Introduction
In recent years, air pollution has become a top environmental issue in
China, and the main concern is fine particulate matter less than 2.5 µm
in diameter (PM2.5) (Huang et al., 2014; Sheehan et al.,
2014). Fine particulate aerosols have a strong adverse effect on human
health, visibility, and directly or indirectly affect weather and climate
(Pui et al., 2014; Chen et al., 2013; Lu et al., 2015; J. Tao et al.,
2014). The negative effects on public health, including damage to the
respiratory and cardiovascular systems, the blood vessels of the brain, and
the nervous system, have triggered both public alarm and official concern in
China (Kessler, 2014). In response to this great concern, the
Chinese government has introduced the Action Plan for Air Pollution
Prevention and Control (2013–17), which aims at marked improvements in air
quality until 2017. In the plan, the strictest regulation for improvement
is a reduction of 25 % in the annual average concentrations of PM2.5
by 2017 (Chinese State Council, 2013). It has been applied in North
China because the region has become the most severely polluted area in
China, characterized by increasingly frequent haze events and regional
expansions of extreme air pollution (Hu et al., 2015; Boynard et al.,
2014).
The key point in reducing PM2.5 concentrations is to control their sources. Reliable source identification and quantification are essential for
the development of effective political abatement strategies. However, the
sources of PM2.5 typically emit a mixture of pollutants, including gas
and particle phases. They would mix further in the atmosphere and can
undergo chemical transformations prior to impacting a specific receptor
site, making it difficult to quantify the impacts (Balachandran et al., 2013). This encourages researchers
to use more sophisticated techniques to quantify the contribution of
individual sources to PM2.5 concentrations, such as the positive matrix
factorization (PMF) modeling (Paatero and Tapper, 1994), chemical
mass balance (CMB) modeling (Chow and Watson, 2002), organic
tracers (Ding et al., 2013) and stable carbon isotopes
(Cao et al., 2011). However, these different approaches
often result in source contributions that can differ in magnitude and/or are
poorly correlated, and the most reliable one cannot be determined
(Balachandran et al., 2013). Radiocarbon (14C)
measurement provides a powerful tool to unambiguously determine fossil and
nonfossil sources of carbonaceous particles, and the method has been used
in source apportionment of carbonaceous aerosols in China (Zhang et al.,
2015; Liu et al., 2013; Liu et al., 2014). The underlying principle of
14C measurement is that radioisotope carbon has become extinct in
fossil fuel due to its age (half-life 5730 years), while nonfossil carbon
sources contain the contemporary or near contemporary radiocarbon level
(Szidat, 2009; Szidat et al., 2004). This method provides a more reliable
source apportionment of PM2.5 through links with other methods, although
it focuses only on carbonaceous aerosols.
In the present study, source apportionment of PM2.5 using PMF linked
with 14C analysis at a regional background site in North China during
winter was carried out using PMF simulation, in which the source contribution
of carbonaceous species was confirmed by the 14C measurement. The effort is vital for the development of
efficient mediation policies to improve the air quality in North China
because regional source apportionment cannot be replaced by those extensively
focused on the metropolitan areas such as Beijing Zhang et al., 2013), Tianjin
(Gu et al., 2011), Jinan (Gu et al.,
2014) and others within North China. Thus, we collected continuous aerosol
samples on Qimu Island during winter to apportion PM2.5 sources. The
objectives of this study are (1) to determine the concentration burden and
the chemical composition of PM2.5, (2) to distinguish the source
signals based on the chemical composition grouped according to the
trajectory clusters, and (3) to apportion PM2.5 sources using the PMF
model linked with 14C measurement.
Results and discussion
General characteristics of PM2.5 and chemical components
Table S2 in the Supplement is a statistical summary of the concentrations of PM2.5,
water-soluble ions, carbonaceous species and metal elements during the
sampling period. As shown, the mean concentration of PM2.5 was 77.6 ± 59.3 µg m-3, which was more than 2 times higher than the
grade I national standards (35 µg m-3; Ministry of Environmental
Protection of China: GB 3095-2012, www.zhb.gov.cn, 29 February 2012). Although the
level of PM2.5 concentration on Qimu Island was higher than the
national standard, it was much lower than that observed in winter in the
megacities of North China, such as in Beijing (208 µg m-3 of
PM2.1 in 2013) (Tian et al., 2014) and Tianjin
(221 µg m-3 in 2013) (Han et al., 2014).
Pie charts showing the relative contribution of species for
PM2.5 in Qimu Island. Note the sum of percentage of identified
species in PM2.5 in (a) is 58.58 %, while that in (b) is 100 %
because the percentage is the ratio of every metal element to the total
identified metal elements.
The relative contribution of species for PM2.5 is displayed in Fig. 3.
Generally, water-soluble inorganic species (WSIS) were the dominant species,
accounting for 43 ± 16 % of PM2.5 mass concentrations. Among
the ions, SO42- ranked the highest with a mean concentration of
14.2 ± 18.0 µg m-3, followed by NO3- (11.9 ± 16.4 µg m-3)
and NH4+ (3.11 ± 2.14 µg m-3). The sum of the three secondary inorganic aerosols constituted the
majority (88 ± 12 %) of the total WSIS concentrations. In addition,
the average concentrations of OC and EC were 6.85 ± 4.81 and 4.90 ± 4.11 µg m-3, accounting for 8.8 ± 2.1
and 6.3 ± 1.8 % of the PM2.5 concentrations, respectively. Total
concentrations of analyzed metal elements were 665 ± 472 ng m-3,
accounting for 0.86 ± 0.50 % of the PM2.5. Among the measured
metal elements, the concentration of Fe (408 ± 285 ng m-3) was
the highest, followed by Zn (107 ± 142 ng m-3) and Pb (88.4 ± 85.7 ng m-3).
At the sampling site, the organic matter was clearly lower but the relative
contributions of SO42-, NO3- and NH4+ to the
PM2.5 were significantly higher than those in the cities within North
China, such as Beijing and Tianjin (Zhang et al., 2013; Zhao et al.,
2013; Tian et al., 2016). The high contributions of SO42-,
NO3- and NH4+ agree with the regional-scale emissions
of their precursors in North China, as it has been reported that SO2,
NOx and NH3 emissions were, respectively, approximately 10, 5 and 5 times
higher compared to OC in the region (Zhao et al., 2012). This finding was also in
agreement with results measured at Changdao Island
(Feng et al., 2012), which is located at the
demarcation line between the Bohai Sea and the Yellow Sea and is a popular
resort with little industry approximately 7 km north of the Shandong
Peninsula (Feng et al., 2012). Measurements at
the island were interpreted as the indicative patterns of atmospheric
outflow and regional pollution in North China (Feng et al., 2012, 2007). It suggested that our measurements also provide a regional
signal of PM2.5 pollution in North China. Furthermore, SO42-
was the largest contributor of PM2.5, and this characteristic is
usually regarded as a regional pollution signal in winter. This is because
there is a lack of a fast conversion rate of SO2 to SO42- in
clouds or aerosol droplets and oxidation reactions via OH free radicals
under low-temperature conditions in PM2.5 source areas (Hu
et al., 2015). Thus, our measurement largely reflected a pollution pattern
on a regional scale, rather than just in source areas.
Source signals based on cluster analysis
As shown in Fig. 1, the 48 h back trajectory clusters indicate that more
than half of the air masses (54 %) during the sampling period were from
the BTH region, followed by the air masses from Mongolia (35 %). Air
masses of these two types traveled about 200 and 250 km, respectively, over
the Bohai Sea before arriving at the sampling site. Thus, the atmospheric
pollutants carried by the two kinds of air masses were mixed well during
transport, creating regional pollution signals. Only a small part of the air
masses (11 %) was from the Shandong Peninsula, potentially reflecting a
mixed contribution of local and regional sources from the southern area of the
sampling site. In addition, only one trajectory in cluster 3 (SDP) passed
the urban area of Longkou, when PM2.5 concentration was measured at
95.3 µg m-3. This level was lower than the average of PM2.5
concentrations in cluster 3, indicating a minor contribution of local source
emissions. In order to reveal the pollution patterns and source signals of
PM2.5 carried by air masses from the three different regions, chemical
species of PM2.5 were grouped according to the three trajectory
clusters, as listed in Table 1.
Statistics of PM2.5 chemical species in different clusters and
significant level by mean test.
Species
Mean ± standard deviation (range)
Significant level
(unit)
Cluster 1 (n=42)
Cluster 2 (n=25)
Cluster 3 (n=9)
1&2
1&3
2&3
PM2.5 (µg m-3)
93.0 ± 66.1 (24.5–305)
41.6 ± 26.7 (12.7–143)
106 ± 42.3 (50.3–193)
0.00
0.59
0.00
EC (µg m-3)
6.53 ± 4.66 (1.39–19.6)
2.50 ± 1.84 (0.800–8.85)
3.94 ± 1.49 (2.53–7.66)
0.00
0.11
0.05
OC (µg m-3)
8.58 ± 5.23 (1.45–21.3)
3.51 ± 2.35 (0.810–11.4)
8.04 ± 2.32 (5.25–13.5)
0.00
0.76
0.00
Cl-(µg m-3)
2.37 ± 2.11 (0.100–8.90)
1.22 ± 0.650 (0.200–2.85)
2.94 ± 1.35 (1.42–5.53)
0.01
0.45
0.00
NO3-(µg m-3)
17.6 ± 19.6 (1.75–87.0)
2.75 ± 4.25 (0.270–20.1)
10.6 ± 6.09 (4.41–20.3)
0.00
0.30
0.00
SO42- (µg m-3)
19.4 ± 21.8 (2.09–96.2)
4.55 ± 4.06 (1.37–19.5)
16.4 ± 8.74 (5.34–35.6)
0.00
0.69
0.00
Na+(µg m-3)
0.380 ± 0.240 (0.05–1.58)
0.550 ± 0.260 (0.180–1.08)
0.310 ± 0.06 (0.220–0.400)
0.01
0.41
0.01
NH4+ (µg m-3)
3.97 ± 2.29 (1.28–10.1)
1.53 ± 0.980 (0.610–4.70)
3.52 ± 0.96 (1.93–4.90)
0.00
0.57
0.00
K+(µg m-3)
1.11 ± 0.740 (0.28–3.10)
0.350 ± 0.360 (0.07–1.69)
2.01 ± 0.93 (0.780–3.95)
0.00
0.00
0.00
Mg2+(µg m-3)
0.03 ± 0.03 (0.01–0.17)
0.03 ± 0.02 (0.01–0.11)
0.02 ± 0.01 (0.01–0.04)
0.66
0.41
0.13
Ca2+(µg m-3)
0.370 ± 0.220 (0.110–1.32)
0.370 ± 0.180 (0.07–0.74)
0.440 ± 0.290 (0.09–0.97)
1.00
0.46
0.46
Ti (ng m-3)
6.96 ± 5.98 (0.350–25.9)
10.9 ± 9.10 (0.01–30.7)
2.51 ± 0.85 (1.16–3.58)
0.04
0.03
0.01
V (ng m-3)
4.68 ± 2.29 (0.760–11.3)
2.83 ± 2.55 (0.450–12.4)
3.24 ± 1.50 (2.05–7.12)
0.00
0.08
0.66
Mn (ng m-3)
33.8 ± 31.3 (1.97–108)
17.6 ± 19.3 (1.38–95.4)
40.9 ± 20.3 (9.14–69.8)
0.02
0.53
0.01
Fe (ng m-3)
404 ± 308 (7.12–1588)
375 ± 263 (9.13–826)
521 ± 188 (244–960)
0.70
0.29
0.15
Co (ng m-3)
0.260 ± 0.200 (0.01–0.73)
0.170 ± 0.140 (0.01–0.48)
0.360 ± 0.130 (0.100–0.590)
0.08
0.14
0.00
Ni (ng m-3)
4.85 ± 2.56 (1.68–13.8)
3.51 ± 1.85 (1.68–6.79)
3.80 ± 1.02 (2.45–5.84)
0.03
0.24
0.67
Cu (ng m-3)
11.6 ± 13.6 (0.720–77.7)
3.06 ± 2.93 (0.03–8.99)
13.9 ± 7.05 (3.90–26.4)
0.00
0.64
0.00
Zn (ng m-3)
146 ± 176 (9.92–987)
46.4 ± 50.1 (5.56–208)
90.4 ± 47.4 (24.2–201)
0.01
0.36
0.03
As (ng m-3)
9.03 ± 9.52 (1.11–43.4)
3.00 ± 2.82 (0.67–14.0)
5.35 ± 3.35 (2.25–13.6)
0.00
0.27
0.06
Cd (ng m-3)
2.70 ± 5.26 (0.110–25.9)
0.450 ± 0.410 (0.04–1.29)
1.54 ± 0.65 (0.490–2.66)
0.04
0.52
0.00
Pb (ng m-3)
110 ± 95.3 (5.30–412)
36.9 ± 44.8 (3.02–176)
128 ± 53.2 (45.4–215)
0.00
0.59
0.00
Generally, the mean test showed that the concentration levels and most abundant
species types of PM2.5 in clusters 1 (BTH) and 3 (SDP) are both
insignificantly different (p>0.05) and statistically
higher than in cluster 2 (MON) (p< 0.01), as shown in
Table 1. The patterns observed are consistent with the spatial distributions
of their emissions and concentrations in North China; as reported, there are
higher emissions and more severe pollution in the BTH region and Shandong
Province than in Inner Mongolia and Liaoning (Zhao et al., 2012; Yang et
al., 2011). Compared with the Shandong Peninsula, the pollution in the BTH
region may be even worse because it travels much longer distances to the
sampling site, yet the difference of the PM2.5 concentrations
attributed to the two areas is trivial. In addition, the mean wind speed of
cluster 2 (MON) was 7.60 m s-1, which was dramatically higher than that
of cluster 1 (BTH) (4.79 m s-1) and cluster 3 (SDP) (4.86 m s-1).
Wind speeds were determined by averaging hourly moving distances of air
masses during a 48 h period. The higher wind speed of cluster 2 might partly
contribute to the lower PM2.5 level at the sampling site, since high wind
speed could provide favorable diffusion conditions for atmospheric
pollutants.
Some anomalies compared with previous discussions provided different source
signals amongst the clusters. For instance, K+ concentration was
significantly higher in cluster 3 (SDP) than in cluster 1 (BTH), while the
Ti concentration was obviously lower. This reflected relatively high
emissions of K+ in the Shandong Peninsula and Ti in the BTH region from
both natural sources and anthropogenic activities. Likewise, the
concentration of Na+ in cluster 2 (MON) was much higher than in
clusters 1 and 3, showing a large contribution from sea salt particles
generated by sea spray under high wind speed to PM2.5 concentrations.
This suggested that sea salt sources should not be ignored in this study
because of the proximity of the sampling site to the Bohai Sea.
Sea salt emissions are comprised of Cl-, SO42-, Na+,
K+, Mg2+ and Ca2+ (Ni et al., 2013). The
amounts of different chemical species in sea salt emissions can be
determined using Na+ as the tracer of sea salt; the amounts of these
species from non-sea-salt (nss) emissions can be expressed as
nss-x=x-[Na+]×a,
where x indicates the Cl-, SO42-, K+, Mg2+ and
Ca2+ concentrations and a is the typical equivalent concentration
ratio of the corresponding species to Na+ in average seawater:
Cl- / Na+ (1.80), SO42- / Na+ (0.250), K+ / Na+
(0.036), Mg2+ / Na+ (0.120) and Ca2+ / Na+ (0.038)
(Ni et al., 2013). If the calculated concentration of non-sea-salt chemical species is negative, then no excess species exist. According
to the calculation, for corresponding total chemical concentration levels
grouped in clusters from 1 (BTH) to 3 (SDP), nss-Cl- accounted for 55 ± 29, 19 ± 24 and 77 ± 10 % of total Cl-;
nss-SO42- accounted for 99 ± 2, 95 ± 4 and 99 ± 0.3 % of total SO42-; nss-K+
accounted for 98 ± 3, 89 ± 9 and 99 ± 0.3 % of total K+;
nss-Ca2+ accounted for 95 ± 4, 91 ± 10 and 96 ± 3 % of total Ca2+. Thus, marked contributions of nss-emission
sources to chemical concentrations at all three clusters were found.
However, these values may be underestimated, since total Na+
concentrations do not necessarily originate from sea salt alone but could
partially come from dust and burning sources
(Zhang et al., 2013). In addition, the loss
of Cl- particles due to a chloride depletion mechanism further supports
the underestimation of Cl-. The contributions of nss sources were lower
in cluster 2 (MON) than in clusters 1 (BTH) and 3 (SDP), which was
attributed to the high emissions of sea spray coupled with high wind speed
in cluster 2. Generally, K+ is often used as a tracer for biomass
burning. The high K+ concentration and the largest contribution of
nss-K+ in cluster 3 (SDP) indicated a clear high emission associated
with agricultural burning in the Shandong Peninsula. This finding agreed
with the fact that Shandong province is the largest producer of crop
residues in North China (Zhao et al., 2012), and
biomass burning is an important source of inorganic and organic aerosols in
the Bohai Sea atmosphere (Feng et al., 2012; Wang et al., 2014). The
contribution of nss-Mg2+ to total Mg2+ concentration was less than
4 % for the all clusters, indicating the species came mostly from sea salt
emission. The mass ratio of Mg2+ to Na+ was 0.07 ± 0.06,
0.06 ± 0.03 and 0.06 ± 0.03 for clusters from 1, 2 and 3,
respectively. The ratios were less than 0.23, also demonstrating that
Mg2+ mainly came from a sea salt source
(Zhang et al., 2013).
The ratios of OC / EC and NO3- / nss-SO42- were used as
tracers to assess the source signals of the three clusters. Low-temperature
burning, such as agricultural residue burning, emits more OC compared with
high-temperature burning, e.g., vehicle exhaust (Gibson et al., 2013; Cui
et al., 2016). Thus, the ratio of OC / EC is often used to evaluate relative
contributions of low- and high-temperature burning emission
(Zhao et al., 2012). The OC / EC ratios were 1.41 ± 0.30, 1.47 ± 0.29 and 2.14 ± 0.50 for clusters 1, 2 and 3,
respectively. The mean test showed that the differences between cluster 1 (BTH)
and cluster 2 (MON) ratios were insignificant at a 95 % confidence level,
and both of them were statistically lower compared with that of cluster 3
(SDP) at the same confidence level. This suggests that low-temperature
burning contributed clearly to the emission in cluster 3 (SDP), while high-temperature burning emission was more distinct in clusters 1 (BTH) and 2
(MON). Furthermore, mobile sources, such as vehicles, emit more NOx
than SO2, while stationary sources, such as coal-fired power plants,
emit more SO2 than NOx (Wang et al.,
2005). These two precursors convert into SO42- and NO3-
in the atmosphere, and the two type sources show different ratios of
NO3- / SO42-. Hence, this ratio is usually adopted as an
indicator of the relative importance of mobile versus stationary sources
of sulfur and nitrogen in the atmosphere (Zhao et al., 2013; Liu et al.,
2014). After deducting the contribution of sea salt to SO42-, the
mean ratios of NO3- / nss-SO42- were 0.96 ± 0.31,
0.47 ± 0.24 and 0.64 ± 0.14 for clusters 1, 2 and 3, respectively.
The mean test showed that the three cluster ratios exhibit significant
differences from each other at a 95 % confidence level. The highest ratio
in cluster 1 (BTH) suggests that mobile sources are the most important
contributors in the BTH region, followed by the Shandong Peninsula (cluster
3). The ratio of NO3- / nss-SO42- in cluster 1 was within
the range of those found in large cities, such as Beijing (1.20), Tianjin
(0.73) and Shijiazhuang (0.76), the capital of Hebei province
(Zhao et al., 2013), reflecting a hybrid
contribution from the BTH region. The value in cluster 2 (MON) was slightly
lower than that in winter in Chengde (0.55), a city located in the northern
mountainous area of Hebei Province (Zhao et
al., 2013). It indicated a more obvious contribution of stationary source
emissions in areas such as eastern Inner Mongolia and the west part of
Liaoning than the BTH region and the Shandong Peninsula. In addition, these
stationary source emissions are possibly associated with coal combustion
because of the lower OC / EC ratio in cluster 2 (MON) compared to cluster 3
(SDP).
Source apportionment of carbonaceous PM2.5
The cluster analysis indicated that PM2.5 concentrations increased
significantly when air masses came from the BTH region and the Shandong
Peninsula during the sampling period. The chemical species in PM2.5
from the BTH region possessed a clear signal of high-temperature burning and
mobile sources, while those from the Shandong Peninsula had more obvious
patterns of low-temperature burning and stationary sources.
Concentration and contemporary carbon fraction of carbonaceous
species in M1 and M2.
M1
M2
M1
M2
PM2.5 (µg m-3)
159 ± 0.510
91.8 ± 0.490
OC (µg m-3)
12.7 ± 0.700
9.01 ± 0.510
fc (OC)
0.59 ± 0.04
0.46 ± 0.04
WSOC (µg m-3)
6.42 ± 0.410
3.70 ± 0.200
fc (WSOC)
0.59 ± 0.03
0.49 ± 0.03
WIOC (µg m-3)
6.30 ± 0.620
5.31 ± 0.400
fc (WIOC)
0.60 ± 0.03
0.43 ± 0.03
EC (µg m-3)
8.60 ± 0.500
5.80 ± 0.310
fc (EC)
0.52 ± 0.02
0.38 ± 0.01
Table 2 lists the concentrations and contemporary carbon fractions of OC,
WSOC, WIOC and EC of the two combined samples, which were selected via a
perfect synoptic process during the sampling period. The fraction of OC was
yielded by the average weights of concentrations of WSOC and WIOC fractions.
It can be expressed as
fc(OC)=[fc(WSOC)×c(WSOC)+fc(WIOC)×c(WIOC)]/[c(WSOC)+c(WIOC)],
where fc(OC), fc(WSOC) and fc(WIOC) are the
contemporary carbon fractions of OC, WSOC and WIOC, and c(WSOC)and c(WIOC)
are the concentrations of WSOC and WIOC, respectively. Generally, WSOC is
mainly associated with biomass burning and secondary formation
(Du et al., 2014), while OC directly emitted from the
combustion of fossil fuel is mostly water insoluble (Weber
et al., 2007). During the earlier stage of the synoptic process, the
concentrations of WSOC and WIOC were 6.42 ± 0.41 and
6.30 ± 0.62 µg m-3, respectively. Later on, the
concentrations of the two carbonaceous fractions fell to 3.70 ± 0.20 and 5.31 ± 0.40 µg m-3 after
the shift of the dominant wind direction from southerly to northwesterly, as
shown in Fig. 2. The fraction of WSOC to OC decreased from 50 ± 3
to 41 ± 3 %, and the WIOC fraction increased from 50 ± 4 to
59 ± 6 % before and after the shift of the dominant wind direction.
This suggested that the contribution of fossil fuel combustion was more
obvious in the BTH region than in the Shandong Peninsula. The contemporary
carbon fractions of WSOC and WIOC decreased from 0.59 ± 0.04 to 0.49 ± 0.03 and from 0.60 ± 0.03 to 0.43 ± 0.03,
respectively, which indicated a decrease in the impact of biogenic and
biomass burning emission and an increase in the contribution of fossil fuel
combustion to the two OC fractions. After the weighted average of the WSOC
and WIOC fractions, the fc(OC) values were 0.59 ± 0.04 and 0.46 ± 0.04
for the M1 and M2 samples, respectively. Together with
fc (EC), we determined that biogenic and biomass burning emission
contributed 59 ± 4 % of OC and 52 ± 2 % of EC
concentrations when air masses were from the Shandong
Peninsula. After the change in wind direction, the contribution of biogenic
and biomass burning emission fell to 46 ± 4 % for OC and 38 ± 1 % for EC, which suggested that fossil fuel combustion
contributed a dominant portion of the carbonaceous aerosols from the BTH
region.
The synoptic process clearly showed a shift of the dominant wind from
southerly to northwesterly, namely from the Shandong Peninsula to the BTH
region. Meanwhile, the pattern of biogenic and biomass burning emission
became more and more weak, and the signal of fossil fuel combustion became
more and more obvious. This was in agreement with our previous discussion.
For instance, emissions in the BTH region exhibited more signals of high-temperature burning and vehicle exhaust. It was characterized by the lower
ratio of OC / EC (1.41 ± 0.30), the higher ratio of
NO3- / nss-SO42- (0.96 ± 0.31) and the relatively
lower concentration of nss-K+ compared with those in the Shandong
Peninsula (2.14 ± 0.50 for OC / EC ratio; 0.64 ± 0.14 for
NO3- / nss-SO42- ratio). The contribution of the biogenic
and biomass burning emission to carbonaceous aerosols in the Shandong
Peninsula was still significant, which has often been mentioned in previous
studies (Feng et al., 2012; Zong et al., 2015; Wang et al., 2014),
although there was much combustion of fossil fuel (e.g., coal) for not only
industrial activity but also heating in winter.
Source apportionment of PM2.5
The EPA PMF 5.0 model was used together with a data set of 76 × 22
(76 samples with 22 species) to further quantitatively estimate the source
contributions of PM2.5 (Bressi et al., 2014; Choi et al., 2013).
After iterative testing from 5 to 15 factors in model exercises, we found
the minimum deviation of the source apportionment of OC and EC between the
results from 14C measurement and a PMF model scenario with an
Fpeak value of 0 and the lowest Q values (6245). In addition, the model
uncertainty was also explored as shown in text S1 in the supplement.
The contribution profiles of eight sources identified by PMF model.
Based on PMF modeling results, eight source factors were identified, as shown
in Fig. 4. Traffic emission has attracted considerable concern in the
megacities of China (e.g., Beijing and Shanghai) due to the remarkable growth
of vehicle numbers in China (Jing et al., 2016; Zheng et al., 2014). In
Beijing in 2012, on-road vehicles were estimated to be the largest local
emission source and contributed 22 % of PM2.5, including primary and
secondary including primary and secondary PM2.5, but excluding
vehicle-induced road dust (S. Zhang et al., 2014). The first source factor
was characterized by high loadings of NO3-, SO42-,
NH4+, OC, EC, Zn and Cu, which matched a vehicle emission profile
(Zhang et al., 2013). Generally,
NO3-, SO42-, OC and EC are mainly from engine exhaust
emissions (Dorado et al., 2003), and NH4+ is from
vehicles equipped with three-way catalytic converters (Chang
et al., 2016). Not only Zn and Cu but also Pb and Cd are emitted directly
as bound particles from exhaust (Tan et al., 2014). In addition,
the high NO3- / SO42- ratio of 1.28 calculated in the PMF
result suggested high-temperature burning and vehicle emissions
(Gelencser et al., 2007). This source was the largest
contributor of NO3-, which contributed 41 % during the sampling
period. The contribution was higher than 31 % of NOx emitted by
traffic sectors in North China in 2003, an expected increase in the
contribution due to the rapid rise of vehicles in North China in recent years
(Y. Shi et al., 2014). This factor was the prevalent anthropogenic PM2.5
source in North China, with an average contribution of 16 % during the
sampling period. The contribution was lower than that in Beijing (S. Zhang et
al., 2014), agreeing with the regional contribution characteristic in our
study, rather than ones in large cities (Zhou et al., 2016). The second
factor consisted of mineral dust elements, such as Mn, Fe and Co, and
chemical species from human activities, such as Zn and EC (Khan et al.,
2016), showing a mixed pattern of natural and anthropogenic emissions.
Vehicle emission is an important source of atmospheric Zn pollution because
it can be emitted from direct exhaust, lubricating oil additives, tire and
brake abrasion, wearing and corrosion from anticorrosion galvanized
automobile sheet metal, and re-entrainment dust enriched with Zn
(Duan and Tan, 2013). Thus, the source factor was
identified as traffic dust under the relatively high contribution of vehicle
emission to PM2.5 concentration.
The third source factor was ship emissions, typically characterized by high
proportions of Ni and V and a high V / Ni ratio
(Cappa et al., 2014). High loading of
these two metals is typically associated with emissions from residual oil,
probably derived from shipping activities and some industrial processes
(Pey et al., 2013). In addition, a V / Ni ratio of more
than 0.7 is always considered a sign of PM2.5 influenced by shipping
emissions (F. Zhang et al., 2014). The average ratio of
V / Ni from the measured data was 0.93 ± 0.46, indicating an obvious
contribution of shipping emission. The average ratio of V / Ni calculated from
the PMF source profile was 1.02, which was the second-highest value amongst
those derived from the eight sources. The highest value of 1.29 was for the
mineral dust source, agreeing with a high ratio of 3.06 for soil background
concentrations of the two metals in mainland China
(Pan et al., 2013).
The fourth factor showed high loadings of Cu, Zn, As, Cd and Pb, which were
treated as signals of industrial processes (Amil et al.,
2016). Emissions from the iron and steel industry are possibly important
amongst those industrial processes for two reasons. One is that the
sintering process in the iron and steel industries emits large amounts of
Pb, Hg, Zn and other heavy metal pollutants, and other processes such as
iron-making and steel-making also emit fugitive dust containing high
concentrations of heavy metals (Duan and Tan, 2013).
The other reason is the huge scale of steel production in North China.
National statistical data show that China produced approximately half the
world's production of crude steel in 2014, and production in the BTH and
Shandong province was 25.3 and 7.8 %, respectively, of the total
amount in China, which is available at the website
(http://www.stats.gov.cn/tjsj/ndsj/). Thus, iron and steel industries are
likely the main atmospheric sources of the metal elements in this study. In
addition, the contribution of the source to SO42- was 12 %,
which was similar to previously reported contributions of industrial
processes to the amount of sulfur dioxide (15 %)
(Zhao et al., 2012).
The fifth source factor was biomass burning, characterized by high
concentrations of K+, OC, EC and NH4+, which have been used
extensively as tracers of biomass burning aerosols (Zhou et al., 2015;
J. Tao et al., 2014). The contribution of this source was significantly higher
in cluster 3 (SDP) than in clusters 1 (BTH) and 2 (MON), as listed in Table 3. Results agreed with more biomass burning emission in the Shandong
Peninsula, characterized by rich K+ and the high OC / EC ratio
(Zong et al., 2015). The average ratio of OC to EC
from this source was also the highest (1.84) amongst the eight identified
sources (0.23–1.84) calculated by the PMF modeling.
Averages of fractional contributions (%) from eight sources
identified by PMF model.
Vehicle
Traffic
Ship
Industrial
Biomass
Mineral
Coal
Sea
emission
dust
emission
process
burning
dust
combustion
salt
All
15.9
4.24
8.95
2.63
19.3
12.8
29.6
6.58
Cluster1
23.6
4.89
8.79
3.64
19.6
6.32
29.2
3.96
Cluster2
3.57
3.60
9.35
1.20
4.88
26.8
37.7
12.9
Cluster3
12.4
3.08
8.67
1.96
52.7
6.46
12.4
2.33
The sixth source factor was mineral dust, characterized typically by crustal
elements, such as Ca2+, Ti and Fe, which are often used as markers of
soil dust (Zhang et al., 2013). The
contribution of this source was obviously higher in cluster 2 (MON) than
that in clusters 1 (BTH) and 3 (SDP), corresponding to the high wind speed
in it. The average ratio of OC to EC (1.53) from this source was obviously
higher than that (0.23) from vehicle dust, possibly suggesting that the
source contributed more OC, mainly derived from biogenic dust, such as plant
debris.
The seventh source factor was characterized by high loadings of Cl-,
Na+, OC, EC, SO42-, Ni and As. Coal combustion is often
indicated by elevated Cl- linked with high Na+, OC and EC
(Zhang et al., 2013). This source was the
largest contributor of SO42- in the present study, matching with
the inventory results in North China (Zhao et al.,
2012). In addition, this source was the largest contributor of PM2.5,
as listed in Table 3, which agreed with the fact that coal combustion is the
predominant source of fine-particle aerosols over China
(Pui et al., 2014). High loadings of As and Ni
in the factor were also used as a marker for coal-fired power plant emissions
(Tan et al., 2016).
The last source factor was sea salt, characterized by high loadings of
Na+, Mg2+ and Cl-, which are related to the primary sea salt
aerosols produced by mechanical disruption of the ocean surface
(Gupta et al., 2015). Similarly to the second source
(mineral dust), high wind speed in cluster 2 (MON) made the contribution of
this source in cluster 2 higher than that in clusters 1 (BTH) and 3 (SDP). In
addition, the higher contribution fraction of Mg2+ compared to Cl-
in this source was in agreement with our previous discussion. The
concentration ratios of Cl- / Na+ and Mg2+ / Na+
calculated from the PMF source profile were 1.79 and 0.11, respectively,
similar to the corresponding ratios of the species (1.80 and 0.12,
respectively) in average seawater (Ni et al., 2013). The sea salt source
contributed 2.53, 15.2 and 1.93 % of OC concentrations in clusters 1, 2 and
3, respectively, but provided no EC contribution in any of the clusters. This
indicated that the source consists of sea-spray organic aerosol, which came
from the marine biogenic activities (Wilson et al., 2015).
The contributions of the eight sources to PM2.5 are summarized in Table 3. The total and cluster fractional contributions (%) from each source
were calculated based on the corresponding sample values simulated by PMF
modeling. Amongst the eight sources, coal combustion, biomass burning and
vehicle emissions were the largest contributors, which accounted for
29.6, 19.3 and 15.9 %, respectively, during the sampling period.
They were followed by mineral dust (12.8 %), ship emissions (8.95 %),
sea salt (6.58 %), traffic dust (4.24 %) and industrial process
(2.63 %). Generally, the source apportionment profile
of PM2.5 in cluster 1 (BTH) was similar to that during the whole
sampling period because the regional-scale pollution exhibited a pattern of
atmospheric outflow of PM2.5 mainly from the BTH region in winter
(Feng et al., 2007, 2012). A slight increase in the
contribution of vehicle emission in cluster 1 (BTH) corresponds to the great
concern about vehicle emission in megacities in China (Huo et
al., 2013). The source signals in cluster 2 (MON) were obviously different
from that in clusters 1 (BTH) and 3 (SDP). The strong northwesterly wind in
it provided more large-scale spatial signals of PM2.5 sources,
indicating that coal combustion (37.7 %) and mineral dust (26.8 %) were
the largest contributors in northern areas of China in winter. The large-scale
PM2.5 pattern linked to coal combustion agreed with the dominant
position of coal consumption in Chinese energy structure. Coal consumption
accounted for 66 % of primary energy in China in 2014 reported by the
national bureau of statistics of China (available at
http://www.stats.gov.cn/tjsj/ndsj/). Other than industrial consumption, coal
is additionally used for residential heating in northern areas of China
during winter (Wang et al., 2013). Although the
household use of coal accounts for a small portion of total coal consumption
in China, its release is still a major source of PM2.5 in winter
(Cao et al., 2012) since household stoves usually run
with no or outdated environmental protection equipment. Traffic emission, of
great concern in large cities, only contributed a minor part (3.57 %) of
PM2.5 on a large spatial scale because motor exhaust concentrates
mainly in urban areas. In addition, biomass burning emission dominated the
PM2.5 pollution when air masses came from the Shandong Peninsula. The
abundant emission from biomass burning was mainly attributed to residential
heating in the cold season (Wang et al., 2002; Hu et al., 2016).
The contributions of coal combustion, vehicle emission, industrial process,
and ship emission derived from the PMF modeling of OC and EC were classified as
fossil fuel combustion for comparison. Sea salt as a marine biogenic source
of OC was merged with biomass burning as contemporary carbon fractions.
However, mineral dust and vehicle dust were not considered for this
classification because they originated from hybrid sources of fossil and
contemporary carbon emissions. Figure 5 shows the comparison of the PMF
results and the 14C measurement.
Comparison of source apportionment of OC and EC in the two
specified samples (M1 and M2) from PMF and 14C measurement. B&B
refers to the source of biogenic and biomass burning. Note: B&B and
fossil emissions from the PMF result do not add up to 100 in the bars
because hybrid sources from B&B and fossil fuel combustion were not considered in the comparison (mineral dust and vehicle dust).
As described in Sect. 2.4, M1 represents the air masses from the Shandong
peninsula, while M2 represents the air masses from the BTH region. In
M1, the biogenic and biomass burning emission identified by PMF modeling
contributed 52 % to OC and 49 % to EC concentrations, which were 7 and
3 % below the fractions indicated by 14C measurement, respectively.
The contributions of fossil fuel combustion to OC and EC in the PMF result
were both 44 %, which is 3 % above and 4 % below the
corresponding values in the 14C result. Similarly, in M2, the biogenic
and biomass burning emission contributed 41 % to OC and 33 % to EC in
the PMF result, 4 and 5 % below the 14C result, respectively. The
contributions of fossil fuel combustion to OC and EC in the PMF result were
52 and 65 %, respectively, which was the same percentage (3 %) below
and above the corresponding values in the 14C result. In general, the
source contributions merged from the PMF result were lower than those from
the 14C measurement. This underestimation may be due to not considering
the contributions of mineral dust and vehicle dust. The largest difference
between PMF and 14C results was 7 %, indicating a minor contribution
of the two sources to carbonaceous species in PM2.5. The substantial
difference was the two overestimations with the same range (3 %); one was
the contribution of fossil fuel combustion to OC in M1, and the other was the
contribution of fossil fuel combustion to EC in M2. The overestimations were
attributed to irrelevantly classifying biogenic and biomass burning emission
as fossil fuel combustion. In conclusion, the minor irrelevant
classification suggested that the PMF result in this study provided a
reasonable source apportionment of regional PM2.5 in North China in
winter.
Implications for PM alleviation
According to the source apportionment results, coal combustion was the
largest contributor of PM2.5 in North China during winter. To alleviate
PM emissions, those generated by coal combustion should be targeted first.
It has been identified as the leading emission source to control in the air
pollution control program. The contribution of traffic emission to
PM2.5 showed a clear spatial pattern in North China. For example,
vehicle emission contributed significantly in the BTH region. Therefore,
vehicle emission ought to be the second major emission source to control.
Biomass burning emission needs close attention because it has only been considered in little detail in the control program. Indeed, the first national
pollution source survey demonstrated that Shandong province is the largest
producer of crop stalks, such as wheat and corn, in China
(Compilation Committee of the first China pollution source census, 2011).
The source survey showed a production of 132 million tons in Shandong in
2007 and about 20 million tons produced in the Shandong Peninsula (including
the cities of Weifang, Yantai, Weihai, Qingdao and Rizhao). Approximately
40 % was household fuel for cooking and heating in the peninsula
countryside. The fraction was significantly higher than in western areas of
Shandong province, such as Zibo (9 %) and Jinan (8 %), and the fraction
of the open burning of crop residues in the peninsula (3 %). The fraction of
biomass open burning in the peninsula was also higher than its average
fraction (1.5 %) in Shandong province in 2007
(Compilation Committee of the first China pollution source census, 2011).
Generally, emissions from agricultural field burning are mainly concentrated
in the harvest season and contribute greatly to regional haze and smog events
in the region, which have attracted particular concern (Feng et al., 2012;
Zong et al., 2015; Wang et al., 2014). Even so, open burning emission was
regarded a minor source contribution in the control program. In addition,
household emission of agricultural waste, another important source for
regional PM2.5, is continuous or semicontinuous. It can also induce
PM2.5 pollution on a regional scale, which has been disregarded or
ignored (Zhang and Cao, 2015).
Since the 1990s, the government has enacted a series of regulations to
prohibit open burning. However, it has not been fully controlled in China
although its supervision has been increased recently. The most basic reason is
the lack of a reasonable alternative to utilize or dispose of huge amounts
of agricultural waste each year. In the current scenario, some agricultural
wastes are stored as fuel for household cooking and heating, while others
are rapidly consumed by open burning in fields for the next planting.
Although farmers know that this disposal of agricultural residues is harmful
to the environment, they still tend to carry it out mainly due to the low costs of
this method. A more permanent solution would be to find higher economic
value of agricultural wastes via the development of renewable techniques. In
fact, agricultural wastes could be used to produce many kinds of renewable
energies, such as biogas, feedstuffs, biochar, bioethanol and bio-succinic
acid. China has enacted relevant energy regulations, legislation and policy
initiatives for rural renewable energy (Li et al.,
2015). The government has also encouraged and sustained the renewable energy
industry to increase the demand for raw feedstock. Through these efforts,
China has achieved some success in renewable development in rural areas.
However, these efforts are not an effective solution to the problem of
surplus crop waste because the costs and benefits of renewable energy could
not be offset. For instance, Zhangziying, a town located in the eastern area
of Beijing, has developed household biogas and straw gas since the 1980s.
But renewable energy only made up approximately 10 % of household energy
consumption in 2011, much lower than the fraction of coal (30 %)
(Li et al., 2015). Before crop residues can achieve a high economic value, the government should compensate
farmers for collecting such residue for use as feedstock for renewable energy and ban crop straw
burning (T. Shi et al., 2014). The revenue from the subsidy and the sale of
crop residues could help alleviate economic burdens on farmers, which would
encourage them to use clean energy, such as electricity, liquefied petroleum
gas, biogas, etc., for household consumption
(Kung and Zhang, 2015). These efforts will not only
significantly improve air quality, but also make farmers learn the
convenience of clean energy and wake from agricultural residue burning.
Summary and conclusion
During the sampling period, the average PM2.5 concentration was 77.6 ± 59.3 µg m-3,
and the SO42- concentration was the
highest among all constituents, with a mean of 14.2 ± 18.0 µg m-3, followed by NO3- (11.9 ± 16.4 µg m-3),
OC (6.85 ± 4.81 µg m-3), EC (4.90 ± 4.11 µg m-3) and NH4+ (3.11 ± 2.14 µg m-3). The
fractions of SO42-, NO3- and NH4+ to
PM2.5 were obviously higher than those in metropolises (e.g., Beijing
and Tianjin) within North China, while fractions of carbonaceous species
were markedly lower; these showed regional pollution signals.
More than half of air masses during the sampling period were from the BTH
region, followed by air masses from Mongolia (35 %) and the Shandong
Peninsula (11 %). The concentrations of PM2.5 and most of the species
carried by air masses from the BTH region and the Shandong Peninsula were
comparable (p>0.05), and they occurred in
statistically greater concentrations than those carried by the air masses
from Mongolia (p < 0.01). The PM2.5 had an obvious
signal of biomass burning emission, characterized by a high OC / EC ratio, low
NO3- / nss-SO42- ratio and high nss-K+ concentration
for the air masses coming from the Shandong Peninsula. In contrast, the
PM2.5 tested from the BTH region showed a vehicle emission pattern,
characterized by a low OC / EC ratio, high NO3- / nss-SO42-
ratio and low nss-K+ concentration. This finding was confirmed by the
14C measurement of OC and EC in two merged samples selected from a
successive synoptic process. The 14C measurement indicated that
biogenic and biomass burning emission contributed 59 ± 4 and 52 ± 2 % of OC and EC concentrations when air masses were from the
Shandong Peninsula, and the contributions fell to 46 ± 4 and 38 ± 1 %, respectively, when the prevailing wind changed and came from
the BTH region.
Based on the PMF modeling result, eight main source factors were identified.
The source contributions of OC and EC from PMF for the two merged samples
were compared with those indicated by the 14C measurement. Two minor
overestimations with the same range (3 %) showed the excellent capacity of
the model, suggesting that the PMF result provided a reasonable source
apportionment of regional PM2.5 in this study. The PMF result indicated
that coal combustion, biomass burning and vehicle emissions were the largest
contributors of PM2.5, accounting for 29.6, 19.3 and 15.8 %
of PM2.5, respectively, during the sampling period. Compared with the overall source apportionment result, the contribution of vehicle emission
increased slightly when air masses came from the BTH region, the fraction of
mineral dust and coal combustion rose clearly when air masses came from Mongolia at a high speed, and biomass burning became the dominant
contributor when air masses were from the Shandong Peninsula. Biomass
burning emission was highlighted in the present study because coal
combustion and vehicle emission have already been considered as major
emission sources in the government air pollution control program. Before the
achievement of a high economic value of biomass, the government should
compensate farmers for collecting it. The subsidy could help alleviate
economic burdens on farmers and encourage them to use clean energy, which will
significantly improve air quality.
Furthermore, the present study proposed that the minimum deviation between
the results from the PMF model and 14C measurement could be employed as a
criterion to select a more reliable solution for the source apportionment of
PM2.5. This method can also be applied to CMB models or other isotopes
(e.g., 13C, 15N and 35S), which will help to improve its scientific significance.