resolved and was partially
influenced by cooking organic aerosol. Previous summer studies (J. Sun et
al., 2010; Y. L. Sun et al., 2012; Zhang et al.,
2015) using quadrupole AMS or ACSM have indeed shown that PMF analysis has
difficulty resolving traffic-related HOA from COA in summer due to their
relatively similar mass spectra measured by unit mass resolution instruments.
In comparison, PMF analysis of the high-resolution mass spectra of OA is able
to separate HOA from COA in summer in Beijing (Huang et al., 2010; Hu et al.,
2016), and the results show substantially different diurnal profiles between
these two factors. Compared to summer, FFOA and COA can be relatively well
separated during other seasons as indicated by the much smaller noon peaks of
FFOA. As shown in Fig. 6, FFOA presents much higher concentrations at
nighttime than daytime due to (1) the enhanced coal combustion emissions, and
(2) traffic emissions from diesel trucks and heavy duty vehicles that are
only allowed inside the city between 22:00–06:00 LT (Han et al., 2009), and (3) shallow boundary
layer (Tang et al., 2016). We also found that COA exceeds FFOA before
midnight during most of the seasons although it shows a rapid decrease after
21:00 when cooking activities are significantly reduced. FFOA then becomes a
more important primary OA after midnight until ∼ 11:00. These results
indicate the different roles of primary OA at different times of the day.
Compared to FFOA and COA, the diurnal profiles of BBOA are less pronounced
with slightly higher concentrations at nighttime.
The diurnal profiles of the two SOA factors are different during most of the
seasons. As shown in Fig. 6c, MO-OOA presents the largest increase during
daytime in winter, and the concentration is continuously increased by a
factor of 2 from 08:00 until 19:00, indicating the strong photochemical
production of SOA. The continuous daytime increase of MO-OOA is interrupted
by a temporal decrease in the late afternoon during the other three seasons,
which is very likely due to the rising boundary layer height with the
dilution effect exceeding the secondary production. Compared to the diurnal
changes in mass concentrations, the contribution of MO-OOA to OA shows
similar diurnal trends during all seasons (Fig. S6), and the contributions
increase gradually from the early morning and reach the maximum values at
16:00–17:00. For example, the contributions increase from 30 to 48 and
53 % in winter 2011 and 2012, respectively (Fig. S6c), and from
∼ 40 to ∼ 56 % in the fall seasons (Fig. S5b). Although the
contributions are decreased slightly at lunchtime in summer due to the
largely increased COA, they are still as high as 47–52 %. Also note that
MO-OOA constitutes the largest fraction in OA throughout the day during all
seasons, highlighting the ubiquity and dominance of SOA at urban sites (Zhang
et al., 2007; Jimenez et al., 2009).
The diurnal profiles of LO-OOA are overall similar during three seasons –
winter, spring, and autumn. The concentrations decrease gradually during
daytime, reach the minimum values typically between 16:00 and 17:00, and then
increase rapidly during the rest time of the day. Such diurnal profiles are
exactly opposite to those of temperatures, likely indicating that gas
partitioning driven by T plays the dominant role. Higher concentrations at
nighttime than daytime also resemble those of FFOA somewhat, which highlights
another possibility, namely that LO-OOA is a combustion-related SOA as
discussed above. It should be noted that the decreases in LO-OOA correspond
to the increases in MO-OOA, which likely indicates the continuous
transformation of LO-OOA into MO-OOA due to photochemical aging (Morgan et
al., 2010; Sun et al., 2011a). LO-OOA presents a very different diurnal
profile in summer compared to those during the other three seasons, which is
characterized by similar COA peaks at lunch and dinner times. One explanation
is that LO-OOA was not well separated from COA in summer and was mixed with
part of COA. Another explanation is that part of LO-OOA in summer was from
the oxidation of VOCs from cooking emissions, which is consistent with a
recent study showing that aging of different cooking aerosol can form less
oxidized SOA (O / C = 0.24–0.46) (Liu et al., 2017b). Overall, SOA
dominates OA throughout the day during all seasons. The contributions vary
between 60 and 80 % in spring, summer, and fall although they are
decreased to ∼ 50–60 % in winter. These results indicate that
secondary formation processes in winter via either photochemical and/or
aqueous-phase reactions could be as important as primary emissions.
RH / T dependence of mass concentrations and mass fractions
of five OA factors for the entire study. The data are grouped into grids
with increments of RH and T being 5 % and 3 ∘C, respectively. Grid
cells with the number of data points fewer than 10 are excluded.
PSCF of five OA factors in each season. The cities marked as solid
circles in each panel are Tianjing (TJ), Langfang (LF), Baoding (BD),
Shijiazhuang (SJZ), and Hengshui (HS). The color scales indicate the values
of PSCF.
RH and T (RH / T) dependence of OA
The RH / T dependence of NR-PM1
species in Beijing is presented in Sun et al. (2015). Here, we mainly focus
on the impacts of RH and T on OA factors. As shown in Fig. 7a1, FFOA
presents the highest concentrations in the bottom right-hand region with low
T and high RH. This is consistent with the significantly enhanced primary
emissions (e.g., coal combustion and biomass burning) in winter. At similarly
low T levels, FFOA concentrations increase substantially as a function of
RH particularly at RH > 50 %. In winter, the periods with
high RH levels are typically characterized by stagnant meteorological
conditions, e.g., low wind speed (Fig. S7a) and shallow boundary layer height
(Tang et al., 2016), which lead to the accumulation of primary pollutants and
the increases in FFOA concentrations. However, the ratios of FFOA / OA
also present high values in the similar bottom right-hand region (Fig. 7a2)
indicating that high RH conditions could also facilitate the formation of
more FFOA from coal combustion emissions (Sun et al., 2013a).
Compared with FFOA, the concentration gradients of COA as a function of
RH / T are much smaller (Fig. 7b). The COA emissions are relatively
constant throughout the year and are not expected to have a strong
RH / T dependence. Thus, the concentration gradients of COA would be
mainly caused by the dilution or accumulation effects. For example, low
concentrations in the left region (RH < 30 %) are mainly caused
by high WS (Fig. S7a), while high values in the bottom right-hand region are
mainly due to the stagnant conditions, which is consistent with
the concentration gradients of FFOA. However, the RH / T dependence of COA / OA
is largely different from that of FFOA / OA. In particular, COA / OA
presents the highest values at low RH levels (< 30 %) across
different T levels. These results highlight the dominant role of COA in OA
in clean periods during all seasons. We further evaluated the RH / T
dependence of FFOA / COA (Fig. S7b). Because FFOA is from both traffic
and seasonal dependent coal combustion emissions, the ratio of FFOA / COA
can be used as a diagnostic for the impacts of source emissions. Indeed, the
highest FFOA / COA ratio (> 1.5) occurred when
T < 6 ∘C and RH > 40 % (Fig. S7b),
supporting more FFOA emissions, e.g., from coal combustion, at lower T and
higher RH levels.
The RH / T dependence of BBOA is similar to that of FFOA (Fig. 7c),
which shows higher concentrations in the bottom right-hand region, indicating
more biomass burning emissions at high RH levels in winter. The contribution
of BBOA to OA is relatively constant at ∼ 6–10 % across different
RH and T levels except for the high contribution in the region with
RH > 70 % and low T (-6–0 ∘C). Such a result
could be potentially important for a better understanding of aqueous-phase
SOA formation as a recent study found that aqueous-phase processing of
biomass burning emissions can contribute to ambient SOA formation
substantially (Gilardoni et al., 2016).
The RH / T dependences of two SOA factors are very different. As shown
in Fig. 7d, LO-OOA presents the highest concentrations in the region with
RH > 40 % and T < 6 ∘C. Although such a
distribution is similar to that of FFOA, we also observed moderately high
concentrations at high T when RH is above 60 %. The RH / T
dependence of LO-OOA / OA is also different from those of other OA
factors. The dominance of LO-OOA (∼ 30 %) is observed in the bottom
region with T below 0 ∘C, while it was similarly important in
other regions with different RH and T levels. These results indicate two
different formation mechanisms of LO-OOA. While gas-particle partitioning
plays a dominant role in LO-OOA formation at low T, photochemical
production becomes more important during other periods. Compared with LO-OOA,
the MO-OOA concentrations show strong RH dependence during all seasons while
the T dependence is not clear (Fig. 7e). Such a distribution illustrates
that aqueous-phase processing might be an important mechanism in the
formation of MO-OOA. This is also supported by the correlations between
MO-OOA and sulfate, a species dominantly from cloud and aqueous-phase
processing, during all seasons. However, the contribution of MO-OOA to OA
presents a strong T dependence with much higher contributions at
T > 18 ∘C than those below 12 ∘C
(∼ > 50 vs. ∼ 30–40 %). Such a T-dependent
MO-OOA/OA clearly highlights the importance of MO-OOA in summer over other
seasons across different RH levels.
Potential source regions of OA
Figure 8 shows the PSCF analysis of five OA factors during four seasons. FFOA
shows high PSCF values in a small region near the sampling site in the summer
seasons of 2011 and 2012, supporting the dominant source of local emissions.
This is consistent with the fact that FFOA in summer is mainly from
traffic-related emissions. While FFOA shows a similarly dominant local source
in the fall of 2011, it is also characterized by a high-PSCF region to the
southwest of Beijing in the fall of 2012. These results indicate that FFOA
can have very different contributions from local sources and regional
transport in the same season in different years. In the winters of 2011 and
2012, FFOA shows high PSCF values in the regions located to the south and
southwest of Beijing, indicating that regional transport plays an important
role for the high concentrations of FFOA in winter in Beijing. In fact, the
coal boilers for residential heating have all been replaced with natural gas
inside Beijing city, and a large contribution of FFOA at the sampling site
would be expected from regional transport (Cheng et al., 2016; Sun et al.,
2017). For example, Sun et al. (2014) found that regional transport may have
contributed 84 % of coal combustion emissions during the peak of severe
haze pollution in 12–13 January 2013. Note that the high PSCF does not
extend beyond the cities of Shijiazhuang and Hengshui, suggesting that
transport is mainly dominant in a region less than 250 km
away. This is consistent with the results from a recent study that state
that the regional transport during severe haze episodes is dominantly from
the south/southwest (e.g., Baoding, Cangzhou, and Hengshui) and
south/southeast (e.g., Langfang and Tianjin), while that from regions farther
away, e.g., Shijiazhuang, Xiangtai, and Handan is negligible (Li et al.,
2017a). The contribution of regional transport to high FFOA concentrations is
even more important in the spring seasons of 2012 and
2013, which is
characterized by a narrow high-PSCF band along Taihang Mountains. The cities
of Baoding and Hengshui (approximately 250 km from Beijing) are both
potential source regions of Beijing FFOA. These results are consistent with
the wind rose plots in Fig. S8 showing more frequency of high WS from the
southwest in spring than winter. Therefore, reducing coal combustion
emissions in the regions to the south/southwest of Beijing would greatly help
mitigate FFOA pollution in Beijing in the winter season.
Compared to FFOA, the regions with high PSCF values for COA are much smaller
during all seasons, consistent with the fact that COA is dominantly from
local cooking emissions. In addition, a small high-PSCF region (typically
less than 50 km) located to the east/southeast is frequently observed in
many seasons, indicating that short-distance transport could contribute the
high COA at our sampling site. However, some hot spots near Hengshui (HS) and
Baoding (BD) are more likely from artifacts and are not expected to be an
important source of COA in Beijing. For example, a recent modeling study
found that the COA concentrations decrease rapidly outside of the city and
could not have a significant impact on rural areas (Ots et al., 2016). As
shown in Fig. 8c, the PSCF regions of BBOA are very similar to those of FFOA
in fall, winter, and spring seasons. We also noticed that BBOA is moderately
correlated with FFOA during these three seasons (r2 = 0.44–0.80,
Fig. S5), suggesting similar seasonal emission characteristics between BBOA
and FFOA. Previous studies have shown that residential emissions contribute
substantially to the regional air pollution in northern China in winter (Liu
et al., 2016). While coal is the dominant fuel for residential heating in
winter, biomass is also an important fuel (Liu et al., 2017a). Not
surprisingly, coal combustion and biomass burning show similar temporal
variations in winter. As discussed above, high concentrations of FFOA and
BBOA in spring and fall mainly occur during the half-month period in the
heating season (15–30 November and 1–15 March), and the high-PSCF regions
of BBOA and FFOA in these two seasons are consistently contributed by the
high concentrations during these two periods. The PSCF region of BBOA is
quite different from that of FFOA in summer, indicating their different
source regions. While FFOA in summer is dominantly contributed by local
traffic emissions, BBOA still has an important source from regional
transport.
The potential source regions of the two SOA factors are quite different in
different seasons (Fig. 8d, e). In the spring seasons of 2012 and 2013, both
LO-OOA and MO-OOA show similar potential source regions as those of FFOA and
BBOA with high PSCF values located to the southwest of Beijing. These results
indicate that regional transport from the southwest is an important
contribution of both primary and secondary OA. Figure 8e also shows
high-potential source regions of MO-OOA that are nearly twice as far away as
those of LO-OOA and FFOA in spring 2012, consistent
with the formation of more oxidized SOA over a wider regional scale. During
the other three seasons, MO-OOA shows ubiquitously wider potential source
regions than LO-OOA, suggesting that the formation of MO-OOA is more regional
than LO-OOA. This is also consistent with the good correlations between
MO-OOA with sulfate that is mainly formed over a regional scale (Zhang et
al., 2012). For instance, the potential source region of LO-OOA is limited to
a small region to the south of Beijing in summer 2011, while that of MO-OOA
is beyond approximately 250 km. These results suggest that LO-OOA in summer
is likely mainly from local photochemical production while MO-OOA has more
contributions from regional transport. Even in the same season, the source
regions could be different. For example, LO-OOA presents two potential source
regions located to the southwest and southeast of Beijing, respectively,
while that of MO-OOA is dominantly from the southeast. Note that a potential
source region over the Bo Hai Sea was also observed for MO-OOA. One
explanation is that MO-OOA from a regional scale was circulated from the Bo
Hai Sea before arriving at Beijing. Indeed, the periods with high
concentrations of MO-OOA in summer 2012 (19–21 June) show clear transport
from Bo Hai Sea to Beijing as indicated by Fig. S9. Compared to other
seasons, the potential source region of MO-OOA in winter is much smaller,
which is mainly located to the southwest in 2011, and the whole south of
Beijing in winter 2012, while the highly polluted cities, e.g., Shijiazhuang
and Hengshui in Hebei province, appear not to contribute significantly to
both POA and SOA in Beijing. These results highlight the importance of
employing different control strategies in reducing the number of severely
polluted days in Beijing in different seasons; in particular, more efforts
are needed to control emissions in the surrounding regions within 250 km in
winter.