Characteristics of PM2.5 emissions from vehicles in the PRD region
PM2.5 mass, OC, EC, WSOC, WSII, metal elements
The PM2.5 mass emission factors ranged from 79.8 to 107 mg vehicle-1 km-1,
with an average of 92.4 ± 8.9 mg vehicle-1 km-1. Average OC and EC emission factors were
16.7 ± 1.9 and 16.4 ± 2.1 mg vehicle-1 km-1,
respectively, and they accounted for 18.1 ± 2.1 and 17.7 ± 2.2 %
of PM2.5 mass emissions. The ratio of OC to EC in the Zhujiang tunnel
ranged from 0.77 to 1.35, with an average of 1.03. Previous studies have
shown that the OC / EC ratio is useful to separate gasoline engine emissions
from diesel emissions. Higher values (> 2) are associated with GV
and LPGV exhaust, and lower values (0.3 to ∼ 0.9) are associated with DV
exhaust (Cadle et al., 1999; Cheng et al., 2010; Gillies and Gertler,
2000). Therefore, the low OC / EC ratios in this study, which are closer to
that from DV exhaust, indicate that diesel vehicles played an important role
in the PM2.5 emissions although the proportion of DV was only 13.7 %
during the sampling. Additionally, it should be noted that emissions of EC
from heavy-duty trucks are expected to be relatively low under the low-speed
operating conditions in the tunnel (Kweon et al., 2002).
Therefore, the ratio could be lower at the actual driving condition of
vehicle fleet with a higher speed on the road. The concentration of WSOC at
the inlet was 6.21 µg m-3 (Table S2) with a
percentage of 31.1 % of OC, which is close to that of ambient air (Ding
et al., 2008; Ho et al., 2006). At the outlet of the tunnel, the
concentration of WSOC was 8.00 µg m-3, representing 17.9 % of
OC. The WSOC had been reported to contribute on average 20 % to OC in the
exit of Marseille roadway tunnel (El Haddad et al., 2009), in which
background influence was included. The calculated emission factor of WSOC in
this study ranged from 0.5 to 2.8 mg vehicle-1 km-1 with an
average of 1.31 mg vehicle-1 km-1, which represents 7.84 %
of that of
OC. Such a WSOC fraction is considerably lower than that previously measured
for biomass burning particles (71 %) (Mayol-Bracero et al., 2002).
However, it could influence the hygroscopicity of particles and the
formation of secondary aerosols (Ho et al., 2006; Rogge et al., 1993b;
Weber et al., 2007) and is worthy of more attention and in-depth research.
The sum of WSII comprised about 9.8 % of the PM2.5 emissions, with
emission factors of 4.17, 0.104, 0.609, 2.88, 0.165, 0.177 and
0.953 mg vehicle-1 km-1 for Cl-, NO3-,
SO42-, Na+, NH4+, Mg2+ and Ca2+, respectively.
The other WSII had a minor contribution
(< 0.1 mg vehicle-1 km-1). In total, 27 measured metal
elements contributed 15.2 % to the PM2.5 emissions. Fe was the most
abundant element, with an emission factor of
3.91 mg vehicle-1 km-1, followed by Na
3.53 mg vehicle-1 km-1, Al 3.15 mg vehicle-1 km-1,
Ca 1.93 mg vehicle-1 km-1, Mg
0.496 mg vehicle-1 km-1 and K
0.338 mg vehicle-1 km-1, which accounted for 4.2, 3.8, 3.4, 2.1,
0.5 and 0.4 % of PM2.5 mass emissions, respectively. These six
elements contributed 95.0 % to the total metal emissions. Emission
factors of other metals ranged from 0.0001 (Ag) to 0.25
(Ba) mg vehicle-1 km-1, with a sum of
0.71 mg vehicle-1 km-1. It is worth noting that emission factors
of elements Na, K, Mg and Ca were significantly higher than those of their
corresponding water-soluble parts (Table S3). The differences can be
attributed to the water-insoluble matter carrying these metal elements, such
as calcium and magnesium carbonates and Na-, K- and Mg-bearing
aluminosilicate species (Pio et al., 2013).
PM2.5 mass was also obtained by summing OM, EC, geological component,
sea salt and major water soluble inorganic ions (NH4+,
SO42-, NO3-). OC was multiplied by 1.4 to estimate mass
of OM (He et al., 2008). The geological component of 35 mg vehicle-1 km-1 was estimated based
on the Al emission data as presented in Table S3. A typical road dust Al composition is 9 % on average
(Tiittanen et al., 1999). Sea salt of 9 mg vehicle-1 km-1 was
estimated by Na, assuming sea salt contains 32 % of Na. Thus, the average
PM2.5 reconstructed mass was 91.8 % of the gravimetric value. This
discrepancy can be attributed to the uncertainties in the weighing process,
the estimation methods and uncalculated components.
Organic compounds
The average emission factors and abbreviated names of 67 individual organic
compounds identified in the Zhujiang tunnel, including n-alkanes, polycyclic
aromatic hydrocarbons (PAHs), hopanes and steranes are listed in Table S4. These organic compounds accounted for 0.59 % of the OM
and 0.11 % of the PM2.5 mass emissions. The distributions of organic
molecular markers associated with PM2.5 are known to be source
indicative despite their small mass fractions (Schauer et al., 1996;
Simoneit, 1986). n-Alkanes are an important class of organic compounds in
atmospheric aerosols, and their homologue distribution may indicate
different pollution sources (Rogge et al., 1993a). In this study, the
n-alkane traces were dominated by C11–C36 with no odd–even carbon number
predominance and the maximum was at C24, consistent with the characteristics
of vehicle emissions reported by Simoneit (1984, 1985). The
emission factors of individual n-alkanes were in the range of 0.22
(C13) ∼ 13.3 (C24) µg vehicle-1 km-1 (Table S4).
There has been a worldwide concern to PAHs due to their known carcinogenic
and mutagenic properties. PAHs are thought to be the result of incomplete
combustion. In total, 15 priority PAHs (the results of naphthalene have not
been discussed in this study due to its low recovery) were identified and
quantified. The emission factor of total PAHs varied from 4.56 to
5.54 µg vehicle-1 km-1 in this study. The emission
factor of benzo[a]pyrene (BaP), which is often used as an indicator of PAHs
and regarded by the World Health Organization as a good index for whole PAH
carcinogenicity, was in the range of 0.37 to
0.46 µg vehicle-1 km-1. The emission factors for other
compounds ranged from 0.006 (acenaphthene) to 0.89
(pyrene) µg vehicle-1 km-1 (Table S4). Pyrene (PYR)was
the most abundant compound, followed by chrysene (CHR), benzo[ghi]perylene
(BghiP) and benz[a]anthracene (BaA), which is different from biomass burning
and coal combustion (Huang et al., 2014; Shen et al., 2012). PAHs diagnostic
ratios have been used as a tool for identifying pollution emission sources
including ANT / (ANT + PHE), FLA / (FLA + PYR),
BaA / (BaA + CHR), BbF / (BbF + BkF),
IcdP / (IcdP + BghiP) and BaP / (BaP + BghiP) (Tobiszewski
and Namiesnik, 2012; Yunker et al., 2002; Zhang et al., 2005). We summarized
PAH ratios mentioned above in Fig. 2 for three combustion sources including
vehicle emissions, biomass burning and coal combustion. On the whole, the six
ratios in this study are similar to the other tunnel experiments, though
environmental conditions of tunnels are different to some extent. It is also
suggested that the ratio of FLA / (FLA + PYR) and
IcdP / (IcdP + BghiP) might be more suitable to distinguish vehicle
emissions from biomass burning and coal combustion.
ANT / (ANT + PHE), FLA / (FLA + PYR),
BaA / (BaA + CHR), BbF / (BbF + BkF),
BaP / (BaP + BghiP) and IcdP / (IcdP + BghiP) ratios for
three source emissions. ANT: anthracene, PHE: phenanthrene, FLA:
fluoranthene, PYR: pyrene, BaA: benz[a]anthracene, CHR: chrysene, BbF:
benzo[b]fluoranthene, BkF: benzo[k]fluoranthene, BaP: benzo[a]pyrene, BghiP:
benzo[ghi]perylene, IcdP: indeno[cd]pyrene. The vehicle emission composition
is from data collected in roadway tunnels (this study; He et al., 2006, 2008;
Ancelet et al., 2011; Ho et al., 2009; Oda et al., 2001).
The biomass burning profiles are obtained from nine straws (Shen et al.,
2011), 26 firewood (Shen et al., 2012), three plant leaves and branches
(Sheesley et al., 2003) and two biomass burning briquettes (Sheesley et
al., 2003). The coal combustion profiles are obtained from the average value of PAH ratios from the combustion of five coals (Shen et al., 2011) and main coal-mining regions in China (Zhang et al., 2008).
δ13C values (‰) of PM from vehicle emissions in
this study and other emission sources.
Emission sources and sampling site
Particle types
δ13C values
Sampling time
Reference
Vehicular fuel emissions
Vehicle emissions (Zhujiang tunnel, China)
PM2.5 / TC
-25.0 ± 0.3
Aug 2013
This study
Vehicle emissions (tunnel of Rio de Janeiro, Brazil)
PM / OC
-25.4
Apr 1985
Tanner and Miguel (1989)
Vehicle emissions (tunnel of Rio de Janeiro, Brazil)
PM / EC
-24.8
Apr 1985
Tanner and Miguel (1989)
Complete combustion of diesel
PM / TC
-29
N/A
Widory (2006)
Complete combustion of gasoline
PM / TC
-27
N/A
Widory (2006)
Vehicle emissions (Cassier tunnel, Canada)
PM2.5 / OC
-27.1
N/A
L. Huang et al. (2006)
Vehicle emissions (Cassier tunnel, Canada)
PM2.5 / EC
-26.9
N/A
L. Huang et al. (2006)
Diesel vehicle emissions (central Camionera del Norte, Mexico)
PM2.5 / TC
-24.6 ± 0.3
Mar 2002
Lopez-Veneroni (2009)
Gasoline vehicle emissions (tunnel of Avenida Chapultepec, Mexico)
PM2.5 / TC
-25.5 ± 0.1
Mar 2002
Lopez-Veneroni (2009)
Vehicle emissions (Mount Victoria tunnel, New Zealand)
PM2.5 / TC
-25.9 ± 0.8
Dec 2008 to Mar 2009
Ancelet et al. (2011)
Non-vehicular fuel sources
Coal combustion (Paris, France)
PM2.5 / TC
-23.9 ± 0.5
May to Sep 2002
Widory et al. (2004)
Coal combustion (Yurihonjo, Japan)
PM2.5 / EC
-23.3
N/A
Kawashima and Haneishi (2012)
Charcoal combustion (Yurihonjo, Japan)
PM2.5 / EC
-27.4 ± 1.7
N/A
Kawashima and Haneishi (2012)
Fireplace soot (Yurihonjo, Japan)
PM / EC
-26.5 ± 0.1
N/A
Kawashima and Haneishi (2012)
Fuel oil combustion (Paris, France)
PM2.5 / TC
-26.0 ± 0.5
May to Sep 2002
Widory et al. (2004)
Dust particles
Street dust (Mexico City, Mexico)
PM2.5 / TC
-21 ± 0.2
Mar 2002
Lopez-Veneroni (2009)
Street dust (Yurihonjo, Japan)
PM2.5 / EC
-18.4∼ -16.4
Nov 2009
Kawashima and Haneishi (2012)
Biomass burning
C4 plant
PM / TC
-13 ± 4
N/A
Boutton (1991)
C4 plant (Yurihonjo, Japan)
PM2.5 / EC
-19.3∼ -16.1
Apr to Nov 2009
Kawashima and Haneishi (2012)
C3 plant
PM / TC
-27 ± 6
N/A
Boutton (1991)
C3 plant (Yurihonjo, Japan)
PM2.5 / EC
-34.7∼ -28.0
Apr to Nov 2009
Kawashima and Haneishi (2012)
Hopanes and steranes are known molecular markers of aerosol emissions from
fossil fuel utilization (Simoneit, 1985). Rogge et al. (1993a)
and Schauer et al. (1996) showed that these petroleum biomarkers can
be used to trace motor vehicle exhaust contributions to airborne PM in the
southern Californian atmosphere. Fourteen major hopanes homologues with
emission factors in the range of 0.46 ∼ 9.14 µg vehicle-1 km-1 and
12
steranes homologues in the range of 0.31 ∼ 0.97 µg vehicle-1 km-1 were identified in
this study. 17α(H),21β(H)-hopane (HP30) was the most abundant
component with the emission factor of 9.14 µg vehicle-1 km-1.
The emission factor of total hopanes was 32.0 µg vehicle-1 km-1. Emissions of the S
hopanes for the extended 17α(H),21β(H)-hopane homologues > C31 were always higher
than those of the corresponding R pairs. All these characteristics of
hopanes in the Zhujiang tunnel are consistent with those in gasoline and
diesel exhausts (Rogge et al., 1993a; Simoneit, 1985) and in other tunnel
studies (see Fig. S1 in the Supplement). Emission factors of individual
sterane ranged from 0.31 to 0.97 ng vehicle-1 km-1, and the sum of
their emission factors was 7.58 µg vehicle-1 km-1. The most
abundant homologue was C29αββ-stigmastane (20R)
(29αββR), followed by 29αααS
and 29αββS.
Vehicle emission standards and limits for PM and NOx
implemented in Guangzhou after 2000.
Emission standard
Yeara
Limit for PM
Limit for NOx
g km-1b
g kWh-1c
g km-1b
g kWh-1c
China I
2001
0.14∼ 0.40
0.40∼ 0.68
–
8.0∼ 9.0
China II
2004
0.08∼ 0.20
0.15
–
7.0
China III
2007
0.05∼ 0.10
0.10∼ 0.21
0.15∼ 0.78
5.0
China IV
2010
0.025∼ 0.060
0.02∼ 0.03
0.08∼ 0.39
3.5
a Year of implementation; b for light-duty vehicles; c for
compression ignition and gas-fueled positive ignition engines of vehicles.
Vehicle fuel standards and limits for sulfur content (mg kg-1) implemented in Guangzhou after 2000.
Standard
China I
China II
China III
China IV
limit
yeara
limit
year
limit
year
limit
year
Gasoline
1000
2001
500
2005
150
2006
50
2010
Diesel
2000
2002
500
2003
350
2010
50
2013
LPG
–
–
270b
2003
–
–
50
2013
a Year of implementation; b unit:
mg m-3.
Stable carbon isotope
Stable carbon isotope analysis of vehicle emissions in Zhujiang tunnel
yielded δ13C values ranging from -25.5 to
-24.7 ‰ with an average value of -25.0 ± 0.2 ‰ and is comparable to previously reported ranges
of -29 to -24.6 ‰ (Table 1) for vehicular fuel emissions.
Generally, the variation in δ13CFuel could affect the
δ13C of hydrocarbons (Keppler et al., 2004; Yamada et al.,
2009). In the PRD region, the δ13C values of gasoline and diesel were on
average -28.6 ± 0.6 ‰ and -27.8 ± 0.2 ‰; small
variations of fuel δ13C were observed (Hu et al., 2014). We
calculated the isotopic differences between δ13CPM2.5 and
δ13CFuel, which represent the apparent isotopic
fractionation occurring during fuel burning. It expressed as Δ13C (‰) and is defined by the following equation
(Yamada et al., 2009).
Δ13CPM2.5-Fuel=δ13CPM2.5+1000δ13CFuel+1000-1×1000
In this study, the value of Δ13CPM2.5-Fuel ranged from 2.7 to
3.5 ‰ with an average of 3.2 ‰,
indicating that an isotopic fractionation occurred during fuel combustion.
Comparing the stable isotopic carbon value of vehicular fuel emissions with
other particulate emission sources (see Table 1), different
emission sources showed different stable carbon isotopic composition. For
total carbon in PM2.5 samples, δ13C ( ‰) of coal and fuel oil combustion are -23.9 ‰ and
-26.0 ‰, respectively, while that of vehicle emissions is
-25.9 ∼ -25.0 ‰. Obviously, the δ13C (‰) of vehicle emissions is not significantly
different from that of coal and fuel oil combustion. However, they are
obviously different from other sources, like dust particles
(-21 ∼ -18.4 ‰), C3 plants
(-19.3 ∼ -13 ‰) and C4 plants
(-34.7 ∼ -27 ‰). Therefore, δ13C might be used to distinguish the fossil fuel combustion
from other sources.
Comparison to previous studies conducted in the same tunnel
To investigate the variation of chemical emission characteristics from
vehicles in the PRD region over the past decade, we compared the chemical
emission characteristics of this study to that of previous study (He et
al., 2008) for the same tunnel in 2004 (see Figs. 3 and 4). Figure 3 shows that
PM2.5 mass, OC and EC decreased significantly from 2004 to 2013. The
reason can be partly attributed to the implementation of pollution control
measures for Chinese vehicle emissions. During this 9-year period, vehicle
emission standards have raised two levels (from China II in 2004 to China IV
in 2013) (Table 2). Additionally, comparing the fleet composition of 2013
to 2004 in Zhujiang tunnel, we found that the proportion of DV and GV
decreased while LPGV increased. LPG is a type of clean energy, and
LPGV is known to emit much less PM mass than GV and DV (Allen et al., 2001; Myung et al., 2014; Yang et al., 2007).
LPG could be combusted more completely than gasoline and diesel. Changes
mentioned above contributed greatly to the decrease of emission factors of
OC and EC (31.3 and 66.9 %) and PM2.5 mass (16.0 %) from 2004
to 2013. However, the emissions of PM2.5 mass, OC and EC are still
significantly higher than those measured in other countries (see Table S5). The implication of these high emission levels is that both the
fuel quality and engine technologies in the PRD region need to be further
improved.
It is also found from Fig. 3 that emission factors of NO3-,
SO42- and NH4+ decreased from 2004 to 2013. Improvement
of fuel quality resulted in decreasing of sulfate emission factor from 3.87
to 0.61 mg vehicle-1 km-1, since the amount of sulfur in fuel is
slashed by 81.5 ∼ 95 % in China IV (2013) compared to that
in China II (2004) (Table 3). The emission levels of nitrate and ammonium
were about one-tenth of those observed in 2004, possibly because NOx
emission standards tightened from 2004 to 2013 (Table 2), leading to lower production of ammonium nitrate. The emission factor of chloride is
significantly
higher than that obtained from Zhujiang tunnel in 2004 and other tunnels.
Chloride was found up to 74 mg vehicle-1 km-1 in PM10 in
the Howell tunnel due to the application of salt to melt ice on roadways in
the winter (Lough et al., 2005). However, it is not applicable in
Guangzhou. The good correlation between Cl- and Na+
(r2=0.992) indicates that resuspension of sea salt particles combined
with vehicle emission PM might be a major source (He et al., 2008).
Emission factors of most of the metal elements increased in Zhujiang tunnel
from 2004 to 2013 with the exception of Cd and Pb. Na emissions increased 3.16 mg vehicle-1 km-1 in 2013 from 2004. Na correlated weakly with
Cl- (r2=0.374) and Na+ (r2=0.429). This indicates
that Na emissions had other sources and was not only from the
resuspension of sea salt particles. The other four most abundant elements
including Fe, Ca, Mg and K increased 1 to 3 times, probably because of
resuspended road dust. However, the wind speed in 2013 was not found to be significantly higher than that in 2004 (3.8 m s-1 in 2013
vs. 3.0 m s-1 in 2004). This minor difference in wind speed could
not account for the large increase. Furthermore, examination of the number
of vehicles per hour in 2013 and 2004 suggests that there were fewer
vehicles per hour in 2013. Therefore, a more plausible explanation is that
there was a lot more dust on the road in 2013. Other sources would also cause the
increased emissions of these elements, such as oil additives (Mg, Ca, Cu, Zn)
(Cadle et al., 1997), the wear of engines (Fe)
(Cadle et al., 1997; Garg et al., 2000) and brakes and tires (Al, Fe,Cu, Mn,
Cd, Ni, Pb and Zn) (Garg et al., 2000; Pio et al., 2013). Additionally,
emissions of Zn, Cu, Mn, Cr, Ni, V, As, Co, U and Tl increased 0.5 to
4.5 times. Although the sum of these elements did not exceed 0.5 % of
PM2.5 mass, they are important for health effects. Lower emission
factors of Pb (0.01 ± 0.0007 mg vehicle-1 km-1) in 2013 than
in 2004 could be a result of the phasing out of leaded gasoline across China
in the late 1990s.
Comparison of PM2.5, OC, EC, WSII and metal emissions in
Zhujiang tunnel sampling in 2004 and 2013.
Comparison of organic compounds emissions in Zhujiang
tunnel sampling in 2004 and 2013.
Figure 4 shows a comparison of organic compound emissions in Zhujiang tunnel
between 2004 and 2013. The n-alkane homologues exhibited a smooth hump-like
distribution with the most abundance at C24, as shown in Fig. 4a. Such a
distribution pattern was similar to patterns observed in Zhujiang tunnel in
2004. However, there are some differences. Firstly, the highest abundant
n-alkane shifted from C23 in 2004 to C24 in 2013. This difference might be
explained by the shift of gas–particle partitioning as alkanes of < C26 are semi-volatile. However, the t test showed that the temperatures were not
significantly different (p=0.14) between this study (33.0 ± 2.3 ∘C) and that in 2004 (31.8 ± 1.0 ∘C). Thus, the
differences of Cmax cannot be regarded as a result of temperature differences. Furthermore,
Cmax was found to be C24 in every test of this study although the
temperature ranged from 28.6 to 36.1 ∘C. It was reported that the
n-alkane in the highest abundance was C20 for DV and C25 or C26 for GV in
dynamometer tests (Rogge et al., 1993a; Schauer et al., 1999, 2002). As
the emissions collected in tunnel studies present a composite result of
emissions from a mixed vehicle fleet, the lower fraction of DV in 2013 was
more likely the cause of the shift of Cmax. Secondly, emission
factors of C16–C26 in 2013 were significantly lower than those in 2004, while this
trend reversed gradually after C27. Emission factors of the PAHs decreased
by 67.6 ∼ 93.4 %. BaP equivalent emission
factors decreased by 88.1 % from 2004 to 2013 (Table S6). This could be attributed to the variation of fleet composition
between 2004 and 2013. PAHs emitted from LPGV are about one-third of that
from GV (Yang et al., 2007), while DV emit more PAHs than GV (Phuleria
et al., 2006). Therefore, the higher proportion of LPGV and lower proportion of
DV resulted in the lower emission factor of PAHs in 2013 than that in 2004.
Emission factors of hopanes also decreased from 2004 to 2013; the percentage
of decrease ranged from 56.2 to 68.7 %. However, the distributions of
hopane series derived from different tunnel studies were very similar (see
Fig. S1). This suggests that the hopane emission
characteristics might be independent of the fleet composition. This is a
reasonable result given that hopanes originate from the lubricating oil used
in DV, GV and LPGV rather than from the fuel (He et al., 2008; Phuleria
et al., 2006). Owing to the fact that more units in heavy-duty vehicles need lubrication,
emission factors of hopanes attributable to heavy-duty vehicles were higher
than those attributable to light-duty vehicles (Phuleria et al., 2006). Reduction of the proportion
of heavy-duty vehicles (buses, heavy-duty trucks, large passenger cars) in fleet
composition in 2013 (11.3 %) compared to that in 2004 (20 %) might be the reason that emission factors of hopanes decreased.
Implications for vehicle emission control policy
Vehicle emission control strategies and policies adopted by Guangdong
province can be classified as emission control on vehicles, fuel-quality
improvements and alternative fuel utilization. PM emission standards for
newly registered vehicles were tightened from China II in 2004 to China IV
in 2013 (Table 2). The reduction of on-road high-PM-emitting vehicles, the phasing in of lower-PM-emitting vehicles and more environmentally friendly
on-road vehicles with more advanced engines following the implementation
of these emission standards were effective for decreasing PM emissions.
Emission factors of PM decreased by 16 % from 2004 to 2013. Also for NOx,
the emission limit was reduced to about half from 2004 to 2013. This change
in emission standards that limit NOx emissions is a major factor
in the decrease of
emission factors of nitrate and ammonium by about 90 %. Additionally,
the national standards have been revised several times to improve fuel
quality to adapt to stringent vehicle emission standards (Table 3).
Sulfur content, for example, showed a sharp decrease by over 90 % from
2004 to 2013, resulting in the decrease of the emission factor of sulfate by
70 %. LPG and liquefied natural gas have gradually
taken the place of diesel and gasoline as the fuel of taxis and buses after
2004; these vehicles now seldom use diesel and gasoline as fuel
(http://www.southcn.com/news/gdnews/nanyuedadi/200707040173.htm). The
application of clean fuel led to nearly complete combustion and resulted in
much lower emissions from taxis and buses. In general, our results suggest
that these strategies are effective to reduce emission factors of PM2.5
mass, as well as OC, EC, WSII and organic compounds in PM2.5. However,
the total vehicle population increased year by year. As shown in Fig. 5a,
the total vehicle population increased by 49.1 % from 2004 to 2013. Total
emissions of vehicle exhaust of PM2.5 mass (calculated as emission
factors multiply by annual average driving distance per car and vehicle
population; Wu et al., 2012) increased by 25.2 % from
2004 to 2013 (Fig. 5b). Consequently, it is demonstrated that more stringent
emission standards are higher quality of fuel and more utilization of clean
fuels are necessary to offset the impacts induced by the growth in vehicle
population and to improve air quality in the PRD region. Additionally, owing
to a lack of mandatory national standards limiting metal content in vehicle
emissions, the emissions of the majority of metals increased from 2004 to 2013
(Figs. 3 and 5b). In China, heavy metals, including As, Cr, Cu, Ni and Tl,
are listed as key substances to be preferentially monitored in
the atmospheric environment (SEPA, 2003); thus, the increase of metal
elements should raise the awareness of the government due to their health
concern.
(a) Growth in total vehicle population in Guangzhou during
2004–2013. (b) Total exhaust emissions of PM2.5 mass, OC, EC, WSII and
metal in 2004 and 2013.