Introduction
In recent years, a vast area in northern China has frequently
been suffering from severe haze pollution (Chan and Yao, 2008; Zhang et al.,
2012, 2015), which has aroused the attention of the public (Guo et al., 2014;
Huang et al., 2014; Cheng et al., 2016; Wang et al., 2016; J. Liu et al.,
2016). The severe haze pollution is mainly due to the high level of fine
particulate matter with an aerodynamic diameter less than 2.5 µm
(PM2.5; Huang et al., 2014; P. Liu et al., 2016). PM2.5
can reduce atmospheric visibility by absorbing or scattering the incident
light (Buseck and Posfai, 1999; Cheng et al., 2006) and increase morbidity
and mortality by penetrating the human bronchi and lungs (Nel, 2005; Poschl,
2005; Peplow, 2014).
To alleviate the serious haze pollution problems, the Chinese government has
performed a series of control measures for major pollution sources (Zhang
et al., 2012; J. Liu et al., 2016; Li et al., 2016b; Wen et al., 2016). For
example, coal-fired power plants have been forced to install flue gas
desulfurization and denitration (Zhang et al., 2012; Chen et al., 2014), coal
has been replaced with natural gas and electricity in megacities (Wang
et al., 2009; Duan et al., 2012; Zhao et al., 2013a; Tan et al., 2016),
stricter emission standards have been implemented for vehicles and industrial
boilers (Zhang et al., 2012; Tang et al., 2016) and so on, resulting in a
decreasing trend of primary pollutants including PM2.5 in recent
years (Ma et al., 2016; Wen et al., 2016; Zhang et al., 2016). However, the
PM2.5 levels were still larger than 1000 µgm-3
in some areas of the Beijing–Tianjin–Hebei (BTH) region during the period of
the red alert for haze in December 2016
(http://english.mep.gov.cn/News_service/media_news/201612/t20161220_369317.shtml).
The stricter control measures (e.g. stopping production in industries and
construction, and the odd and even number rule) had been performed (Y. Li
et al., 2016), implying that sources other than industries, construction and
vehicles might make dominant contributions to atmospheric PM2.5 in
the region. Residential coal combustion, which prevails for heating
during winter in the region, was suspected to be a dominant source of
atmospheric PM2.5. Although annual residential coal consumption
(about 42 Tgyear-1) in the BTH region only accounts for a small
fraction (about 11 %) of the total coal consumption
(http://www.qstheory.cn/st/dfst/201306/t20130607_238302.htm), the
emission factors of primary pollutants including PM2.5 from the
residential coal combustion have been found to be about 1–3 orders of
magnitude greater than those from coal combustion of industries and power
plants (Revuelta et al., 1999; Chen et al., 2005; Xu et al., 2006; Zhang
et al., 2008; Geng et al., 2014; Yang et al., 2016). In addition, annual
residential coal consumption mainly focuses on the 4 months in winter.
Although the Chinese government has implemented control measures for
residential coal combustion (e.g. replacement of traditional coal stoves by
new stoves, bituminous coal by anthracite and coal by electricity and
natural gas), the implementation strength of the control measures is still
very limited. Additionally, the promoted new stoves still have large
smoke emissions due to the lack of clean combustion techniques, and the anthracite
is not welcomed by farmers because of its extremely slow combustion rate in
comparison with bituminous coal.
There are a few studies focusing on the influence of residential coal
combustion on atmospheric particles in northern China. W. Li et al. (2014)
concluded that strong sources of PM10 in rural residential areas
were from household solid fuel combustion, based on annual mean
PM10 concentrations observed in urban regions (180±171 µgm-3) and rural villages (182±154 µgm-3) in northern China. Duan et al. (2012)
inferred that the lower OC/EC ratios at the rural site
compared to the urban site were ascribed to coal combustion prevailing in the
rural area. Our previous study revealed that residential coal combustion made
an evident contribution to atmospheric water-soluble ions (WSIs) in Beijing (P.
Liu et al., 2016). Based on Weather Research and Forecasting model coupled
with Chemistry, J. Liu et al. (2016) recently estimated that the residential
sources (solid fuel) contributed 32 and 53 % of the primary
PM2.5 emissions in the BTH region during the whole year and during
the winter of 2010, respectively.
In this study, daily samples of PM2.5 were simultaneously collected at
the four sampling sites (Beijing city, Baoding city, Wangdu county and
Dongbaituo) during the winter and spring of 2014–2015, and the
direct evidence for the influence of residential coal combustion on regional
PM2.5 in the region was found, based on the PM2.5 levels,
PM2.5 composition characteristics, correlations among key species in
PM2.5, back trajectories and chemical mass closure.
Materials and methods
Sampling sites
China (a), the North China Plain (b) and Baoding city in
Hebei Province (c). The locations of sampling sites (BJ, BD, WD and DBT) as
well as Tianjin municipality and Shijiazhuang, the provincial capital of Hebei
are marked.
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The two sampling sites on a rooftop in Beijing city and Dongbaituo were
selected, which have been described in detail by our previous study (P. Liu
et al., 2016), (approximately 25 and 5 m above ground, respectively)
of the Research Center for Eco-Environmental Sciences, Chinese Academy of
Sciences (RCEES, CAS) and a field station in the agricultural field of
Dongbaituo village, Baoding, Hebei Province, respectively. Another two
sampling sites in Baoding city and Wangdu county were chosen, both of which
were on the rooftops of local environmental monitoring stations (about 30 and
20 m above ground, respectively), which are located in the centres of
the cities and surrounded by some commercial and residential areas. The
spatial locations of the four sampling sites are presented in Fig. 1 and the
distances between Beijing and Baoding, Baoding and Wangdu, Wangdu and
Dongbaituo are about 156, 36 and 12 km, respectively. Thereafter, the
sampling sites of Beijing, Baoding, Wangdu and Dongbaituo are abbreviated as
BJ, BD, WD and DBT, respectively.
Sample collection and analysis
PM2.5 samples at BJ and DBT were collected simultaneously on PTFE
filters (90 mm, Millipore) by medium-volume PM2.5 samplers
(LaoYing-2034) at a flow rate of 100 Lmin-1 from 15 January 2014
to 31 May 2015, in winter (15 January–25 February 2014,
18 November 2014–20 January 2015 and 11 February–15 March 2015) and spring
(21 April–4 May 2014 and 20 March–31 May 2015). An enhanced observation at
BJ, BD, WD and DBT was carried out from 21 January to 10 February 2015, and
PM2.5 samples were collected in the same way on the quartz fibre
filters (90 mm, Munktell). The sampling duration was 24 h
(from 15:00 to 15:00 of the following day in local time, UTC + 8). All
the samples were put in the appropriative dishes (90 mm, Millipore)
after sampling and preserved in a refrigerator immediately until analysis.
As for the quartz fibre filters, half of each filter was extracted
ultrasonically with 10 mL of ultrapure water for half an hour. The
solutions were filtered through a micro-porous membrane (pore size,
0.45 µm; diameter, 13 mm) before the analysis, and the
WSIs (Cl-, NO3-, SO42-, Na+,
NH4+, Mg2+, Ca2+ and K+) in the treated
filtrates were analysed by ion chromatography (IC, WAYEE IC6200), which has
been described in detail by our previous study (P. Liu et al., 2016).
A quarter of each filter was cut into fragments and digested with
5 mL 65 % HNO3 and 2 mL 30 % H2O2
(Li et al., 2015) by a microwave digestion system (SINEO, MASTER-40). The
digestion solution was diluted to 25 mL with ultrapure water to
ensure that the solution acidity was below 10 %, and the trace elements (Al, Mn,
Fe, Cu, Zn, As, Se, Sr, Tl and Pb) in the diluted solution were analysed by
a triple-quadrupole inductively coupled plasma mass spectrometry (ICP-MS/MS,
Agilent 8800). The standard reference material (GBW07427) was also digested
in the same way as the samples and the recoveries of the trace elements were
within the allowable ranges of the certified values (100±15 %).
Another quarter of each filter was analysed by a DRI thermal optical carbon
analyser (DRI-2001A) for carbon components (OC and EC). In addition, the PTFE
filters were only used for analysing the WSIs (P. Liu et al., 2016).
Chemical mass closure
A chemical mass closure (CMC) method was adopted by considering secondary
inorganic aerosols (SIA, the sum of SO42-, NO3- and
NH4+), sea salt and coal combustion (derived from Cl- and
Na+), biomass burning (characterized by K+), mineral dust, EC,
primary organic carbon (POC), secondary organic carbon (SOC) and trace
element oxide (TEO; Hsu et al., 2010b; Zhang et al., 2013; Mantas et al.,
2014; Tian et al., 2014; Kong et al., 2015).
Atmospheric Na+ and Cl- were considered to be from sea salt
(Brewer, 1975; van Eyk et al., 2011), coal combustion (Bläsing and
Müller, 2012; Yu et al., 2013; Wu et al., 2014; He et al., 2015; P. Liu
et al., 2016) and biomass burning (Zong et al., 2016; Yao et al., 2016).
However, biomass burning in the NCP region mainly focuses on the harvest
seasons of summer and autumn (Zong et al., 2016), and a few farmers are
currently combusting crop straws for household cooking and heating because
of the inconvenience of biomass in comparison to coal and liquid gas. Thus,
only sea salt and coal combustion were considered for the estimation of mass
concentrations for atmospheric Na+ and Cl- in this study based on
the following equations:
Clcc-+Clss-=Cl-,Nacc++Nass+=Na+,[Clcc-]/35.5[Nacc+]/23=1.4,[Clss-]/35.5[Nass+]/23=1.18,
where [Clss-] and [Nass+] are the mass
concentrations of Cl- and Na+ from sea salt, and
[Clcc-] and [Nacc+] are the mass concentrations
of Cl- and Na+ from coal combustion. The molar ratio of
Clss-- to Nass+ at 1.18 was adopted, which
represented the typical ratio from sea salt (Brewer, 1975). The molar ratio
of Clcc--to-Nacc+ was chosen to be 1.4 in this
study according to our preliminary measurements from the raw bituminous coal
that prevailed in northern China, and the value of 1.4 has been recorded by the
previous study (Bläsing and Müller, 2012). If the molar ratios of
atmospheric Cl--to-Na+ in PM2.5 were greater than
the value of 1.4 or lower than the value of 1.18, atmospheric Cl- and
Na+ would be considered to be entirely from coal combustion or sea
salt.
Because the average Al content accounts for about 7 % in mineral dust
(Zhang et al., 2003; Ho et al., 2006; Hsu et al., 2010a; Zhang et al.,
2013), the mineral dust was estimated based on the following equation:
Mineral dust=[Al]0.07.
POC and SOC were calculated by the EC-tracer OC/EC method
(Cheng et al., 2011; Zhao et al., 2013b; G. J. Zheng et al., 2015; Cui
et al., 2015) as follows:
POC=EC×(OC/EC)pri=KEC+M,SOC=OC-POC.
The values of K and M are estimated by linear regression analysis using
the data pairs with the lowest 10 % percentile of ambient
OC/EC ratios. It should be mentioned that POC could be
underestimated and SOC could be overestimated by the EC-tracer
OC/EC method, because the lowest 10 % percentile of
OC/EC ratios measured were usually less than those from
dominant sources of coal combustion and biomass burning in autumn and winter
(Ding et al., 2012; Cui et al., 2015).
Enrichment factor values for trace elements in
PM2.5.
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To estimate the contribution of heavy metal oxide, the enrichment factors
(EFs) of various heavy metal elements were calculated by the following
equation (Hsu et al., 2010b; Zhang et al., 2013):
EF=([Element]/[Al])aerosol([Element]/[Al])crust,
where ([Element]/[Al])aerosol is the ratio of the element to Al in aerosols and
([Element]/[Al])crust is the ratio of the element to Al in the average crust (Taylor, 1964). According
to the method developed by Landis et al. (2001), the atmospheric concentrations of elements were multiplied by a factor of 0, 0.5
and 1 if their EFs were less than 1, between 1 and 5, and greater than 5, respectively. Based on the EFs (Fig. 2), the equation
for estimating TEO was derived as follows:
TEO=1.3⋅(Cu+Zn+Pb+As+Se+Tl+0.5⋅Mn).
The value of 1.3 was the conversion factor of metal abundance to oxide
abundance. It should be mentioned that some other elements such as Cd and Ba
were not measured in this study, probably resulting in underestimating the
proportion of TEO. Nevertheless, the biases are probably insignificant
because the proportion of TEO only accounted for less than 2 % in
PM2.5.
Meteorological, trace gases and back trajectory
The wind speed, wind direction, RH, temperature and
barometric pressure at BD and BJ during the sampling period in the winter of
2015.
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Meteorological data, including wind speed, wind direction, relative humidity
(RH), temperature, barometric pressure, as well as air quality index (AQI)
based on PM2.5, SO2, NO2, CO, O3 at BJ, BD
and WD, were obtained from the Beijing urban ecosystem research station in
RCEES, CAS (http://www.bjurban.rcees.cas.cn/), environmental
protection bureau of Baoding city (http://bdhb.gov.cn/) and
environmental monitoring station of Wangdu county
(http://www.wdx.gov.cn/), respectively. The meteorological data at BJ
and BD are shown in Fig. 3 and the average concentrations of SO2 and
NO2 at BJ, BD and WD are listed in Table 2 during the sampling period
in the winter of 2015, which will be discussed in Sect. 3.2 and 3.3.
The air mass backward trajectories were calculated for 24 h through
the National Oceanic and Atmospheric Administration (NOAA) Hybrid
Single-Particle Lagrangian Integrated Trajectory Version 4 model (HYSPLIT 4
model) with global data from the National Centers for Environmental
Prediction (NCEP). The backward trajectories arriving at 500 m above
the sampling position were computed at 00:00, 06:00, 12:00 and 18:00 (UTC)
for each sampling day. A K-means cluster method was then used for classifying
the trajectories into several different clusters and suitable clusters were
selected for further analysis.
The average mass concentrations of WSIs in PM2.5 at DBT
and BJ during the sampling period in winter and spring of 2014–2015
(µgm-3).
WSIs
Spring
Winter
DBT
BJ
DBT
BJ
Na+
1.0±0.5
1.4±0.5
2.4±1.3
3.1±1.4
Mg2+
0.2±0.2
0.3±0.2
0.7±0.5
0.8±0.7
Ca2+
1.7±2.4
3.4±2.5
2.6±2.1
3.4±2.3
K+
0.5±0.5
0.7±0.4
3.2±3.0
3.0±6.0
NH4+
6.1±5.1
4.8±4.7
23.1±17.9
13.2±11.6
NO3-
12.5±11.2
13.6±13.2
28.4±28.0
19.0±20.0
SO42-
10.5±8.2
9.2±8.6
29.0±28.1
17.4±16.5
Cl-
2.9±2.2
1.8±1.6
14.1±9.4
7.2±6.0
Total
35.3±26.7
35.1±28.7
103.3±81.3
67.0±55.2
Results and discussion
Comparison of atmospheric WSIs between the two sampling sites of BJ and DBT
The mass concentrations of the WSIs in PM2.5 at DBT
and BJ during the sampling period in winter and spring of 2014–2015.
![]()
The D values of the mass concentrations of WSIs in
PM2.5 between DBT and BJ during the sampling period in winter and
spring of 2014–2015.
![]()
The daily variations of atmospheric WSIs during the sampling periods at BJ
and DBT are shown in Fig. 4. It is evident that the variations of the WSIs
between the two sampling sites of BJ and DBT exhibited similar trends, but
the mass concentrations of the WSIs were remarkably greater at DBT than at
BJ during the two winter seasons. As listed in Table 1, the average
concentrations of the typical WSIs were a factor of 1.5–2.0 greater at DBT
than at BJ during the two winter seasons, whereas they were approximately
the same at the two sampling sites during the two spring seasons. To clearly
reveal the differences, the daily D values (the concentrations of WSIs at
DBT minus those at BJ) of several typical WSIs as well as the total WSIs
between the two sampling sites of DBT and BJ are individually illustrated in
Fig. 5. With an exception only for Ca2+ (typical mineral dust
component), the D values of NH4+, NO3-, SO42-
and Cl- between the two sampling sites of DBT and BJ exhibited positive
values during the majority of sampling days in the two winter seasons, implying
that the sources related to mineral dust could be excluded for explaining
clearly higher concentrations of the WSIs at DBT than at BJ. The
sampling site of DBT is adjacent to Baoding city, where the AQI during
winter always ranked among the top three Chinese cities in recent years
(http://113.108.142.147:20035/emcpublish/), and hence the
relatively greater concentrations of the WSIs at DBT might be due to the
regional pollution. However, the emissions of pollutants from industries,
power plants and vehicles are usually relatively stable, so could not
account for the remarkable differences in the D values between winter
and spring (Fig. 5). If the relatively high concentrations of the WSIs
at DBT during winter were ascribed to the regional pollution, there
would be additional strong sources of them in the area of Baoding. To
explore whether regional pollution was responsible for the relatively
high concentrations of WSIs at DBT in winter, the various species in
PM2.5 collected simultaneously at DBT and its neighbouring cities of WD, BD
and BJ in the winter of 2015 were further investigated in the following
section.
Daily variations of the species in <inline-formula><mml:math id="M193" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> at the four sampling sites
Daily variation of the species in PM2.5 at
the four sampling sites during the sampling period in the winter of 2015.
![]()
The mass proportions of OC, EC and WSIs from residential
coal combustion under the flaming and smoldering combustion processes (a),
and the average mass proportions of the typical species in PM2.5 at the
four sampling sites during the sampling period in the winter of 2015 (b).
![]()
The daily variations of the species in PM2.5 at the four sampling
sites also exhibited similar trends (Fig. 6), but there were obvious
differences (p<0.01) in the concentrations of OC, EC, NH4+,
NO3-, SO42-, Cl- and K+ among the four
sampling sites, ranked as BJ < WD < BD < DBT. The meteorological
conditions, especially the wind speed and planetary boundary layer (PBL),
play pivotal roles in the dispersion and accumulation of atmospheric
pollutants (Xu et al., 2011; Tao et al., 2012; Sun et al., 2013; Chen et al.,
2015; M. Gao et al., 2016), which can cause spatial and temporal differences
in concentrations of pollutants. As for the sampling sites of BD, WD and DBT,
the meteorological conditions could be considered similar because of the
short distances (<36 km) between them, and hence the spatial
difference in the concentrations of PM2.5 and the major components
at the three sampling sites was rationally ascribed to the different source
strengths. Although the distance between the sampling sites of BJ and BD is
about 156 km, there was no significant difference in the wind speeds
between the two sampling sites during the sampling period (1.4±1.4 ms-1 for BJ and 1.7±1.1 ms-1 for BD,
Fig. 3). Therefore, the spatial difference in the concentrations of
PM2.5 and the major components between the sampling sites of BJ and
the other three could not be ascribed to the difference in the wind speeds.
Because the information on PBL was not available in the region of Baoding, it
is difficult to discuss the impact of PBL on the spatial difference in the
concentrations of the pollutants. As listed in Table 2, the average
concentration of the total species at DBT was about a factor of 2.7, 1.8 and
1.4 higher than those at BJ, WD and BD, respectively. The largest levels of
the key species in PM2.5 at DBT among the four sampling sites
implied that the pollutants at the rural site were not transported through
the air parcel from the neighbouring cities but mainly ascribed to the local
emissions or formation. Vehicles and industries could be rationally excluded
to explain the largest levels of the key species in PM2.5 at DBT,
because these sources are very sparse in the rural area around DBT (see
Sect. 3.4). Compared with the cities, the distinct source of atmospheric
pollutants at DBT in winter is the residential coal combustion because
residential coal combustion is prevailingly used for heating and cooking in
rural areas of northern China. The emissions of various pollutants from
residential coal combustion were very large due to the lack of any control
measures, and thick smoke could be seen in the chimneys of residential coal
stoves. The emission factors of OC and EC from residential coal combustion
were reported to be 0.47–7.82 gkg-1 coal and
0.028–2.75 gkg-1 coal, respectively (Chen et al., 2005; Zhang
et al., 2008). The emission factors of various pollutants from a typical
residential coal stove fuelled with raw bituminous coal were also
investigated in our group according to farmers' customary uses of coal stoves
under the alternation cycles of flaming and smoldering (Du et al., 2016; Liu
et al., 2017). The emission factors of OC and EC under the entire combustion
process can be as high as 10.99±0.95 gkg-1 coal and 0.84±0.06 gkg-1 coal, respectively (Table 3). Considering the
high density of farmers in the rural area, the largest levels of atmospheric
OC and EC at DBT could be rationally ascribed to residential coal combustion.
However, the proportion of WSIs from residential coal combustion (Fig. 7a)
was extremely low compared to that of the atmosphere. Therefore, the largest
levels of the key WSIs in PM2.5 at DBT were suspected to occur from
secondary formation via heterogeneous or multiphase reactions, which might
be accelerated by the OC and EC particles (Han et al., 2013; Zhao et al.,
2016) emitted from residential coal combustion.
The average mass concentrations (Mean ± SD) of PM2.5
species, NO2 and SO2 at the four sampling sites during the
sampling period in the winter of 2015 (µgm-3).
Species
BJ
BD
WD
DBT
Na+
2.5±0.7
4.8±2.0
4.5±1.7
4.3±1.2
Mg2+
0.3±0.1
0.4±0.1
0.3±0.1
0.4±0.2
Ca2+
1.8±0.9
2.6±0.8
1.7±0.6
2.0±0.8
K+
0.7±0.8
2.5±1.0
2.0±1.4
3.1±1.3
NH4+
6.0±5.0
13.3±11.0
9.3±9.5
18.7±11.7
NO3-
11.7±10.1
16.6±10.3
13.0±8.2
21.0±12.2
SO42-
11.2±6.5
18.1±14.1
14.5±14.5
24.1±16.1
Cl-
5.0±3.6
9.5±4.2
7.8±3.5
13.4±6.0
OC
28.6±19.6
70.2±31.2
57.2±21.3
100.0±42.9
EC
5.5±4.5
13.5±7.8
11.4±4.7
21.6±10.2
Al
0.6±0.8
0.6±0.1
0.5±0.2
0.5±0.1
Mn
0.1±0.1
0.1±0.1
0.1±0.1
0.2±0.3
Fe
2.1±0.8
0.6±0.2
0.8±0.6
1.3±0.6
Cu
0.6±0.3
0.3±0.1
0.2±0.1
0.1±0.1
Zn
0.1±0.1
0.2±0.1
0.1±0.1
0.1±0.1
As
0.1±0.1
0.3±0.1
0.2±0.1
0.1±0.1
Se
0.1±0.0
0.1±0.1
0.1±0.0
0.1±0.0
Sr
0.0±0.0
0.1±0.0
0.0±0.0
0.0±0.0
Tl
0.0±0.0
0.0±0.0
0.0±0.0
0.0±0.0
Pb
0.2±0.2
0.4±0.3
0.2±0.1
0.3±0.1
Total
80.1±47.7
159.5±70.3
121.7±51.8
218.4±87.1
NO2
36.5±17.4
60.4±23.4
76.1±19.2
–
SO2
63.9±31.7
181.7±62.4
101.3±39.4
–
The emission factors (mean ± SD; gkg-1 coal) of
OC and EC from residential coal combustion during the flaming combustion
process, the smoldering combustion process and the entire combustion
process.
Emission
The flaming
The smoldering
The entire
factors
combustion
combustion
combustion
process
process
process
OC
1.83±1.19
17.11±0.79
10.99±0.95
EC
1.40±0.11
0.46±0.03
0.84±0.06
Although the three sampling sites of DBT, WD and BD are closely adjacent, the
lowest concentrations of the key species in PM2.5 were observed at
WD, which was probably ascribed to the replacement of coal with natural gas
for the central heating in the county of WD (a main pipe of natural gas is
just across the county); e.g. the average concentration of NO2 was
higher at WD than at BD, whereas the average concentration of SO2
showed the opposite pattern (Table 2).
The city of BD and the county of WD are fully surrounded by countryside with
high farmer density, whereas the city of BJ only neighbours the countryside
in the south–southeast–southwest directions, and thus the residential coal
combustion was also suspected to be responsible for the remarkably higher
concentrations of the key species in PM2.5 at BD and WD than at BJ.
To confirm the above assumptions, the chemical composition and source
characteristics of the species in PM2.5 were further analysed in
the following section.
Chemical composition of <inline-formula><mml:math id="M342" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> at the four sampling sites
The average mass proportions of the species in PM2.5 during the
sampling period at the four sampling sites are illustrated in Fig. 7b. OC,
EC, NH4+, NO3- and SO42- were found to be the
principal species, accounting for about 82–88 % of the total species in
PM2.5 at each sampling site, which is in line with previous studies
(Zhao et al., 2013a; X. J. Zhao et al., 2013; Tian et al., 2014; Huang
et al., 2014). As for the proportions of individual species, there were
obvious differences between the sampling site of BJ and the sampling sites of
BD, WD and DBT. The average mass proportions of OC and EC at BD, WD and DBT
were very close, accounting for about 45.7–47.1 and 9.0–10.4 % of the
total species in PM2.5, respectively, which were much greater than
those (37.9 % for OC and 7.4 % for EC) at BJ. In contrast to OC and
EC, the average mass proportions of NO3- (10.1–10.8 %) and
SO42- (11.2–11.7 %) at BD, WD and DBT were slightly less
than those (15.1 % for NO3- and 14.0 % for
SO42-) at BJ. The obvious differences in the mass proportions of
OC, EC, NO3- and SO42- between the sampling site of BJ
and the sampling sites of BD, WD and DBT indicated that the sources of the
principal species at BJ were different from the other three sampling sites.
The mass proportions of OC, EC, NO3- and SO42- at BD and
WD were very close to those at DBT, implying that residential coal combustion
might also be the dominant source of the species in PM2.5 at BD and
WD. The residential sector (dominated by residential coal combustion) in the
region of BTH during winter has been recognized as the dominant source of
atmospheric OC and EC (Chen et al., 2017), and was estimated to contribute 85
and 65 % of primary OC and EC emissions, respectively (J. Liu et al.,
2016). Because the sampling sites of DBT, BD and WD are located in or fully
surrounded by a high density of rural areas, the contribution of residential
coal combustion to atmospheric OC and EC at DBT, BD and WD must evidently
exceed the regional values estimated by J. Liu et al. (2016).
Although the mass proportions of NO3- and SO42- were
evidently lower at BD, WD and DBT than at BJ, the average mass concentrations
of NO3- and SO42- were lower at BJ (Table 2). Atmospheric
NO3- and SO42- are mainly from secondary formation via
heterogeneous, multiphase or gas-phase reactions, which are dependent on the
concentrations of their precursors (NO2 and SO2) and OH
radicals, the surface characteristics and areas of particles, and RH
(Ravishankara, 1997; Wang et al., 2013; Quan et al., 2014; Nie et al., 2014;
He et al., 2014; Yang et al., 2015; B. Zheng et al., 2015). The remarkably
higher concentrations of NO2, SO2 and PM2.5 at BD,
WD and DBT (Liu et al., 2015) compared to BJ (Table 2) favoured secondary
formation of NO3- and SO42-, resulting in the relatively
high concentrations of NO3- and SO42-.
The back trajectory cluster analysis and the
corresponding PM2.5 concentrations in Beijing during the sampling
period in the winter of 2015.
![]()
The correlations between the OC/EC ratios and
the PM2.5 concentrations at the four sampling sites during the
sampling period in the winter of 2015.
![]()
As shown in Fig. 8, the major pollution episodes at BJ usually occurred
during the periods with the air parcel from the southwest–south directions,
where farmers reside in high densities, and thus residential coal combustion
might also make an evident contribution to atmospheric pollutants at BJ.
Because the average concentrations of the species in PM2.5 were
mainly controlled by the highest concentration values, and the relatively
high concentration level of the species in PM2.5 at BJ usually
occurred during the major pollution episodes, the proportions of the species
in PM2.5 were dominated by major pollution events. The highest
NO3- and SO42- proportions and the lowest OC and EC
proportions at BJ among the four sampling sites might be partly ascribed to
the conversions of NO2 and SO2 to NO3- and
SO42- during the air parcel transportation from the
south–southwest directions. The contribution of the transportation to
atmospheric OC and EC at BJ could be verified by the relations between the
OC/EC ratios and the PM2.5 levels (Fig. 9). The
OC/EC ratios (about 4.9±0.7) at WD and DBT were
almost independent of the PM2.5 levels, whereas the
OC/EC ratios at BJ and BD significantly decreased with
increasing PM2.5 levels and reached the almost same value (about
4.8±0.5) as those at WD and DBT when the concentrations of
PM2.5 were above 150 µgm-3 (the major pollution
events). Because there were relatively sparse emissions from vehicles and
industries at WD and DBT, the almost constant OC/EC
ratios under the different levels of PM2.5 at WD and DBT further
revealed that atmospheric OC and EC were dominated by the local residential
coal combustion. The similar OC/EC ratios at the four
sampling sites with concentrations of PM2.5 greater than
150 µgm-3 indicated that residential coal combustion also
made a dominant contribution to atmospheric OC and EC in the two cities
during the severe pollution period. Our previous study (C. Liu et al., 2016)
also found that the contribution from residential coal combustion to
atmospheric VOCs increased from 23 to 33 % with increasing pollution
levels in Beijing.
It should be mentioned that the OC/EC ratios observed at
DBT and WD were about a factor of 2.7 less than that (13.1) of the emission
from the residential coal combustion and, however, the
OC/EC ratios observed at BJ and BD were too high to be
explained by direct emissions from diesel (0.4–0.8) and gasoline (3.1)
vehicles (Shah et al., 2004; Geller et al., 2006). The OC emitted from the
residential coal combustion might be easily degraded or volatilized in the
atmosphere, resulting in the relatively low OC/EC ratios
observed at DBT and WD. In China, aromatic compounds as typical pollutants
from vehicle emissions are very reactive, favouring secondary organic
aerosols (SOAs) formation (Zhang et al., 2017), which was suspected to make
an evident contribution to the OC/EC ratios at BJ and BD
when the atmospheric EC concentrations were relatively low. For example, the
extremely high OC/EC ratios (> 6.0) at BJ and BD only
occurred when the atmospheric EC concentrations were less than
3.2 µgm-3 at BJ and 5.4 µgm-3 at BD.
Because the atmospheric EC concentrations at BJ and BD were about a factor of
4–6 greater during the major pollution events than during the minor
pollution events, the effect of SOA formation on the
OC/EC ratios would be smaller during the major pollution
events if the SOA formation rate was kept constant.
Correlations among the species in <inline-formula><mml:math id="M409" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>
The correlations of several typical species in PM2.5
at the four sampling sites during the sampling period in the winter of 2015.
n=21
BJ
Mg2+
Ca2+
K+
Cl-
NO3-
SO42-
NH4+
OC
EC
Mg2+
1
Ca2+
0.895b
1
K+
0.634b
0.862b
1
Cl-
0.856b
0.899b
0.791b
1
NO3-
0.803b
0.768b
0.637b
0.905b
1
SO42-
0.679b
0.660b
0.590b
0.804b
0.950b
1
NH4+
0.718b
0.667b
0.543a
0.834b
0.971b
0.959b
1
OC
0.845b
0.751b
0.560b
0.848b
0.919b
0.838b
0.895b
1
EC
0.849b
0.851b
0.679b
0.932b
0.877b
0.769b
0.823b
0.936b
1
n=21
BD
Mg2+
Ca2+
K+
Cl-
NO3-
SO42-
NH4+
OC
EC
Mg2+
1
Ca2+
0.805b
1
K+
0.697b
0.556b
1
Cl-
0.714b
0.659b
0.789b
1
NO3-
0.554b
0.560b
0.675b
0.757b
1
SO42-
0.022
0.107
0.491a
0.499a
0.764b
1
NH4+
0.315
0.331
0.659b
0.721b
0.920b
0.941b
1
OC
0.743b
0.576b
0.705b
0.936b
0.674b
0.369
0.614b
1
EC
0.698b
0.560b
0.702b
0.939b
0.660b
0.410
0.633b
0.984b
1
n=19
WD
Mg2+
Ca2+
K+
Cl-
NO3-
SO42-
NH4+
OC
EC
Mg2+
1
Ca2+
0.897b
1
K+
0.226
0.457a
1
Cl-
0.532a
0.663b
0.598b
1
NO3-
0.468a
0.677b
0.712b
0.796b
1
SO42-
0.097
0.358
0.874b
0.552a
0.770b
1
NH4+
0.306
0.563b
0.906b
0.735b
0.901b
0.945b
1
OC
0.463a
0.543a
0.372
0.816b
0.471a
0.222
0.581a
1
EC
0.553a
0.638b
0.339
0.763b
0.510a
0.214
0.565a
0.925b
1
n=20
DBT
Mg2+
Ca2+
K+
Cl-
NO3-
SO42-
NH4+
OC
EC
Mg2+
1
Ca2+
0.721b
1
K+
0.191
0.407
1
Cl-
-0.061
0.316
0.519a
1
NO3-
-0.241
0.161
0.579b
0.642b
1
SO42-
-0.133
0.109
0.458a
0.482a
0.744b
1
NH4+
-0.223
0.125
0.558a
0.697b
0.928b
0.914b
1
OC
0.067
0.159
0.419
0.772b
0.570b
0.293
0.557a
1
EC
0.051
0.169
0.419
0.838b
0.585b
0.400
0.624b
0.977b
1
a,b represent p<0.05 and p<0.01.
The correlations among the WSIs, OC and EC in PM2.5 at the four
sampling sites are listed in Table 4. The number of species involved in
significant correlations (p<0.05) evidently increased from the countryside
to the cities and was 18, 28, 30 and 36 at DBT, WD, BD and BJ, respectively.
The significant correlations among the species could be classified into three
types: (1) associated with OC and EC, (2) associated with Ca2+ and
Mg2+ and (3) associated with K+. Three types of significant
correlations at DBT were independent of each other, whereas they were
involved in interrelation more and more from WD to BJ. The independence of
the three types of significant correlations at DBT further confirmed that
residential coal combustion was the preferentially dominant source of
atmospheric OC and EC. The strong correlations among OC, EC, NO3-,
NH4+ and Cl- at DBT indicated that the OC and EC that
emitted from residential coal combustion could quickly accelerate secondary
formation of NO3-, NH4+ and Cl- via heterogeneous
or multiphase reactions of NOx, NH3 and HCl. It has been
verified that they are emitted from residential coal combustion (Wang et al.,
2005; Shapiro et al., 2007; Bläsing and Müller, 2010; Meng et al.,
2011; Zhang et al., 2013; Gao et al., 2015; Li et al., 2016a; Huang et al.,
2016). The interrelation for the three types of significant correlations at
WD, BD and BJ implied that complex sources including local emissions and
regional transportation were dominant for atmospheric species in the cities.
The species associated with Ca2+ and Mg2+ from
construction and road dust (Yang et al., 2011; Liang et al., 2016) as well as
the species associated with K+ from biomass (municipal solid waste)
burning (Gao et al., 2011; J. Li et al., 2014; Yao et al., 2016) in the
cities would accumulate under stagnant air conditions at the earth surface;
meanwhile the OC and EC concentrations could also increase due to the air
parcel transportation with abundant OC and EC in the upper layer from the
south–southwest directions (Fig. 8). It is interesting to note that the
strong correlations among OC, EC, NO3-, NH4+ and
Cl- were found at the four sampling sites, whereas the strong
correlation between OC (or EC) and SO42- was only found at BJ.
Because the sampling sites of DBT, WD and BD are close to the source of OC
and EC from the residential coal combustion, the strong correlations among
OC, EC, NO3-, NH4+ and Cl- and the non-existent
correlation between OC (or EC) and SO42- implied that the
formation rate of SO42- via heterogeneous or multiphase reactions
might be relatively slower than those of NO3-, NH4+ and
Cl-. The reactive uptake coefficients of SO2 oxidation by
O3 have been reported to be from 4.3×10-8 to 7×10-7 on different mineral aerosols and from 1×10-6 to 6×10-6 on soot particles (Wu et al., 2011; Song et al., 2012), which
is at least 1 order of magnitude less than those of NO2 (1.03×10-2–3.43×10-3 on soot particles and 1.03×10-6–1.2×10-5 on mineral aerosols; Underwood et al., 2001;
Esteve et al., 2004; Ma et al., 2011, 2017). The OC, EC and SO2
emitted from the residential coal combustion experienced a relatively long
period of transportation to Beijing, resulting in a strong correlation
between OC (or EC) and SO42- at BJ.
The statistical correlations for [Cu+Zn] vs. [Pb]
and [As+Se] vs. [Pb] in PM2.5 at the four sampling
sites during the sampling period in the winter of 2015. The uncorrelated
results are also marked below zero on the y axis. The red and black symbols
represent p<0.05 and p<0.01.
![]()
The correlations between [Zn] vs. [Cu] and [As] vs. [Se] in
PM2.5 at the four sampling sites during the sampling
period
in the winter of 2015.
Elements
BJ
BD
WD
DBT
(n=21)
(n=21)
(n=19)
(n=20)
[Zn] vs. [Cu]
0.607b
0.479a
0.620a
0.659b
[As] vs. [Se]
0.662b
0.664b
0.959b
0.871b
a,b represent p<0.05 and p<0.01.
As listed in Table 5, the pronounced correlations for [As] vs. [Se] and [Cu]
vs. [Zn] at the four sampling sites indicated that the two pairs of elements
were from the common sources. Based on the remarkable elevations of As and Se
near a coal-fired power plant in comparison to the background site,
Jayasekher (2009) pointed out that their significant correlation can be used
as the tracer for coal combustion. Because Cu and Zn have been found to be
mainly released from the additives of vehicle-lubricating oils, brake and
tyre wear during transportation activities (Yu et al., 2013; Zhang et al.,
2013; Tan et al., 2016), their significant correlation has been used as a
tracer for vehicle emissions. Both coal combustion and vehicle emissions
could make a contribution to atmospheric Pb (Zhang et al., 2013; J. Gao
et al., 2016), and thus the correlations for [Pb] vs. [Cu +Zn] and [Pb] vs.
[As +Se] could reflect their local dominant sources. As shown in Fig. 10, a
moderately strong correlation between [Pb] and [Cu +Zn] but non-existent
correlation between [Pb] and [As +Se] was found at BJ, whereas the
correlations at the rural site of DBT indicated that atmospheric Pb, Cu and
Zn at BJ were mainly related to the vehicle emissions, and atmospheric Pb, As
and Se at DBT were dominated by residential coal combustion. Because the
sampling sites of BD and WD were affected by both vehicle emissions and
residential coal combustion, the moderately strong correlations between [Pb]
and [Cu +Zn] as well as [Pb] and [As +Se] were found at the two sampling
sites. Although there was a non-existent correlation between [Pb] and [As
+Se] at BJ, the contribution of residential coal combustion to atmospheric
PM2.5 in the city of BJ could not be excluded because the trace
elements from coal combustion are mainly present in relatively large
particles (0.8–2.5 µm), which might quickly deposit near their
sources (Wang et al., 2008).
Source apportionment of <inline-formula><mml:math id="M667" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">PM</mml:mi><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> at the four sampling sites
The proportions of source species under the constructed
chemical mass closures for PM2.5 at the four sampling sites during the
sampling period in the winter of 2015. Average mass concentrations of
PM2.5 at each sampling site, including all of source species and
unidentified fractions, are also marked above the bars.
![]()
The source characteristics of PM2.5 at the four sampling sites were
analysed by the CMC method, which has been described in detail in Sect. 2.3.
The average proportions of the species from different sources in
PM2.5 during the sampling period at the four sampling sites are
compared in Fig. 11. It is evident that secondary aerosols (SIA + SOC)
accounted for the largest proportion (about 32–41 %) in PM2.5,
followed by POC (about 24–28 %), EC (about 6–8 %), mineral dust
(about 2–8 %) and Clcc- (about 2–5 %) at the
four sampling sites. The proportion of mineral dust was highest at BJ and
lowest at DBT among the four sampling sites, whereas the proportion of
Clcc- was the other way around. Because the concentrations of
the mineral dust compounds were much higher under stagnant weather conditions
than under clear days at BJ, the remarkably high proportion of mineral dust
at BJ was mainly ascribed to the emissions from road dust and construction
(Liang et al., 2016) during the sampling period. The obviously high
proportion of Clcc- at DBT was ascribed to the emission from
residential coal combustion (Shen et al., 2016). In addition, the proportions
of TEO, Kbb+ and Clss- were less than about
2 %, which were insignificant to the sources of PM2.5 at the
four sampling sites during the sampling period.
The estimated contributions of coal combustion to the
PM2.5 at the four sampling sites during the sampling period in the
winter of 2015.
![]()
Atmospheric primary organic matter (POM) and Clcc- at the
four sampling sites could be estimated based on
POM≈POC×1.6 (Cheung et al., 2005; Hsu
et al., 2010b; Han et al., 2015) and the formulas (1)–(4), respectively. The
sum of POM, EC and Clcc- at DBT was assumed to be solely from
residential coal combustion, accounting for about 58 % in PM2.5
(Fig. 12). Assuming that the ratio of Clcc- to the sum of POM,
EC and Clcc- was constant for coal combustion at the four
sampling sites, the primary contribution of coal combustion to atmospheric
PM2.5 at BJ, BD and WD could be estimated to be 32, 49 and 43 %
(Fig. 12), respectively. The annual residential coal consumption mainly
focused on the 4 months in winter, accounting for about 11 % of the
total coal consumption in the region of BTH. Because the emission factor of
PM2.5 from residential coal combustion (about
1054–12 910 mgkg-1) was about 1–3 orders of magnitude greater
than those from industry boilers or coal power plants (about
16–100 mgkg-1; Chen et al., 2005; Zhang et al., 2008), the
estimated proportions of the contribution of coal combustion to atmospheric
PM2.5 at the four sampling sites during the winter were mainly
ascribed to residential coal combustion. If only primary PM2.5 was
considered, the contribution of residential coal combustion to the
primary PM2.5 at BJ would be about 59 %, which was
in line with the value of 57 % estimated by J. Liu et al. (2016) for the
winter of 2010 in Beijing.