Beijing has suffered from heavy local emissions as well as regional
transport of air pollutants, resulting in severe atmospheric fine-particle
(PM2.5) pollution. This study developed a combined method to
investigate source types of PM2.5 and its source regions during winter
2016 in Beijing, which include the receptor model (positive matrix
factorization, PMF), footprint and an air quality model. The PMF model was
performed with high-time-resolution measurements of trace elements, water
soluble ions, organic carbon and elemental carbon using online instruments
during the wintertime campaign of the Air Pollution and Human Health in a Chinese Megacity – Beijing
(APHH-Beijing) program in 2016. Source types and their contributions
estimated by PMF model using online measurements were linked with source
regions identified by the footprint model, and the regional transport
contribution was estimated by an air quality model (the Nested Air Quality
Prediction Model System, NAQPMS) to analyze the specific sources and source
regions during haze episodes. Our results show that secondary and biomass-burning sources were dominated by regional transport, while the coal
combustion source increased with local contribution, suggesting that strict
control strategies for local coal combustion in Beijing and a reduction of
biomass-burning and gaseous precursor emissions in surrounding areas were
essential to improve air quality in Beijing. The combination of PMF with
footprint results revealed that secondary sources were mainly associated
with southern footprints (53 %). The northern footprint was characterized
by a high dust source contribution (11 %), while industrial sources
increased with the eastern footprint (10 %). The results demonstrated the
power of combining receptor model-based source apportionment with other
models in understanding the formation of haze episodes and identifying
specific sources from different source regions affecting air quality in
Beijing.
Introduction
Presently, haze in China has the characteristics of high frequency and long
duration on a regional scale and has influenced public life and human
health (Xie et al., 2016). High concentrations of fine particulates, which
can significantly reduce atmospheric visibility, are one of the main factors
in the formation of haze episodes (Y. Sun et al., 2016; Watson et al., 2002;
Yang et al., 2015). Previous studies have found that PM2.5 can be
emitted from various sources, including residential coal combustion, biomass
burning, traffic-related sources, industrial sources and dust (Gao et al.,
2016; Kotchenruther et al., 2016; Taghvaee et al., 2018; Watson et al.,
2001; Zong et al., 2016). Therefore, it is important to have a better
understanding of the major source types and their contribution to PM2.5
in order to formulate effective science-based policies and regulations.
As the capital of China, Beijing has suffered from heavy emissions from
various sources, resulting in severe PM2.5 pollution (D. Li et al., 2017;
Lv et al., 2016). The source apportionment of PM2.5 in Beijing has
received great attention in recent years, which is mostly based on receptor
models (Gao et al., 2016; Y. Li et al., 2017; Lv et al., 2016; Song et al.,
2006; Yang et al., 2016). Receptor models, including the chemical mass
balance model (CMB) and positive matrix factorization model (PMF), are the
most commonly used methods of source apportionment in China and are
implemented by application of mathematical methods to measurements of
chemical composition of fine particles at receptor sites (Cooper et al.,
1980; Gao et al., 2016; Lv et al., 2016; Zheng et al., 2005). The receptor
model can identify and quantify the contribution of multiple source types
based on in situ measurements and specific source tracers. Gao et al. (2016)
employed two receptor models, PMF and Multilinear Engine 2 (ME2), to conduct
a high-time-resolution source apportionment of PM2.5 in summer in
Beijing. The results showed that PMF and ME2 corresponded well with each
other, and a secondary sources were predominant in Beijing (38 %–39 %). Similar
source apportionment results were reported by in Peng et al. (2016) with
secondary sources contributing 35 %–40 %. Y. Sun et al. (2016) used online
instruments and PMF to investigate the rapid evolution of a severe haze
episode in winter in Beijing and showed the variation of chemical components
during four stages of haze. By conducting receptor models based on high-time-resolution online measurements, the source types and source contributions in
Beijing have been analyzed in previous studies (Gao et al., 2016; Peng et
al., 2016; Song et al., 2006). However, these studies still have limitations
in that source apportionment based on receptor models are only restricted to
one or several receptor sites without information about detailed source
regions or the local and regional source contributions.
Previous studies have indicated that PM2.5 pollution in Beijing has
been significantly influenced by regional transport and meteorological
conditions (Han et al., 2015; D. Li et al., 2017; Zhao et al., 2013). With the
development of the function of source apportionment in air quality models,
source regions and relative contributions to the receptor site can be
qualitatively estimated, based on emission inventory of pollution sources and
meteorological fields (Burr et al., 2011; Kwok et al., 2013; Zhang et al.,
2015). Li et al. (2016) found that regional transport highly contributed to
the rapid increase stage of PM2.5, with the transport height ranging
from 200 to 700 m above ground level with application of the Nested Air
Quality Prediction Model System (NAQPMS). Han and Zhang (2018) used a regional
air quality modeling system coupling with ISAM (integrated source
apportionment method) and found that air pollutants derived from Hebei and
Shandong provinces were major contributors to PM2.5 in Beijing, with
contributions up to 25 % and 10 %, respectively. The air quality model
has advantages of analyzing spatial distribution and regional transport of
pollutants, but it also has large uncertainties due to the emission inventory,
boundary layer meteorological processes and complex atmospheric chemical
processes.
Due to the importance of the regional transport contribution to PM2.5
in Beijing, the limitations of receptor models cannot be ignored. The source
types and source contribution derived from receptor models can be combined
with the contribution and direction of regional transport derived from
chemical transport models. In this study, we employed the receptor model
(PMF), the air quality model (NAQPMS) and a footprint model simultaneously
based on high-time-resolution online measurement data to investigate sources
and regional transport of PM2.5 in Beijing during November to December
in 2016, as part of the Air Pollution and Human Health in a Chinese Megacity (APHH) campaign. The
goal of the study is to link the contribution of different sources by PMF
with the source regions by footprint and the regional transport
contribution by NAQPMS. The combination of multiple models gives greater
power to identify specific sources and source regions.
Materials and methodsOnline measurements of PM<inline-formula><mml:math id="M14" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
Online sampling of PM2.5 was conducted from November to December 2016 in winter, which was within the heating period of Beijing. The sampler
was operated at the Peking University monitoring site (PKU, 39∘59′21′′ N, 116∘18′25′′ E) in the northwestern part of
Beijing city. There are no obvious emission sources locally, except two
major roads (150 m to the east and 200 m to the south). Situated in a mixed
district of teaching, residential and commercial areas, the sampling site
is representative of the Beijing urban area (Liu et al., 2018; Yan et al.,
2015). The sampling site is located on the sixth floor of a teaching
building within PKU. The inlet of the instrument is about 20 m above the
ground.
Online PM2.5 mass concentrations were continuously measured using a
tapered element oscillating microbalance (TEOM 1405F, Thermo Fisher
Scientific Inc.). Organic carbon (OC) and elemental carbon (EC) were
simultaneously monitored by a semi-continuous OCEC carbon aerosol analyzer
(Sunset Laboratory Inc.) with 1 h time resolution. The Sunset OC/EC analyzer
uses a modified NIOSH 5040 thermal–optical protocol as its default protocol,
which produces a relatively reliable determination of OC, EC and the OCEC
split (Bauer et al., 2009). More detailed information can be found in
Bauer et al. (2009).
An in situ Gas and Aerosol Compositions monitor (IGAC, Model S-611,
Fortelice International Co. Ltd.), which collects both gases and
particles simultaneously, was applied to measure water-soluble ions online
with 1 h time resolution in this study. A detailed description of IGAC can
be found in Young et al. (2016). Briefly, IGAC was composed of three major
units, including a wet annular denuder (WAD), to collect gases into aqueous
solution, a scrub and impact aerosol collector (SCI) to collect particles
into solution and a sample analysis unit comprised of two ion chromatographs
(DionexICS-1000) for analyzing anions and cations (IC). Ambient air was
drawn through a PM10 inlet followed by a PM2.5 cyclone at a flow
rate of 16.7 L min-1, and then gases and PM2.5 were separately
collected by WAD and SCI. Both gaseous and aerosol samples were injected
into 10 mL glass syringes which were connected to the IC for analysis
(30 min time resolution for each sample). The concentrations of eight
water-soluble inorganic ions (e.g., NH4+, Na+, K+,
Ca2+, Mg2+, SO42-, NO3- and Cl-) in the
fine particles were measured.
Twenty-three trace elements in PM2.5 were measured by an Xact 625
Ambient Metal Monitor (Cooper Environmental Services LLC, USA) with a 1 h time
resolution. Among them 12 elements (e.g., K, Ca, Ba, Cr, Mn, Fe, Cu, Ni,
Zn, As, Se, Pb) were selected for further analysis, while other trace
elements (such as V, Co, Tl) were not used here due to the low
concentrations (below the method detection limit). The ambient air was
sampled on a Teflon filter tape inside the instrument through a PM2.5
cyclone inlet at a constant flow rate of 16.7 L min-1, and then the
sample was automatically analyzed by nondestructive energy-dispersive X-ray
fluorescence (XRF) to determine the mass of the species. This instrument has
been documented with Environmental Technology Verification (ETV) and certified
by the US Environment Protection Agency (EPA, 2012). The detection limit of
each species measured by the online instruments can be seen in Table S1 in the Supplement.
Strict quality assurance (QA) and quality control (QC) protocols for online
instruments were performed during the whole sampling period. For IGAC, the
internal standard (LiBr) was added continuously to each sample and analyzed
by the IC system during the analysis to check the stability of the IGAC
instrument. During the sampling period, the mean concentrations of Li+
and Br- were within the range of 3 standard deviations,
suggesting a stable condition of the IGAC (see Fig. S1 in the Supplement). As shown in Fig. S2, the slope of the linear fitting
between the anions and cations was 0.93,
and R2 was 0.96. As for the OC/EC analyzer, external standard
calibration using the stock sucrose solution was conducted before operation
to calibrate carbon analysis. For the Xact, a Pd rod was used as an automatic
internal quality control to check the performance of the instrument on a
daily basis (see Fig. S3). Additionally, a QA energy calibration test and QA
energy level test were performed for half hour after midnight every day to
monitor any possible shift and instability of the XRF. During our field
campaign, the Xact remained stable and reliable.
Chemical closure has been done between the measured and reconstructed
PM2.5. Organic matter (OM) was calculated as OM =1.6× OC
(Turpin and Lim, 2001). Mineral species was calculated as mineral =1.89 Al
+2.14 Si +1.4 Ca +1.43 Fe +1.66 Mg (Zhang et al., 2003). The
concentrations of Al, Si, Fe and Mg were calculated by the concentration of
Ca and the composition of urban soils of Beijing: Al =1.7 Ca, Si =7.3 Ca,
Fe =0.7 Ca and Mg =0.3 Ca (An et al., 2016). “Others” were calculated by
subtracting OM, EC, mineral and secondary inorganic aerosol (SIA, including
SO42-, NO3-, NH4+) concentration from total
PM2.5 concentration. The correlation of measured and reconstructed
PM2.5 mass can be seen in Fig. S6 with R2=0.915.
MethodologyPositive matrix factorization (PMF)
To qualitatively and quantitatively identify sources of PM2.5 and
estimate the associated contributions, the USEPA PMF 5.0 model was adopted
in this study. The principle and detailed information of this model can be
found in Paterson et al. (1999) and the EPA 5.0 Fundamentals and User Guide.
Factor contributions and profiles were derived by minimizing the objective
function Q in the PMF model, which was determined as follows (Norris et al.,
2014; Paatero and Tapper, 2010; Paatero et al., 2014; Paatero, 1997):
Q=∑i=1n∑j=1mxij-∑k=1pgikfkjuij2.
Data values below the MDL were substituted with MDL/2. Missing data values
were substituted with median concentrations. If the concentration was less
than or equal to the MDL, the uncertainty (Unc) was calculated using a fixed
fraction of the MDL:
Unc=56MDL.
If the concentration was greater than the MDL provided, the calculation was
based on the following equation:
Unc=error fraction×concentration2+0.5×MDL2.
In total, 19 chemical components were used in the PMF model, including
OC, EC, Cl-, SO42-, NO3-, Na+, NH4+,
K, Ca, Ba, Cr, Mn, Fe, Cu, Ni, Zn, As, Se and Pb. To determine the optimal
number of source factors, a string of effective tests, in which the number of factors
was from four to nine, was carried out. The resulting Q parameters
are shown in Fig. S4. Obviously, the lowest QRobust value was
(13087) at six factors when moving from four to nine factors. Although
Qexpected has been decreasing in the process, Q/Qexpected shared
similar variation with QRobust showing the lowest value at six factors
(see Fig. S4). Bootstrapping (BS), displacement (DISP) and bootstrapping
with displacement (BS-DISP) were conducted to analyze the uncertainty of the
PMF model at six factors. The results were stable with all factors mapped in
BS in 100 % and no swaps with DISP and all BS-DISP runs, indicating a
convincing source apportionment result (see Table S2).
Footprint analysis model
The footprint model developed by Peking University was used to simulate the
potential source region of air pollution. The footprint is a transfer
function in a diffusion problem linking the source and the measurement
result at a point (receptor) (Pasquill and Smith, 1977). That is,
cr=∫RQr+r′fr,r′dr′,
where c is the measured concentration at a spatial location r,
Q is the source strength with spatial location r+r′, f is the footprint or the transfer function and R is the
integration domain. The footprint links point measurements (receptors) in
the atmosphere to upstream forcings, in which turbulent dispersion plays a
central role. The Lagrangian stochastic (LS) particle models was used to
calculate the footprint function (Cai et al., 2007; Leclerc and Thurtell,
1990; Kurbanmuradov and Sabelfeld, 2000).
The meteorological data used to drive the footprint model were provided by the
Weather Research and Forecasting model (WRF-ARWv3.6.1)
(http://www.wrf-model.org/, last access: 15 January 2018), initialized using the Final Analysis (FNL) data
from the United States National Centers for Environmental Prediction (NCEP).
Two nested domains were used in this study with horizontal resolutions of 15
and 5 km and 28 vertical levels. The simulation period was from 1 November
to 31 December 2016, with a 12 h spinup time before the start for each 48 h simulation. The domain of the footprint model is the same as the domain 2
in WRF which covers the North China Plain (500×600 km), and the
horizontal resolution is 2.5×2.5 km. Every hour, 5000 particles
were released 10 m above the ground at the center of Beijing, and then each
particle was tracked backward in time for 48 h. The residence time of
all particles 0–100 m above the ground was recorded to obtain the
footprint. This model has undergone rigorous theoretical discussion and
verification and more detailed principle and calculation methods of the
model can be found in Cai et al. (2007).
The Nested Air Quality Prediction Model System (NAQPMS)
In this study, the NAQPMS model was applied to analyze the contribution of
local emissions and regional transport to PM2.5 in winter in Beijing.
NAQPMS is a 3-D Eulerian chemical transport model with terrain-following
coordinates, developed by the Institute of Atmospheric Physics, Chinese
Academy of Sciences (IAP/CAS) and has been validated by the Ministry of
Environmental Protection of China (CMEP, 2013). The main modules in the
model include horizontal and vertical advection and diffusion, dry and wet
deposition, and gaseous, aqueous, aerosol and heterogeneous chemistry (Li et
al., 2007; J. Li et al., 2017). A more detailed description of the model can
be found in Li et al. (2008, 2014, 2016; J. Li et al., 2017).
Three nested model domains were used in this study. The coarsest domain (D1)
covered most of China and East Asia with a 27 km resolution. The second
domain (D2) included most anthropogenic emissions within the North China
Plain with a 9 km resolution. The innermost domain (D3) covered the
Beijing–Tianjin–Hebei region at a 3 km resolution. The first level of model
above the surface is 30 m in height, and the average vertical layer spacing
between 30 m and 1 km is around 100 m. The MIX
(http://www.meicmodel.org/dataset-mix.html, last access: 12 November 2018) anthropogenic emission inventory
was used (M. Li et al., 2017), with the original resolution of 0.25∘
(about 25 km at midlatitudes) and the year of 2010. The NAQPMS
meteorological fields were provided by the Weather Research and Forecasting
model (WRF-ARWv3.6.1) (http://www.wrf-model.org/) driven by the NCEP FNL data. The
simulation was conducted from 10 November to 15 December 2016.
The combination of multiple models
The footprint model was used to provide the direction of source regions,
while the NAQPMS model was run to calculate the contribution of local
emission and regional transport. To verify the consistency between the two
models, the footprint with a time resolution of 6 h was divided into
four types (local, south, north and east) according to the direction of
potential source regions, and average local contributions of different types
obtained from NAQPMS were calculated (See Table S3). Based on the input data
availability, the footprint simulation was performed from 1 to 31 December,
while the NAQPMS model analysis was carried out from 10 November to
15 December. Therefore, we use the data from 1 to 15 December
for the consistency test of NAQPMS and the footprint model. A
typical example of different types of footprint can be seen in Fig. S5. The
average local contribution estimated by NAQPMS was highest for the local
footprint (85 %) and lower for southern (68 %), northern (63 %) and eastern footprints (66 %). The results of the two models correlated well with each
other.
Based on online measurements of PM2.5 species including specific source
tracers, the receptor model (PMF) can be used to obtain precise source
apportionment results but with no information on regional transport.
Therefore, the footprint and NAQPMS model were simultaneously conducted and
combined with the PMF model to link the source type and contribution to
PM2.5 in Beijing by receptor models with different source regions.
Chemical composition of PM2.5 during sampling period (red for
SO42-, blue for NO3-, yellow for NH4+, green
for OM, black for EC, pink for mineral and grey for others). Dates are mm/dd and are from 7 November to 22 December.
Results and discussionMass concentration and chemical composition of PM<inline-formula><mml:math id="M89" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula>
Temporal variation in the chemical composition of PM2.5 during the field
campaign is shown in Fig. 1. The statistical summary of identified species
of PM2.5 in the entire sampling period was summarized in Table S1.
Figure 1 shows that SIA and OM were the predominant PM2.5 components in
winter in Beijing, accounting for 57 % and 24 % of total PM2.5 mass, respectively. The average concentration of OC was
20.8±17.0µg m-3, and the average concentration of EC was 5.6±4.4µg m-3. The OC/EC
ratio is often used to indicate the contribution
of primary emission sources and secondary organic aerosols (SOAs) (Lim and Turpin, 2002;
Zheng et al., 2014). In this study, the OC/EC ratio ranged from
1.36 to 7.92 with an average ratio of 3.91±0.91, which was lower than
that in the winter of Beijing in 2013 (5.73±2.47) (Yan et al., 2015).
SO42- is the predominant ion in SIA with an average concentration
of 23.5±20.8µg m-3, which was similar to that of
NO3- (22.0±23.3µg m-3). The concentration of
elemental components ranked from high to low as
K > Fe > Ca > Zn > Pb > Mn > Ba > Cu > As > Cr > Se > Ni, with K
contributing 2 % to PM2.5. In general, the large contribution of SIA
and OM, as well as the high OC/EC ratio, indicated the importance of secondary
formation in winter in Beijing (Y. Sun et al., 2016), while the high
concentration of species like SO42- and K suggested a significant
contribution of combustion sources including coal combustion and biomass
burning to PM2.5 (Achad et al., 2018; Chen et al., 2017; H. Li et al.,
2017).
Variation in (a) chemical composition and (b) elemental species
with PM2.5 concentration. The white bars represent the frequency of
PM2.5 concentration.
Figure 2 shows the large differences in chemical composition of PM2.5
concentration between nonhaze and haze episodes. The average concentration
of PM2.5 and identified species in different haze and nonhaze periods
were summarized in Table S4. Under low PM2.5 concentration (< 50 µg m-3), SO42- was one of the major components of
PM2.5 with a contribution of around 24 %. When PM2.5 was
50–150 µg m-3, OM was the dominant composition
(about 38 %). When PM2.5 was greater than 150 µg m-3, the
contribution of SIA increased with concentration level (up to 55 %).
The contribution of mineral components decreased from 8 % to 2 % when
PM2.5 concentration increased from below 50 to over
250 µg m-3. The proportion of K, Pb, As and Se, which were tracers
of biomass burning and coal combustion (Achad et al., 2018; Chen et al.,
2017; Vejahati et al., 2010), increased with PM2.5 concentration.
However, the contributions of Ca, Ba, Fe and tracers of dust sources (Amato et al., 2013;
Shen et al., 2016) decreased with PM2.5 concentration. Taken together,
all these variations of source-specific PM2.5 compositions suggested
a more significant influence of combustion sources to PM2.5 in haze
episodes and relatively higher contributions of dust sources in nonhaze
periods.
Source apportionment during haze and nonhaze periods
To conduct high-time-resolution source apportionment in Beijing, a PMF model
was applied to 1 h online measurement data. The six-factor solution gave the
best performance. The profile for each factor is shown in Fig. S7.
Contribution of different factors to PM2.5 were estimated after
considering major sources in Beijing, the similarity of the PMF source
profiles with relevant source emission profiles, and distinctively different
marker species for different sources. Factor 1 was heavily weighted by
secondary inorganic ions (SO42-, NO3- and
NH4+) and moderately weighted by OC, which was typical of the
secondary source profiles (Gao et al., 2016; Peng et al., 2016; Shi et al.,
2017). Factor 2 was highly loaded on metal species including Mn, Fe, Cu and
Zn, which were mostly used as indicators for industrial sources (Hu et al.,
2015; Y. Li et al., 2017; Pan et al., 2015; Yu et al., 2013). Factor 3
presented high loading of Ca, Ba and Fe, and the two crustal elements were
mainly emitted from dust sources (Amato et al., 2013; Shen et al., 2016).
Factor 4 was mostly loaded by EC, OC and moderately loaded by Cu and Zn,
which were mainly emitted from lubricant additive of vehicles (Kim et al.,
2003; Tao et al., 2014) and wear of brake and tyre (Pant and Harrison,
2013). High loading of As and Se and moderate loading of OC and EC were observed
in Factor 5, indicating a typical source profile of coal combustion
(Vejahati et al., 2010). Factor 6 was characterized by high loading of K,
SO42- and OC, which were identified as indicators of biomass
burning (Duan et al., 2004). In previous studies, cooking was
one of the important sources of PM2.5, contributing to about 10 % on
average in East Asia (Chafe et al., 2014; Sun et al., 2013), but in this
study, cooking sources were not identified by PMF due to the lack of organic
tracers. The relationships between the tracers of identified sources and
sources mass concentrations are shown in Fig. S8.
Source contribution of PM2.5(a) in the whole sampling period
and (b) in different pollution episodes and nonhaze periods (yellow for
dust source, green for biomass burning, pink for industrial source, red for
coal combustion, black for traffic sources and blue for secondary sources). Dates are mm/dd.
The source apportionment result of PMF in winter in Beijing is shown in
Fig. 3. During the campaign, the source contribution in Beijing ranked as
secondary sources (44 %) > traffic sources (18 %) > coal combustion (16 %) > biomass
burning (9 %) > industrial sources (8 %) > dust (5 %). The high contribution
of secondary sources in winter was similar to previous studies (Gao et al.,
2016; Peng et al., 2016; Zhang et al., 2013), which might be attributed to
regional transport and heterogeneous reactions (Ma et al., 2017).
Meteorological conditions during pollution episodes and nonhaze
periods.
Considering data integrity and representativeness, four typical pollution
episodes (EP1-4) and two nonhaze periods (NH1 and NH2) were selected. The
average PM2.5 concentrations in four haze episodes were all above 97 µg m-3 (see Table S4). EP1 (14–19 November) and EP2 (24–27 November)
represented the pollution episodes in November, and EP3 (1–5 December) and EP4
(16–21 December) were two severe pollution processes in December. The four
pollution episodes were characterized by low wind speed around 2 m s-1
and high relative humidity (RH) compared to nonhaze periods (see in Table 1). The chemical composition and sources of the four pollution episodes
varied from each other, but relatively high contributions of secondary sources
were observed in all episodes (32 %–57 %), and the contribution increased
with PM2.5 concentration (see Fig. 2). EP4 was characterized by the
highest contribution of secondary sources (57 %). The contribution of coal
combustion and industrial sources in EP1 was the most significant compared
with other episodes, which were 22 % and 17 %. The traffic
source contributions in EP2 and EP3 were higher than other pollution
episodes, accounting for about 22 %. The source contribution in nonhaze
periods was significantly different from that in pollution episodes. The
contribution of secondary sources in the two nonhaze periods, NH1 (22–23 November) and NH2 (13–15 December), decreased to 18 % and 25 %, while traffic
and dust source contributions to PM2.5 increased to about 30 % and
10 %, which could be influenced by local emission and regional transport
from northern areas to Beijing.
Generally, secondary sources were predominant (∼50 %) to
PM2.5 in pollution episodes, while traffic sources (∼30 %) became more important in nonhaze periods. However, source
contributions of PM2.5 could vary from episode to episode. EP1 was more
influenced by primary sources, while EP4 was characterized by high secondary
source contributions (57 %).
Evolution of different types of haze episodes
The high-time-resolution source apportionment result by PMF was combined
with the NAQPMS and footprint modeling outcomes to investigate the
variation in source types and contributions with source regions in different
haze episodes in Beijing. EP1 and EP4, with the longest duration and
significantly different source compositions, were selected as two case
episodes for further analysis.
Variation in sources and local contribution during EP1. The above
pie charts show the daily local (Beijing as BJ) and regional contributions
(labeled “others”). The pie charts below show the daily source type and
contribution.
A haze episode dominated by local emission
Figure 4 shows the variation in sources and local contribution and Fig. 5
shows the footprint regions and daily source apportionment results by PMF in
EP1. The spatial mass concentrations of PM2.5, wind speed and wind
direction during EP1 by NAQPMS can be found in Fig. S9. It can be seen
that EP1 was characterized with high local contributions (69 %–89 %) and
primary source contributions to PM2.5. On November 14, the footprint
located in the northeastern part of Beijing (mainly Inner Mongolia) with a low
PM2.5 concentration, while the contribution of dust sources was
significant (52 %). On November 16 when the formation stage of EP1
started, the footprint concentrated in local areas of Beijing and the local
contribution by NAQPMS (82 %) increased simultaneously. The daily average
source contribution ranked as traffic sources (29 %) > coal
combustion (28 %) > industrial sources (15 %) > dust and secondary sources (12 %) > biomass burning (6 %). The
contribution of primary sources, especially for traffic sources, increased when
footprints were primarily located in the local area.
(a) Source regions by the footprint model and (b) daily source
apportionment results by PMF in EP1.
The relationship between source apportionment and the footprint model
results can also be found in the daily variation of 17 November (see Fig. 5). From 01:00 to 12:00 of the day, the footprint remained in local
areas, while primary sources were predominant. However, when the footprint changed
to southwestern areas to Beijing from 13:00 to 18:00, the contribution of
secondary sources increased significantly to 42 %. After the footprint
changed back to a local type from 19:00 to 24:00, the secondary source
contribution decreased to the previous level (19 %).
A haze episode dominated by regional transport
Figure 6 shows the variation of sources and Fig. 7 shows the footprint
regions and daily source apportionment results by PMF in EP4. In contrast to
EP1, the footprint in EP4 was mostly located in the southwestern area of
Beijing, where there were heavily polluted cities including Baoding and
Shijiazhuang (see Fig. 7). The daily local and regional contribution by
NAQPMS of this episode was not provided due to lack of data. From the
formation stage (16–17 December) to the peak (20 December) of EP4, the
contribution of secondary sources increased from 34 % to 58 %, while the
contributions of coal combustion and biomass burning were also significant
among primary sources (see Fig. 6). Figure 7 shows that the footprint on
December 17 was more concentrated in the local and eastern areas of Beijing,
while it gradually moved to southwestern areas along with the increase of
PM2.5 concentration and the secondary source contribution.
Source contribution in EP4. The pie charts show the daily source
type and contribution. Dates are mm/dd.
(a) Source regions by the footprint model and (b) daily source
apportionment results by PMF in EP4.
The above results confirmed that high-time-resolution source apportionment
result can be integrated with footprint and NAQPMS model to identify the
rapid evolution of different episodes – EP1 was mainly caused by
local emission from transportation and coal combustion, while EP4 was typical
of regional transport from southwestern areas of Beijing with increasing
contributions of secondary sources.
Relationship of PM<inline-formula><mml:math id="M161" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> sources by PMF with regional transport estimated
by NAQPMSSources dominated by local emission and regional transport
Receptor models which are used for source apportionment have the limitation
that they cannot quantify the local or regional transport contribution.
Therefore, the receptor model was combined with the chemical transport model
NAQPMS to investigate the correlation of source contribution with
local or regional transport. As shown in Sect. 3.2, secondary and combustion
sources were predominant in haze episodes in Beijing. To better control
those major sources in winter, it is essential to determine the correlation of
source contribution with the contribution of local emission or regional
transport. Figure 8 shows the correlations of relative contribution of
secondary sources, coal combustion and biomass-burning sources by PMF with
local contribution by NAQPMS during the sampling period. The results showed
that, for PM2.5 in Beijing, the secondary source contribution decreased when
the local emission was more significant (p < 0.05, r=0.4), while coal
combustion, as a primary combustion source, showed an increasing trend along
with local contribution estimated by NAQPMS (p < 0.05, r=0.3).
Comparing with Fig. 8b and c, the two primary combustion sources showed
the opposite relationship to the local contribution, indicating that the
pollutants from biomass burning were mainly transported from the surrounding
areas of Beijing, while those from coal combustion were more
influenced by local emission. According to previous studies, biomass burning
was an important source in provinces around Beijing including Shandong,
Hebei and Inner Mongolia (Khuzestani et al., 2018; J. Sun et al., 2016; Zhang
et al., 2010; Zhao et al., 2012; Zong et al., 2016). The surrounding
provinces and cities of Beijing are shown in Fig. S10. The results suggested
that locally emitted coal combustion contributed significantly to PM2.5
in Beijing in winter 2016 and the strict control strategies for coal
combustion were essential to improving air quality in Beijing. In the
meantime, more control of biomass burning and precursors of secondary sources
in surrounding areas are also needed to mitigate air pollution in Beijing.
Correlations of local contribution by NAQPMS with the relative
contribution by PMF of (a) secondary sources, (b) coal combustion sources and
(c) biomass-burning sources.
Sources dominated in different potential source regions
The combination of the PMF result with the footprint model was used to further
identify specific source type and contribution in different source regions.
As mentioned in Sect. 2.2.4, the footprint with the time resolution of 6 h was divided into four types (local, south, north and east) according
to the direction of potential source regions. The typical examples of
different types of footprint are shown in Fig. S5. The local footprint
referred to the cases with source region located within Beijing. The southern footprint mainly covered southwestern areas in Hebei province including
Baoding, Shijiazhuang and Xingtai. The northern footprint included Zhangjiakou
and Inner Mongolia. The eastern footprint covered the northern part of Hebei, such
as Tangshan and Qinhuangdao, and the southern part of the Liaoning province. The
local footprint was predominant in winter in Beijing (N=79) with the
contribution of 38 %, followed by northern and southern footprint (N=51, 45). The amount of eastern footprint was the lowest in winter. The
average value and box chart of source contribution in four types of
footprint during the whole sampling period are shown in Fig. 9. It can be
seen that local footprint was characterized by traffic (23 %) and coal
combustion sources (25 %), while the contribution of secondary sources
(26 %) was the lowest among the four types. On the contrary, secondary
sources were predominant in southern footprint cases with the contribution of
53 %, while the contribution of traffic sources decreased to 15 %. The
results corresponded well with the analysis of two typical episodes in Sect. 3.3. The northern footprint was characterized by the highest contribution of dust
sources (11 %), which could be influenced by dust transported from Inner
Mongolia (Hoffmann et al., 2008; Park and Park, 2014). Eastern footprint, which
mainly covered heavy industrial areas such as Tangshan and Shenyang, showed
higher contribution of industrial sources (10 %) and coal combustion
sources (18 %). Figure 9b shows that the variation in source
contribution had the smallest local footprint, indicating a relatively
stable local emission of Beijing, while the source contribution varied more
significantly in the eastern footprint.
(a) The average source contribution (in percentage) for each type
of footprint, and (b) a box chart of source contribution for four types of
footprint during the whole sampling period. N in (a) represents the
number of cases. The capital letters in (b) stand for the type of footprint
(L for local, S for south, N for north, E for east) and the lowercase letters
stand for different sources (s for secondary source, c for coal combustion,
t for traffic source, i for industrial source, d for dust and b for biomass
burning).
The results of PMF and the footprint model showed that the source contribution in
winter in Beijing was influenced by the potential source regions, and the
predominant source could change specifically for different footprint types,
which might suggest that source apportionment and footprint analysis need to
be combined to better control specific sources from different source
regions.
Future prospects
In this study, the high-time-resolution online measurements were conducted by
Xact, IGAC and the Sunset OCEC analyzer, which could measure inorganic
species including water-soluble ions, elemental components, OC and EC. As a
result, most of the tracers selected for PMF source apportionment were
inorganic species. In previous studies based on online measurements, organic
tracers are also not commonly used due to current technical difficulties in
carrying out online and quantitative measurements of organic species with
high-time resolution (Gao et al., 2016; Y. Li et al., 2017; Peng et al.,
2016). However, some organic tracers are believed to be more specific for
certain sources, such as levoglucosan for biomass burning, hopane and
sterane for traffic sources and cholesterol for cooking sources (Fraser et
al., 2000; Yin et al., 2010; Zhao et al., 2015). Therefore, future online
measurements of organic species could be conducted, which will be very
helpful in identifying sources. Besides, vertical measurements of PM2.5
are important for a better understanding of sources and regional transport of
PM2.5 in Beijing. Li et al. (2017) found that the height of regional
transport ranged from 200 to 700 m above ground level using the NAQPMS
model. In the future, the integration of ambient measurements with the air
quality model should be considered at a vertical level as well.
Conclusions
High-time-resolution online measurements of PM2.5 were conducted during
the APHH winter campaign in Beijing. Considering the limitation of receptor
models, which could not identify and quantify regional transport, the
receptor model PMF was combined with multiple models, including NAQPMS and
the footprint model, to analyze the specific sources from different source
regions during haze episodes in Beijing. The source apportionment results by
PMF during our sampling period showed that secondary sources were predominant
(∼50 %) to PM2.5 in pollution episodes, while traffic
sources (∼30 %) became more important in nonhaze periods.
Source contributions of PM2.5 can vary from episode to episode.
The multiple models were combined to analyze the evolution of two typical
pollution episodes in Beijing. The high-time-resolution results indicated
that source contribution can vary rapidly and significantly with source
regions within different types of haze episodes. EP1, with a locally
concentrated footprint and high local emission, was characterized by coal
combustion and traffic sources, while EP4 with a more southwestern footprint was
typical of a high secondary source contribution. The relationship of
PM2.5 sources by PMF with regional transport during the whole sampling
period was further investigated. As the predominant sources of PM2.5 in
Beijing, secondary and biomass burning sources were more influenced by
regional transport, while coal combustion sources increased with local
contribution. The source regions of PM2.5 in Beijing were classified
into four types and source contribution varied significantly with potential
source regions, with traffic sources dominating in the local footprint, secondary
sources dominating in southern footprint and dust and industrial sources increasing in
northern and eastern footprints. The results suggested that source
contributions of PM2.5 in winter in Beijing could change significantly,
along with the contribution and direction of regional transport. Therefore,
the combined use of the receptor model, meteorological model and chemical
transport model was important in identifying specific sources from different
source regions.
Data availability
The data in this study are available from the corresponding author upon
request (mzheng@pku.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-6595-2019-supplement.
Author contributions
MZ, XC and JL designed the research. MZ organized the field campaign. YL,
TZ and CY conducted the measurements. YL wrote the paper. YL, MY and HD
analyzed the data. XW, ZS, RH, QZ and KH took part in data analysis and revised and
commented on the paper. All authors contributed to the discussion of this
paper.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-Beijing) (ACP/AMT inter-journal SI)”. It is not associated with a conference.
Acknowledgements
The authors
gratefully thank for the assistance of Jinting Yu in Peking University for
maintaining the online instruments in this work.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 41571130033, 41430646, 41571130035, 91744203, and 41571130034) and the UK Natural Environment Research Council (grant nos. NE/N006992/1 and NE/R005281/1).
Review statement
This paper was edited by Leiming Zhang and reviewed by three anonymous referees.
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