Wei-Chun Wang1, Nguyen Duy Dat1,2, Kai-Hsien Chi3, Moo-Been Chang This email address is being protected from spambots. You need JavaScript enabled to view it.1

1 Graduate Institute of Environmental Engineering, National Central University, Taoyuan 32001, Taiwan
2 Faculty of Chemical & Food Technology, Ho Chi Minh City University of Technology and Education, Thu Duc, Ho Chi Minh 700000, Viet Nam
3 Environmental and Occupational Health, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan

Received: April 1, 2021
Revised: August 28, 2021
Accepted: September 27, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.210074  

Cite this article:

Wang, W.C., Dat, N.D., Chi, K.H., Chang, M.B. (2021). Characterization of PM2.5 and Particulate PAHs Emitted from Vehicles via Tunnel Sampling in Different Time Frames. Aerosol Air Qual. Res. 21, 210074. https://doi.org/10.4209/aaqr.210074


  • Particulate PAHs emitted from vehicle during different time frames are investigated.
  • Diesel vehicle strongly affects the concentration of particulate PAHs measured.
  • 15 EU-PAHs should be included for evaluating PAH emission from vehicles.


The present study investigated the emission characteristics of PM2.5 and particulate PAHs (25 congeners) emitted from vehicles in real running conditions via air sampling in the longest tunnel in Taiwan. The average PM2.5 concentrations measured in the tunnel inlet and outlet were 21.9 ± 6.9 µg m–3 and 46.1 ± 12 µg m–3, respectively, which are significantly higher than that measured at an ambient station nearby (12.5 ± 6.2 µg m–3). Total particulate PAHs (P-PAHs) concentration measured at the inlet of the tunnel was 1.68 ± 1.4 ng m–3, which was significantly lower than that measured at the outlet of the tunnel (6.31 ± 4.8 ng m–3). Meanwhile, the average concentration of P-PAHs found in ambient air station was only 0.275 ± 0.062 ng m–3. A higher concentration difference (ΔC = Cout – Cin) of particulate PAHs was found on weekday compared with that observed during the weekend due to the higher number of diesel vehicles passing through the tunnel. The concentration differences of these pollutants were higher in the daytime compared with that in the nighttime because of higher vehicle number. Pyr and PL were the dominant contributors in terms of mass concentration while BcFE was the main contributor to TEQ concentration. The results also indicate that the list of 15 EU-PAHs should be considered for evaluation of the health risk associated with the emission of PAHs from vehicles.

Keywords: Mobile sources, Tunnel sampling, timeframe emission, EU-PAHs, Benzo[c]fluorene


Exposure to the high concentration of particulate matter (PM) causes numerous adverse health effects such as exacerbation of chronic respiratory and cardiovascular diseases, decreased lung function, and premature mortality (Guaita et al., 2011; Halonen et al., 2009; Perez et al., 2012; Samoli et al., 2008, Xing et al., 2016). Especially, fine particles (PM2.5) are the main causes of visibility reduction and climate change due to direct effects of scattering and absorption of solar radiation, and their association with cloud formation (Adesina et al., 2016; Samset et al., 2016; Srivastava et al., 2018). Additionally, polycyclic aromatic hydrocarbons (PAHs) are a group of chemically related compounds that are environmentally persistent with various structures and varied toxicity (Nisbet and LaGoy, 1992). Association of PM with toxic PAHs has been investigated and reported worldwide due to their synergistic effect on human health (Dat and Chang, 2016). Both PM2.5 and particulate PAHs emitted from mobile sources are regarded as a major contributor to air quality degradation in urban areas (Guo et al., 2003; Zhang et al., 2013; Liang et al., 2020; Wang et al., 2020; Xing et al., 2020; Nadali et al., 2021), indicating that they need to be controlled for reducing their levels in the environment. Vehicles are reported as one of the main contributors to ambient PAHs in urban areas of Taiwan, such as in Kaohsiung (Lai et al., 2016), Hsinchu (Yang et al., 2016) and Taoyuan (Dat et al., 2018). Therefore, investigation of emission factor and characteristics of PM and particle-bound PAHs (P-PAHs) emitted from mobile sources is deemed necessary for enacting effective regulations on reducing the occurrence of PAHs in the environment.

PAHs are discharged from vehicles via three main mechanisms, including the synthesis of low-molecular-weight PAHs through combustion processes, emission of the PAHs originally present in the fuel, and the cracking of the lubricating oil (Sonntag et al., 2012). Emission factors and characteristics of particulate PAHs emitted from vehicles are mainly affected by engine type, operating condition, compositions of fuel and lubricant used, vehicle mileage, and the catalytic converter adopted (Cheruiyot et al., 2015). Lyu et al. (2019) investigated the emission of 27 PAHs including 16 US-EPA and 15+1 EU-PAHs from coal-fired processes and indicated that 15+1 EU-PAHs contributed to 99% total Ba-P TEQ concentration of PAHs emitted from a coal-fired plant. Similar results were obtained by the investigation of PAHs in different environmental matrices (Richter-Brockmann and Achten, 2018), suggesting the importance of including EU-PAHs as target compounds for evaluating the health risk of PAHs. However, limited information regarding these PAHs emitted from vehicles has been reported worldwide. Therefore, the 15 EU-PAHs are included as the target compounds (25 congeners) in this study for evaluating their contributions on the total emission and TEQ. Additionally, the contribution of 15 EU-PAHs to total PAHs in terms of mass and TEQ concentrations will be compared, which would provide useful information for future research regarding the emission and risk assessment of PAHs.

The tunnel with a semi-enclosed space can be selected to investigate the characteristic of pollutants discharged from a wide ranges of vehicle type (or fleet composition) operated at different conditions because of the least influences of meteorological parameters and atmospheric reactions occurring inside the tunnel (Kim et al., 2012). This study conducted the tunnel sampling with the primary aims to (1) characterize the distribution of 25 PAHs in particulate phase (PM2.5); (2) assess the influence of fleet composition on emission characteristics of PM2.5 and particulate PAHs at different sampling time frames including weekday, weekend, daytime and nighttime; (3) evaluate the diagnostic ratios and emission factors of PM2.5 and particulate PAHs emitted from vehicles. This study is expected to provide the comparison between characteristics of 16 popular PAHs and EU-PAHs in particulate phase of a tunnel in different timeframes, which would provide good insights into the PAHs emission from mobile sources.


2.1 Tunnel Sampling

The tunnel with a length of 1,875 m connecting New Taipei City and Keelung City in northern Taiwan (as shown in Fig. 1) was selected to investigate the characteristics of PAHs emitted from vehicles. The tunnel has two separate bores with three lanes per bore, which was equipped with a vertical flow ventilation jet fan with a total width of 10.3 meters. There are 1.0-meter wide pedestrian walkways on both sides of the road. The height of the tunnel is 8.5 m and the sectional area is about 106 m2. In the tunnel, samples were collected at two sampling points 50 m away from the tunnel inlet (located in Keelung City, Station A) and the outlet (located in New Taipei City, Station B), respectively. Furthermore, ambient air samples were also collected at the same time at an air quality monitoring station nearby the tunnel (Station C). To evaluate the characteristics of PAHs emitted in different periods, samples were collected simultaneously at three stations in a period of seven days, being divided into weekdays (Monday–Friday) and weekend (Saturday and Sunday) samples. Furthermore, single-day samples were divided into daytime (7:00 AM–19:00 PM) and nighttime (19:00 PM–7:00 AM) samples. The sampling campaign was conducted from 5th March 2019 to 11th March 2019, and the number and type of vehicles crossing the tunnel were identified via a radar vehicle counter and a video recorder.

Fig. 1. Relevant locations for tunnel and ambient samplings.
Fig. 1. Relevant locations for tunnel and ambient samplings.

All samples were collected by high flow rate samplers operating with the sampling flow rate of 500 L min1. The PM2.5 and particulate PAHs were collected on a 150 mm round quartz fiber filter (QFF) which was pre-cleaned in a furnace at the temperature of 900°C. After the sampling campaign, the QFF was stored in a tight box with ice, sent back to the laboratory immediately and then stored in a refrigerator until extraction. PAHs were extracted and analyzed within three weeks after sampling.

2.2 Analysis of PM2.5 and Particulate PAHs

Before analysis, the filters were conditioned under specific conditions (20°C, and relative humidity of 40%) for 24 hrs and weighed to obtain the mass of particle collected for calculating PM2.5. For the analysis of particulate PAHs, all QFFs were then spiked with internal standards (PAH-LCS-B, Wellington Laboratories Inc.) and extracted for 24 hours by Soxhlet extraction with dichloromethane (DCM). The DCM extract was then concentrated to approximately 10 mL by rotary evaporation. Thereafter, DCM was replaced with n-hexane for three times by adding 70–80 mL of n-hexane and concentrating to completely remove DCM. The sample was then passed through a clean-up column packed with 10 g of activated silica gel and 1 g Na2SO4. The column was eluted by hexane and DCM to obtain PAHs in the purifying solution and then, the extract was condensed to 2 mL by rotary evaporation. The collected eluent was reconcentrated to approximately 500 µL with a gentle nitrogen stream, and the recovery standards were for analysis. In this study, 25 PAHs were analyzed by gas chromatography-mass spectrometry (Agilent 6890-5973N) using a fused silica capillary column DB-5 MS (60 m × 0.25 mm × 0.25 m) under positive EI conditions, and data were obtained in the selected ion monitoring (SIM) mode. The relevant information regarding target compounds and their abbreviations was presented in Table S1.

2.3 Emission Factor and BaP Equivalency Factors

In this study, emission factor (EF) of particulate PAHs was calculated for vehicles passing through the tunnel during the sampling period. The EF was calculated following Riccio et al. (2016) using the vehicle kilometers traveled-based emission factors (EFVKT) calculation method as shown in Eq. (1):


where EFVKT is the emission factor of vehicle mileage (ng PAHs emitted km-vehicle traveled1); V is the air flow velocity (m s1); A is the tunnel cross-sectional area (m2); N is the number of vehicles per unit time (number-of-vehicles total-time1); L is the distance between two sampling points (km) and ∆c is the difference in PAHs concentrations (ng m3) measured at the two sampling points of the tunnel.

The BaP equivalent concentration (BaPeq) was used to assess the carcinogenic risk of PAHs, and the toxic equivalency factors (TEFs) were obtained from previous studies (Nisbet and LaGoy, 1992; Richter-Brockmann and Achten, 2018; U.S. EPA, 2010; Andersson et al., 2015; OEHHA, 1994). The BaPeq of the PAHs was calculated according to Eq. (2):


where PAHi is the concentration of the individual PAH in each sample and TEFi is the TEF of the individual PAH. The carcinogenic risk of 25 PAHs is calculated based on the sum of individual BaPeq. The TEF values of individual PAH are summarized in Table S1.

2.4 Quality Assurance and Quality Control (QA/QC)

The QC and QA for analysis of PM2.5 and PAHs were carried out according to the methods prescribed by the European Commission and Taiwan EPA, including EN-14907 and NIEA A212.10B for sampling and analysis of PM2.5 and particulate PAHs in tunnel and ambient air, respectively. Field blanks and method blanks were extracted and analyzed simultaneously with field samples. Only low contents of PHE, Ant, FL, and Pyr were detected in the PUF and filter of field blank samples (< 2 µg, < 0.3 µg, < 0.1 µg, < 0.1 µg and < 0.2 µg per cartridge, respectively). The recoveries of internal standards ranged from 75 to 110% for most PAHs.


3.1 Traffic Flow and PM2.5 Emission

The vehicles passing through the tunnel were divided into two categories including diesel and gasoline engines. The average speed of vehicles in daytime and nighttime were 80.9 ± 1.2 and 91.3 ± 0.87 (km hr1), respectively, and no significant difference between weekend and weekday was found. The average volume and composition of the fleet recorded at different time frames are presented in Fig. 2. The vehicle number ranged from 52,000 to 57,000 per day with a slightly lower traffic flow during weekdays (4,400 ± 200 vehicles hr1) compared with the weekend (4,600 ± 100 vehicles hr1). The number of vehicles recorded in nighttime accounted for 26 ± 2% of total vehicles passing through the tunnel on weekdays, while this figure increased to 32 ± 1% for the weekend. Gasoline vehicles predominated at all time and the diesel vehicle accounted for 16.2% and 8.6% of the fleet on weekday and weekend, respectively. Furthermore, the proportion of diesel vehicles in the fleet was higher in the daytime compared with nighttime, especially a significant difference was observed on weekdays.

Fig. 2. (a) Composition and fleet number in different time frames; (b) Average PM2.5 concentrations measured at the ambient air, tunnel inlet and outlet on weekday and weekend, respectively; (c) Average PM2.5 concentrations measured at the inlet and outlet of the tunnel in daytime and nighttime, respectively. The error bar refers to the standard deviation (± SD).Fig. 2. (a) Composition and fleet number in different time frames; (b) Average PM2.5 concentrations measured at the ambient air, tunnel inlet and outlet on weekday and weekend, respectively; (c) Average PM2.5 concentrations measured at the inlet and outlet of the tunnel in daytime and nighttime, respectively. The error bar refers to the standard deviation (± SD).

The average PM2.5 concentrations measured at the inlet and outlet of the tunnel were 21.9 ± 6.9 µg m–3 and 46.1 ± 12 µg m–3, respectively, which are significantly higher than that measured at ambient station (12.5 ± 6.2 µg m–3), indicating that vehicle emissions contributed significantly to the level of PM2.5 in the tunnel. The concentration of PM2.5 measured in this study was relatively lower than those reported worldwide for PM2.5 concentration measured in tunnel (Zhang et al., 2015; Pant et al., 2017; de P. Pereira et al., 2002). Interestingly, the ambient PM2.5 concentration (Fig. 2(b)) measured during the weekend (13.5 ± 7.9 µg m–3) was slightly higher than that measured during the weekdays (11.7 ± 5.8 µg m–3), while the PM2.5 concentrations measured at the inlet and outlet of tunnel during the weekday (inlet: 26.3 ± 11 µg m–3, outlet: 55.3 ± 16 µg m–3) were higher than those measured during the weekend (inlet: 17.2 ± 3.3 µg m–3, outlet: 33.3 ± 7.2 µg m–3). It is noted that the difference of inlet and outlet PM2.5 concentrations measured during weekday and weekend are 29.0 µg m–3 and 16.1 µg m–3, respectively, which is consistent with the significantly higher number of diesel vehicle passing through the tunnel during the weekday (1.76 times higher) in comparison with that on the weekend. Additionally, the difference of inlet and outlet PM2.5 concentration measured during the daytime (27.4 µg m–3) was significantly higher than that measured during the nighttime (16.4 µg m–3), resulting from the higher traffic volume in the daytime compared with that in the nighttime (Fig. 2(c)). A similar trend was also reported by Perez-Martinez et al. (2014) for the levels of NOx and PM10 measured in a tunnel located in Brazil.

3.2 Mass Concentration and Distribution of PAHs on PM2.5

The concentrations of particulate PAHs (P-PAHs) measured at the inlet and outlet of the tunnel, respectively, are shown in Fig. 3. The total P-PAHs measured at the inlet of the tunnel was 1.68 ± 1.4 ng m–3, which is significantly lower than that measured at the outlet of the tunnel (6.31 ± 4.8 ng m–3), while the average concentration of P-PAHs found in ambient air station was only 0.275 ± 0.062 ng m–3. Interestingly, the levels of P-PAHs measured in this study were notably lower than those reported by previous studies. For instance, Fang et al. (2018) indicate that the levels of 15 PAHs in PM2.5 measured at the inlet and outlet of the tunnel in Nanjing (China) were 6.34 ± 3.1 and 32.1 ± 7.5 ng m–3, respectively. Kim et al. (2014) revealed that the levels of particle-PAHs measured in three tunnels located in South Korea were in the range of 28.2 to 37.4 ng m–3. The differences (ΔC = Coutlet – Cinlet) of PM2.5-PAH congeners (outlet – inlet) measured at the tunnel at different time frames of this study are presented in Fig. 4. The differences found in daytime, nighttime, weekday and weekend were 4.28 ± 3.9 and 4.46 ± 2.4, 5.27 ± 3.6 and 4.28 ± 1.2 ng m3, respectively. Although the fleet number in the daytime was significantly higher than that in the nighttime, the differences of P-PAH concentration measured in daytime and nighttime were comparable, even the PM2.5 concentration measured in the daytime was significantly higher than that measured in nighttime (Fig. 2). The level of PAHs on PM2.5 collected in nighttime at the outlet of tunnel (0.166 ± 0.069 ng µg1) was significantly higher than that in daytime (0.108 ± 0.087 ng µg1). Similar results were also reported by Ho et al. (2009) for particulate-PAHs in different time frames. It might be attributed to the adsorption/desorption of PAHs onto particles and the decline of temperature at nighttime. After being emitted into the atmosphere of the tunnel at a low temperature, gas-phase PAHs of high affinity with particles (normally high-ring PAHs) would immediately associate with fine particles, resulting in the higher level of PAHs on PM2.5 collected in nighttime (Dat et al., 2017). On the other hand, a higher difference of P-PAHs concentration was found during weekdays compared with that on weekends, while the total number of vehicles recorded on weekends was slightly higher than that on weekdays. This trend was similar to that of PM2.5, which was resulted from the higher number of diesel vehicles compared with gasoline vehicles in the daytime. Previous studies indicated that the PM and PAH emission factors of diesel engines were significantly higher than that of gasoline engines. For example, Yang et al. (2019) investigated the emission factors of PM2.5 from gasoline and diesel vehicles in Taiwan, and they revealed that average emission factor of PM2.5 from diesel engines (57.8 mg km–1) was significantly higher than that from gasoline engines (1.57 mg km1). Chen et al. (2013) reported that the emission factor of PAHs from diesel vehicles was 3.37 times higher compared with that from gasoline vehicles. Ho et al. (2009) indicated that diesel vehicles emitted 5.45 times PAHs compared to gasoline vehicles. Several factors are reported to affect the PAH emission factor of vehicles, such as engine type, engine temperature, service age, mileage, and maintenance (Cheruiyot et al., 2015). Survival of PAHs in the fuel and pyrosynthesis of PAHs in combustion chamber are two major mechanisms of PAHs formation in vehicular engines (Elghawi et al., 2010). Souza and Corrêa (2016) indicated that 1–5% of diesel was not destroyed in diesel engines, which could be the reason for higher PAHs emission from diesel vehicles compared to that from gasoline vehicles because of the significantly higher level of PAHs in diesel fuel compared with that in gasoline (Pohjola et al., 2010). The result indicates that the emission of PAHs from diesel vehicles constituted a major contribution to total P-PAHs measured in the tunnel despite of lower diesel vehicles in the total fleet number.

Fig. 3. The concentrations of P-PAHs measured at the ambient air station, and inlet, outlet of the tunnel, respectively (the error bar represents one SD of the data)Fig. 3. The concentrations of P-PAHs measured at the ambient air station, and inlet, outlet of the tunnel, respectively (the error bar represents one SD of the data).

Fig. 3 also presents the congener profile of PAHs on PM2.5 collected at the inlet and outlet of the tunnel. It can be seen that the profile of P-PAHs collected in the ambient air station was significantly different from those collected at the inlet and outlet of the tunnel. In the samples collected from ambient station, the 5-ring PAHs dominated, accounting for 36.6 ± 8.7% of total PAHs, followed by 6-ring and 4-ring PAHs, which made up 28.9 ± 5.7% and 24.1 ± 5.7% of total P-PAHs, respectively; and the 3-ring PAHs only contributed to 10.5 ± 8.7% of total P-PAHs. Specifically, BbF and BaP were the dominant congeners among the 5-ring group, contributing to 11.7 ± 2.2% and 9.5 ± 1.6%, respectively, of total P-PAHs. As for the 6-ring PAHs group, the main contributors to total P-PAHs include BghiP (16.0 ± 6.2%) and IND (11.4 ± 1.5%). FL (7.5 ± 2.8%) and Pyr (6.6 ± 1.6%) were the major contributors to total P-PAHs in the 4-ring group, while PA of 3-ring group contributed to 4.7 ± 1.5% of total P-PAHs. On the other hand, Fig. 3 also reveals a similar profile of P-PAHs collected at the inlet and outlet of the tunnel, with the dominance of 4-ring PAHs (54.0–57.8%), followed by 5-ring PAHs (19.7–21.4%), 6-ring PAHs (12.5–14.3%), 3-ring PAHs (9.9–10.3%). The results are consistent with those reported by Ho et al. (2009) and Liu et al. (2015) for the P-PAHs collected in the tunnel located in Hongkong and Shanghai, respectively. It is noted that the dominant congener found at the inlet and outlet of the tunnel was Pyr (4-ring congener), accounting for 23.7 ± 7.3% and 23.5 ± 7.6%, respectively, which is remarkably higher than that found in the ambient air samples. BeP was the highest contributor to total P-PAHs in the 5-ring group, constituting 5.0 ± 2.7%, while BghiP was the major contributor of 6-ring PAHs with 7.1 ± 3.3% of total P-PAHs. PA was the biggest congener contributing to total P-PAHs among the 3-ring group, accounting for 6.6 ± 1.9%. The congener profile of PAHs was routinely employed as basic information for suggesting major sources of PAHs. Different congener profiles of PAHs in the samples collected from ambient air station and the tunnel indicated different sources contributed to PAHs collected at two sampling locations. The PAHs collected from ambient air might be originated from several sources, while those found in the tunnel should be resulted from the evaporation or/and incomplete combustion of diesel and gasoline fuels (Wallington et al., 2006). Some studies reported that the enrichment of low-ring PAHs (≤ 4-ring PAHs) denotes the contribution of diesel engine emission, while the dominance of high-ring PAHs (5- and 6-ring PAHs) indicates the emission of gasoline engines (Ancelet et al., 2011; Kam et al., 2012). Although the fleet composition showed that gasoline engines were the major vehicle, the high contribution of 4-ring PAHs indicates the emission of PAHs from diesel vehicles contributed mainly to the PAHs collected. This result confirms that diesel engines emitted significantly higher PAHs than gasoline engines.

Fig. 4 depicts the concentration differences of PAHs in PM2.5 collected at different time frames. The differences observed in the weekday and weekend were 5.25 ± 3.6 and 2.81 ± 2.8 ng m–3, respectively, while the differences found in daytime and nighttime were 4.25 ± 3.9 and 4.46 ± 2.4 ng m–3, respectively. As presented in Fig. 4(a), the profiles of P-PAHs emitted in the tunnel are similar at all different time frames with the dominance of 4-ring PAHs (Pry and PL). A higher P-PAHs concentration was found in the tunnel during weekday compared with that on the weekend, which is in line with the higher number of vehicles recorded on the weekday. Fig. 4(b) shows the concentrations of PAHs measured in daytime and nighttime, respectively, which reveals that the concentrations of low-ring PAHs (3- and 4-ring groups) were higher in daytime compared with those in nighttime, however, the opposite trend was found for high-ring PAHs (5- and 6-ring groups). It is noted that the proportion of diesel vehicles in nighttime was lower than in day time. Many studies indicated that diesel engines mainly emit low-ring PAHs, while high-ring PAHs dominate in gas exhaust emitted from gasoline engines (Zielinska et al., 2004; Oliveira et al., 2011; Kou et al., 2012). This might be the reason for different distributions of PAHs in particles in night and day times.

Fig. 4. The differences of P-PAH concentrations measured at different time frames: (a) weekend and weekday, (b) daytime and nighttime (the bar represents one SD of the data).Fig. 4. The differences of P-PAH concentrations measured at different time frames: (a) weekend and weekday, (b) daytime and nighttime (the bar represents one SD of the data).

3.3 TEQ Concentration and Distribution

The TEQ concentration of P-PAHs measured at ambient air station and inlet, outlet of the tunnel, and the TEQ concentration difference at different time frames are shown in Fig. 5. The TEQ concentration of P-PAHs measured in ambient air station was low (0.051–0.217 ng BaP-TEQ m–3), which is lower than 1 ng BaP-TEQ m–3 for the ambient PAHs standard as recommended by the European Commission (European Commission, 2005) and within the range reported by Dat et al. (2018) for P-PAHs concentration in northern Taiwan, while those measured at the inlet and outlet of the tunnel were in the ranges of 0.405–7.14 and 1.55–23.2 ng BaP-TEQ m–3, respectively. In terms of concentration differences (ΔC = Cout – Cin), as shown in Fig. 5, the average concentration differences measured during weekday, weekend, daytime, and nighttime were 5.41, 1.56, 4.44, 3.07 ng BaP-TEQ m–3, respectively.

Fig. 5. TEQ concentrations of P-PAHs measured at three sampling sites and the TEQ concentration differences found in the tunnel at different time frames.Fig. 5. TEQ concentrations of P-PAHs measured at three sampling sites and the TEQ concentration differences found in the tunnel at different time frames.

Furthermore, Fig. 5 also reveals that the BcFE is the predominant congener contributing to the total BaP-TEQ concentration of P-PAHs collected in the tunnel, accounting for 63.8–92.8% of total TEQ. The following major contributors to total TEQ concentration are BaP (2.94–9.52%) and DBalP (1.44–19.45%), and all other congeners contribute less than 1% of total TEQ (as shown in Fig. S1). These results are conceivable considering the relatively high toxic equivalent factors of these species (BcFe: 20; BaP: 1; DBalP: 10), which is in agreement with those reported by previous studies if these congeners are included in the target compounds. Richter-Brockmann and Achten (2018) investigated the occurrence of 24 PAHs in various kinds of environmental samples and found that BcFe contributed to about 93% of the total TEQ concentration. Lyu et al. (2019) reported that BcFe, DalP were the main contributors to total TEQ of P-PAHs emitted from coal-fired power plants.

It is important to evaluate the contribution of 15 EU-PAHs to the total TEQ concentration. The lists of US-EPA PAHs and EU-PAHs and their toxic equivalent factors are presented in Table S1. In this study, 15 EU-PAHs consistently contributed to 99.9% of the total TEQ concentration in all samples collected in the tunnel, which is consistent with those reported by Richter-Brockmann and Achten (2018) and Lyu et al. (2019) for environmental samples and stack gas samples emitted from a coal-fired power plant, respectively. The figure for ambient air samples ranges from 59.3 to 82.7%. On the other hand, 15 US-EPA PAHs only contribute to 4.96 to 15.0% of total TEQ for tunnel samples and 87.6 to 93.1% of total TEQ for ambient air samples. This finding implies that 15 EU-PAHs should be included for the evaluation of health risk associated with PAHs.

3.4 Diagnostic Ratio and Emission Factor (EF)

In PAH study, diagnostic ratios were usually used as a common tool to identify preliminary sources of PAHs (Tobiszewski et al., 2012). Several diagnostic ratios used for distinguishing diesel and gasoline-related emissions calculated from the data of concentration differences obtained in this study are listed and compared in Table 1. It can be seen that the BaA/(BaA + Chr) > 0.35 and BaP/BghiP = 0.65 (on average) > 0.5 are in agreement with the literature for identifying vehicular emission. The ratios of IND/(IND + BghiP), BaA/(BaA + Chr), BbF/BkF, Pyr/BaP reveal that the PAHs collected were closer to diesel vehicles rather than gasoline vehicles. The ratios of Flu/(Flu + Pyr) and Flu/BeP are out of the ranges reported in the literature (Sicre et al., 1987; Rogge et al.,1993; Oda et al., 2001), which should be carefully considered if they are employed for identifying the sources of PAHs in ambient air.

Table 1. Comparison of diagnostic ratios obtained in this study and literature.

The average EFVKT (PM2.5) of the tunnel sampling calculated in this study is 10.9 ± 1.1 mg km-vehicle1, which is lower than those reported previously. For instance, Ferm and Sjöberg (2015) reported that the EF of PM2.5 in Sweden was 20 mg km-vehicle1, and 22.8 ± 7.4 mg km-vehicle1 was reported by Mancilla and Mendoza (2012) for the tunnel study conducted in Mexico. Marinello et al. (2020) reviewed various studies conducted at different countries and indicated that the EF of PM2.5 was in a wide range of 3–715 (mg km-vehicle1). Many reasons might be attributed to the lower EF of PM found in this study compared with previous studies such as the ventilation rate in the tunnel, the detailed composition fleet and the maintenance quality of vehicles passing through the tunnel during sampling time, which should be clarified in the future studies.

The EFs of particulate PAHs emitted from vehicles passing through the tunnel at different time frames are shown in Table 2. The emission factors of total P-PAHs are higher in weekday and nighttime compared with weekend and daytime, respectively. The contribution of individual PAHs to total EF follows the trend of concentration difference with the highest EF of Pyr (6.61 µg km-vehicle1), followed by FL (4.11 µg km-vehicle-1) and PA (1.91 µg km-vehicle1). The total EFs ranged from 9.29 to 81.7 (µg km-vehicle1) or 28.0 ± 22 (µg km-vehicle1) on average, which are in the ranges of 22.0 to 354.2 and 71.1 ± 15 and 43.11 ± 15 (µg km-vehicle1) found in tunnels in Hongkong, Portugal, and China, respectively (Ho et al., 2009; Alves et al., 2016; and Fang et al., 2018).

Table 2. The emission factors of particulate PAHs measured in the tunnel investigated.


This study investigated the characteristics of PM2.5 and 25 PAHs on PM2.5 emitted from vehicles in different time frames via air sampling conducted in a tunnel located in northern Taiwan. The emissions of PM2.5 and P-PAHs depended on the size and composition of the fleet. Generally, higher concentration differences of PM2.5 and P-PAHs were found on weekday compared with that on weekend due to the higher number of diesel vehicles. Furthermore, the concentration differences of these pollutants were higher in the daytime compared with that in the nighttime because of the larger fleet size in daytime. In terms of mass concentration, 4-ring PAHs (Pyr and PL) were the dominant contributors in all samples; however, another 4-ring PAHs (BcFE) was the major contributor to total TEQ concentration due to its high toxic equivalence. On the other hand, the predominant contribution of 15 EU-PAHs indicates that they should be included for the evaluation of the health risk associated with PAHs emission. The diagnostic ratios reveal that the source of P-PAHs collected was closer to emission of diesel vehicles, which is consistent with the higher emission factor of diesel vehicles compared with gasoline vehicles. The EF of PM5 was lower than those reported in previous studies, while the EF of P-PAHs was within the range of those reported worldwide.

As indicated in this study, different characteristics of PM and particulate PAHs emitted from vehicles passing through this tunnel in different time frames were attributed from different fleet compositions and number of vehicles in different time frames. To the best of our knowledge, this study is the first investigation including 15 EU-PAHs emitted from vehicles via tunnel sampling and the results obtained suggest that these PAHs are important to be taken into account as the risk of PAHs is assessed. Benzo[c]fluorine was found as the main contributor to total TEQ concentration of particulate PAHs emitted from vehicles. Therefore, this study could provide useful insights for future research regarding the emission characteristics of PAHs from mobile sources.

This study only investigated the particulate PAHs emitted from two types of vehicle based on fuels used (diesel and gasoline) in a tunnel in different time frames. Therefore, it is suggested that future studies should investigate emission characteristics of PAHs from different kinds of vehicles based on powers. The gas-phase PAHs normally are the main contributors to total PAHs emitted from vehicles, which should be included in the next investigation. Furthermore, the emission of 15-EU PAHs should be characterized for not only mobile sources but also stationary sources because of their highly adverse effects to human health and environment.


The authors gratefully acknowledge the financial support provided by the Ministry of Science and Technology (MOST-106-EPA-F-005-003).


  1. Adesina, A.J., Kumar, K.R., Sivakumar, V. (2016). Aerosol-cloud-precipitation interactions over major cities in south Africa: Impact on regional environment and climate change. Aerosol Air Qual. Res. 16, 195-211. https://doi.org/10.4209/aaqr.2015.03.0185

  2. Alves, C.A., Vicente, A.M.P., Gomes, J., Nunes, T., Duarte, M., Bandowe, B.A.M. (2016). Polycyclic aromatic hydrocarbons (PAHs) and their derivatives (oxygenated-PAHs, nitrated-PAHs and azaarenes) in size-fractionated particles emitted in an urban road tunnel. Atmos. Res. 180, 128-137. https://doi.org/10.1016/j.atmosres.2016.05.013

  3. Ancelet, T., Davy, P.K., Trompetter, W.J., Markwitz, A., Weatherburn, D.C. (2011). Carbonaceous aerosols in an urban tunnel. Atmos. Environ. 45, 4463–4469. https://doi.org/10.1016/j.atmosenv.2011.05.032

  4. Andersson, J.T., Achten, C. (2015). Time to say goodbye to the 16 EPA PAHs? Toward an up-to-date use of PACs for environmental purposes. Polycyclic Aromat. Compd. 35, 330–354. https://doi.org/10.1080/10406638.2014.991042

  5. Chen, F., Hu, W., Zhong, Q. (2013). Emissions of particle-phase polycyclic aromatic hydrocarbons (PAHs) in the Fu Gui-shan Tunnel of Nanjing, China. Atmos. Res. 124, 53–60. https://doi.org/10.1016/j.atmosres.2012.12.008

  6. Cheruiyot, N.K., Lee, W.J., Mwangi, J.K., Wang, L.C., Lin, N.H., Lin, Y.C., Cao, J., Zhang, R., Chang-Chien, G.P. (2015). An overview: Polycyclic aromatic hydrocarbon emissions from the stationary and mobile sources and in the ambient air. Aerosol Air Qual. Res. 15, 2730–2762. https://doi.org/10.4209/aaqr.2015.11.0627

  7. Dat, N.D., Chang, M.B. (2017). Review on characteristics of PAHs in atmosphere, anthropogenic sources and control technologies. Sci. Total Environ. 609, 682–693. https://doi.org/10.1016/j.scitotenv.2017.07.204

  8. Dat, N.D., Lyu, J.M., Chang, M.B. (2018). Variation of atmospheric PAHs in northern Taiwan during winter and summer seasons. Aerosol Air Qual. Res. 18, 1019-1031. https://doi.org/10.4209/aaqr.2018.01.0038

  9. de P. Pereira, P.A., Andrade, J.B. de, Miguel, A.H. (2002). Measurements of semivolatile and particulate polycyclic aromatic hydrocarbons in a bus station and an urban tunnel in Salvador, Brazil. J. Environ. Monit. 4, 558–561. https://doi.org/10.1039/B201990F

  10. Elghawi, U.M., Mayouf, A., Tsolakis, A., Wyszynski, M.L. (2010). Vapour-phase and particulate-bound PAHs profile generated by a (SI/HCCI) engine from a winter grade commercial gasoline fuel. Fuel 89, 2019–2025. https://doi.org/10.1016/j.fuel.2010.01.002

  11. Fang, X., Wu, L., Zhang, Q., Zhang, J., Wang, A., Zhang, Y., Zhao, J., Mao, H. (2019). Characteristics, emissions and source identifications of particle polycyclic aromatic hydrocarbons from traffic emissions using tunnel measurement. Transp. Res. D. Transp. Environ. 67, 674-684. https://doi.org/10.1016/j.trd.2018.02.021

  12. Ferm, M., Sjöberg, K. (2015). Concentrations and emission factors for PM2.5 and PM10 from road traffic in Sweden. Atmos. Environ. 119, 211-219. https://doi.org/10.1016/j.atmosenv.2015.08.037

  13. Grimmer, G., Jacob, J., Naujack, K.-W. (1983). Profile of the polycyclic aromatic compounds from crude oils. Z. Anal. Chem. 314, 29–36. https://doi.org/10.1007/BF00476507

  14. Guaita, R., Pichiule, M., Maté, T., Linares, C., Díaz, J. (2011). Short-term impact of particulate matter (PM2.5) on respiratory mortality in Madrid. Int. J. Environ. Health Res. 21, 260-274. https://doi.org/10.1080/09603123.2010.544033

  15. Guo, H., Lee, S.C., Ho, K.F., Wang, X.M., Zou, S.C. (2003). Particle-associated polycyclic aromatic hydrocarbons in urban air of Hong Kong. Atmos. Environ. 37, 5307-5317. https://doi.org/10.1016/j.atmosenv.2003.09.011

  16. Halonen, J.I., Lanki, T., Yli-Tuomi, T., Tiittanen, P., Kulmala, M., Pekkanen, J. (2009). Particulate air pollution and acute cardiorespiratory hospital admissions and mortality among the elderly. Epidemiology 20, 143–153. https://doi.org/10.1097/EDE.0b013e31818c7237

  17. Ho, K.F., Ho, S.S.H., Lee, S.C., Cheng, Y., Chow, J.C., Watson, J.G., Louie, P.K.K., Tian, L. (2009). Emissions of gas- and particle-phase polycyclic aromatic hydrocarbons (PAHs) in the Shing Mun Tunnel, Hong Kong. Atmos. Environ. 43, 6343-6351. https://doi.org/10.1016/j.atmosenv.2009.09.025

  18. Kam, W., Liacos, J.W., Schauer, J.J., Delfino, R.J., Sioutas, C. (2012). On-road emission factors of PM pollutants for light-duty vehicles (LDVs) based on urban street driving conditions. Atmos. Environ. 61, 378-386. https://doi.org/10.1016/j.atmosenv.2012.07.072

  19. Katsoyiannis, A., Terzi, E., Cai, Q.Y. (2007). On the use of PAH molecular diagnostic ratios in sewage sludge for the understanding of the PAH sources. Is this use appropriate? Chemosphere 69, 1337-1339. https://doi.org/10.1016/j.chemosphere.2007.05.084

  20. Kavouras, I.G., Koutrakis, P., Tsapakis, M., Lagoudaki, E., Stephanou, E.G., Von Baer, D., Oyola, P. (2001). Source apportionment of urban particulate aliphatic and polynuclear aromatic hydrocarbons (PAHs) using multivariate methods. Environ. Sci. Technol. 35, 2288-2294. https://doi.org/10.1021/es001540z

  21. Kim, K.H., Anthwal, A., Chan, G.P., Jo, S.J., Chae, Y.Z., Park, J.A., Jong, H.J., Jong, R.S., Oh, J.M. (2012). Monitoring of polyaromatic hydrocarbons and volatile organic compounds in two major traffic tunnels in Seoul, Korea. Environ. Technol. 33, 1963–1976. https://doi.org/10.1080/09593330.2012.655316

  22. Kuo, C.Y., Chien, P.S., Kuo, W.C., Wei, C.T., Rau, J.Y. (2013). Comparison of polycyclic aromatic hydrocarbon emissions on gasoline- and diesel-dominated routes. Environ. Monit. Assess. 185, 5749-5761. https://doi.org/10.1007/s10661-012-2981-6

  23. Lai, Y.C., Tsai, C.H., Chen, Y.L., Chang-Chien, G.P. (2017). Distribution and sources of atmospheric polycyclic aromatic hydrocarbons at an industrial region in Kaohsiung, Taiwan. Aerosol Air Qual. Res. 17, 776-787. https://doi.org/10.4209/aaqr.2016.11.0482

  24. Liang, B., Su, Z., Tian, M., Yang, F., Gao, M., Chen, Y., Zhang, L., Xiang, L. (2020). Sources and potential health risks of PM2.5-bound PAHs in a megacity of southwest China: Importance of studying from a health risk perspective. Polycyclic Aromat. Compd. https://doi.org/10.1080/10406638.2020.1753218

  25. Liu, Y., Wang, S., Lohmann, R., Yu, N., Zhang, C., Gao, Y., Zhao, J., Ma, L. (2015). Source apportionment of gaseous and particulate PAHs from traffic emission using tunnel measurements in Shanghai, China. Atmos. Environ. 107, 129-136. https://doi.org/10.1016/j.atmosenv.2015.02.041

  26. Lu, C.M., Dat, N.D., Lien, C.K., Chi, K.H., Chang, M.B. (2019). Characteristics of fine particulate matter and polycyclic aromatic hydrocarbons emitted from coal combustion processes. Energy Fuels 33, 10247-10254. https://doi.org/10.1021/acs.energyfuels.9b02201

  27. Mancilla, Y., Mendoza, A. (2012). A tunnel study to characterize PM2.5 emissions from gasoline-powered vehicles in Monterrey, Mexico. Atmos. Environ. 59, 449-460. https://doi.org/10.1016/j.atmosenv.2012.05.025

  28. Marinello, S., Lolli, F., Gamberini, R. (2020). Roadway tunnels: A critical review of air pollutant concentrations and vehicular emissions. Transp. Res. D. Transp. Environ. 86, 102478. https://doi.org/10.1016/j.trd.2020.102478

  29. Nadali, A., Leili, M., Bahrami, A., Karami, M., Afkhami, A. (2021). Phase distribution and risk assessment of PAHs in ambient air of Hamadan, Iran. Ecotoxicol. Environ. Saf. 209, 111807. https://doi.org/10.1016/j.ecoenv.2020.111807

  30. Nisbet, I.C.T., LaGoy, P.K. (1992). Toxic equivalency factors (TEFs) for polycyclic aromatic hydrocarbons (PAHs). Regul. Toxicol. Pharm. 16, 290-300. https://doi.org/10.1016/0273-2300(92)90009-X

  31. Oliveira, C., Martins, N., Tavares, J., Pio, C., Cerqueira, M., Matos, M., Silva, H., Oliveira, C., Camões, F. (2011). Size distribution of polycyclic aromatic hydrocarbons in a roadway tunnel in Lisbon, Portugal. Chemosphere 83, 1588-1596. https://doi.org/10.1016/j.chemosphere.2011.01.011

  32. Pandey, P.K., Patel, K.S., Lenicek, J. (1999). Polycyclic aromatic hydrocarbons: Need for assessment of health risks in India? Study of an urban-industrial location in India. Environ. Monit. Assess. 59, 287–319. https://doi.org/10.1023/A:1006169605672

  33. Pant, P., Shi, Z., Pope, F.D., Harrison, R.M. (2017). Characterization of traffic-related particulate matter emissions in a road tunnel in Birmingham, UK: Trace metals and organic molecular markers. Aerosol Air Qual. Res. 17, 117-130. https://doi.org/10.4209/aaqr.2016.01.0040

  34. Park, S.S., Kim, Y.J., Kang, C.H. (2002). Atmospheric polycyclic aromatic hydrocarbons in Seoul, Korea. Atmos. Environ. 36, 2917-2924. https://doi.org/10.1016/S1352-2310(02)00206-6

  35. Pérez-Martínez, P.J., Miranda, R.M., Nogueira, T., Guardani, M.L., Fornaro, A., Ynoue, R., Andrade, M.F. (2014). Emission factors of air pollutants from vehicles measured inside road tunnels in São Paulo: Case study comparison. Int. J. Environ. Health Res. 11, 2155-2168. https://doi.org/10.1007/s13762-014-0562-7

  36. Pohjola, S.K., Savela, K., Kuusimäki, L., Kanno, T., Kawanishi, M., Weyand, E. (2004). Polycyclic aromatic hydrocarbons of diesel and gasoline exhaust and DNA adduct detection in calf thymus DNA and lymphocyte DNA of workers exposed to diesel exhaust. Polycyclic Aromat. Compd. 24, 451-465. https://doi.org/10.1080/10406630490471465

  37. Ravindra, K., Wauters, E., Tyagi, S.K., Mor, S., Van Grieken, R. (2006). Assessment of air quality after the implementation of compressed natural gas (CNG) as fuel in public transport in Delhi, India. Environ. Monit. Assess. 115, 405-417. https://doi.org/10.1007/s10661-006-7051-5

  38. Ravindra, K., Sokhi, R., Van Grieken, R. (2008). Atmospheric polycyclic aromatic hydrocarbons: Source attribution, emission factors and regulation. Atmos. Environ. 42, 2895-2921. https://doi.org/10.1016/j.atmosenv.2007.12.010

  39. Riccio, A., Chianese, E., Monaco, D., Costagliola, M.A., Perretta, G., Prati, M.V., Agrillo, G., Esposito, A., Gasbarra, D., Shindler, L., Brusasca, G., Nanni, A., Pozzi, C., Magliulo, V. (2016). Real-world automotive particulate matter and PAH emission factors and profile concentrations: Results from an urban tunnel experiment in Naples, Italy. Atmos. Environ. 141, 379-387. https://doi.org/10.1016/j.atmosenv.2016.06.070

  40. Richter-Brockmann, S., Achten, C. (2018). Analysis and toxicity of 59 PAH in petrogenic and pyrogenic environmental samples including dibenzopyrenes, 7H-benzo[c]fluorene, 5-methylchrysene and 1-methylpyrene. Chemosphere 200, 495-503. https://doi.org/10.1016/j.chemosphere.2018.02.146

  41. Rogge, W.F., Hildemann, L.M., Mazurek, M.A., Cass, G.R., Simoneit, B.R.T. (1993). Sources of fine organic aerosol. 2. Noncatalyst and catalyst-equipped automobiles and heavy-duty diesel trucks. Environ. Sci. Technol. 27, 636-651. https://doi.org/10.1021/es00041a007

  42. Samoli, E., Peng, R., Ramsay, T., Pipikou, M., Touloumi, G., Dominici, F., Burnett, R., Cohen, A., Krewski, D., Samet, J., Katsouyanni, K. (2008). Acute effects of ambient particulate matter on mortality in Europe and north America: Results from the APHENA study. Environ. Health Perspect. 116, 1480-1486. https://doi.org/10.1289/ehp.11345

  43. Samset, B.H., Myhre, G., Forster, P.M., Hodnebrog, Ø., Andrews, T., Faluvegi, G., Fläschner, D., Kasoar, M., Kharin, V., Kirkevåg, A., Lamarque, J.F., Olivié, D., Richardson, T., Shindell, D., Shine, K.P., Takemura, T., Voulgarakis, A. (2016). Fast and slow precipitation responses to individual climate forcers: A PDRMIP multimodel study. Geophys. Res. Lett. 43, 2782-2791. https://doi.org/10.1002/2016GL068064

  44. Sicre, M.A., Marty, J.C., Saliot, A., Aparicio, X., Grimalt, J., Albaiges, J. (1987). Aliphatic and aromatic hydrocarbons in different sized aerosols over the Mediterranean Sea: Occurrence and origin. Atmos. Environ. 21, 2247-2259. https://doi.org/10.1016/0004-6981(87)90356-8

  45. Simcik, M.F., Eisenreich, S.J., Lioy, P.J. (1999). Source apportionment and source/sink relationships of PAHs in the coastal atmosphere of Chicago and Lake Michigan. Atmos. Environ. 33, 5071-5079. https://doi.org/10.1016/S1352-2310(99)00233-2

  46. Sonntag, D.B., Bailey, C.R., Fulper, C.R., Baldauf, R.W. (2012). Contribution of lubricating oil to particulate matter emissions from light-duty gasoline vehicles in Kansas city. Environ. Sci. Technol. 46, 4191-4199. https://doi.org/10.1021/es203747f

  47. Souza, C.V., Corrêa, S.M. (2016). Polycyclic aromatic hydrocarbons in diesel emission, diesel fuel and lubricant oil. Fuel 185, 925-931. https://doi.org/10.1016/j.fuel.2016.08.054

  48. Srivastava, A.K., Bisht, D.S., Singh, S., Kishore, N., Soni, V.K., Singh, S., Tiwari, S. (2018). Scattering and absorption characteristics of aerosols at an urban megacity over IGB: Implications to radiative forcing. Atmos. Res. 205, 107-117. https://doi.org/10.1016/j.atmosres.2018.01.018

  49. Tobiszewski, M., Namieśnik, J. (2012). PAH diagnostic ratios for the identification of pollution emission sources. Environ. Pollut. 162, 110-119. https://doi.org/10.1016/j.envpol.2011.10.025

  50. Wallington, T.J., Kaiser, E.W., Farrell, J.T. (2006). Automotive fuels and internal combustion engines: A chemical perspective. Chem. Soc. Rev. 35, 335-347. https://doi.org/10.1039/B410469M

  51. Wang, B., Lau, Y.S., Huang, Y., Organ, B., Chuang, H.C., Ho, S.S.H., Qu, L., Lee, S.C., Ho, K.F. (2021). Chemical and toxicological characterization of particulate emissions from diesel vehicles. J. Hazard. Mater. 405, 124613. https://doi.org/10.1016/j.jhazmat.2020.124613

  52. Xing, X., Chen, Z., Tian, Q., Mao, Y., Liu, W., Shi, M., Cheng, C., Hu, T., Zhu, G., Li, Y., Zheng, H., Zhang, J., Kong, S., Qi, S. (2020). Characterization and source identification of PM2.5-bound polycyclic aromatic hydrocarbons in urban, suburban, and rural ambient air, central China during summer harvest. Ecotoxicol. Environ. Saf. 191, 110219. https://doi.org/10.1016/j.ecoenv.2020.110219

  53. Xing, Y.F., Xu, Y.H., Shi, M.H., Lian, Y.X. (2016). The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 8, E69-E74. https://doi.org/10.3978/j.issn.2072-1439.2016.01.19

  54. Yang, H.H., Dhital, N.B., Wang, L.C., Hsieh, Y.S., Lee, K.T., Hsu, Y.T., Huang, S.C. (2019). Chemical characterization of fine particulate matter in gasoline and diesel vehicle exhaust. Aerosol Air Qual. Res. 19, 1439-1449. https://doi.org/10.4209/aaqr.2019.04.0191

  55. Yang, T.T., Hsu, C.Y., Chen, Y.C., Young, L.H., Huang, C.H., Ku, C.H. (2017). Characteristics, Sources, and health risks of atmospheric PM2.5-bound polycyclic aromatic hydrocarbons in Hsinchu, Taiwan. Aerosol Air Qual. Res. 17, 563-573. https://doi.org/10.4209/aaqr.2016.06.0283

  56. Zhang, S., Wu, Y., Liu, H., Wu, X., Zhou, Y., Yao, Z., Fu, L., He, K., Hao, J. (2013). Historical evaluation of vehicle emission control in Guangzhou based on a multi-year emission inventory. Atmos. Environ. 76, 32-42. https://doi.org/10.1016/j.atmosenv.2012.11.047

  57. Zhang, Y., Wang, X., Li, G., Yang, W., Huang, Z., Zhang, Z., Huang, X., Deng, W., Liu, T., Huang, Z., Zhang, Z. (2015). Emission factors of fine particles, carbonaceous aerosols and traces gases from road vehicles: Recent tests in an urban tunnel in the Pearl River Delta, China. Atmos. Environ. 122, 876-884. https://doi.org/10.1016/j.atmosenv.2015.08.024

  58. Zielinska, B., Sagebiel, J., McDonald, J.D., Whitney, K., Lawson, D.R. (2004). Emission rates and comparative chemical composition from selected in-use diesel and gasoline-fueled vehicles. J. Air Waste Manage. Assoc. 54, 1138-1150. https://doi.org/10.1080/10473289.2004.10470973

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