Introduction
Traffic emissions are an important source of particulate matter (PM)
(Sternbeck et al., 2002; Birmili et al., 2006; Lough et al., 2005; Johansson
et al., 2009) in the urban atmosphere. Exposure to traffic-derived PM poses
adverse effects on human health and increases the risk of respiratory
illness, cardiovascular diseases, and asthma (Brauer et al., 2002; Defino et
al., 2005), resulting in increased mortality (Nel, 2005). Airborne
traffic-related PM is emitted mainly by tailpipe exhaust from gasoline and
diesel engines (exhaust emissions), wear from brake linings and tires, and
resuspension of road dust (non-exhaust emissions) by moving
vehicles (Rogge et al., 1993; Cadle et al., 1999; Garg et al., 2000;
Wåhlin et al., 2006; Lawrence et al., 2013). Exhaust emissions
contribute a large amount of fine particulate matter (aerodynamic diameter
less than 2.5 µm, PM2.5), whereas non-exhaust emissions mainly
consist of larger particles (Abu-Allaban et al., 2002; Sanders et al.,
2003). With regard to elemental compositions, Pb, Zn, Ni, and V in submicron
particles have commonly been attributed to pipe emissions and fuel oil combustion
of both gasoline and diesel engines as shown in Table S1 in the Supplement (Lin et al., 2005;
Wang et al., 2003; Shafer et al., 2012). Silicon (Si), Fe, Ca, Na, Mg, Al,
and K are essentially found in larger particles and are associated with
resuspension of road dust. Large amounts of Ca and K observed in submicron
particles occasionally originate from the tailpipe emission of lubricating
oil as well as the vaporization of volatile K compounds and potassium
titanate (K2O ⋅ nTiO2), which is used for improving heat
resistance and wear characteristics (Hee and Filip, 2005; Iijima et al.,
2007; Kuo et al., 2009). Meanwhile, Cu, Ba, Sb, Fe, Cd, Cr, Ga, Sn, and Zn,
which are commonly associated with wear dust from brake linings and tires,
are predominant in coarse PM (Lough et al., 2005; Grieshop et al., 2006;
Thorpe and Harrison, 2008).
A number of studies have investigated the chemical and physical properties of
traffic-originated PM by performing conventional dynamometric tests and
field measurements near roads and inside tunnels (Sternbeck et al., 2002;
Sanders et al., 2003; Birmili et al., 2006; Wåhlin et al., 2006; Iijima
et al., 2007; Ning et al., 2007; Harrison et al., 2012; Dall'Osto et al.,
2013; Lawrence et al., 2013). Dynamometric tests may allow optimal control
of experimental conditions; however, the limitations of such tests are the
costs and inadequate representative of real-world traffic emissions on the
roads (Jamriska et al., 2004). A field measurement near the roadside is
another method to well characterize the traffic-derived PM (Ning et al.,
2008), but it may be influenced by local meteorological conditions and
traffic activities (Jamriska et al., 2004; Ntziachristos et al., 2007).
Accordingly, a tunnel study may be an alternative way to address this issue.
Tunnel aerosol sampling is designed to explore size distributions, chemical
compositions, and emission factors of traffic-related aerosols and their
associated compositions (Weingartner et al., 1997; Funasaka et al., 1998;
Gillies et al., 2001; Sternbeck et al., 2002; Grieshop et al., 2006; Chiang
and Huang, 2009; Pio et al., 2013). Pio et al. (2013) discriminated three
main types of aerosols in Marquês Tunnel, Portugal, namely,
carbonaceous, soil component, and vehicle mechanical wear. They also
suggested that Cu is a good tracer for wear emissions of road traffic. Wear
emission elements such as Zn, Sb, and Ba exhibited a peak mode in the size
range of 3.2 to 5.6 µm. In comparison, Pb, Ca, and Fe
partitioned within 0.1 µm are mostly emitted from combustion of fuel
and lubricant oil or vaporization from hot brake surfaces (Lough et al.,
2005). Sternbeck et al. (2002) collected aerosol samples in two tunnels in
Sweden and analyzed trace metals through inductively coupled plasma mass
spectrometry (ICP-MS). They concluded that vehicle-related metals – such as
Cu, Zn, Cd, Sb, Ba, and Pb – originated mainly from wear rather than from
combustion, and that heavy-duty vehicles (HDVs), rather than light-duty
vehicles (LDVs), are the leading emitter of Ba and Sb. They further suggested
that a Sb / Cu ratio of ∼ 0.22 indicates the presence of brake
wear-related particles.
In this work, a series of aerosol sampling was conducted at two sites in
Hsuehshan Tunnel by using micro-orifice uniform deposited impactors (MOUDIs)
to characterize the physical and chemical properties of metallic aerosols
under real driving conditions. In the past, several intensive measurements of
aerosols have been carried out inside Hsuehshan Tunnel (Chang et al., 2009;
Chen et al., 2010; Cheng et al., 2010a; Zhu et al., 2010); for instance, Zhu
et al. (2010) characterized different temperature carbonaceous aerosols in
fine PM and then identified their sources by the positive matrix factorization
(PMF) approach. Moreover, number concentrations of ultrafine particles (UFPs)
measured by Cheng et al. (2010a) indicate that UFPs, on average, were about
1.0×105–3.0×105 particles cm-3, while
higher UFP numbers were found at a traffic jam. They also suggested that
gas-to-particle conversion is a crucial way to produce nucleation PM at the
entrance of the tunnel, and coagulation growth of nucleation particles is an
important mechanism for forming Aitken mode PM at the middle and exit
section. Furthermore, gaseous pollutants – including VOC, O3, CO, and
NOx – inside this tunnel have been also studied previously (Chang et
al., 2009; Li et al., 2011, Lai and Peng, 2012). Thus, Hsuehshan Tunnel is a
suitable study area for characterizing the behaviors of air pollutants
associated with vehicle fleets. During the experimental campaigns, a total
of 24 sets of size-resolved aerosol samples were collected; 36 target metals
were analyzed by ICP-MS. Elemental compositions, size distributions, and
fingerprinting metal ratios in traffic aerosols are reported in this paper.
The resulting comprehensive data set would provide useful insight into health
effect studies, source apportionment of atmospheric metals, and emissions
inventory of traffic-related particulate metals.
Summary of sampling dates; mass concentrations (µg m-3) of
PM1.8–10, PM1–1.8, and PM1; traffic flow; and wind
speed in Hsuehshan Tunnel during the sampling periods in 2013.
Sampling
Date
Inlet site
Outlet site
Vehicle fleet
Wind
No.
PM1.8–10
PM1–1.8
PM1
PM1.8–10
PM1–1.8
PM1
LDV
HDV
Speed
(µg m-3)
(µg m-3)
(No. h-1)
(No. h-1)
(m s-1)
1
17 May 2013
17
4
32
17
9
155
1272
72
4.7
2
18 May 2013
18
7
43
18
11
128
1777
88
4.6
3
19 May 2013
19
6
35
21
12
208
1843
109
4.7
4
19 Jul 2013
16
4
27
26
9
83
1277
104
4.3
5
20 Jul 2013
16
3
34
15
9
142
1400
118
4.8
6
21 Jul 2013
13
3
33
20
9
168
1680
126
4.7
7
8 Aug 2013
17
4
26
15
11
142
1354
109
4.7
8
9 Aug 2013
19
4
39
9
10
87
1460
133
5.2
9
10 Aug 2013
9
3
23
16
10
126
1712
81
4.9
10
27 Sep 2013
27
4
22
28
10
125
1334
81
4.7
11
28 Sep 2013
22
4
39
16
9
85
1764
101
5.0
12
29 Sep 2013
15
4
34
18
10
180
1909
121
4.7
Methodology
Site description
With a length of 12.9 km, Hsuehshan Tunnel is the second-longest road tunnel
in Asia and the fifth longest in the world. Opened to traffic on June 2006,
Hsuehshan Tunnel connects Pingling in New Taipei City and Toucheng in Yilan
County. The tunnel has two separate two-lane bores and ascends steadily from
44 m a.m.s.l. (meters above mean sea level) at the south end (Toucheng) to
208 m a.m.s.l. at the north end (Pingling), that is, a slope of 1.26 %.
Only passenger cars and light-duty trucks (which are both classified as
LDVs) as well as shuttle buses (categorized as HDVs) are allowed to travel
inside the tunnel, with vehicle speed limited to 90 km h-1. Four aerosol
sampling campaigns were conducted in the northbound bore between May and
September 2013; each campaign lasted for 3 days: Friday to Sunday.
During the sampling period, the traffic volume passing through Hsuehshan
Tunnel at the northbound, in general, approximated 1800 vehicles per hour on
the weekend, which was 1.3 times higher than the workdays (see in Table 1).
However, the traffic flow increased to 2300 vehicles h-1 from Sunday afternoon
to evening, when people traveled back to Taipei; as a result, a traffic jam
always occurred inside the tunnel beginning Sunday afternoon, probably
influencing traffic-related PM metal emissions.
A ventilation system composed of three air exchange stations and three air
interchange stations was built inside the tunnel to maintain air quality.
Exchange and interchange stations are located alternatively at intervals of
nearly 2 km. In exchange stations, polluted air is exchanged with outer
fresh air by using separate fresh and exhaust shafts equipped with two sets
of fans. Fans are typically triggered at temperatures higher than 40 ∘C or CO concentrations higher than 75 ppm. In interchange
stations, the air in each bore is diverted into another bore by two sets of
fans, which are also triggered when CO concentration exceeds 75 ppm. During
the sampling periods, the ventilation system operated regularly, particular
in July and August campaigns, when the air temperature near the outlet site
is frequently more than 40 ∘C.
Sampling and analysis
During the sampling campaigns, two aerosol samplers were installed at 1.7
and 10.6 km from the entrance. The intakes of both aerosol instruments were
placed 1.6 m above the pavement. MOUDIs (model 100, MSP Corporation,
Minneapolis, Minnesota) equipped with pre-weighed Teflon filters (PTFE, 47 mm in diameter and 1.0 mm in pore size, Pall Gelman, East Hills, New York)
were used to collect size-resolved aerosol samples. MOUDIs consist of 10
size-fractionating stages with 50 % cut-off diameters of 10, 5.6, 3.2,
1.8, 1.0, 0.56, 0.32, 0.18, 0.10, and 0.056 µm, plus an inlet (nominal
cut size of 18 µm) and an after-filter (< 0.018 µm) at the
base. Flow rate was calibrated prior to each sampling run and maintained at
30 L min-1. Each sample was collected for 12 h (typically from 09:00 to
21:00 local time, UTC + 8) daily.
After sampling, filter samples were conditioned for 48 h, followed by
gravimetric measurement at 23 ∘C and RH of 30 ± 5 % with a
microbalance (METTLER TOLEDO, MX5, AX205, precision 1 µg) to
determine the net mass of collected aerosol particles, which is needed to
calculate the PM mass concentration. The samples were then subjected to acid
digestion with the use of an ultra-high-throughput microwave digestion
system (MARSXpress, CEM Corporation, Matthews, NC, USA). The vessels were
acid-cleaned thoroughly prior to sample digestion. A half of each sample
filter was digested in an acid mixture (1.5 mL of 60 % HNO3 and 1.5 mL
of 48 % HF). After digestion, the vessels were transferred to the
XpressVap™ accessory sets (CEM) for evaporation of the remaining
acids. When nearly dried, 2 mL of concentrated HNO3 was added into each
vessel and reheated. The resulting solution was then diluted with Milli-Q
water to a final volume of 15 mL for analysis. The digestion procedure has
been detailed in previous studies (Hsu et al., 2008, 2009; Zhang et al.,
2013).
A total of 36 target elements in aerosols were analyzed by ICP-MS (Elan
6100, Perkin ElmerTM SCIEX, USA). For each run, a blank reagent and three
filter membrane blanks were subjected to the same procedure as that for the
samples. Indium (In) was added to the digestion as an internal standard with a
final concentration of 10 ng mL-1 for ICP-MS analysis. The QA/QC of data is
guaranteed by the analysis of a standard reference material, SRM 1648 (urban
atmospheric particulate matter prepared by the National Institute of Standards
and Technology, NIST). The recoveries of target elements mostly fell within
10 % (n=7) of certified or reference values (Table S2). The method
detection limits (MDLs) for the analyzed elements are also presented.
Details of the ICP-MS analysis have been extensively discussed by Hsu et al. (2010) and Zhang et al. (2013).
Enrichment factor and principal component analysis
In addition to size distribution, three approaches – namely, enrichment
factor (EF), correlation matrix, and principal component analysis (PCA) – were
applied to explore the possible sources and associations of elements. EF is
used to assess the influence of crustal source on a given metal (Xi), which
can be calculated by using the following equation:
EF(Xi)=(Xi/Al)PM(Xi/Al)Crust,
where (Xi/Al)PM is the concentration ratio of a given element X to Al in
tunnel particulate matters and (Xi/Al)crust is the concentration ratio
of an interested element X to Al in the average crustal abundance (Taylor,
1964).
PCA can elucidate variance in a given data set in terms of minimum number of
significant components. This technique has been employed in the tunnel
studies concerning source apportionment of airborne metals (Lin et al.,
2005; Lawrence et al., 2013). The software used here is STATISTICA 12
(Statsoft Inc.). A factor loading of > 0.7 was adopted in this
study to assign source identification to a given principal component.
Result and discussions
Chemical compositions
Table 1 summarizes the data on PM mass concentrations in
size-resolved aerosols at both the inlet and outlet sites in Hsuehshan
Tunnel. The aerosols are treated into three size bins: submicron (PM1),
fine (PM1–1.8), and coarse (PM1.8–10) modes. During the sampling
periods, the mass concentrations of PM10, which were determined as the
sum of aerosol masses at all corresponding stages with a cut-off diameter
less than 10 µm, ranged from 35 to 68 µg m-3 (average:
54 ± 9 µg m-3) at the inlet site and from 106 to 241 µg m-3 (average: 162 ± 42 µg m-3) at the outlet site.
Submicron particles were the predominant fraction, accounting for 60 ± 6 and 82 ± 3 % of PM10 mass at the entrance and the exit,
respectively. The abundance of submicron PM may indicate that combustion
processes are significant sources of tunnel aerosols, which are presumably
dominated by carbonaceous particles (Zhu et al., 2010; Pio et al., 2013).
Compared with the inlet site, higher concentrations of PM1–1.8 and
PM1 were observed at the outlet site by a factor of 2.5 and 4.4,
respectively. For PM1.8–10, the concentration at the outlet site was
nearly equal to that at the inlet site (outlet-to-inlet ratio:
∼ 1.1). The outlet-to-inlet ratio of PM mass concentration
increases with decreasing PM size, indicating that smaller particles are
relatively efficiently transported from the entrance to the exit; previous
studies have attributed such efficient transport to the “piston effect” (Chang
et al., 2009; Cheng et al., 2010a; Moreno et al., 2014). These authors
suggested that passing vehicles pick up air pollutants emitted from vehicle
fleets and the flows lead them to the exit, resulting in the accumulation of
large quantities of air pollutants in that area. On the other hand, the
discrepancies of out-to-inlet ratios in PM1.8–10, PM1–1.8, and
PM1 could also be ascribed to the distinct emission rates for PM in
different size fractions inside the tunnel.
(a) Elemental compositions of PM10 collected at both the
inlet and outlet sites in Hsuehshan Tunnel; (b) partitioning of trace metals
within PM of three sizes; (c) outlet-to-inlet ratio for each element in
PM10. The sequence of metallic species is in order of decreasing
concentrations (ng m-3) at the outlet site. N denotes the number of
aerosol samples.
Figure 1a shows the average elemental concentrations of PM10 at the two
sites in Hsuehshan Tunnel, and Fig. 1b depicts the partitioning of trace
elements among three size bins. As shown in Fig. 1a, Fe was the most
abundant element, with a mean concentration of 2384 ± 1416 ng m-3.
In addition to Na, Ca, and Al (300 to 500 ng m-3), Zn, K, Ba, Cu,
and Mg (up to 100 ng m-3) were also major metals in PM10, followed
by Ti (73 ng m-3), Mn (29 ng m-3), and Sb (23 ng m-3), and then
followed by Mo, Pb, Ga, Sr, Ni, V, and Ce (1 to 10 ng m-3).
The rest of the elements have concentrations less than 1 ng m-3 (i.e.,
0.9 ng m-3 for Bi to 0.02 ng m-3 for U). Most elements exhibited
significantly higher concentrations at the exit than at the entrance (p < 0.05, Fig. 1c), with the exception of a number of crustal
elements such as Al, K, Mg, and Rb. This suggests that a lower road dust
reservoir is present inside the freeway tunnel (Amato et al., 2012).
Considerably high outlet-to-inlet ratios (ranging from 2.2 for Sr to 4.3 for
Zn) were found for traffic-derived elements, including Zn, Cu, Ba, Mn, Sb,
Sn, Pb, Ga, Sr, and Cd.
Size distributions
The average size distributions of some of the analyzed metals are shown
in Figs. 1b, 2, and S1. Barium (Ba), Cd, Cu, Fe, Ga, Mn, Mo, Sb, and
Sn were predominant in coarse mode at the inlet site (Fig. 1b). These
elements displayed a typical mono-modal distribution with a major peak in
the size range of 3.2–5.6 µm, while they had another small peak at
1.0–1.8 µm at times (Figs. 2 and S2). The size distribution
patterns of these metals were consistent with the results observed by
Harrison et al. (2012) at a curbside in central London. The authors assigned
the elements Fe, Cu, Sb, Ba, and Zn to the non-exhaust traffic particles. At
the outlet site, those elements (Ba, Cd, Cu, Fe, Ga, Mn, Mo, Sb, and Sn)
similarly had a mono-modal size distribution, but the main peak shifted to
1.0–1.8 µm (Figs. 2 and S2). Similar to that in PM mass, this shift
was perhaps due to the piston effect, which, as previously mentioned,
facilitated the transport of finer PM to the exit.
Zinc (Zn) showed a bimodal distribution for most samples at the entrance,
with a major peak in the size range of 3.2–5.6 µm and a second peak
in the size range of 0.56–1.0 µm. Meanwhile, a mono-modal pattern
with a major peak at 1.0–1.8 µm was found at the exit. Lead (Pb)
displayed two peaks at the inlet site: one at 0.56–1.0 µm and another
one at 3.2–5.6 µm. However, Pb exhibited a typical mono-modal
distribution at the outlet site, peaking at 0.32–0.56 µm. Vanadium
(V) revealed a bimodal size pattern with a major peak at 0.32–0.56 µm and a second peak at 3.2–5.6 µm at the inlet site, whereas it
peaked at 0.18–0.32 or 0.32–0.56 µm at the exit.
Aluminum (Al), Ca, and Mg of predominant geological origins showed a typical
mono-modal size distribution at the inlet site with a major peak
at 3.2–5.6 µm; however, a peak was occasionally found in the
submicron particles (Figs. S1 and S3). For example, the abundance of Al,
Ca, and K was observed at submicron size in two sets of samples (21 July and
10 August). This abundance was ascribed to non-crustal sources such as
vaporization from lubricating oil and diesel emissions (Wang et al., 2003),
which perhaps alters the size distributions of these crustal elements.
Submicron mode, which is an indicator of combustion or high-temperature
processes, contributes non-negligible Ca and K, which are usually regarded
as crustal elements. Potassium titanate and a number of volatile compounds
are known to contain K and therefore may be the sources of
submicron K (Hee and Filip, 2005; Iijima et al., 2007). Submicron Ca
probably originated from tailpipe emissions of lubricating oil (Kuo et al.,
2009). Like traffic elements, these crustal elements had a major peak that
shifted to 1.0–1.8 µm at the outlet site, which also arose from
the piston effect. At the inlet site, rare earth elements (REEs) – such
as La, Ce, Nd, Pr, and Sm – revealed a mono-modal size distribution with a
major peak at 3.2–5.6 µm. At the exit, these elements essentially
showed a mono-modal distribution that peaked at 1.0–1.8 µm (Fig. S3).
Average size distributions of traffic-derived elements observed at
the inlet and outlet sites inside Hsuehshan Tunnel.
Sources of trace metals
Figure 3 presents the results of enrichment factor analysis for all analyzed
elements in three size bins of size-segregated particles at both the inlet
and outlet sites. EF values for all species were higher at the outlet than
at the inlet site, suggesting that the influence of resuspended road dust
was insignificant for most metals at the exit. Enrichment factor values for
Ca, K, Mg, Rb, Sr, and Ti in the three size-resolved particles were
generally close to unity at both sites, demonstrating that these elements
originated mainly from the resuspension of soil and road dust. EF values for
these geological metals increased with decreasing size, indicating that
these elements in smaller particles would be significantly influenced by
anthropogenic sources such as diesel emissions, lubricating oil, and
additives in oil fuels. For lanthanides, lower enrichment was found for La,
Pr, Nd, and Sm in all three sizes of PM, although high EFs were occasionally
found. This indicates that, although such elements mainly originate from
geological sources, they are sometimes from mixed sources of dust and
anthropogenic emissions such as automotive catalysts (Kulkarni et al., 2006).
Cerium (Ce), which is one of the lanthanides, had higher EF values
(> 10) in all size-resolved particles than La, Pr, Nd, and Sm,
demonstrating that Ce is highly influenced by anthropogenic emissions. For
the three size-resolved particles, Ce is highly correlated not only with La,
Pr, Nd, and Sm but also with a number of anthropogenic elements, again
implying that Ce originated from traffic emissions such as automotive
catalysts and fuel additives of diesel vehicles as well as from a crustal
source (Kulkarni et al., 2006; Cassee et al., 2011).
High EFs (> 10) were obtained for As, Ba, Cd, Cu, Cr, Ga, Mo, Sb,
Se, and Sn, indicating their anthropogenic origins. Among these elements, Cu
is an additive in high-temperature lubricant and is present in brake
linings, approximately 1–10 % by weight (Sanders et al., 2003), and it
has been used successfully as a good tracer for wear emission of road
traffic (Pio et al., 2013). Correlation analyses (Table 2) illustrate that
Ba, Cd, Ga, Mo, Sb, and Sn are well correlated with Cu (r > 0.93)
in both coarse and fine modes, suggesting that, similar to Cu, these
elements in Hsuehshan Tunnel originated mainly from wear-abrasive sources.
This could be supported by the presence of both BaSO4- and
Sb2S3-containing particles in both brake lining materials, in
which the former is utilized as a filler and the latter is utilized as an
alternative to asbestos (Ingo et al., 2004). Moreover, the use of organic Sb
compounds in grease and motor oil is another road traffic emission source of
Sb (Huang et al., 1994; Cal-Prieto, 2001).
Correlation matrix of selected elements in coarse (top side triangle) and
fine particles (lower side triangle) observed in Hsuehshan Tunnel.
Correlation coefficients higher than 0.8 are marked in bold.
Al
Fe
Mg
K
Ca
Sr
Ba
Ti
Mn
Ni
Cu
Zn
Mo
Cd
Sn
Sb
Pb
V
Cr
Rb
Cs
Ga
La
Ce
Pr
Nd
Al
–
0.29
0.42
0.44
0.44
0.45
0.29
0.35
0.34
0.05
0.25
0.43
0.26
0.19
0.17
0.17
0.49
0.20
0.10
0.47
0.41
0.30
0.40
0.20
0.45
0.25
Fe
0.69
–
0.27
0.31
0.43
0.88
0.97
0.96
1.00
-0.03
0.99
0.66
0.98
0.97
0.97
0.98
0.64
0.74
0.57
0.41
0.38
0.96
0.71
0.91
0.73
0.90
Mg
0.78
0.84
–
0.74
0.61
0.62
0.36
0.44
0.30
-0.09
0.24
0.31
0.23
0.29
0.29
0.24
0.62
0.42
0.03
0.75
0.62
0.38
0.66
0.44
0.55
0.50
K
0.71
0.89
0.84
–
0.61
0.63
0.45
0.44
0.36
0.25
0.22
0.56
0.25
0.32
0.31
0.26
0.68
0.45
0.37
0.91
0.88
0.45
0.68
0.48
0.69
0.55
Ca
0.70
0.86
0.82
0.86
–
0.75
0.57
0.56
0.48
-0.05
0.38
0.59
0.39
0.48
0.47
0.46
0.90
0.49
0.13
0.81
0.74
0.61
0.82
0.47
0.74
0.55
Sr
0.64
0.99
0.86
0.89
0.88
–
0.94
0.93
0.90
-0.04
0.83
0.76
0.83
0.88
0.87
0.86
0.87
0.75
0.45
0.75
0.67
0.94
0.90
0.86
0.88
0.90
Ba
0.60
0.98
0.81
0.87
0.82
0.99
–
0.95
0.97
-0.04
0.93
0.77
0.94
0.96
0.96
0.96
0.75
0.73
0.52
0.56
0.52
1.00
0.81
0.91
0.82
0.92
Ti
0.68
0.99
0.85
0.87
0.84
0.98
0.97
–
0.96
0.02
0.96
0.70
0.95
0.96
0.96
0.96
0.75
0.79
0.50
0.55
0.51
0.95
0.80
0.88
0.75
0.89
Mn
0.63
0.95
0.80
0.90
0.91
0.95
0.95
0.93
–
0.45
0.98
0.69
0.97
0.96
0.97
0.97
0.68
0.75
0.60
0.46
0.43
0.97
0.74
0.90
0.76
0.90
Ni
0.01
0.08
0.02
0.11
-0.01
0.05
0.06
0.06
0.12
–
-0.09
-0.02
-0.07
-0.11
-0.10
-0.09
-0.03
-0.02
0.73
0.15
0.16
-0.05
0.05
0.00
0.06
0.03
Cu
0.66
0.99
0.83
0.85
0.82
0.98
0.97
1.00
0.93
0.06
–
0.63
0.99
0.97
0.98
0.98
0.60
0.73
0.51
0.32
0.30
0.93
0.66
0.87
0.64
0.85
Zn
0.22
0.50
0.34
0.55
0.61
0.50
0.52
0.47
0.72
0.43
0.45
–
0.63
0.72
0.67
0.67
0.76
0.49
0.31
0.56
0.51
0.78
0.67
0.56
0.65
0.60
Mo
0.61
0.98
0.81
0.84
0.82
0.98
0.98
0.99
0.93
0.05
0.99
0.47
–
0.98
0.99
0.99
0.60
0.75
0.54
0.34
0.33
0.93
0.67
0.88
0.65
0.86
Cd
0.56
0.96
0.76
0.86
0.87
0.95
0.96
0.95
0.98
0.17
0.95
0.70
0.96
–
1.00
0.99
0.68
0.73
0.49
0.43
0.41
0.96
0.72
0.87
0.69
0.86
Sn
0.60
0.98
0.80
0.84
0.83
0.98
0.97
0.99
0.93
0.05
0.99
0.48
1.00
0.96
–
0.99
0.66
0.74
0.51
0.41
0.40
0.95
0.72
0.89
0.69
0.88
Sb
0.63
0.99
0.81
0.85
0.85
0.98
0.98
0.99
0.94
0.05
0.99
0.50
0.99
0.97
1.00
–
0.64
0.74
0.52
0.38
0.36
0.95
0.71
0.87
0.68
0.86
Pb
0.59
0.73
0.76
0.84
0.89
0.75
0.79
0.71
0.85
0.21
0.69
0.72
0.68
0.80
0.70
0.70
–
0.62
0.27
0.81
0.73
0.77
0.91
0.64
0.78
0.70
V
0.28
0.39
0.31
0.49
0.35
0.38
0.38
0.41
0.37
0.22
0.40
0.20
0.42
0.40
0.39
0.38
0.44
–
0.45
0.54
0.52
0.73
0.71
0.72
0.65
0.75
Cr
0.20
0.41
0.28
0.30
0.27
0.38
0.39
0.38
0.44
0.84
0.39
0.60
0.38
0.49
0.39
0.38
0.40
0.11
–
0.31
0.32
0.49
0.37
0.53
0.44
0.64
Rb
0.64
0.81
0.74
0.92
0.92
0.83
0.79
0.78
0.89
0.07
0.75
0.64
0.75
0.82
0.76
0.77
0.90
0.40
0.27
–
0.96
0.56
0.82
0.57
0.83
0.65
Cs
0.50
0.65
0.56
0.80
0.82
0.67
0.64
0.61
0.77
0.11
0.58
0.65
0.58
0.70
0.60
0.61
0.84
0.44
0.23
0.95
–
0.51
0.74
0.53
0.77
0.61
Ga
0.60
0.99
0.81
0.85
0.85
0.99
0.99
0.98
0.95
0.06
0.98
0.53
0.98
0.97
0.99
0.98
0.71
0.38
0.40
0.79
0.63
–
0.82
0.90
0.82
0.91
LA
0.71
0.87
0.81
0.88
0.94
0.88
0.82
0.85
0.88
0.04
0.83
0.52
0.83
0.84
0.84
0.84
0.87
0.44
0.32
0.89
0.77
0.84
–
0.79
0.89
0.84
Ce
0.60
0.89
0.80
0.81
0.78
0.90
0.86
0.88
0.81
0.00
0.90
0.30
0.90
0.81
0.89
0.87
0.67
0.36
0.32
0.72
0.54
0.87
0.87
–
0.83
0.99
Pr
0.68
0.90
0.81
0.89
0.82
0.92
0.89
0.87
0.87
0.01
0.86
0.43
0.86
0.82
0.85
0.85
0.70
0.28
0.31
0.85
0.68
0.87
0.85
0.87
–
0.88
Nd
0.62
0.91
0.82
0.83
0.82
0.92
0.88
0.91
0.84
0.01
0.92
0.32
0.92
0.84
0.91
0.89
0.70
0.36
0.32
0.76
0.57
0.89
0.90
1.00
0.90
–
Enrichment factors of trace metals in (a) PM1,
(b) PM1–1.8 and (c) PM1.8–10 observed at the inlet and outlet sites in
Hsuehshan Tunnel.
Lead (Pb) and Zn show high enrichment in all size fractions, indicating that
both elements are contributed primarily by traffic emissions, rather than a
natural origin. According to the bimodal distribution (Fig. 2) and the
good correlations with Cu, Ba, and Sb (r > 0.63) in PM1.8–10
(Table 2), Zn appears to originate from traffic emissions, and two traffic
sources could account for the observed Zn. For the coarse mode, Zn is
associated with wear tire debris because Zn is added to tires during
vulcanization and is responsible for 1–2 % of the tires by weight
(Degaffe and Turner, 2011; Taheri et al., 2011). This is in concert with
previous results (Adachi and Tainosho, 2004; Councell et al., 2004; Tanner
et al., 2008; Harrison et al., 2012). For the fine mode, Zn is probably
contributed by lubrication oil via pipe emissions (Huang et al., 1994).
Emissions from vehicle exhaust and wear abrasion are both important sources
of Pb. In this work, Pb showed a good correlation with Cu, Sb, Ba, and Zn (r > 0.60) in coarse PM, indicating wear debris origins. In fine
mode, Pb correlated well with Cu, Sb, and Ba (r > 0.69), suggesting
wear abrasion dust. Moreover, a good correlation between Pb and Zn (r=0.75,
in Table 2) was also found, revealing that Pb in fine PM might also be
derived from diesel engines since both species were detected together and
constituted up to 2 % in fresh diesel PM (Sharma et al., 2005; Agarwal et
al., 2015). However, Pb only correlated well with Zn (r=0.77) in
submicron size (in Table S3), reflecting that Pb was preferentially
contributed by the combustion process from vehicle fleets (Wang et al., 2003).
Iron (Fe), which is considered an important crustal element, exhibited
enrichment factors of 5 to 11 at the entrance and 12 to 21 at the exit,
indicating that Fe in the tunnel was mainly produced from anthropogenic
emissions other than road dust. Previous studies have pointed out that, in
addition to road dust, wear debris from brake linings and tires as well as
diesel engine emissions are main sources of Fe in areas near traffic
emissions (Cadle et al., 1997; Garg et al., 2000; Wang et al., 2003). In the
present study, Fe correlated well with Cu, Ba, and Sb in all sizes (r > 0.87, Tables 2 and S3), demonstrating that wear dust is a major
anthropogenic source of Fe in Hsuehshan Tunnel, as is the case for those
elements.
Summaries of principal component analysis for trace metals in coarse, fine
and submicron particles observed in Hsuehshan Tunnel. Factor loadings lower
than ±0.4 are not given. Factor loadings greater than 0.7 are marked in
bold.
Coarse
Fine
Submicron
PC1
PC2
PC1
PC2
PC3
PC1
PC2
PC3
PC4
Al
0.55
0.52
–
0.68
–
–
0.88
–
Fe
0.98
–
0.94
–
–
0.82
0.52
–
–
Na
–
0.81
–
–
0.93
–
–
–
–
Mg
–
0.89
0.70
–
0.66
0.69
–
–
–
K
–
0.88
0.77
–
0.44
–
0.57
–
–
Ca
–
0.75
0.81
–
–
0.65
–
0.53
–
Ba
0.94
–
0.95
–
–
0.96
–
–
–
Ti
0.93
–
0.94
–
–
0.73
–
–
–
Mn
0.97
–
0.90
–
–
–
0.96
–
–
Ni
–
–
–
0.76
–
–
–
0.56
0.72
Cu
0.98
–
0.95
–
–
0.96
–
–
–
Zn
0.65
0.42
0.44
0.79
–
–
0.97
–
–
Mo
0.99
–
0.96
–
–
0.96
–
–
–
Cd
0.97
–
0.92
–
–
–
0.90
–
–
Sb
0.99
–
0.96
–
–
0.90
–
–
–
Pb
0.58
0.71
0.62
0.61
–
–
0.84
–
–
V
0.72
–
–
–
–
–
–
–
0.92
Rb
–
0.90
0.73
0.44
–
–
0.63
–
–
Ga
0.93
–
0.96
–
–
0.94
–
–
–
La
0.65
0.68
0.80
–
–
0.81
–
–
–
Ce
0.86
–
0.86
–
–
0.92
–
–
–
Potential
Wear
Dust
Wear debris +
Diesel
Dust
Wear debris +
Diesel
Dust
Fuel
source
debris
dust + gasoline
auto catalyst
oil
Ratios of specific elements to Cu in tunnel PM.
Tunnel studies
Size
Fe / Cu
Ba / Cu
Sb / Cu
Sn / Cu
Referenceb
Hatfield Tunnel (United Kingdom)
PM10
19
1.23
0.13
–
1
Marquês de Pombal Tunnel (Portugal)
PM0.5–10
16
0.27
0.08
0.23
2
Tingstad Tunnel (Sweden)
PM10
28
0.74
0.18
–
3
Lundby Tunnel (Sweden)
PM10
60
1.34
0.24
–
3
Malraux Tunnel (France)
PM2.5
15
–
0.14
0.14
4
Squirrel Hill Tunnel (USA)a
PM2.5
37
2.48
0.21
0.48
5
Sepulveda Tunnel (USA)a
PM2.5
16
2.12
0.88
0.82
6
Loma Largo Tunnel (Mexico)
PM2.5
7
0.13
0.76
0.49
7
Jãnio Tunnel (Brazil)
PM2.5
20
–
0.12
–
8
Belway Rodonael Mário Covas Tunnel (Brazil)
PM2.5
45
–
0.36
–
8
Shing Mun Tunnel (Hong Kong)
PM2.5
17
0.58
0.14
0.29
9
Zhujiang Tunnel (China)
PM2.5
28
1.08
–
–
10
Hsuehshan Tunnel (Taiwan)
PM1.8–10
14
0.80
0.14
0.09
This study
PM1–1.8
14
1.07
0.16
0.09
PM1
15
1.10
0.16
0.11
a The ratios of Squirrel Hill Tunnel and Sepulveda Tunnel are obtained
from the ratios of elemental emission factors.
b 1. Lawrence et al. (2013); 2. Pio et al. (2013); 3. Sternbeck et al. (2002);
4. Fabretti et al. (2009); 5. Grieshop et al. (2006); 6. Gillies et al. (2001);
7. Mancilla and Mendoza (2012); 8. Brito et al. (2013); 9. Cheng et al. (2010b); 10. He et al. (2008).
PCA results are presented in Table 3, in which the data (samples) are
divided into three size groups. Two possible sources are identified for
coarse PM. As seen, PC1 was associated with Fe, Ba, Mn, Cu, Mo, Cd, Sb, Ti,
V, and Ga; moderate loadings were found for Zn and Pb. This indicates that
PC1 was likely attributed to wear debris (Wåhlin et al., 2006; Lawrence
et al., 2013; Pio et al., 2013). In PC2, high loadings were found for Na,
Mg, K, Ca, and Rb, implying road dust origins. For fine particles, Fe, Ba,
Mn, Cu, Mo, Cd, Sb, Mg, K, Ca, Rb, La, and Ce had high loadings, whereas
Pb had moderate loadings in PC1; brake abrasion mixed with resuspended dust
and gasoline emissions might explain this factor. In PC2, high positive
loading was found for Zn and moderate loading for Pb; thus, PC2 could be
explained by diesel emissions (Sharma et al., 2005; Agarwal et al., 2015).
The third component was identified as road dust because of the correlations
among Na, Al, and Mg (loadings > 0.6). For submicron particles,
high loadings were found for Fe, Ba, Cu, Mo, Sb, Ga, and Ce in PC1. As
previously mentioned, Ce in smaller PM may be associated with catalytic
converters and fuel additives; therefore, PC1 might be grouped into mixed
sources of wear abrasion and automotive catalysts. In PC2, high positive loadings
were found for Pb and Zn, illustrating that exhaust from diesel engine was a
potential source in this component. However, PC3, which had a high loading
of Al and a moderate loading of Ca, indicates that road dust could be the
potential source. PC4 is a component with high loading for V and Ni.
Previous studies have suggested that V and Ni in submicron particles were
commonly attributed to fuel oil combustion of gasoline and diesel engines
(Wang et al., 2003; Shafer et al., 2012), but higher emission rates for
gasoline exhaust compared to diesel engines (Cheng et al., 2010b).
Consequently, PC4 in submicron PM may be associated preferentially to
gasoline engines. Overall, wear abrasion dust and road dust are major
sources of many airborne metals through all PM size ranges inside Hsuehshan
Tunnel, and combustion processes from vehicle fleets are additional sources
of fine- and submicron-particle-bound metals.
Scatterplots of (a) Fe, (b) Ba, (c) Sb, (d) Sn, (e) Ga, and (f) Mo
against Cu concentrations (ng m-3) in different size-segregated
particles observed in Hsuehshan Tunnel.
Fingerprinting ratios of traffic-derived metals
Cu is used as an indicator for wear debris, and the ratios of wear-derived
elements to Cu obtained by the linear regression approach can be applied to
determine the contribution of specific metals from wear debris in the urban
atmosphere. Figure 4 presents the scatterplots of Fe, Ba, Sb, Sn,
Ga, and Mo against Cu in PM1, PM1–1.8, and PM1.8–10 at the two
sites. These elements had strong correlations (r > 0.9), and
these ratios were constant in different size-resolved PM, strongly
suggesting that these ratios can be applied as good fingerprinting ratios of
wear emissions. The mean mass ratios of Fe / Cu, Ba / Cu, Sb / Cu, Sn / Cu, and
Ga / Cu were 14, 1.05, 0.16, 0.10, and 0.03, respectively. Table 4 compares our ratios to those established by other tunnel studies. The
ratios of Fe / Cu held around 14 to 15 over all sizes in the present work,
which agrees with that (14) acquired by dynamometer tests (Sanders et al.,
2003) and is also comparable to those observed in different tunnels (Gillies
et al., 2001; Fabretti et al., 2009; Cheng et al., 2010b; Pio et al., 2013).
However, the Fe / Cu ratio is also significantly distinct from those (37 to
60) found in other tunnels; this difference may have arisen from
discrepancies in ingredients of brake pads and in driving conditions (Garg
et al., 2000). Ba / Cu ratios of 0.8–1.1 were similar to those found in
Europe but slightly lower than that (> 2) found in the United
States. Our Sb / Cu ratio of 0.16 is consistent with the result obtained in
Hong Kong but lower than that (0.76 to 0.88) occasionally measured in
American countries (Gillies et al., 2001; Mancilla and Menodza, 2012). In
Japan, Iijima et al. (2007), with the use of dynamometer tests, reported
Sb / Cu ratios ranging from 0.05 to 0.11 for different brake pads. They also
pointed out that Sb-free brake pads have been utilized recently in Japanese
passenger cars. According to the Taiwan Transportation Vehicle Manufactures
Association 44 and 13 % of vehicle fleets in Taiwan are Japanese and
American cars, respectively. The abundance of Japanese cars in Taiwan may
have caused the lower Sb / Cu values in this work. For the Mo-against-Cu
scatterplot, two slopes are obtained: 0.05 for coarse and fine particles
and 0.12 for particles with aerodynamic diameter less than 0.56 µm. The
enhancement of Mo in these submicron particles is perhaps attributed to an
additional source of Mo such as diesel exhausts (Kuo et al., 2009). Previous
studies show that the ratio of V / Ni has been widely used as a fingerprinting
ratio of specific anthropogenic origins. For example, heavy oil combustion
shows a narrow range of V / Ni ratio (3 to 4) (Hedberg et al., 2005; Mazzei et
al., 2008). Combustion origins from gasoline and diesel vehicles have
smaller V / Ni ratios (< 2.0) (Qin et al., 1997; Watson et al.,,
2001). On the other hand, small quantities of V and Ni are also found in
soil with a V / Ni ratio of < 1.5 (Hsu et al., unpublished data). In
this work, V / Ni ratios were typically lower than 2.0 in fine and submicron
PM; the ratios were alternatively acquired directly from their mass
concentrations (instead of linear regression) because V is not strongly
correlated with Ni (r < 0.5, Tables 2 and S3) in three different
sizes. In fine and submicron PM, the lower V / Ni ratios with higher EF value
(> 10) for both elements suggest that they were contributed
mostly by oil combustion from traffic fleets. In coarse PM, a low V / Ni ratio
(< 2) with a low EF value (∼ 2) for V indicates that V
was associated with soil origins; however, high EF for Ni suggests that Ni
was contributed by combustion sources. The Pb / Cu ratios in the tunnel
particles averaged at 0.07, which is much lower than those (much higher than
unity) usually observed in ambient air (Fang et al., 2005). In addition, the
tunnel particles had As / Sb and Se / Sb ratios of 0.1 and 0.05, respectively,
which are also evidently lower than those (around unity) measured in ambient
aerosols (Querol et al., 2007). These results imply that traffic emissions
are not major sources of Pb, As, and Se in ambient atmospheres.
Figure 5 illustrates the relationships of La against Ce, Pr, Nd, and Sm.
Their correlations weaken with decreasing particle size, suggesting that the
REEs in smaller particles were disturbed by certain anthropogenic sources. A
ratio of La / Ce has been successfully used to distinguish natural sources
from anthropogenic origins (Kulkarni et al., 2006). In this work, the La / Ce
ratios that range from 0.15 to 0.18 and from 0.10 to 0.12 at the inlet and
outlet sites, respectively, are significantly lower than that of
average crust (∼ 0.50) (Taylor, 1964) and soils
(∼ 0.7) (Kulkarni et al., 2006). Such values agree with those
of Kulkarni et al. (2006) and Huang et al. (1994), who reported that La / Ce
ratios for traffic emissions were 0.20 and 0.13, respectively. As discussed
in Sect. 3.2, the EF values of Ce were mostly higher than unity at both
the inlet and outlet sites, with even some of the values being 1 order of
magnitude higher (Fig. 3), revealing that soil dust is not the sole source
of Ce. Thus, the low La / Ce values found in the present study could be
attributed to an additional supply of Ce from vehicular emissions.
Summary and concluding remarks
Size-fractionated aerosol samples were collected in Hsuehshan Tunnel to
characterize particulate metals emitted by vehicle fleets. A total of 36
elements were analyzed by ICP-MS. Compared to the entrance, enhanced
concentrations for most metals at the exit are due to the piston effect.
With regard to enrichment factor, correlation matrix, and principal
component analysis, the analyzed metals were categorized into three groups,
namely, wear abrasion (Cu, Cd, Cu, Fe, Ga, Mn, Mo, Sb, and Sn), resuspended
dust (Ca, Mg, K, and Rb), and pipe emissions (Zn, Pb, and V in fine mode).
Size distributions of these elements were significantly different because of
their origins. For wear-related metals and geological elements, a mono-modal
size distribution was found and the major peak shifted from the range of 3.2–5.6 µm at the entrance to the range of 1–1.8 µm at the exit.
However, elements attributed to combustion sources were predominant mainly
in submicron particles and peaked at 0.56–1.0 µm at the inlet site
and at 0.18–0.32 or 0.32–0.56 µm at the outlet site. By
adopting Cu as an indicator element of wear debris, fingerprinting ratios
were constructed, including Fe / Cu, Ba / Cu, Sb / Cu, Sn / Cu, and Ga / Cu. These
ratios can effectively apportion the source of specific elements in urban
environments from wear abrasion.
Scatterplots of La and (a) Ce, (b) Pr, (c) Nd, and (d) Sm
concentrations (ng m-3) in different size-segregated particles observed
in Hsuehshan Tunnel.
In this work, we characterized traffic-derived PM metals using a tunnel
study. The data would be useful for future studies on traffic emission
inventory and health effects, especially for submicron PM. Wear abrasion
appeared to be a major source of specific toxic elements. While the
government focuses on exhaust emission control, the contribution of wear
from brake linings and tires should not be ignored. Thus, stringent
implementations of measures for reducing wear emissions are needed in the
future.