To investigate the sources and evolution of haze pollution in different
seasons, long-term (from 15 August to 4 December 2015) variations in
chemical composition of PM1 were characterized in Beijing, China.
Positive matrix factorization (PMF) analysis with a multi-linear engine (ME-2)
resolved three primary and two secondary organic aerosol (OA) sources, including
hydrocarbon-like OA (HOA), cooking OA (COA), coal combustion OA (CCOA),
local secondary OA (LSOA) and regional SOA (RSOA). The sulfate source
region analysis implies that sulfate was mainly transported at a large
regional scale in late summer, while local and/or nearby sulfate formation
may be more important in winter. Meanwhile, distinctly different
correlations between sulfate and RSOA or LSOA (i.e., better correlation with
RSOA in late summer, similar correlations with RSOA and LSOA in autumn, and
close correlation with LSOA in early winter) confirmed the regional
characteristic of RSOA and local property of LSOA. Secondary aerosol species
including secondary inorganic aerosol (SIA – sulfate, nitrate, and ammonium) and SOA (LSOA and RSOA)
dominated PM1 during all three seasons. In particular, SOA contributed
46 % to total PM1 (with 31 % as RSOA) in late summer, whereas SIA
contributed 41 % and 45 % to total PM1 in autumn and early winter,
respectively. Enhanced contributions of secondary species (66 %–76 % of
PM1) were also observed in pollution episodes during all three seasons,
further emphasizing the importance of secondary formation processes in haze
pollution in Beijing. Combining chemical composition and meteorological
data, our analyses suggest that both photochemical oxidation and
aqueous-phase processing played important roles in SOA formation during all
three seasons, while for sulfate formation, gas-phase photochemical
oxidation was the major pathway in late summer, aqueous-phase reactions were
more responsible during early winter and both processes had contributions
during autumn.
Introduction
Atmospheric particulate matter (PM) has broad impacts on the environment,
including air quality (Molina et al., 2007; Sun et al., 2010,
2013; Huang et al., 2014), regional and global climate (Kaufman et al.,
2002; IPCC, 2013; Molina et al., 2015), and human health (Pope et al., 2002;
Lelieveld et al., 2015). Over the past decades, PM pollution in China has
become one of the most serious environmental problems (Li et al., 2017; An et
al., 2019). Beijing, the capital of China, has been suffering from severe
haze events, with annual concentrations of PM2.5 frequently exceeding
the Chinese National Ambient Air Quality Standard (35 µg m-3 as an
annual average) (He et al., 2001; Streets et al., 2007; Huang et al., 2014;
Wang et al., 2015). Effective mitigation of PM pollution requires a better
understanding of the emission sources and atmospheric evolution processes
(Cao et al., 2012a, b; Huang et al., 2014; Guo et al., 2014; Sun et al., 2014).
The Aerodyne aerosol mass spectrometers (AMSs) have been widely used to
obtain real-time measurements of the chemical composition of the
non-refractory PM (NR-PM), including organic aerosol (OA), sulfate, nitrate,
ammonium and chloride. Real-time techniques such as that employed by an AMS overcome some
limitations of offline techniques, for instance, measurement artifacts or
limited time resolution (DeCarlo et al., 2006; Canagaratna et al., 2007; Ng
et al., 2011b). The aerosol chemical speciation monitor (ACSM), which is a
simplified version of AMS, was designed for long-term measurements of
NR-PM1. In Beijing, a number of online and offline studies have been
conducted in recent years to investigate the chemical composition, emission
sources and formation mechanisms of PM (Chan and Yao, 2008; Zhao et al.,
2013; Huang et al., 2014; Tian et al., 2014; Ho et al., 2015; Wang et al.,
2015; Xu et al., 2015; Yang et al., 2015; Elser et al., 2016). It has been
found that OA is the most dominant contributor to fine PM and that
the secondary aerosol plays an important role in haze formation (Huang et al.,
2014; Elser et al., 2016).
Atmospheric receptor models, e.g., positive matrix factorization (PMF;
Paatero and Tapper, 1994), have been successfully used to perform OA source
apportionment based on the OA mass spectral data (Lanz et al., 2007; Ulbrich
et al., 2009; Thornhill et al., 2010; Sun et al., 2012, 2013; Elser et al.,
2016; Wang et al., 2017). Primary OA (POA) sources such as hydrocarbon-like
OA (HOA), cooking OA (COA), and biomass burning OA (BBOA) or coal combustion
OA (CCOA) have been identified, while secondary OA (SOA) factors could be
resolved either based on oxidation state (i.e., less-oxidized oxygenated OA
(LO-OOA) and more-oxidized oxygenated OA (MO-OOA)) or based on volatility
(i.e., semi-volatility oxygenated OA (SV-OOA) and low-volatility oxygenated
OA (LV-OOA)) (Huang et al., 2012; Crippa et al., 2013; Hu et al., 2013; Wang
et al., 2017). PMF analyses have been used in a number of studies in Beijing
(Huang et al., 2010; Sun et al., 2013, 2014, 2016, 2018; Huang et al., 2014;
Elser et al., 2016; Hu et al., 2016).
Despite the large number of aforementioned studies, the major sources and
mechanisms responsible for the PM pollution during haze events are not well
constrained, mainly due to complex interplay among local emission, regional
transport, secondary reaction and meteorological influence (Volkamer
et al., 2006; Ma et al., 2010; Tao et al., 2012; Sun et al., 2014; Zhang et
al., 2017). For example, Hu et al. (2016) reported a stable ∼80 % contribution of secondary species to PM1 in summertime Beijing,
while PM1 mass concentration in winter changed dramatically due to
different meteorological conditions and enhanced primary emissions. However,
Huang et al. (2014) and Elser et al. (2016) found that secondary aerosol
formation also plays a crucial role in wintertime haze events in Beijing.
The formation mechanisms of secondary aerosol during haze events are not
well constrained. Besides photochemical reactions, aqueous-phase reactions
have been suggested to contribute to SOA formation. For example, PMF studies
show that an aqueous OOA factor contributed 12 % of total OA in wintertime
Beijing and that the oxidation degree of OA increased at high RH levels
(> 50 %) (Sun et al., 2016). In combination with the
back-trajectory analysis, it is found that high PM1 concentrations in
Beijing were associated with air masses from the south and southwest and
characterized by high fractions of MO-OOA and secondary inorganic aerosol,
whereas direct emissions from local sources were the main contributor during
clean events (Sun et al., 2015). These results show the inhomogeneity in the
contribution to PM pollution depending on different sampling locations and
seasons, highlighting the need for more studies on chemical composition,
sources and atmospheric evolution of PM.
In this study, we discuss the seasonal characteristics of the chemical nature,
sources and atmospheric evolution of PM1 in urban Beijing.
Specifically, the formation mechanisms of secondary species and the impacts
of meteorological conditions on the haze pollution are elucidated.
ExperimentalMeasurement site
Measurements were conducted at an urban site in the National Center for
Nanoscience (39.99∘ N, 116.32∘ E) in Beijing, which is
close to the fourth ring of Beijing and surrounded by residential,
commercial and traffic areas. All instruments were deployed on the roof of a
five-story building (∼20 m above the ground) and the
measurements were performed from 15 August to 4 December 2015.
Instrumentation
NR-PM1 species including organics, sulfate, nitrate, ammonium and
chloride were continuously measured by an Aerodyne quadrupole ACSM (Q-ACSM)
with a time resolution of ∼30 min. Detailed descriptions of
ACSM operation can be found elsewhere (Ng et al., 2011a; Wang et al., 2017).
Briefly, the ambient aerosol was sampled at a flow rate of ∼3 L min-1 through a 3/8 in. stainless steel tube and a University Research Glass ware (URG) cyclone
(Model: URG-2000-30ED); a size cut of 2.5 µm in front of the
sampling inlet was used to remove coarse particles. A Nafion dryer
(MD-110-48S; Perma Pure, Inc., Lakewood, NJ, USA) was applied to dry aerosol
particles before they entered the ACSM, and the submicron aerosol was subsampled
into the ACSM with a flow rate of 85 cc min-1 fixed by a 100 µm diameter critical aperture. The submicron particles were focused into a
narrow beam by an aerodynamic lens and impacted a hot vaporizer
(∼600∘). The resulting vapor was ionized with
electron impact and chemically characterized with a quadrupole mass
spectrometer. Monodispersed 300 nm ammonium nitrate particles, generated by
an atomizer (Model 9302, TSI Inc., Shoreview, MN, USA) and selected by a
differential mobility analyzer (DMA; TSI model 3080), were used to determine
the response factor (RF) and calibrate the ionization efficiency (IE) (Ng et
al., 2011a).
An aethalometer (Model AE-33, Magee Scientific) was used for the
determination of black carbon (BC) concentration with a time resolution of 1 min. SO2
was measured by an Ecotech EC 9850 sulfur dioxide analyzer, CO by a Thermo
Scientific Model 48i carbon monoxide analyzer, NOx by a Thermo
Scientific Model 42i NO-NO2-NOx analyzer and O3 by a Thermo
Scientific Model 49i ozone analyzer. Meteorological parameters, including
wind speed, wind direction, relative humidity (RH) and temperature, were
measured by an automatic weather station (MAWS201, Vaisala, Vantaa, Finland)
and a wind sensor (Vaisala Model QMW101-M2).
Data analysisACSM data analysis
The standard ACSM data analysis software in Igor Pro (WaveMetrics, Inc.,
Lake Oswego, Oregon USA) was used to analyze the ACSM dataset. IE was
determined by comparing the response factors of ACSM to the mass calculated
with the known particle size and the number concentration from a condensation particle counter (CPC; TSI model 3772). Standard
relative ionization efficiencies (RIEs) were used for organics, nitrate and
chloride (i.e., 1.4 for organics, 1.1 for nitrate and 1.3 for chloride) and
RIEs for ammonium (6.4) and sulfate (1.2) were estimated from the IE
calibrations using NH4NO3 and NH4SO4. The collection
efficiency (CE) was introduced to correct for the particle loss due to
particle bounce, which is influenced by aerosol acidity, composition and the
aerosol water content. As aerosol was dried before entering the ACSM and
particles are overall neutralized, the influences of particle phase water
and acidity are expected to be negligible. Therefore, CE was determined as
CEdry= max (0.45,0.0833+0.9167× ANMF), where ANMF
represents the mass fraction of ammonium nitrate in NR-PM1 (Middlebrook et al., 2012).
Source apportionment
PMF was used to perform the source apportionment on the organic spectral
data as implemented by the multilinear engine (ME-2; Paatero, 1997) via the
interface SoFi (Source Finder) coded in Igor Wavemetrics (Canonaco et al.,
2013). First, a range of solutions with two to eight factors from
unconstrained runs were examined. The POA factors mixed seriously with the
SOA factors in the three-factor solution, and there was no new interpretable
factor when increasing the factor numbers above 4 in the PMF analysis.
Therefore, the four-factor solution (HOA + CCOA, COA, OOA1 and OOA2) was
adopted (Fig. S1 in the Supplement). In the four-factor solution, the COA factor was
well-defined through the much higher contribution of m/z 55 than m/z 57 in its
profile and the symbolic diurnal cycle of three peaks corresponding to the
time of breakfast, lunch and dinner, supporting the assignment of the COA
factor. Although the COA profile was well-defined, HOA and CCOA were totally
mixed in the four-factor PMF solution, and the mixed factor had
hydrocarbon-like fragments of CnH2n-1 and CnH2n+1 as
in HOA but substantial amounts of polycyclic aromatic hydrocarbon (PAH)-related ions as in CCOA. This mixed
HOA + CCOA factor could not be further separated when increasing the
number of factors, likely due to the low mass resolution in ACSM data and
limited capacity of PMF in separating similar factors. The mixture of HOA
and CCOA factors was also observed in Sun et al. (2018), suggesting the
difficulty in separating HOA and CCOA with PMF for the ACSM dataset.
Compared to PMF, the ME-2 approach can direct the apportionment towards an
environmentally meaningful solution by introducing a priori information (profiles)
for certain factors (Canonaco et al., 2013; Crippa et al., 2014; Frohlich et
al., 2015). The ME-2 runs of five factors were performed to separate HOA from
CCOA and further optimize the apportionment solutions. We first constrained
the HOA using the HOA profile from Ng et al. (2011b), which is the average over
15 sites all over the world (including China, Japan, Europe and the United
States). Previous studies have suggested that the HOA spectra from Europe
and China are similar (Ng et al., 2011b; Elser et al., 2016) despite the
different vehicle fuel patterns in China and Europe. When HOA was
constrained, a new CCOA factor could be resolved. However, this CCOA factor
was seriously mixed with OOA as indicated by a relatively higher intensity
at m/z 44 in the CCOA profile (Fig. S2). We thus further constrained the CCOA
profile to decrease the influence of OOA on the CCOA factor. A CCOA profile
from our previous study (Wang et al., 2017) was used to constrain CCOA. To
minimize the effect from nonlocal input profiles (for both HOA and CCOA),
the a value approach was used to adjust the input profiles to a certain
extent. In addition, we also constrained the COA profile from the four-factor PMF
solution with an a value of 0, which is a well-defined local profile as
discussed above.
We tested a values for HOA and CCOA profiles between 0 and 1 with an interval
of 0.1 and obtained 121 possible results, among which six solutions were
reasonable based on the verification of the rationality of unconstrained
factors, distinct mass spectra and time series, interpretable diurnal cycles, and good correlations with external tracers for all factors. The final
profiles and time series of individual factor were averaged from these six
solutions, and the standard deviations of intensities at each m/z were shown as
error bars.
Liquid water content
Aerosol liquid water content (ALWC) was predicted using the ISORROPIA-II
model (Fountoukis and Nenes, 2007) with ACSM aerosol composition and
meteorological parameters (temperature and relative humidity) as input. The
ISORROPIA-II model then calculated the composition and phase state of a
NH4+–SO42-–NO3-–Cl-–H2O system
in thermodynamic equilibrium, and the concentration of H+ and ALWC could
be resolved.
Time series of (a) temperature (T) and relative humidity (RH), (b) wind speed (WS) and wind direction (WD), (c)O3 and SO2, (d) CO
and NOx, (e) PM1 species, and (f) mass fractions of PM1 species
during the entire study. Seven clean episodes (C1–C7), seven moderate-pollution
episodes (M1–M7) and five high-pollution episodes (H1–H5) are marked for
further discussion. The dates in this and other figures are given as year/month/day.
Results and discussionOverview of mass concentration and chemical composition
Figure 1 shows the time series of meteorological parameters, trace gases and
PM1 composition during the entire measurement period. The relatively
clean events and polluted episodes occurred alternatively during the entire
campaign. As shown in Fig. 1, the variations in PM1 species are
strongly associated with meteorological conditions. For example, clean
periods were generally associated with northerly and northwesterly winds
with high wind speeds. However, serious pollution episodes were related to
southerly winds with low wind speeds (< 1 m s-1), indicating
the important role of stagnant meteorological conditions in haze pollution
(Takegawa et al., 2009; Huang et al., 2010; Sun et al., 2014). The mass
concentration of PM1 varied from 0.4 to 260.7 µg m-3. Considering that the long-term measurements in our study have
different meteorological conditions, we separated the entire study into
three periods as late summer (15 August to 10 September), autumn (11 September to 10 November) and early winter (11 November to 4 December) in
order to discuss the seasonal variations in PM1 mass concentration and
chemical composition.
Summary of PM1 mass concentrations and composition as well as
OA composition in Beijing during different seasons.
% of PM1% of OA YearSeasonPM1OASO4NO3NH4ChlBCPOASOAReference(characteristic)(µg m-3)2008Summer63.138271616134357Huang et al. (2010)(Olympic games)2010Winter60.050131011896931Hu et al. (2016)2011Summer84.0312620161535652011Summer50.0401825161–3664Sun et al. (2012)2011Winter66.8521416135–6931Sun et al. (2013)2011Autumn53.3501221133–––Sun et al. (2015)2011Winter58.7511317145–––2012Spring52.3411425173–––2012Summer61.6401725171–––2012Winter56.04812189494555Wang et al. (2015)(non-heating)2012Winter84.25016129776238(Heating)2013Winter64.060151186–5743Sun et al. (2016)2014Autumn88.038142611474654Xu et al. (2015)(beforeAsia-PacificEconomicCooperation(APEC)summit)2014Autumn & 41.6529199566634(during APEC)2015Autumn19.45518128163565Zhao et al. (2017)(parade control)2015Autumn45.440202012263565(non-parade control)2015Late summer21.6641467182971This paper2015Autumn43.349112282839612015Early winter64.346152010365347
The average mass concentration of PM1 was 21.6 µg m-3 in
late summer (Fig. S3), which was much lower than that measured in
July–August 2011 (50.0 µg m-3; Sun et al., 2012) and in
August–September 2011 (84.0 µg m-3; Hu et al., 2016) (see Table 1). This lower PM1 concentration was likely associated with the 2015
China Victory Day parade control from 23 August to 3 September, which
significantly improved air quality in Beijing (Zhao et al., 2017). OA
constituted a major fraction of PM1 mass (64 %), followed by sulfate
(14 %), BC (8 %), ammonium (7 %), nitrate (6 %) and chloride
(1 %). During autumn, the mean concentration of PM1 increased to 43.3 µg m-3, which was 2 times higher than that in late summer. OA
contributed a mass fraction of 49 %, followed by nitrate, sulfate,
ammonium, BC and chloride with the mass fractions of 22 %, 11 %, 8 %,
8 % and 2 %, respectively. Compared to late summer, the mass fraction of
OA decreased to 49 % (but the OA mass increased from 13.8 to 21.2 µg m-3), and the mass fraction of inorganic species increased
correspondingly. The increase in inorganics was particularly noticeable for
nitrate, which increased from 6 % to 22 % (or from 1.3 to 9.5 µg m-3). The mean concentration of PM1 was 64.3 µg m-3 in
early winter, even higher than those in late summer and autumn. This
PM1 average concentration in wintertime Beijing is similar to other
studies, such as Hu et al. (2016) (60.0 µg m-3), Sun et al. (2013)
(66.8 µg m-3) and Sun et al. (2016) (64.0 µg m-3). OA
accounted for 46 % of PM1 mass in early winter, followed by 20 % of
nitrate, 15 % of sulfate, 10 % of ammonium, 6 % of BC and 3 % of
chloride (Fig S3).
As shown in Figs. 1f and S3, OA dominated PM1 mass in late summer.
In autumn and early winter, however, the contribution of OA decreased and
secondary inorganic aerosol increased to be equally important. It should
also be noted that nitrate had a more important contribution than sulfate to
PM1 during autumn and early winter, with nitrate/sulfate mass ratios of
2.0 and 1.3 in autumn and early winter, respectively. This phenomenon is
likely due to the efficient emission reduction of SO2 and the
continuous increase in NOx because of dramatic growth of the vehicle
fleets and large emissions from industries (Xu et al., 2015). Therefore,
nitrate is expected to play a more important role in PM pollution in the
near future and controlling NOx emission would greatly help mitigate air pollution in Beijing.
The diurnal cycles of PM1 species during different seasons are shown in
Fig. S4. OA was characterized by three peaks occurring in the morning
(06:00–09:00 LT), at noon (12:00–14:00 LT) and in the evening (19:00–22:00 LT) during
all three seasons. Such diurnal patterns were partially influenced by the
emission behavior of pollution sources, i.e., traffic, cooking and/or coal
burning emissions (Huang et al., 2012; Sun et al., 2012; Crippa et al.,
2013). Due to the relatively flat planetary boundary layer (PBL) height
related to stagnant meteorological conditions in early winter compared to
that in autumn and late summer, the noon peak of OA was more evident in
early winter. The morning peak of OA was even more pronounced than the noon
peak in late summer. Such a diurnal cycle was likely related to the
efficient photochemical oxidation in the morning and efficient dilution
effect resulted from PBL height increase at noon.
The diurnal cycle of nitrate varied significantly during different seasons
due to the seasonal difference in photochemical production and gas-particle
partitioning (Sun et al., 2015). Compared to nitrate, sulfate showed a
relatively flat diurnal cycle in all seasons. A clear increase in sulfate in
the afternoon was observed during late summer and autumn due to enhanced
photochemical processes (Takegawa et al., 2009). In the winter, however,
sulfate showed a decreasing trend in the afternoon, suggesting low
photochemical production as discussed below. Chloride presented a morning
peak and then rapidly decreased to a low concentration level at ∼ 18:00 LT during late summer, while in both autumn and winter, chloride displayed a
diurnal cycle with higher concentrations at nighttime, which may be related
to the local emission from coal combustion. BC also showed a similar
diurnal cycle with higher concentrations at nighttime and lower
concentrations in the daytime during all three seasons.
Mass spectra (left) and time series (right) of five resolved OA
factors. Error bars of mass spectra represent the standard deviation of
each m/z over all accepted solutions. The time series of BC, m/z 55, chloride and
nitrate are shown for comparison.
Primary OA factors
Three POA factors were resolved in this study: HOA, COA and CCOA.
As shown in Fig. 2a, the HOA mass spectrum is characterized by prominent
hydrocarbon ion series of CnH2n-1 and CnH2n+1,
particularly m/z 27, 29, 41, 43, 55, 57, 67 and 71. The HOA spectrum is similar to
previously reported HOA spectra at various urban sites (He et al., 2011; Ng
et al., 2011; Sun et al., 2012). The time series of HOA is also correlated
well with that of BC, which is an external tracer of incomplete combustion
(R2=0.56). The mass fractions of HOA (10 %–13 %) and diurnal
cycles in different seasons are rather consistent. There are two peaks from
rush hours, i.e., 07:00–09:00 LT in the morning and around 20:00 LT in the evening.
The nighttime concentrations are generally high (Fig. S4), likely due to
increased diesel fleets, which are allowed in urban Beijing only at night, and
the decrease in PBL during nighttime.
The COA profile is characterized by prominent ion peaks at m/z 55 and m/z 57 (Fig. 2b) and a higher ratio of intensity at m/z 55 over that at m/z 57 (=2.3)
compared to the other two primary OA components (∼1), which
have been shown to be robust markers for COA (He et al., 2010; Mohr et al.,
2012; Crippa et al., 2013; Elser et al., 2016). This COA mass spectrum is
highly correlated with other COA profiles reported in previous studies
(Crippa et al., 2013; Elser et al., 2016; Wang et al., 2017), and the time
series correlated well with that of m/z 55 with R2=0.81. The COA
diurnal cycle showed two obvious peaks at lunch (12:00 LT) and dinner (20:00 LT)
time and a smaller peak at breakfast time (07:00 LT) (Fig. S4). Similar diurnal
behaviors of COA have been observed in Beijing and at other urban sites (Allan
et al., 2010; Sun et al., 2010, 2013). COA had a lower mass fraction of
11 % during late summer compared to autumn (20 %) and early winter
(16 %).
The mass spectrum of CCOA is dominated by unsaturated hydrocarbons,
particularly PAH-related ion peaks (e.g., 77, 91 and 115) (Dall'Osto et
al., 2013; Hu et al., 2013). It shows a similar spectral pattern with the
ambient CCOA mass spectra in Beijing and Xi'an (Elser et al., 2016). The
presence of CCOA can be further validated by the good correlation with
external combustion tracer chloride (R2=0.77) (Zhang et al.,
2012). The time series of CCOA shows that the mass concentration of CCOA was
much lower in August and September but increased dramatically after
November, indicating the large emissions from residential coal combustion
for domestic heating. Also, the nighttime CCOA concentrations were much
higher than the daytime concentrations, further confirming the enhanced coal
combustion emissions from domestic heating in wintertime nights.
Specifically, on average, the mass fraction of CCOA increased from 5 %
(0.7 µg m-3) in late summer to 9 % (2.0 µg m-3) in
autumn and then to 26 % (7.7 µg m-3) in early winter (Fig. S3).
(a) Correlation between time series of SO4 and LSOA; (b) correlation between time series of SO4 and RSOA; (c) bivariate polar
plots of SO4 during late summer (left), autumn (middle) and early
winter (right) as functions of wind direction and wind speed (m s-1).
Secondary OA factors and sulfate sources: regional transport vs. local
formation
In order to analyze sources of sulfate in our study period, the bivariate
polar plots of sulfate during different seasons are displayed in Fig. 3.
During late summer, the high mass concentration of sulfate was mainly located in
the south and southwest regions from the sampling site, suggesting regional
transport was the major source of sulfate in late summer. However, high
sulfate was located both at the sampling site and in the south and southeast
regions from the sampling site in autumn, which indicates that both local
formation and regional transport contributed to the sulfate concentration.
When it comes to the early winter, high mass concentration of sulfate was mainly located at the sampling site coming from local formation and there was
almost no contribution from regional transport. These results indicate that
transported sulfate at a large regional scale was more important during late
summer, while local formation was the major source of sulfate in early
winter due to residential heating.
Two oxygenated OA factors with very different time series were identified in
our study, which we defined as local SOA (LSOA) and regional SOA (RSOA) as
characterized below in detail. As shown in Fig. 3, different correlations
between sulfate and RSOA or LSOA were found during different seasons. The
time series of RSOA correlated well with that of sulfate during late summer
with R2=0.71. This correlation coefficient decreased to 0.62
during autumn, and there was almost no correlation between RSOA and sulfate
(R2=0.02) in early winter. By contrast, the correlations
between LSOA and sulfate displayed the opposite variation with the
correlation coefficient (R2) increasing from 0.40 in late summer to 0.66
in autumn and 0.86 in early winter (Fig. 3a). As we have discussed that
sulfate mainly came from regional transport during late summer, while the
contribution of local formation increased during autumn and further became
the dominant source of sulfate, these correlation variations (i.e., better
correlation with RSOA in late summer, similar correlations with RSOA and
LSOA in autumn and close correlation with LSOA in early winter) suggested
that RSOA is related to the regional source of OOA and LSOA indicates a local
source and subsequent local formation. These two SOA factors show similar
mass spectra with high ratios of intensity at m/z 44 over that at m/z 43
(f44/43), and the f44/43 of RSOA (4.8) is higher than that of LSOA
(2.9), suggesting that RSOA from regional transport is more oxygenated (more
aged) than locally formed SOA (Sun et al., 2014, 2015). The attribution of
LSOA and RSOA is further supported by the bivariate polar plots (Fig. S5),
which show clearly that LSOA is mainly located at the sampling site while
RSOA is mainly from the south of the sampling site. The average mass
concentration of LSOA increased from 3.2 in late summer to
9.2 µg m-3 in autumn and to 12.1 µg m-3 in early
winter with an increase in mass fraction from 23 in late summer to
43 % in autumn and 41 % in early winter. By contrast, the average
mass concentration of RSOA decreased from 6.6 in late
summer to 3.8 µg m-3 in autumn and to 1.8 µg m-3 in
early winter, with a dramatic decrease in mass fraction from 48 % in
late summer to 18 % in autumn and to 6 % in early winter (Fig. S3).
These seasonal variations in LSOA and RSOA indicate that RSOA related to
regional transport was more important during late summer, while locally
formed LSOA played a dominant role in autumn and early winter.
Contribution of secondary species to PM pollution
The average PM1 concentration increased from late summer (21.6 µg m-3) to early winter (64.3 µg m-3) (Fig. S3) and the chemical
composition showed a seasonal difference. The mass concentrations of secondary
species increased from 15.7 µg m-3 in late summer to 30.8 µg m-3 in autumn and to 42.8 µg m-3 in early winter, but the
mass fraction in PM1 decreased from 72 % in late summer to 66 % in
early winter. In particular, SOA had a dominant contribution in late summer
(9.8 µg m-3, 46 % of PM1), while secondary inorganic aerosol (SIA) played a key role
during autumn (17.8 µg m-3, 41 % of PM1) and early winter
(28.9 µg m-3, 45 % of PM1) (Fig. S3). The high SOA fraction
in summer is likely associated with active photochemical oxidation, while
the increased SIA fraction in autumn and early winter is likely due to
enhanced gas-particle partitioning of nitrate and aqueous-phase formation of
sulfate.
Relative contributions of PM1 species and OA sources on clean days, M-pollution days and H-pollution days during late summer (a), autumn (b) and early winter (c).
Figure 4 shows the PM1 composition and OA sources on clean days (daily
average PM1 < 20 µg m-3), medium-pollution days
(M-pollution; 40 µg m-3 < daily average PM1 < 80 µg m-3) and high-pollution days (H-pollution; daily
average PM1 > 80 µg m-3) during different
seasons. The mass concentrations of PM1 species and OA factors, gaseous
pollutants, and meteorological parameters during different periods are
summarized in Table S1 in the Supplement. The average concentration of PM1 was 46.9 µg m-3 during M-pollution days, about 3 times higher than that
during clean days (15.6 µg m-3) in late summer. In autumn and
early winter, the average PM1 concentrations during H-pollution days
(110.5 and 109.7 µg m-3, respectively) were
2 times higher than those on M-pollution days (54.2 and
43.5 µg m-3, respectively) and 10 times higher than those on clean days (9.3 and 8.1 µg m-3,
respectively). As shown in Fig. 4, the mass fraction of secondary aerosol
species (SIA and SOA) increased from clean days (52 %–70 %) to M-pollution
days (67 %–76 %) and H-pollution days (66 %–74 %) during all three seasons,
emphasizing the significant enhancements of secondary aerosol formation in
haze pollution events (Huang et al., 2014; Jiang et al., 2015; Zheng et al.,
2015). In late summer, the mass concentration of LSOA increased from 2.2 µg m-3 (21 % of OA) during clean days to 6.7 µg m-3 (24 % of OA) during M-pollution days, and the mass concentration
of RSOA increased from 5.0 µg m-3 (48 % of OA) during
clean days to 13.8 µg m-3 (49 % of OA) during M-pollution
days, suggesting that regional transport played a more important role than
local formation in both clean and haze pollution events during late summer.
The mass concentration of LSOA increased from 1.5 on clean days to 10.2 µg m-3 on M-pollution days and to 25.4 µg m-3 on H-pollution days during autumn and increased from 1.5 µg m-3 on clean days to 7.5 µg m-3 on M-pollution days and to 20.7 µg m-3 on H-pollution days during early winter.
In comparison, the mass concentration of RSOA increased from 1.5 and 0.6 µg m-3 on clean days to 5.9 and 2.0 µg m-3 on M-pollution days and to 6.6 and 2.5 µg m-3 on H-pollution days during autumn and
early winter, respectively. The increased rates of LSOA were much higher than
that of RSOA; thus, the mass fraction of LSOA increased dramatically from
clean days to M-pollution and H-pollution days in autumn and early winter
(i.e., 26 % to 40 % and 50 % during autumn and 33 % to 37 % and
42 % during early winter), whereas the mass fraction of RSOA decreased
from clean days to M-pollution and H-pollution days (i.e., 25 % to 23 %
and 13 % during autumn and 14 % to 10 % and 5 % during early
winter). These observations suggest that locally formed SOA had more
important contributions than regional sources in haze pollution during
autumn and early winter, implying a different contribution of secondary
aerosol in different seasons.
Summary of (a) meteorological parameters (RH, T, WS), (b) gaseous
species (SO2, NOx, O3), (c) OA factors and (d) PM1
composition for episodes C1–C7, M1–M7 and H1–H5.
Episodic analysis and meteorological effects
The clean and pollution episodes occurred in “sawtooth cycles”, in which
meteorological conditions, regional transport, local emissions and secondary
formation intertwine and play different roles in the evolution of PM
pollution. To get a better insight into aerosol sources and atmospheric
processes, seven clean episodes (average PM1 concentration < 20 µg m-3), seven M-pollution episodes (40 µg m-3 < average PM1 concentration < 80 µg m-3)
and five H-pollution episodes (average PM1 concentration > 100 µg m-3) were selected for further analysis. As shown in Fig. 5, the pollution episodes were generally associated with higher RH and lower
wind speeds (< 1 m s-1) than in clean episodes in autumn
and early winter, with RH usually higher than 60 % in pollution episodes
(both M-pollution and H-pollution) and lower than 45 % in clean episodes.
Specifically, an M-pollution (M1; 47.6 µg m-3) episode in late
summer had similar RH and wind speed to the adjacent clean period (C1;
14.1 µg m-3). However, the contribution of organic species
decreased from 68 % in C1 to 61 % in M1, but the mass fraction of
secondary inorganic species (particularly sulfate) increased from 23 % in
C1 to 33 % in M1. This phenomenon may result from enhanced photochemical
formation of secondary species in M1 due to higher oxidation capacity as M1
had higher O3 concentration (54.1 ppb) than C1 (31.0 ppb). In autumn,
the mass concentrations of organics increased from 4.8 to 6.3 during C2–C5 to 21.2–27.8 µg m-3 during M2–M6, while
the contributions decreased from 56 %–71 % to 39 %–55 %, and the
corresponding contributions of secondary inorganic species increased from
17 %–29 % during C2–C5 to 36 %–52 % during M2–M6 with mass concentrations
increasing from 1.6–2.9 to 16.7–33.1 µg m-3.
The contributions of secondary organic species to OA also increased from
50 %–61 % to 55 %–73 % with mass concentrations increasing from 2.7–3.6 to 14.1–19.4 µg m-3. This indicates a
notable production and accumulation of secondary aerosol during pollution
events. Compared to M-pollution episodes, there was no further increase in
the contribution of secondary inorganic species during H1–H3 (42 %–47 %)
although the mass concentrations increased to 45.3–56.6 µg m-3
due to the systematic concentration growths of all species from M-pollution
to H-pollution. Secondary organic species also had similar contributions to
OA during H1–H3 (52 %–75 %) to that during M2–M6 (55 %–73 %) although the
mass concentrations increased from 14.1–19.4 to
25.6–38.5 µg m-3. Further analysis shows that the RH during
H1–H3 (71.7 %–81.6 %) is lower than that during M2–M6
(74.1 %–91.8 %), which indicates that the stronger aqueous-phase
chemistry during M2–M6 may lead to the efficient formation of secondary
species, and the mass concentration growths of secondary species were faster
than that of other species in PM1; thus, the mass fraction of secondary
species in M2–M6 was higher or similar to that in H1–H3. A similar
phenomenon was also found in early winter. The contributions of secondary
species increased from clean episodes (C6 and C7) to pollution episodes (M7,
H4 and H5), while the contributions of secondary species were similar in M7,
H4 and H5 because of similar RH. These PM evolution characteristics observed
here highlight the importance of meteorological conditions for driving
particulate pollution (Li et al., 2017) and imply different formation
mechanisms of PM pollution during different seasons.
The relationship between sulfur oxidation ratio (FSO4) and
Ox concentration during late summer (a), autumn (b) and early winter (c) and the relationship between FSO4 and ALWC at RH > 65 % and Ox < 60 ppb during late summer (d), autumn (e) and
early winter (f).
Photochemical oxidation and aqueous-phase chemistry
To further elucidate the formation mechanisms of secondary aerosol, the
sulfur oxidation ratio (FSO4) (Sun et al., 2006) was calculated
according to Eq. (1):
FSO4=n[SO4]nSO4+n[SO2],
where n[SO4] and n[SO2] are the molar concentrations of
sulfate and SO2, respectively. Figure 6a–c plots FSO4 versus Ox
(=O3 + NO2) concentration which is a tracer to indicate
photochemical processing during late summer, autumn and early winter,
respectively. During late summer, positive correlations between FSO4
and Ox with similar slopes and correlation coefficients in RH < 65 % and RH > 65 % were observed, suggesting the important
role of photochemical oxidation during late summer irrespective of the RH
range. During autumn and early winter, at RH < 65 % sulfate was
also formed by photochemical oxidation because of the positive correlations
between FSO4 and Ox, while there was no correlation between
FSO4 and Ox at RH > 65 %, indicating that other
processes (e.g., aqueous-phase reactions) may contribute to the sulfate
formation in high-RH conditions. This is supported by the relationships
between FSO4 and ALWC at RH > 65 % and low atmospheric
oxidative capacities of Ox < 60 ppb (Fig. 6d–f). There were
positive correlations between FSO4 and ALWC during all three seasons in
high-RH conditions, indicating the contribution of aqueous-phase processing
to the sulfate formation. Meanwhile, we found that FSO4 was up to
∼0.6 with Ox while it was only up to ∼0.3 with
ALWC during late summer, suggesting the more important role of photochemical
oxidation for the sulfate formation during late summer. By contrast,
during early winter the increase in FSO4 with ALWC (from
∼0.05 to ∼0.5) was more efficient than that
with Ox (from ∼0.05 to ∼0.2), indicating
that aqueous-phase reactions were more responsible during early winter.
During autumn, FSO4 was up to about 0.4–0.5 both with Ox and ALWC,
suggesting that for sulfate formation during autumn both photochemical
oxidation and aqueous-phase reaction had important contributions. It should
be noted that at the typical atmospheric level of OH radicals, the lifetime
of SO2 from the reaction with OH is about 1 week (Seinfeld and Pandis,
2016; Zhang et al., 2015), and the bivariate polar plots of Ox in late
summer also showed a regional source (Fig. S6). Thus, SO2 oxidation
into sulfate may proceed during long-range transport in late summer (Rodhe
et al., 1981), consistent with our results in Fig. 3.
Variations in the mass fractions and mass concentrations of LSOA and RSOA as functions of ALWC or Ox in (a, d) late summer, (b, e) autumn
and (c, f) early winter. The data were binned according to the ALWC
concentration (5 µg m-3 increment in late summer, 50 µg m-3 increment in autumn and early winter) and Ox concentration (20 ppb increment in late summer, 10 ppb increment in autumn and early winter).
We further investigated the formation mechanisms of SOA during different
seasons. Figure 7 shows the effects of ALWC and Ox on the mass
concentrations and mass fractions of LSOA and RSOA during different seasons.
During late summer, the ALWC ranged from 2.1 to 53.6 µg m-3; both the mass concentrations of LSOA and RSOA increased
as ALWC increased when ALWC was higher than ∼25–35 µg m-3. In comparison, the ALWC concentrations during autumn and early
winter were much higher than that during late summer, and the increasing
trends of SOA were much obvious than that during late summer. The mass
concentrations of LSOA and RSOA increased from 7.3 to
33.3 and 3.5 to 11.5 µg m-3, respectively, when ALWC increased from 12.3 to 519.6 µg m-3, and the mass fraction of SOA increased
from 30 % to 38 % during autumn. In comparison, during winter, the mass
concentration of LSOA increased from 5.6 to 37.9 µg m-3 when ALWC increased from 9.7 to 436.6 µg m-3 with the mass fraction of LSOA increasing from 37 % to
42 %. RSOA displayed no clear increase trend with ALWC as it played a
minor contribution during early winter. These variations indicated the influence of aqueous-phase processes on the formation of SOA especially
during autumn and early winter with higher ALWC. Variations in the mass
concentrations and fractions of LSOA and RSOA as functions of Ox during
different seasons are also shown in Fig. 7. The mass concentrations of SOA
increased clearly with the increase in Ox concentration during all
three seasons, and the mass fraction of SOA also increased from 64 % to
76 % during late summer and increased from 59 % to 80 % during autumn
as Ox increased from 30 to 120 ppb. Similar to that of ALWC, the
increasing rates of LSOA and RSOA as functions of Ox were substantially
different among different seasons. In late summer, both LSOA and RSOA
presented linear increases with the increase in Ox. As a comparison,
LSOA showed higher increase rates with Ox than that of RSOA during
autumn and early winter as LSOA played a dominant role in the haze formation
during autumn and early winter. These results clearly indicate that both
photochemical processing and aqueous-phase reactions played important roles
in the formation of SOA during all three seasons.
Conclusions
In this study, an ACSM combined with an aethalometer were applied for
real-time measurements of PM1 species (organics, sulfate, nitrate,
ammonium, chloride and BC) from 15 August to 4 December 2015 in Beijing.
The average mass concentration of PM1 varied from 21.6
in late summer to 64.3 µg m-3 in early winter, indicating that PM
pollution was very serious in wintertime due to enhanced emissions, low
temperatures and stagnant meteorological conditions. OA contributed the
major fraction (46 %–64 %) to PM1 mass during all three seasons,
followed by nitrate (6 %–22 %) or sulfate (11 %–15 %). Regarding the
OA factors, three primary OAs (HOA, COA and CCOA) and two secondary OAs (LSOA
and RSOA) were resolved. Seasonal variations suggested that SOA dominated OA
during late summer and autumn, whereas POA played a more important role in
early winter due to the dramatically increased fraction of CCOA in the heating
season (from 5 % in late summer to 26 % in early winter). A higher RSOA
fraction (48 % of OA) in late summer and higher LSOA fractions in autumn
(43 % of OA) and early winter (41 % of OA) and different correlations
between RSOA and sulfate were found in our study, suggesting that regional
transport played a more important role in SOA and sulfate sources in late
summer, while local formation was important in winter due to heating.
Haze evolution and formation mechanisms of PM1 were also discussed.
Results suggested that secondary aerosol species including SIA (sulfate,
nitrate and ammonium) and SOA (LSOA and RSOA) dominated PM1 species
during all three seasons with fractions of 72 %, 71 % and 66 % during
late summer, autumn and early winter, respectively. SOA had a dominant
contribution to PM1 in late summer, while SIA played a key role during
autumn and early winter. Higher contributions of secondary species (SIA and
SOA) further observed in pollution episodes emphasized the importance of the
secondary formation processes in haze pollution in Beijing. We explored the
formation mechanisms of secondary aerosol during different seasons and found
that both photochemical processing and aqueous-phase processing played
important roles in SOA formation during all three seasons. In comparison,
for sulfate formation, both photochemical oxidation and aqueous-phase
reaction had contributions during autumn, while photooxidation played a more
important role during late summer and aqueous-phase reactions were more
responsible during early winter.
Data availability
Raw data used in this study are archived at the Institute of Earth
Environment, Chinese Academy of Sciences, and are available on request by
contacting the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-10319-2019-supplement.
Author contributions
RJH and JC designed the study. JD, YG, YW and HZ performed the online
measurements. Data analysis and source apportionment were done by JD, RJH
and CL. JD and RJH wrote the paper. JD and RJH interpreted data and
prepared display items. All authors commented on and discussed the
paper.
Competing interests
Douglas R. Worsnop is an employee of Aerodyne Research, Inc. (ARI), and an ACSM produced by Aerodyne was used in this study.
Special issue statement
This article is part of the special issue “Regional transport and transformation of air pollution in eastern China”. It is not associated with a conference.
Acknowledgements
This work was supported by the National Natural Science Foundation of China
(NSFC) under grant no. 91644219 and no. 41877408 and the National Key
Research and Development Program of China (no. 2017YFC0212701). The authors acknowledge financial support from the Cross Innovative Team fund from the State Key Laboratory of Loess and Quaternary Geology (SKLLQG) (no. SKLLQGTD1801)
Financial support
This research has been supported by the National Natural Science Foundation of China (NSFC) (grant nos. 91644219 and 41877408) and the National Key Research and Development Program of China (grant no. 2017YFC0212701) and the Cross Innovative Team fund from SKLLQG (grant no. SKLLQGTD1801).
Review statement
This paper was edited by Luisa Molina and reviewed by two anonymous referees.
ReferencesAllan, J. D., Williams, P. I., Morgan, W. T., Martin, C. L., Flynn, M. J., Lee, J., Nemitz, E., Phillips, G. J., Gallagher, M. W., and Coe, H.: Contributions from transport, solid fuel burning and cooking to primary organic aerosols in two UK cities, Atmos. Chem. Phys., 10, 647–668, 10.5194/acp-10-647-2010, 2010.An, Z., Huang, R.-J., Zhang, R., Tie, X., Li, G., Cao, J., Zhou, W., Shi,
Z., Han, Y., Gu, Z., and Ji, Y.: Severe haze in northern China: A synergy of
anthropogenic emissions and atmospheric processes, P. Natl. Acad. Sci. USA,
116, 8657–8666, 10.1073/pnas.1900125116, 2019.Canagaratna, M. R., Jayne, J. T., Jimenez, J. L., Allan, J. D., Alfarra, M.
R., Zhang, Q., Onasch, T. B., Drewnick, F., Coe, H., Middlebrook, A., Delia,
A., Williams, L. R., Trimborn, A. M., Northway, M. J., DeCarlo, P. F., Kolb,
C. E., Davidovits, P., and Worsnop, D. R.: Chemical and microphysical
characterization of ambient aerosols with the Aerodyne aerosol mass
spectrometer, Mass Spectrom. Rev., 26, 185–222,
10.1002/mas.20115, 2007.Canonaco, F., Crippa, M., Slowik, J. G., Baltensperger, U., and Prévôt, A. S. H.: SoFi, an IGOR-based interface for the efficient use of the generalized multilinear engine (ME-2) for the source apportionment: ME-2 application to aerosol mass spectrometer data, Atmos. Meas. Tech., 6, 3649–3661, 10.5194/amt-6-3649-2013, 2013.Cao, J. J., Shen, Z. X., Chow, J. C., Watson, J. G., Lee, S. C., Tie, X. X.,
Ho, K. F., Wang, G. H., and Han, Y. M.: Winter and summer PM2.5
chemical compositions in fourteen Chinese cities, J. Air Waste Manage., 62, 1214–1226, 10.1080/10962247.2012.701193,
2012a.Cao, J. J., Wang, Q., Chow, J. C., Watson, J. G., Tie, X., Shen, Z., Wang,
P., and An, Z.: Impacts of aerosol compositions on visibility impairment in
Xi'an, China, Atmos. Environ., 59, 559–566,
10.1016/j.atmosenv.2012.05.036, 2012b.Chan, C. K. and Yao, X.: Air pollution in mega cities in China, Atmos.
Environ., 42, 1–42, 10.1016/j.atmosenv.2007.09.003,
2008.Crippa, M., DeCarlo, P. F., Slowik, J. G., Mohr, C., Heringa, M. F., Chirico, R., Poulain, L., Freutel, F., Sciare, J., Cozic, J., Di Marco, C. F., Elsasser, M., Nicolas, J. B., Marchand, N., Abidi, E., Wiedensohler, A., Drewnick, F., Schneider, J., Borrmann, S., Nemitz, E., Zimmermann, R., Jaffrezo, J.-L., Prévôt, A. S. H., and Baltensperger, U.: Wintertime aerosol chemical composition and source apportionment of the organic fraction in the metropolitan area of Paris, Atmos. Chem. Phys., 13, 961–981, 10.5194/acp-13-961-2013, 2013.Crippa, M., Canonaco, F., Lanz, V. A., Äijälä, M., Allan, J. D., Carbone, S., Capes, G., Ceburnis, D., Dall'Osto, M., Day, D. A., DeCarlo, P. F., Ehn, M., Eriksson, A., Freney, E., Hildebrandt Ruiz, L., Hillamo, R., Jimenez, J. L., Junninen, H., Kiendler-Scharr, A., Kortelainen, A.-M., Kulmala, M., Laaksonen, A., Mensah, A. A., Mohr, C., Nemitz, E., O'Dowd, C., Ovadnevaite, J., Pandis, S. N., Petäjä, T., Poulain, L., Saarikoski, S., Sellegri, K., Swietlicki, E., Tiitta, P., Worsnop, D. R., Baltensperger, U., and Prévôt, A. S. H.: Organic aerosol components derived from 25 AMS data sets across Europe using a consistent ME-2 based source apportionment approach, Atmos. Chem. Phys., 14, 6159–6176, 10.5194/acp-14-6159-2014, 2014.Dall'Osto, M., Ovadnevaite, J., Ceburnis, D., Martin, D., Healy, R. M., O'Connor, I. P., Kourtchev, I., Sodeau, J. R., Wenger, J. C., and O'Dowd, C.: Characterization of urban aerosol in Cork city (Ireland) using aerosol mass spectrometry, Atmos. Chem. Phys., 13, 4997–5015, 10.5194/acp-13-4997-2013, 2013.DeCarlo, P. F., Kimmel, J. R., Trimborn, A., Northway, M. J., Jayne, J. T.,
Aiken, A. C., Gonin, M., Fuhrer, K., Horvath, T., Docherty, K. S., Worsnop,
D. R., and Jimenez, J. L.: Field-deployable, high-resolution, time-of-flight
aerosol mass spectrometer, Anal. Chem., 78, 8281–8289,
10.1021/ac061249n, 2006.Elser, M., Huang, R.-J., Wolf, R., Slowik, J. G., Wang, Q., Canonaco, F., Li, G., Bozzetti, C., Daellenbach, K. R., Huang, Y., Zhang, R., Li, Z., Cao, J., Baltensperger, U., El-Haddad, I., and Prévôt, A. S. H.: New insights into PM2.5 chemical composition and sources in two major cities in China during extreme haze events using aerosol mass spectrometry, Atmos. Chem. Phys., 16, 3207–3225, 10.5194/acp-16-3207-2016, 2016.Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–NH4+–Na+–SO42-–NO3−–Cl–H2O aerosols, Atmos. Chem. Phys., 7, 4639–4659, 10.5194/acp-7-4639-2007, 2007.Fröhlich, R., Crenn, V., Setyan, A., Belis, C. A., Canonaco, F., Favez, O., Riffault, V., Slowik, J. G., Aas, W., Aijälä, M., Alastuey, A., Artiñano, B., Bonnaire, N., Bozzetti, C., Bressi, M., Carbone, C., Coz, E., Croteau, P. L., Cubison, M. J., Esser-Gietl, J. K., Green, D. C., Gros, V., Heikkinen, L., Herrmann, H., Jayne, J. T., Lunder, C. R., Minguillón, M. C., Močnik, G., O'Dowd, C. D., Ovadnevaite, J., Petralia, E., Poulain, L., Priestman, M., Ripoll, A., Sarda-Estève, R., Wiedensohler, A., Baltensperger, U., Sciare, J., and Prévôt, A. S. H.: ACTRIS ACSM intercomparison – Part 2: Intercomparison of ME-2 organic source apportionment results from 15 individual, co-located aerosol mass spectrometers, Atmos. Meas. Tech., 8, 2555–2576, 10.5194/amt-8-2555-2015, 2015.Guo, S., Hu, M., Zamora, M. L., Peng, J., Shang, D., Zheng, J., Du, Z., Wu,
Z., Shao, M., Zeng, L., Molina, M. J., and Zhang, R.: Elucidating severe
urban haze formation in China, P. Natl. Acad. Sci. USA, 111,
17373–17378, 10.1073/pnas.1419604111, 2014.He, K., Yang, F., Ma, Y., Zhang, Q., Yao, X., Chan, C. K., Cadle, S., Chan,
T., and Mulawa, P.: The characteristics of PM2.5 in Beijing, China, Atmos.
Environ., 35, 4959–4970, 10.1016/S1352-2310(01)00301-6,
2001.He, L.-Y., Lin, Y., Huang, X.-F., Guo, S., Xue, L., Su, Q., Hu, M., Luan, S.-J., and Zhang, Y.-H.: Characterization of high-resolution aerosol mass spectra of primary organic aerosol emissions from Chinese cooking and biomass burning, Atmos. Chem. Phys., 10, 11535–11543, 10.5194/acp-10-11535-2010, 2010.He, L.-Y., Huang, X.-F., Xue, L., Hu, M., Lin, Y., Zheng, J., Zhang, R., and
Zhang, Y.-H.: Submicron aerosol analysis and organic source apportionment in
an urban atmosphere in Pearl River Delta of China using high-resolution
aerosol mass spectrometry, J. Geophys. Res.-Atmos., 116, D12304,
10.1029/2010JD014566, 2011.Ho, K. F., Huang, R.-J., Kawamura, K., Tachibana, E., Lee, S. C., Ho, S. S. H., Zhu, T., and Tian, L.: Dicarboxylic acids, ketocarboxylic acids, α-dicarbonyls, fatty acids and benzoic acid in PM2.5 aerosol collected during CAREBeijing-2007: an effect of traffic restriction on air quality, Atmos. Chem. Phys., 15, 3111–3123, 10.5194/acp-15-3111-2015, 2015.Hu, W., Hu, M., Hu, W., Jimenez, J. L., Yuan, B., Chen, W., Wang, M., Wu,
Y., Chen, C., Wang, Z., Peng, J., Zeng, L., and Shao, M.: Chemical
composition, sources, and aging process of submicron aerosols in Beijing:
Contrast between summer and winter, J. Geophys. Res.-Atmos., 121,
1955–1977, 10.1002/2015JD024020, 2016.Hu, W. W., Hu, M., Yuan, B., Jimenez, J. L., Tang, Q., Peng, J. F., Hu, W., Shao, M., Wang, M., Zeng, L. M., Wu, Y. S., Gong, Z. H., Huang, X. F., and He, L. Y.: Insights on organic aerosol aging and the influence of coal combustion at a regional receptor site of central eastern China, Atmos. Chem. Phys., 13, 10095–10112, 10.5194/acp-13-10095-2013, 2013.
Huang, R. J., Zhang, Y. L., Bozzetti, C., Ho, K. F., Cao, J. J., Han, Y. M.,
Daellenbach, K. R., Slowik, J. G., Platt, S. M., Canonaco, F., Zotter, P.,
Wolf, R., Pieber, S. M., Bruns, E. A., Crippa, M., Ciarelli, G.,
Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J.,
Zimmermann, R., An, Z., Szidat, S., Baltensperger, U., Haddad, I. E., and
Prevot, A. S. H.: High secondary aerosol contribution to particulate pollution
during haze events in China, Nature, 514, 218–222, 2014.Huang, X.-F., He, L.-Y., Hu, M., Canagaratna, M. R., Sun, Y., Zhang, Q., Zhu, T., Xue, L., Zeng, L.-W., Liu, X.-G., Zhang, Y.-H., Jayne, J. T., Ng, N. L., and Worsnop, D. R.: Highly time-resolved chemical characterization of atmospheric submicron particles during 2008 Beijing Olympic Games using an Aerodyne High-Resolution Aerosol Mass Spectrometer, Atmos. Chem. Phys., 10, 8933–8945, 10.5194/acp-10-8933-2010, 2010.Huang, X.-F., He, L.-Y., Xue, L., Sun, T.-L., Zeng, L.-W., Gong, Z.-H., Hu, M., and Zhu, T.: Highly time-resolved chemical characterization of atmospheric fine particles during 2010 Shanghai World Expo, Atmos. Chem. Phys., 12, 4897–4907, 10.5194/acp-12-4897-2012, 2012.
IPCC: Climate Change 2013: The Physical Science Basis, Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, Cambridge University Press, Cambridge, UK and New
York, NY, USA, 2013.Jiang, Q., Sun, Y. L., Wang, Z., and Yin, Y.: Aerosol composition and sources during the Chinese Spring Festival: fireworks, secondary aerosol, and holiday effects, Atmos. Chem. Phys., 15, 6023–6034, 10.5194/acp-15-6023-2015, 2015.Kaufman, Y. J., Tanre, D., and Boucher, O.: A satellite view of aerosols in
the climate system, Nature, 419, 215–223, 10.1038/nature01091, 2002.Lanz, V. A., Alfarra, M. R., Baltensperger, U., Buchmann, B., Hueglin, C., and Prévôt, A. S. H.: Source apportionment of submicron organic aerosols at an urban site by factor analytical modelling of aerosol mass spectra, Atmos. Chem. Phys., 7, 1503–1522, 10.5194/acp-7-1503-2007, 2007.
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The
contribution of outdoor air pollution sources to premature mortality on a
global scale, Nature, 525, 367–371, 2015.Li, H., Zhang, Q., Zhang, Q., Chen, C., Wang, L., Wei, Z., Zhou, S., Parworth, C., Zheng, B., Canonaco, F., Prévôt, A. S. H., Chen, P., Zhang, H., Wallington, T. J., and He, K.: Wintertime aerosol chemistry and haze evolution in an extremely polluted city of the North China Plain: significant contribution from coal and biomass combustion, Atmos. Chem. Phys., 17, 4751–4768, 10.5194/acp-17-4751-2017, 2017.Li, Y. J., Sun, Y., Zhang, Q., Li, X., Li, M., Zhou, Z., and Chan, C. K.:
Real-time chemical characterization of atmospheric particulate matter in
China: A review, Atmos. Environ., 158, 270–304,
10.1016/j.atmosenv.2017.02.027, 2017.Ma, J., Chen, Y., Wang, W., Yan, P., Liu, H., Yang, S., Hu, Z., and
Lelieveld, J.: Strong air pollution causes widespread haze-clouds over
China, J. Geophys. Res., 115, D18204, 10.1029/2009JD013065,
2010.Middlebrook, A. M., Bahreini, R., Jimenez, J. L., and Canagaratna, M. R.:
Evaluation of composition-dependent collection efficiencies for the Aerodyne
aerosol mass spectrometer using field data, Aerosol Sci. Tech., 46,
258–271, 10.1080/02786826.2011.620041, 2012.Mohr, C., DeCarlo, P. F., Heringa, M. F., Chirico, R., Slowik, J. G., Richter, R., Reche, C., Alastuey, A., Querol, X., Seco, R., Peñuelas, J., Jiménez, J. L., Crippa, M., Zimmermann, R., Baltensperger, U., and Prévôt, A. S. H.: Identification and quantification of organic aerosol from cooking and other sources in Barcelona using aerosol mass spectrometer data, Atmos. Chem. Phys., 12, 1649–1665, 10.5194/acp-12-1649-2012, 2012.Molina, L. T., Kolb, C. E., de Foy, B., Lamb, B. K., Brune, W. H., Jimenez, J. L., Ramos-Villegas, R., Sarmiento, J., Paramo-Figueroa, V. H., Cardenas, B., Gutierrez-Avedoy, V., and Molina, M. J.: Air quality in North America's most populous city – overview of the MCMA-2003 campaign, Atmos. Chem. Phys., 7, 2447–2473, 10.5194/acp-7-2447-2007, 2007.Molina, L. T., Gallardo, L., Andrade, M., Baumgardner, D., Borbor-Cordova,
M., Borquez, R., Casassa, G., Cereceda-Balic, F., Dawidowski, L., Garreaud,
R., Huneeus, N., Lambert, F., McCarty, J. L., Mc Phee, J., Mena-Carrasco,
M., Raga, G. B., Schmitt, C., and Schwarz, J. P.: Pollution and its impacts
on the South American cryosphere, Earth's Future, 3, 345–369,
10.1002/2015EF000311, 2015.Ng, N. L., Herndon, S. C., Trimborn, A., Canagaratna, M. R., Croteau, P. L.,
Onasch, T. B., Sueoer, D., Worsnop, D. R., Zhang, Q., Sun, Y. L., and Jayne,
J. T.: An Aerosol Chemical Speciation Monitor (ACSM) for routine monitoring
of the composition and mass concentrations of ambient aerosol, Aerosol Sci.
Tech., 45, 770–784, 10.1080/02786826.2011.560211, 2011a.Ng, N. L., Canagaratna, M. R., Jimenez, J. L., Zhang, Q., Ulbrich, M., and
Worsnop, D. R.: Real-time methods for estimating organic component mass
concentrations from aerosol mass spectrometer data, Environ. Sci. Technol.,
45, 910–916, 10.1021/es102951k, 2011b.Paatero, P.: Least squares formulation of robust non-negative factor
analysis, Chemometr. Intell. Lab., 37, 23–35,
10.1016/S0169-7439(96)00044-5, 1997.Paatero, P. and Tapper, U.: Positive Matrix Factorization: A Non-Negative Factor Model with Optimal Utilization of Error Estimates of Data Values, Environmetrics, 5, 111–126, 10.1002/env.3170050203, 1994.
Pope, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito,
K., and Thurston, G. D.: Lung cancer, cardiopulmonary mortality, and
long-term exposure to fine particulate air pollution, J. Am. Med. Assoc.,
287, 1132–1141, 2002.
Rodhe, H., Crutzen, P., and Vanderpol, A.: Formation of Sulfuric and
Nitric-Acid in the Atmosphere during Long-Range Transport, Tellus, 33,
132–141, 1981.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics: from
air pollution to climate change, John Wiley & Sons, 2016.Streets, D. G., Fu, J. S., Jang, C. J., Hao, J., He, K., Tang, X., Zhang,
Y., Wang, Z., Li, Z., Zhang, Q., Wang, L., Wang, B., and Yu, C.: Air quality
during the 2008 Beijing Olympic games, Atmos. Environ., 41, 480–492,
10.1016/j.atmosenv.2006.08.046, 2007.Sun, J., Zhang, Q., Canagaratna, M. R., Zhang, Y., Ng, N. L., Sun, Y.,
Jayne, J. T., Zhang, X., Zhang, X., and Worsnop, D. R.: Highly time- and
size-resolved characterization of submicron aerosol particles in Beijing
using an Aerodyne Aerosol Mass Spectrometer, Atmos. Environ., 44, 131–140,
10.1016/j.atmosenv.2009.03.020, 2010.Sun, Y., Zhuang, G., Tang, A., Wang, Y., and An, Z.: Chemical
characteristics of PM2.5 and PM10 in haze-fog episodes in Beijing, Environ.
Sci. Technol., 40, 3148–3155, 10.1021/es051533g, 2006.Sun, Y., Jiang, Q., Wang, Z., Fu, P., Li, J., Yang, T., and Yin, Y.:
Investigation of the sources and evolution processes of severe haze
pollution in Beijing in January 2013, J. Geophys. Res.-Atmos., 119,
4380–4398, 10.1002/2014JD021641, 2014.Sun, Y., Du, W., Fu, P., Wang, Q., Li, J., Ge, X., Zhang, Q., Zhu, C., Ren, L., Xu, W., Zhao, J., Han, T., Worsnop, D. R., and Wang, Z.: Primary and secondary aerosols in Beijing in winter: sources, variations and processes, Atmos. Chem. Phys., 16, 8309–8329, 10.5194/acp-16-8309-2016, 2016.Sun, Y., Xu, W., Zhang, Q., Jiang, Q., Canonaco, F., Prévôt, A. S. H., Fu, P., Li, J., Jayne, J., Worsnop, D. R., and Wang, Z.: Source apportionment of organic aerosol from 2-year highly time-resolved measurements by an aerosol chemical speciation monitor in Beijing, China, Atmos. Chem. Phys., 18, 8469–8489, 10.5194/acp-18-8469-2018, 2018.Sun, Y. L., Wang, Z., Dong, H., Yang, T., Li, J., Pan, X., Chen, P., and
Jayne, J. T.: Characterization of summer organic and inorganic aerosols in
Beijing, China with an Aerosol Chemical Speciation Monitor, Atmos. Environ.,
51, 250–259, 10.1016/j.atmosenv.2012.01.013, 2012.Sun, Y. L., Wang, Z. F., Fu, P. Q., Yang, T., Jiang, Q., Dong, H. B., Li, J., and Jia, J. J.: Aerosol composition, sources and processes during wintertime in Beijing, China, Atmos. Chem. Phys., 13, 4577–4592, 10.5194/acp-13-4577-2013, 2013.Sun, Y. L., Wang, Z. F., Du, W., Zhang, Q., Wang, Q. Q., Fu, P. Q., Pan, X. L., Li, J., Jayne, J., and Worsnop, D. R.: Long-term real-time measurements of aerosol particle composition in Beijing, China: seasonal variations, meteorological effects, and source analysis, Atmos. Chem. Phys., 15, 10149–10165, 10.5194/acp-15-10149-2015, 2015.Takegawa, N., Miyakawa, T., Kuwata, M., Kondo, Y., Zhao, Y., Han, S., Kita,
K., Miyazaki, Y., Deng, Z., Xiao, R., Hu, M., van Pinxteren, D., Herrmann,
H., Hofzumahaus, A., Holland, F., Wahner, A., Blake, D. R., Sugimoto, N., and
Zhu, T.: Variability of submicron aerosol observed at a rural site in
Beijing in the summer of 2006, J. Geophys. Res., 114, D00G05,
10.1029/2008jd010857, 2009.Tao, M., Chen, L., Su, L., and Tao, J.: Satellite observation of regional
haze pollution over the North China Plain, J. Geophys. Res., 117, D12203,
10.1029/2012JD017915, 2012.Thornhill, D. A., Williams, A. E., Onasch, T. B., Wood, E., Herndon, S. C., Kolb, C. E., Knighton, W. B., Zavala, M., Molina, L. T., and Marr, L. C.: Application of positive matrix factorization to on-road measurements for source apportionment of diesel- and gasoline-powered vehicle emissions in Mexico City, Atmos. Chem. Phys., 10, 3629–3644, 10.5194/acp-10-3629-2010, 2010.Tian, S., Pan, Y., Liu, Z., Wen, T., and Wang, Y.: Size-resolved aerosol
chemical analysis of extreme haze pollution events during early 2013 in
urban Beijing, China, J. Hazard. Mater., 279, 452–460,
10.1016/j.jhazmat.2014.07.023, 2014.Ulbrich, I. M., Canagaratna, M. R., Zhang, Q., Worsnop, D. R., and Jimenez, J. L.: Interpretation of organic components from Positive Matrix Factorization of aerosol mass spectrometric data, Atmos. Chem. Phys., 9, 2891–2918, 10.5194/acp-9-2891-2009, 2009.Volkamer, R., Jimenez, J. L., Martini, F. S., Dzepina, K., Zhang, Q.,
Salcedo, D., Molina, L. T., Worsnop, D. R., and Molina,M. J.: Secondary
organic aerosol formation from anthropogenic air pollution: Rapid and higher
than expected, Geophys. Res. Lett., 33, L17811,
10.1029/2006GL026899, 2006.Wang, L., Liu, Z., Sun, Y., Ji, D., and Wang, Y.: Long-range transport and
regional sources of PM2.5 in Beijing based on long-term observations from
2005 to 2010, Atmos. Res., 157, 37–48,
10.1016/j.atmosres.2014.12.003, 2015.Wang, P., Cao, J. J., Shen, Z. X., Han, Y. M., Lee, S. C., Huang, Y., Zhu,
C. S., Wang, Q. Y., Xu, H. M., and Huang, R.-J.: Spatial and seasonal
variations of PM2.5: mass and species during 2010 in Xi'an, China, Sci.
Total Environ., 508, 477–487, 10.1016/j.scitotenv.2014.11.007, 2015.Wang, Q., Sun, Y., Jiang, Q., Du, W., Sun, C., Fu, P., and Wang, Z.:
Chemical composition of aerosol particles and light extinction apportionment
before and during the heating season in Beijing, China, J. Geophys. Res.-Atmos., 120, 12708–12722, 10.1002/2015JD023871, 2015.Wang, Y. C., Huang, R. J., Ni, H. Y., Chen, Y., Wang, Q. Y., Li, G. H., Tie,
X. X., Shen, Z. X., Huang, Y., Liu, S. X., Dong, W. M., Xue, P.,
Fröhlich, R., Canonaco, F., Elser, M., Daellenbach, K.R., Bozzetti, C.,
Haddad, EI., and Cao, J. J.: Chemical composition, sources and secondary
processes of aerosols in Baoji city of northwest China, Atmos. Environ.,
158, 128–137, 10.1016/j.atmosenv.2017.03.026, 2017.Xu, W. Q., Sun, Y. L., Chen, C., Du, W., Han, T. T., Wang, Q. Q., Fu, P. Q., Wang, Z. F., Zhao, X. J., Zhou, L. B., Ji, D. S., Wang, P. C., and Worsnop, D. R.: Aerosol composition, oxidation properties, and sources in Beijing: results from the 2014 Asia-Pacific Economic Cooperation summit study, Atmos. Chem. Phys., 15, 13681–13698, 10.5194/acp-15-13681-2015, 2015.
Xu, Z. J., Wen, T. X., Li, X. R., Wang, J. G., and Wang, Y. S.:
Characteristics of carbonaceous aerosols in Beijing based on two-year
observation, Atmos. Pollut. Res., 6, 202–208,
10.5094/APR.2015.024, 2015.Yang, Y., Liu, X., Qu., Y., Wang, J., An, J., Zhang, Y., and Zhang, F.:
Formation mechanism of continuous extreme haze episodes in the megacity
Beijing, China, in January 2013, Atmos. Res., 155, 192–203,
10.1016/j.atmosres.2014.11.023, 2015.Zhang, H., Wang, S., Hao, J., Wan, L., Jiang, J., Zhang, M., Mestl, H. E.
S., Alnes, L. W. H., Aunan, K., and Mellouki, A. W.: Chemical and size
characterization of particles emitted from the burning of coal and wood in
rural households in Guizhou, China, Atmos. Environ., 51, 94–99,
10.1016/j.atmosenv.2012.01.042, 2012.Zhang, R. Y., Wang, G. H., Guo, S., Zamora, M. L., Ying, Q., Lin, Y., Wang,
W. G., Hu, M., and Wang, Y.: Formation of urban fine particulate matter.
Chem. Rev., 115, 3803–3855,
10.1021/acs.chemrev.5b00067, 2015.Zhang, Y., Tang, L., Croteau, P. L., Favez, O., Sun, Y., Canagaratna, M. R., Wang, Z., Couvidat, F., Albinet, A., Zhang, H., Sciare, J., Prévôt, A. S. H., Jayne, J. T., and Worsnop, D. R.: Field characterization of the PM2.5 Aerosol Chemical Speciation Monitor: insights into the composition, sources, and processes of fine particles in eastern China, Atmos. Chem. Phys., 17, 14501–14517, 10.5194/acp-17-14501-2017, 2017.Zheng, G. J., Duan, F. K., Su, H., Ma, Y. L., Cheng, Y., Zheng, B., Zhang, Q., Huang, T., Kimoto, T., Chang, D., Pöschl, U., Cheng, Y. F., and He, K. B.: Exploring the severe winter haze in Beijing: the impact of synoptic weather, regional transport and heterogeneous reactions, Atmos. Chem. Phys., 15, 2969–2983, 10.5194/acp-15-2969-2015, 2015.Zhao, J., Du, W., Zhang, Y., Wang, Q., Chen, C., Xu, W., Han, T., Wang, Y., Fu, P., Wang, Z., Li, Z., and Sun, Y.: Insights into aerosol chemistry during the 2015 China Victory Day parade: results from simultaneous measurements at ground level and 260 m in Beijing, Atmos. Chem. Phys., 17, 3215–3232, 10.5194/acp-17-3215-2017, 2017.Zhao, X. J., Zhao, P. S., Xu, J., Meng,, W., Pu, W. W., Dong, F., He, D., and Shi, Q. F.: Analysis of a winter regional haze event and its formation mechanism in the North China Plain, Atmos. Chem. Phys., 13, 5685–5696, 10.5194/acp-13-5685-2013, 2013.