Shahzada Amani Room1, Chia En Lin1, Shih Yu Pan1, Ta Chih Hsiao2, Charles C.-K. Chou3, Kai Hsien Chi This email address is being protected from spambots. You need JavaScript enabled to view it.1,4 

1 Institute of Environmental and Occupational Health Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
2 Graduate Institute of Environmental Engineering, National Taiwan University, Taipei 106, Taiwan
3 Research Center for Environmental Changes, Academia Sinica, Taipei 115, Taiwan
4 One Health Research Center, Research Center of National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan

Received: September 11, 2022
Revised: December 26, 2022
Accepted: January 25, 2023

 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.

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Cite this article:

Room, S.A., Lin, C.E., Pan, S.Y., Hsiao, T.C., Chou, C.C.K., Chi, K.H. (2023). Incremental Lifetime Cancer Risk of PAHs in PM2.5 via Local Emissions and Long-Range Transport during Winter. Aerosol Air Qual. Res. 23, 220319.


  • PM2.5 and PAHs were higher during the day due to high traffic and human activities.
  • Gasoline, diesel and coal combustion were the key sources of PAHs in the urban site.
  • The ion had higher levels during the day due to high contents of nss-SO42 and NO3.
  • Metals at tunnel were higher in the daytime due to high Fe and Ca levels from brakes.
  • Among all metal species, Cr6+ showed the highest carcinogenic risk at 2.60 × 105.


Our study covers the chemical characteristics and health risks of PM2.5 from 2019–2020 at National Taiwan University and City Tunnel in Taipei, Taiwan. Positive Matrix Factorization (PMF) was used to quantify the potential sources of polycyclic aromatic hydrocarbon (PAH). The influences of local pollution (LP), long range transport (LRT) and Power Plant are shown by using bivariate polar plot (BPP), Potential source contribution function (PSCF) and Enrichment Factor (EF) with Positive Matrix Factorization (PMF). Besides, chemical analysis included PAHs, carbonaceous species (OC/EC), water-soluble ions, and trace metals, respectively. Our results indicated that vehicle emissions (gasoline engines: 20.8%, diesel engines: 23.3%) and coal combustion (55.9%) were the major sources of PAHs in the urban site, with higher levels in LP than in LRT. The mean PM2.5 concentrations during the daytime was 13.0 ± 5.64 µg m–3, higher than 10.4 ± 5.16 µg m–3 at nighttime, reflecting high traffic emissions and anthropogenic aerosols during the day. Similar phenomenon was observed for PAHs in the daytime of 3.26 ± 2.36 ng m–3 and 1.91 ± 1.14 ng m–3 at nighttime. Also, due to higher NO3- and K levels, the ions and metals had higher values in the daytime and weekdays than at nighttime and weekends. In contrast, in the City Tunnel, the PM2.5 concentrations difference at the tunnel inlet and tunnel outlet during the daytime was significantly higher 36.4 µg m–3 compared to 27.4 µg m–3 at nighttime due to the heavy traffic during the day. As well, the OC/EC mass fractions at the tunnel inlet and tunnel outlet were 4.00 and 2.73. PSCF revealed a greater value (> 0.6) in the coastal areas of Inner Mongolia and Mainland China, due to long-range transport and industrial emissions. The incremental lifetime cancer risk (ILCR) of Cr6+ was higher in metal (2.60 × 10–5) but did not exceed its tolerable limits.

Keywords: PM2.5, City tunnel, Urban site, Positive matrix factorization, ILCR


Air pollution is one of the major problems around the world due to its negative impacts on human health (Lelieveld et al., 2015). Nearly 90% of the world’s population lives in areas with high levels of polluted air (WHO, 2016). Recent studies have showed that every year 8.8 million people die globally from air pollution (Lelieveld et al., 2019; Li et al., 2018). Particulate matter (PM) is one of the significant components of air pollution (Veld et al., 2021). In terms of their sizes, they are usually divided into two groups, including coarse particles (with diameter less than 10 µm, PM10) and inhalable fine particles (with diameter smaller than 2.5 µm, PM2.5) (Kim et al., 2015). PM with a diameter 2.5 µm or less is considered more hazardous to human health because it contains a large number of toxic components which cause cardiovascular and pulmonary diseases (Brunekreef and Holgate, 2002). Over 3.2 million people die globally as a direct consequences of PM2.5, which can easily penetrate the respiratory tract through inhalation and cause lung disease (Gauderman et al., 2015). PM2.5 is currently one of the major problems in Taiwan (Chang and Lee, 2008), as it contains organic matter such as polycyclic aromatic hydrocarbons with multiple aromatic rings (Tang et al., 2017). Several epidemiological studies on PM2.5 in Taiwan have demonstrated the adverse effects of PM2.5 on lungs and heart functions as well as on mortality (Wang et al., 2021a). About 7.5% of Taiwan's deaths in 2016 were caused by PM2.5, as it is one of the major risk factors for common and complex illnesses (Guo et al., 2018). The ambient PM2.5 in Taiwan is strongly influenced by three main sources, including traffic, long-range transport and industrial activities (Ngo et al., 2019). A study reported by Chi et al. (2017) found that the average PM2.5 concentration in northern Taiwan was 17.7 ± 13 µg m–3, whereas in central Taiwan this concentration was 29.3 ± 102 µg m–3 (Cheng et al., 2008), however 43.2 ± 20 µg m–3 concentration was reported in the southern part of Taiwan (Cheng et al., 2014). Positive matrix factorization (PMF), a technique based on the content of ionic components and carbon and trace metals in particles, has been applied in several studies (Stortini et al., 2009; Tao et al., 2014) due to its large advantages over other receptor models (Liang et al., 2017).

Taipei is the most populous city with a population of about 2.6 million people (Chi et al., 2022). Its average population density is 9,288 people km2, covers an area of 272 km2 (Chen et al., 2022), and is located in northern Taiwan (Lai, 2020). There are three administrative units in this region, including Taipei City, New Taipei City and Keelung City. Due to its unique geographical location, its air quality is mainly affected by long-range transport, road dust and anthropogenic aerosols coming from the Asian continent (Chen et al., 2019a). Additionally, traffic emissions are also one of the leading contributors to air pollution in Taipei city (Yang et al., 2004). There are 700,000 cars, over a million motorcycles, and 6,536 cars per square kilometer (Chang and Lee, 2008). Furthermore, by the end of 2019, the city has a total of 410,405 meters of roads with a total area of 22,200,944 m2 (Taipei Department of Transportation, 2020). As we all know, particulate matter consists of a complex mixture of different chemical species. Therefore, the major aims of this study were (i) to investigate PM2.5 concentrations and its chemical composition at different timescales and scenarios in the urban and tunnel site in Taipei city (ii) to estimate the health risk assessment associated with inhalation exposure to PAHs over time. Our findings will help to promote regulatory action to improve air quality within the city.


2.1 Sampling Site Description and Collection of PM2.5 Samples

The PM2.5 samples were taken from 24 December 2019 to 12 January 2020 at two different locations, including an urban site and a City Tunnel in Taipei (Taiwan), as shown in Fig. 1. The urban site is in the National Taiwan University campus (25.0102°N, 121.3238°E), where traffic emissions, especially during rush hours, are a major contributor to air pollution. The city tunnel is the Ziqiang Tunnel in Zhongshan District (25.05169°N, 121.32561°E), which is the longest double-hole road tunnel in Taipei City, connecting Shilin to Dazhidu. Its length varies: 819 meters (southbound lane) and 821 meters (northbound lane), whereas its width is 9 meters long. It is restricted for vehicles over 3.5 meters in height. The vehicle type composition depended on the time of day, 45% petrol, 52% locomotive, and 3% diesel were recorded during the daytime, and 52% petrol, 44% locomotive, and 4% diesel were recorded at nighttime. In addition, the samples were taken from two spots inside the tunnel 100 m away from the tunnel inlet and tunnel outlet. The average wind speed during the sampling period was recorded 2.20 m s-1 during the daytime and 1.50 m s1 at nighttime.

Fig. 1. Sampling location of urban and tunnel site.Fig. 1. Sampling location of urban and tunnel site.

In this study, two types of high-volume air samplers (HVS) were used. At the urban site, PM2.5 samples were collected on a 150 mm Whatman cellulose filters using DHA-80 (Digital, Switzerland), for 24 h from (7:00 AM–6:30 PM) to (7:00 PM–6:30 AM) local time, for three consecutive weeks (24 December 2020–1 December 2020). While, in the Ziqiang Tunnel, a Shibata (HV-1000R) was used to collect PM2.5 using an 8 × 10-inch square quartz fiber filters, over a 24 h periods from (18 March 2020) to (23 March 2020) between (7:00 AM–1:00 PM) to (2:00 PM–8:00 AM) local time.

A total of 42 PM2.5 samples were collected from both sites with flow rates of 500 L min–1 (DHA-80) and 1000 L min–1 (Shibata HV-1000R). First, the quartz filters were conditioned at 500°C for 24 h and stored in aluminum foil for 48 h at a temperature of 20 ± 2°C and 50 ± 5% relative humidity. After sampling, filters were cased, enfolded in aluminum foil to avoid direct sunlight, placed in a bag, and kept that in a freezer at −18°C to evade volatile organic compounds before further analysis. After bringing the filters to the laboratory, they were placed in a controlled room and left to equilibrate for 48 h. they were then weighed in triplicate on a micro-analytical balance (CP225D, Sartorius, Germany) with an accuracy of 0.1 mg. The PM2.5 mass level was predicted by taking the variance of the filter mass before and after sampling (Liu et al., 2020). Also, a field blank sample was used for all six PM2.5 samples and analyzed separately in the same way (Liu and Corma, 2018).

2.2 Chemical Analysis of PM2.5

Different types of chemical compositions of PM2.5 were analyzed with different instruments, The PAHs were measured on a Thermo Scientific™ TSQ 8000 Evo Triple Quadrupole GC-MS/MS (GC MS/MS, Thermo Fisher Scientific Taiwan Co., Ltd., Taiwan). Although, the Trace metals were investigated by using inductively coupled plasma mass spectrometry (ICP-MS; Platform ICP, Micomass Inc., Wilmslow, UK) using (ICP-MS, THERMO-ELEMENT XR, Waltham, MA, US). While the ionic components were examined through a DionexTM ICS-1000 (Thermo Fisher Scientific Ion Chromatograph), and finally, carbonaceous compounds (OC/EC) were identified using a Thermo-Optical Carbon Analyzer (Desert Research Institute Model 2015, Atmoslytic Inc. Calabasas, CA, USA) with Thermo-Optical Reflection (TOR) method under IMPROVE-A (Interagency Monitoring of Protected Visual Environment) protocol. For further details about analytical method and chemical species please refer to Text S1 in the supplementary data.

2.3 Source Apportionment

In this study, we used bivariate polar plots, potential source contribution function and positive matrix factorization to find the major atmospheric emission sources in the urban site of Taipei city.

2.3.1 Positive matrix factorization

The PMF model was used to analyze potential sources of PAH contamination in atmospheric air (Hsu et al., 2016). This model was developed by (Paatero and Tapper, 1994) and has been applied in a previous study (Yu et al., 2013). It is an air quality assessment model that splits the given sample data into two matrices such as factor contribution and factor profile (Jindamanee et al., 2020). For the operation method of this software, data analysis was performed in PMF mode, referring to the user manual "EPA PMF 5.0 User Guide" published by (U.S. EPA Environmental Protection Agency), which is described below in Eq. (1).


where x includes i samples and j compounds, j is the species, k is the number of pollution sources, gik is the contribution of the kth source to the ith sample, gik > 0, fkj is the profile of species j by the kth source, fkj > 0, and eij is the residual of the ith sample and the jth species concentration value and its analytical value. The uncertainty calculations were done with the help of the Following Eq. (2) and Eq. (3):

When the pollutant level was below or equal to the detection limit (MDL).


When the pollutant concentration was higher than MDL


Error fraction shows the error percentage.

2.3.2 Bivariate polar plot

BBP was used to investigate how changes in pollutant levels are related to polar wind direction and speed. A detailed description of this model can be found somewhere (Carslaw and Beevers, 2013; Carslaw and Ropkins, 2012; Jeričević et al., 2019). In our study, the meteorological (temperature, wind direction and speed) and pollutants data (SO2, O3, and NOx) were obtained from the Environmental Protection Agency (Taiwan). R statistical software was used to measure the pollutants levels. Additionally, Controlled Permeability Formwork (CPF) was used to investigate the effects of local wind sources from different wind directions (Kim et al., 2003). Eq. (4) and Eq. (5) are showing the calculation method for CPF and BPP.


The mΔθ stands for the number of samples whose species concentration is equal to or higher than the threshold standard (x) in a certain wind domain (Δθ), x is the high percentage digits of all concentration values (75th or 90th) and nΔθ: is the number of samples in a certain wind domain (Δθ). The Δθ was set to 22:5 and wind speeds below 1 m s-1 were omitted from the analysis. The threshold criteria for each source were set to the top 20 percentile of partial source contributions for each source (Chen et al., 2016b).


mΔθ,Δu is the number of samples with species concentration equal to or higher than the threshold standard (x) in a certain wind domain (Δθ) and wind speed in a certain interval (Δu) and nΔθ, Δu is the number of samples in the wind domain (Δθ) and the wind speed in a certain interval (Δu).

2.3.3 Potential source contribution function

The potential source contribution function is an important tool for determining air mass trajectory analysis (Liu et al., 2020). It was developed by Meteo-Info using TrajStat software for data analysis and applied in various studies as a tool at different scales. The PSCF method was used in combination with the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory, HYSPLIT) model ( to investigate the location of potential regional sources of air pollutants in Taipei City. The calculation method is given bellow Eq. (6):


The nij stands for the number of segments or points of air parcel in the ij-the grid and mij is the number of segments and points in the air parcel trajectory combined with pollutant data in the ij-th grid.

2.4 PM2.5 Health Risk Assessment

Health risk assessment is the process used to identify threats to human health from various hazardous compounds (Ceron-Breton et al., 2021). This study contains the Incremental Lifetime Cancer Risk (ILCR) method for measuring ILCR inhalation risk assessment of exposure to heavy metals and PAHs in Taipei City.

2.4.1 Heavy metal

The study, refers the method overview by (Hsu et al., 2021), for assessing the ILCR of PM2.5 in atmospheric metals. The carcinogenic risk factor values and their unit for each type of metal are given in Table S1. ILCR risk for metals was measured using the following Eq. (7):


where C is the concentration of individual metal (µg m–3) and URi is the unit of carcinogenic risk of individual metal (µg m–3)–1.

2.4.2 Carcinogenicity risk assessment of PAHs

The Carcinogenic risk of PAHs at urban site was carried through the following Eq. (8). The ILCR risk for inhalation exposure to airborne PAHs was determined with the help of Eq. (9). Moreover, the Benzo[a]pyrene of toxic equivalency factors of individual species is given in Table S2.


The BaP_TEQ indicates the toxic equivalent concentration of PAHs (ng m–3), Ci is the concentration of individual species of PAHs and BaP_TEFi is the Benzo[a]pyrene of toxic equivalency factors of individual species of PAHs.


where URBaP is the unit carcinogenic risk factor.


3.1 Chemical Composition and Spatio-Temporal Variation of PM2.5 at Urban Site

A summary of the mean concentrations of PM2.5 measured under different time periods and scenarios at urban site is given in Table S3. The average PM2.5 concentration measured during the daytime was 13.0 ± 5.64 µg m–3 slightly greater than 10.4 ± 5.16 µg m–3 at nighttime, indicating heavy traffic volume, road dust and more anthropogenic activities during the daytime compared to nighttime (Shivkumar et al., 2022). Traffic emissions, especially during rush hour, are a major contributor to PM2.5 in the urban areas (Pani et al., 2017). Road dust particles combined with traffic-related turbulence play a significant role in urban PM2.5 levels (Chen et al., 2019b). A comparable trends were reported by (Qiao et al., 2015; Shi et al., 2012). Additionally, the study also divided PM2.5 emissions into two groups of days (weekdays and weekends) to distinguish pollution sources. The days of the week consisted of Monday to Friday, while Saturday and Sunday are being treated as weekends. As Table S3, the ambient PM2.5 showed a higher level of 12.2 ± 4.94 µg m–3 on weekdays than 10.3 ± 3.47 µg m–3 on weekends, representing heavy traffic density, road dust and high anthropogenic activities (Kulshrestha et al., 2009). A similar phenomenon were observed by (Sahu and Kota, 2017; Tong et al., 2020), for PM2.5 on weekdays vs. weekends. Indeed, traffic emissions are dramatically reduced on weekends compared to weekdays due to easier communication and less road congestion. likewise, a decline in anthropogenic activity over the weekends also had a significant impact on PM2.5 levels (Khan et al., 2018). Besides, at different scenarios, the PM2.5 level was higher in LPs 21.7 followed by LRT 15.6 ± 5.45 and normal 9.70 ± 3.62 µg m–3 as given in Table S3. This may be due to heavy traffic, road dust, and significant stationary sources (power plants, oil refineries, etc.), as the study area is close to busy roads and industrial zones (New Taipei City, Keelung City), which may contribute highly to PM2.5 levels. In addition, metrological factors are also one of the major issue increasing the PM2.5 content of LPs (Hsu et al., 2017). A study reported by (Cheng et al., 2014), found that air pollutants from LPs in Taiwan contained large amounts of PM2.5. As well, a positive coloration was found between LRT and PM2.5, which is attributed to the pollutants entering Taiwan from elsewhere in China via the northeast monsoon, accumulating and increased PM2.5 concentrations on the land (Lai, 2015; Liang et al., 2015). Parallel results were investigated by Tang et al. (2020), who studied the source profile of PM2.5 in Taiwan.

Table S4 shows the greater ionic concentrations of 5.43 ± 2.55 and 5.24 ± 2.51 µg m–3 during the daytime and on weekdays, compared with 4.82 ± 3.06 and 4.82 ± 3.53 µg m–3 at nighttime and weekends respectively. Additionally, no significant change in ionic composition was noticed between daytime and nighttime, with dominant species were nss-SO42, NO3, and NH4+, respectively, as shown in Fig. S1. Similar results have been reported by (Shen et al., 2020; Tsai et al., 2016). However, a slight difference was observed between SO42 and NO3, with proportion of 54% and 12% on weekdays compared to 51% and 16% on weekends. SO42 and NO3 are mainly associated with fuel combustion and vehicle emissions (Huang et al., 2015). In a previous study Yin et al. (2012), SO42–, NO3 and NH4+ were identified as the major ions contributing to PM2.5 in China. Furthermore, the ionic values in LP, LRT and normal were 8.56, 7.74 ± 1.22 and 4.20 ± 1.73 µg m3, respectively, shown in Fig. 2. Interestingly, Na+ was found to have a higher level in LP than LRT, which is mainly associated with marine aerosols. This may be because the study area is close to the sea, and sea-derived Na+ ions were relatively high, or it may link with long range transport of pollutants from the outside. To minimize the effects of marine aerosols, Eqs. (10–13) were used, to evaluate contribution level of the non-sea-salt ions (Pani et al., 2017).


Fig. 2. Ion distribution in different scenarios at an urban site.Fig. 2. Ion distribution in different scenarios at an urban site.

where Na+ is the concentration of sodium, nss-SO42– is sulfate concentration of non-sea salt ingredients, SO42– is the concentration of sulfate, nss-Mg2+ is magnesium concentration of non-sea salt ingredients, Mg2+ is the concentration of magnesium, nss-Ca2+ is calcium concentration of non-sea salt ingredients, Ca2+ is the concentration of calcium, nss-K+ is potassium concentration of non-sea salt ingredients and K+ is the concentration of potassium.

Table S5 displays the average metallic concentrations of PM2.5 in different period and scenarios in an urban site. The Metals showed a higher value of 718 ± 220 ng m–3 during the daytime compared to 532 ± 182 ng m–3 at nighttime, however, weekdays and weekends deviations were 679 ± 176 ng m–3 and 490 ± 85 ng m–3, respectively, with the main species being Na, K, Fe and Ca, as shown in Fig. S2. Interestingly, we found that the K values were significantly higher during the day and weekdays than at night and weekends. This suggests that high coal combustion, biomass combustion and fugitive dust are the main contributors to K values (Srimuruganandam and Shiva Nagendra, 2011). Moreover, a positive correlation was found between different time periods and scenarios as indicated in Table S5. Additionally, in different scenarios, the metal concentrations in Normal, LRT and LP were 571 ± 152, 799 ± 198, and 801 ng m–3, respectively. Interestingly, the metals distribution was similar between Normal and LRT, while, the highest concentrations were found in LP, with Fe (27%) and K (22%), as indicated in Fig. 3. Fe is associated with brake pads and rusts (Chang et al., 2018), while K is allied with coal combustion, biomass burning and traffic sources (Srimuruganandam and Shiva Nagendra, 2011). Although, Taipei City is primarily associated with high transport emissions and has no significant sources of coal or biomass burning, they might be associated with neighborhoods areas that contain many powers plant sources, which contributes to a highly amount of potassium.

Fig. 3. Metal distribution in different scenarios at an urban site.Fig. 3. Metal distribution in different scenarios at an urban site.

3.1.1 PAHs and BaPeq concentrations in PM2.5 at urban site

As shown in Table S6, the total concentrations of ∑27 PAHs were higher 3.26 ± 2.36 in the daytime than 1.91 ± 1.14 ng m–3 at nighttime, whereas their corresponding BaPeq values were 5.62 ± 13.0 and 4.39 ± 7.87 ng m–3, respectively, probably due to high traffic emissions, decline in photochemical degradation and temporal variations. Moreover, the relative contribution of the nighttime PAHs values of the BghiP, IND, and BeP were slightly higher as compared to daytime, with high molecular weight (HMW 66.8%), presented in Fig. S3, the reasons may be related to emissions from diesel and gasoline vehicles, especially from heavy load vehicles at night (Javed et al., 2021) or decline in temperature at nighttime (Zhang et al., 2019). This suggests that the BeP and BghiP, INDs, are mainly associated with particles (> 98%), do not volatilize readily at low temperatures, and accumulate instead of volatilizing, leading to increased PAH levels at nighttime. Similar results was also stated by Chen et al. (2021). Furthermore, the total ∑27 PAH concentrations during the weekdays and weekends were 2.30 ± 0.713 and 3.27± 1.84 ng m–3, respectively as shown in Table S6, and the dominant species were Bghip, IND and BeP, respectively, shown in Fig. S4. Interestingly, higher medium molecular (MMW) and low molecular weight (LMW) concentrations were observed during weekends as compared to weekdays. Conflicting to other study Javed et al. (2019), this may be due to temporary increase in the urban construction activities on weekends. A similar results was reported by Chen et al. (2016a). Furthermore, in different scenarios the LP hold the highest PAH value 3.53, followed by LRT 2.51 ± 0.293 and normal 1.31± 0.969 ng m–3, respectively. However, their corresponding BaPeq values were recorded 5.30, 7.35 ± 6.57, and 4.53 ± 6.89 ng m–3, respectively. BghiP was the most abundant species of LP with HMW (77.5%) as compared to LRT and Normal, representing high diesel and gasoline vehicles, as the study area is located to busy roads which emits huge amount of pollutant into the air.

3.2 Chemical Composition and Spatio-Temporal Variation of PM2.5 at Tunnel Site

As shown in Fig. S5, the average PM2.5 concentrations measured in the inlet and outlet of the tunnel were 49.1 ± 12.9 and 81.4 ± 15.6 µg m–3, respectively, however, the difference between the inlet and outlet during the daytime was 36.4 µg m–3, significantly higher than 27.4 µg m–3 at nighttime, indicating heavy traffic volume during the day than at night. Similar trend was reported by Wang et al. (2021b) for PM2.5 and PAHs levels at tunnel site, Taiwan. Moreover, the total ion concentrations at tunnel inlet and tunnel outlet were 12.1 ± 4.34, and 13.7 ± 4.46 µg m–3, respectively, as given in Table S7. Overall, no significant changes were noticed in the ionic compositions between the tunnel inlet and tunnel outlet, dominated by NO3, SO42–, and Na+, respectively, shown in Fig. 4. Interestingly, NO3 showed the highest diurnal variations within the tunnel, and their proportions were higher in PM2.5 during the daytime (inlet: 32%, outlet: 31%) compared to nighttime (inlet: 19%, outlet: 21%). NO3 is a secondary aerosol and is mainly associated with diesel vehicle emissions (Cheng et al., 2010) from heavy traffic leading to higher NOx formations.

Fig. 4. Ion distribution in the tunnel site.Fig. 4. Ion distribution in the tunnel site.

Besides, the metallic level of tunnel outlet was 3,226 ± 1,092 ng m–3 which was higher than 2,240 ± 1,886 ng m–3 at tunnel inlet, as shown in Table S8. As well, at inlet K (56%) and Fe (24%) were the most abundant species contributing to PM2.5, followed by outlet Fe (39%), Ca (21%) and K (12%) as illustrated in Fig. 5. Additionally, the metal showed a higher value during the daytime (inlet: 2,426 ± 2,085, outlet: 3,185 ± 1,325 ng m–3) compared to nighttime (inlet: 2,361 ± 1,882, outlet: 3,131 ± 779 ng m–3), due the higher Fe and Ca levels. Fe is associated with traffic pollution emitted from gasoline vehicles as exhaust emissions linked with brake wear (Ngo et al., 2019), while Ca is allied with road dust and increases with heavy traffic volume (Chang et al., 2018), especially, in rush hour during the daytime. Besides, the total concentrations of OC, EC, TC and as well as OC/EC mass ratios at the tunnel inlet were 9.81 ± 1.98, 2.63 ± 1.09, 12.4 ± 2.99 and 4.00 ± 0.801 µg m–3, while the tunnel outlet were 19.0 ± 3.37, 7.39 ± 2.50, 26.4 ± 5.74 and 2.73 ± 0.584 µg m–3 respectively, as given in Table S9. OC is mainly associated with gasoline vehicles (Wang et al., 2022), however, EC is formed due to incomplete combustion of fossil fuels and biomass (Shi et al., 2021). Remarkably, higher levels of carbonaceous species were observed during the daytime than at nighttime. It indicates high traffic emissions from gasoline, diesel vehicles during the day vs. night, another study also reported that coal combustion and diesel emissions were the main sources of organic aerosols of 35% and 25%, whereas secondary organic aerosols consisted of gasoline 14% and diesel 12% (Hu et al., 2015).

Fig. 5. Metal distribution in the tunnel site.Fig. 5. Metal distribution in the tunnel site.

3.2.1 PAH and BaPeq concentrations in PM2.5 at tunnel site

The results of the average concentrations of PAHs and BaPeq are shown in Table S10. The total value of PAHs measured at inlet was 5.22 ± 2.03 compared to 8.02 ± 3.93 ng m–3 at outlet, while the total BaPeq was recorded 33.9 ± 14.9 at inlet and 56.2 ± 39.8 ng m–3 at outlet of the tunnel. Additionally, the PAHs and BaPeq showed significantly higher concentrations at outlet compared to inlet during the daytime. The reason might be associated with high traffic emissions during the daytime vs. nighttime. A similar trend was reported by Ngo et al. (2022). Our results were much smaller than Fang et al. (2019) (inlet: 6.43 ± 3.1 outlet: 32.1 ± 7.5 ng m–3) at China, Kim et al. (2015) proposed 28.2 to 37.4 ng m–3 at South Korea. Moreover, the average PAH distribution at inlet and outlet of the tunnel is given in Fig. S6. It can be clearly seen that the dominant species of the tunnel inlet were including 3-rings BcFE, 6-rings IND and BghiP, however, the major species distributed highly at tunnel outlet were 3-rings BcFE, 2-rings Pyr and 2-Min. Notably, the difference in PAH contribution was observed to be higher at the tunnel inlet with HMW (53.9%) compared to the tunnel outlet. A possible reason for the higher inlet PAH distribution could be related to diesel and gasoline vehicles (Zhao et al., 2020). HMW of PAH are linked with gasoline engine. BcFE, IND and BghiP are highly molecular compounds, and they can easily accumulate at low temperatures, and contributing highly to HMW (Chen et al., 2013).

3.2.2 Emission factor (EF) of PM2.5 at tunnel site

EF is one of the most important tools for analyzing atmospheric chemistry (Alleman et al., 2010; Lim et al., 2010) was used to estimate the amount of contamination source (anthropogenic or crustal), causing pollutants. Eq. (14) showing the method of calculating the emission factor of PM2.5.


where EF is the emission factor by vehicle fleet (mg km-vehicle–1), Cex and Cent are the chemical concentration at inlet and outlet (µg m–3), A is the cross-sectional area of the tunnel (m2), WS is the average wind speed in the tunnel (m s–1), t is the sampling time (second), N is the number of vehicle during the sample time and L is the distance between the inlet and outlet (km).

There is no specific rule for the selection of a reference element, Although terrigenous must be taken (Gugamsetty et al., 2012). Elements with EF values less than 10 are likely due to crustal emissions, while elements with EF values greater than 10 are considered to be from sources other than the crust (Chester et al., 1999). The diurnal variations of PM2.5 emission factor for all vehicle types during the daytime and nighttime are given in Table S11. As the results, the PM2.5 EF ranged from 3.08 to 6.30 mg km-vehicle–1, with an average of 5.27 ± 1.78 (daytime) 4.19 ± 1.17 (nighttime) mg km-vehicle–1. In addition, the average EC, ion, metal, PAH and Bapeq mass factor during the daytime were 771 ± 319, 510 ± 241, 299 ± 147, 1.01 ± 0.594 and 6.43 ± 3.37 µg km-vehicle–1, respectively, higher than the EF values 632 ± 235, 204 ± 134, 245 ± 118 and 7.13 ± 2.54 µg km-vehicle–1, at nighttime. In addition, OC was the dominant component in PM2.5 emissions factor in the city tunnel ranged from 1.07 to 2.29 during the daytime and nighttime due to high emissions of primary and secondary aerosols. These results were 22 ± 8 mg km-vehicle–1 lower than the previous study (Ferm and Sjöberg, 2015). This may be due to lower EF values as the samples were collected from the urban tunnel with low traffic volume and the vehicle speeds was within 50 km h–1.

3.3 Source Apportionment

3.3.1 Positive matrix factorization analysis at urban site

42 samples were collected to analyze ∑16 PAHs using the PMF 5.0 model to determine the source contributions of 16 PAHs at an urban site in Taipei City. Three major sources were classified; gasoline vehicles, diesel vehicles and coal-fired power plants as demonstrated in Fig. S7.

Factor 1 loaded 20.8% to the total PAHs, and was associated with highly loadings of PHE, NAP and FLT. The presence of PHE, NAP, and FLT is indicative of traffic emissions, especially linked with gasoline vehicle emissions (Wang et al., 2020). Therefore, Factor 1 was supposed to be gasoline combustion source, and when we analyzed the trend of this factor over time, we found that its concentration was higher during the daytime and on weekdays compared to nighttime and weekends due to high transportation.

Factor 2, which explained 23.3% of the total PAHs, shown high loading of PYR, BKF and BaP. PYR and BKF are associated with diesel vehicles (Mihankhah et al., 2020). Therefore, Factor 2 was suggested to be related to diesel vehicle emission sources, and its concentration was higher during the daytime and weekdays compared to nighttime and weekends due to heavy traffic density.

Factor 3, which accounts for 55.9% of all PAHs, was a high load of CHR, IND, and BghiP, which were found to be stationary sources emission (Kulkarni and Venkataraman, 2000). CHR, BghiP were identified as a distinct markers for coal combustion (Wang et al., 2020) and Its concentration was higher during the daytime and weekdays than at night and weekends due to high emissions from stationary and anthropogenic sources. Among all factors, factor 3 contributed the most to total PAH. There are no significant PAH stationary sources (power plants, oil refineries, etc.) in Taipei City and the active substances can be released from the large stationary sources near the sampling site. For example, there are two largest power plants near Taipei City. The Hsieh-ho-power plant is situated nearly 26 km north of Taipei City, and the Linkou Coal-Fired Power Plant is located nearly 24 km west of Taipei City (Tang et al., 2020).

3.3.2 Bivariate polarization plot (BPP) analysis at urban site

In this study, BPP was analyzed to detect the pollutant source trends under different scenarios at Taipei, City, Taiwan. During the normal days, the wind speed and direction vary more erratically, although the maximum winds speed were between 1.8 m s–1 and 3 m s–1, and the average PM2.5 concentration was found to be relatively low. As shown in Fig. 6 as the wind blows from the northeast to the east, high PM2.5 levels were observed in Taiwan, suggesting that pollutants were brought to the island from mainland China, South Korea, and Japan with high-pressure through long-range transport. A similar pattern was also described in earlier studies (Chi et al., 2017; Pani et al., 2017). Remarkably, high values of PM2.5 have also been detected in the northwestern part of mainland Taiwan. The air parcels may have originated from northern Taiwan, likely due to heavy traffic and coal-fired power plants.

Fig. 6. BPP results in different scenarios at an urban site.Fig. 6. BPP results in different scenarios at an urban site.

3.3.3 Potential source contribution factor at urban site

PSCF was used to represent the results of cluster analysis of air mass tracking trajectories at Taipei City, Taiwan as shown in Fig. 7. Three groups of air masses were identified in this study, including Cluster 1 in blue, Cluster 2 in red, and Cluster 3 in purple, shown in Fig. S8. Cluster 1 accounting for approximately 48.1% of the trajectories; originate from the northeastern part of mainland China. Cluster 2, accounting for almost 43.2%, originates from the Pacific Ocean below South Korea, however, Cluster 3, comprising 8.6% of the total trajectories, originates from the southwestern coast of Taiwan and carries over to northern Taiwan. In addition, PM2.5 showed a higher PSCF values (> 0.6) in coastal areas of Inner Mongolia and Mainland China, suggesting that the potential sources of PM2.5 in northern Taiwan is originated from the coastal areas of Inner Mongolia and central China, which passes through the Loess Desert anthropogenic aerosols-rich areas along the coastal of Central China. Finally, Cluster 1, originating from mainland China, is the most polluted of the three clusters with the highest average PM2.5 concentrations and is strongly influenced by long-range transport and industrial emissions. A similar phenomenon was also observed by (Chi et al., 2017), who measured PM2.5 and PCCD/F concentrations in Taiwan.

Fig. 7. PSCF analysis results in the urban site. Fig. 7. PSCF analysis results in the urban site.

3.3.4 Incremental lifetime cancer risk of PM2.5 and PAHs at urban site

Six metallic concentrations (Cr, Co, As, Ni, Cd, and Pb) in the ambient PM2.5 were taken from the urban site to evaluate the ILCR as given in Table S12. Generally, Cd, As and Pb are associated with coal combustion (Yang et al., 2017), Ni is allied to fuel oil (Almeida et al., 2020) while, Co and Cr are linked with traffic sources (Hsu et al., 2021), respectively. Generally, the ILCR value was higher during the daytime (2.05 × 10-5) than at nighttime (1.65 × 10–5), and between the weekdays (2.03 × 10-5) than at weekends (1.41 × 10–7), respectively, given in Fig. S9. Our findings were within the tolerance range (10–6–10–4), set by the United States Environmental Protection Agency (U.S. EPA). In addition, Cr6+ had the highest carcinogenic risk among all metal types, mainly associated with traffic sources, car brakes and tire wear (Lin et al., 2015). The results were similar with (Hsu et al., 2021; Xue et al., 2022). Besides, we also assessed PAHs in PM2.5 as illustrated in Fig. 8. Results showed that LP had the highest cancer risk (2.60 × 10–5), followed by LRT (1.47 × 10–5) and Normal (1.29 × 10–5), respectively, but did not exceed acceptable limits (10–6–10–4). Furthermore, we found that ILCR in LRT was lower than (Hsu et al., 2019). Similarly, Normal also had a lower value (1.29 × 10–5), compared to (3.55 × 105) reported by (Wu et al., 2011). Indeed, our research focused mainly on traffic emissions and anthropogenic aerosols, even though no significant stationary sources (power plants, oil refineries, etc.) were involved in this study.

Fig. 8. The ILCR result of PAHs inhaled exposure in different scenarios.Fig. 8. The ILCR result of PAHs inhaled exposure in different scenarios.


The study investigated the chemical composition, source analysis and associated health risks of PM2.5 from 2019 to 2020 at two different locations, including an urban and city tunnel in Taipei, Taiwan. Based on the results, the overall mean PM2.5 concentration during the daytime was higher than at nighttime in both locations in Taipei. Additionally, no significant changes were found in the ionic composition of the urban site during the daytime and nighttime, dominated by SO42, NO3 and NH4+ respectively, although there was a slight difference between SO42 (54%) and NO3 (12%) on weekdays as well as on weekends, up to (51%) and (16%) respectively, which are related to fuel combustion and vehicle emissions. Moreover, under different scenarios, metal concentrations were found to be similar in long-range transport (LRT) and normal, while a higher concentration of (801 ng m–3) was observed in local pollution (LP), with dominant species being Fe (27%) and K (22%) which are linked with brake pads and rusts. Furthermore, the PMF model indicated that traffic emissions and coal-fired power plants were the main sources of PAHs with 44.1%, and 55.9%, respectively in the urban site. Besides, the major potential source regions with high PSCF values (> 0.6) in winter mainly consisted of Inner Mongolia and mainland China, due to long range transport and industrial emissions.

In contrast, in the City Tunnel, a significant difference of 36.4 µg m–3 and 27.4 µg m–3 was measured during the daytime vs. nighttime between the inlet and outlet of the tunnel due to the heavier traffic volume during the day than at night. In addition, no significant changes were observed in the ionic composition between the tunnel inlet and tunnel outlet, with SO42 (42%), NO3 (27%), and Na+ (2%) predominating, respectively. Also, due to the higher Fe and Ca content, the average metal concentrations in the tunnel inlet and tunnel outlet were 2,240 ± 1886 and 3,226 ± 1,092 ng m–3, respectively, with higher levels during the daytime compared to nighttime. Similar trends were also noted in PAHs during the daytime and nighttime because of high traffic emissions during the day vs. night. Furthermore, OC was the major component of the PM2.5 emissions factor in the city tunnel, which ranges from 1.07 to 2.29 in the daytime and nighttime due to high primary and secondary aerosol formations. Finally, among all the metals, Cr6+ showed the greatest carcinogenic risk of 2.60 × 105, and its concentration was higher during the daytime and weekdays but did not exceed its acceptable limits of 106–104, set by Unites State Environmental Protection Agency (U.S. EPA).


The authors gratefully acknowledge the Taiwan Environmental Protection Administration (EPA109F032), the Ministry of Science and Technology of Taiwan (MOST 108-2111-M-010-001-MY2 and MOST 110-2111-M-A49A-501) and Higher Education Sprout Project, Ministry of Education (MOE) in Taiwan for financial support.


  1. Alleman, L.Y., Lamaison, L., Perdrix, E., Robache, A., Galloo, J.C. (2010). PM10 metal concentrations and source identification using positive matrix factorization and wind sectoring in a French industrial zone. Atmos. Res. 96, 612–625.

  2. Almeida, S.M., Manousakas, M., Diapouli, E., Kertesz, Z., Samek, L., Hristova, E., Šega, K., Alvarez, R.P., Belis, C.A., Eleftheriadis, K. (2020). Ambient particulate matter source apportionment using receptor modelling in European and Central Asia urban areas. Environ. Pollut. 266, 115199.

  3. Brunekreef, B., Holgate, S.T. (2002). Air pollution and health. Lancet 360, 1233–1242.

  4. Carslaw, D.C., Ropkins, K. (2012). openair — An R package for air quality data analysis. Software 27–28, 52–61.

  5. Carslaw, D.C., Beevers, S.D. (2013). Characterising and understanding emission sources using bivariate polar plots and k-means clustering. Environ. Modell. Software 40, 325–329.

  6. Ceron-Breton, J.G., Cerón Bretón, R.M., Kahl, J.D.W., Sánchez Rico, G., Carranco Lozada, S.E., de la Luz Espinosa Fuentes, M., Uc Chi, M.P. (2021). Concentrations, sources, and health risk associated with exposure to BTEX at ten sites located in an urban-industrial area in the Bajio Region, Mexico. Air Qual. Atmos. Health 14, 741–761.

  7. Chang, S.C., Lee, C.T. (2008). Evaluation of the temporal variations of air quality in Taipei City, Taiwan, from 1994 to 2003. J. Environ. Manage. 86, 627–635.​jenvman.2006.12.029

  8. Chang, Y., Huang, K., Xie, M., Deng, C., Zou, Z., Liu, S., Zhang, Y. (2018). First long-term and near real-time measurement of trace elements in China’s urban atmosphere: Temporal variability, source apportionment and precipitation effect. Atmos. Chem. Phys. 18, 11793–11812.

  9. 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.​10.1016/j.atmosres.2012.12.008

  10. Chen, H.S., Lin, Y.C., Chiueh, P.T. (2022). High-resolution spatial analysis for the air quality regulation service from urban vegetation: A case study of Taipei City. Sustain. Cities Soc. 83, 103976.

  11. Chen, P.Y., Tan, P.H., Chou, C.C.K., Lin, Y.S., Chen, W.N., Shiu, C.J. (2019a). Impacts of holiday characteristics and number of vacation days on “holiday effect” in Taipei: Implications on ozone control strategies. Atmos. Environ. 202, 357–369.​2019.01.029

  12. Chen, S., Zhang, X., Lin, J., Huang, J., Zhao, D., Yuan, T., Huang, K., Luo, Y., Jia, Z., Zang, Z., Qiu, Y., Xie, L. (2019b). Fugitive road dust PM2.5 emissions and their potential health impacts. Environ. Sci. Technol. 53, 8455–8465.

  13. Chen, T., He, J., Lu, X., She, J., Guan, Z. (2016a). Spatial and temporal variations of PM2.5 and its relation to meteorological factors in the urban area of Nanjing, China. Int. J. Environ. Res. Public Health 13, 921.

  14. Chen, W.H., Hsieh, M.T., You, J.Y., Quadir, A., Lee, C.L. (2021). Temporal and vertical variations of polycyclic aromatic hydrocarbon at low elevations in an industrial city of southern Taiwan. Sci. Rep. 11, 3453.

  15. Chen, Y.C., Chiang, H.C., Hsu, C.Y., Yang, T.T., Lin, T.Y., Chen, M.J., Chen, N.T., Wu, Y.S. (2016b). Ambient PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in Changhua County, central Taiwan: Seasonal variation, source apportionment and cancer risk assessment. Environ. Pollut. 218, 372–382.

  16. Cheng, M.T., Chou, W.C., Chio, C.P., Hsu, S.C., Su, Y.R., Kuo, P.H., Tsuang, B.J., Lin, S.H., Chou, C.C.K. (2008). Compositions and source apportionments of atmospheric aerosol during Asian dust storm and local pollution in central Taiwan. J. Atmos. Chem. 61, 155–173.​10.1007/s10874-009-9131-8

  17. Cheng, Y., Lee, S.C., Ho, K.F., Chow, J.C., Watson, J.G., Louie, P.K.K., Cao, J.J., Hai, X. (2010). Chemically-speciated on-road PM2.5 motor vehicle emission factors in Hong Kong. Sci. Total Environ. 408, 1621–1627.

  18. Cheng, Y.H., Lin, C.C., Liu, J.J., Hsieh, C.J. (2014). Temporal characteristics of black carbon concentrations and its potential emission sources in a southern Taiwan industrial urban area. Environ. Sci. Pollut. Res. 21, 3744–3755.

  19. Chester, R., Nimmo, M., Preston, M.R. (1999). The trace metal chemistry of atmospheric dry deposition samples collected at Cap Ferrat: A coastal site in the Western Mediterranean. Mar. Chem. 68, 15–30.

  20. Chi, K.H., Li, Y.N., Hung, N.T. (2017). Spatial and temporal variation of PM2.5 and atmospheric PCDD/FS in Northern Taiwan during winter monsoon and local pollution episodes. Aerosol Air Qual. Res. 17, 3151–3165.

  21. Chi, K.H., Huang, Y.T., Nguyen, H.M., Tran, T.T.H., Chantara, S., Ngo, T.H. (2022). Characteristics and health impacts of PM2.5-bound PCDD/Fs in three Asian countries. Environ. Int. 167, 107441.

  22. 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. Part D Transp. Environ. 67, 674–684.

  23. Ferm, M., Sjöberg, K. (2015). Concentrations and emission factors for PM2.5 and PM10 from road traffic in Sweden. Atmos. Environ. 119, 211–219.​08.037

  24. Gauderman, W.J., Urman, R., Avol, E., Berhane, K., McConnell, R., Rappaport, E., Chang, R., Lurmann, F., Gilliland, F. (2015). Association of improved air quality with lung development in children. N. Engl. J. Med. 372, 905–913.

  25. Gugamsetty, B., Wei, H., Liu, C.N., Awasthi, A., Tsai, C.J., Roam, G.D., Wu, Y.C., Chen, C.F. (2012). Source characterization and apportionment of PM10, PM2.5 and PM0.1 by using positive matrix factorization. Aerosol Air Qual. Res. 12, 476–491.

  26. Guo, C., Zhang, Z., Lau, A.K.H., Lin, C.Q., Chuang, Y.C., Chan, J., Jiang, W.K., Tam, T., Yeoh, E.K., Chan, T.C., Chang, L.Y., Lao, X.Q. (2018). Effect of long-term exposure to fine particulate matter on lung function decline and risk of chronic obstructive pulmonary disease in Taiwan: a longitudinal, cohort study. Lancet Planet. Heal. 2, e114–e125.

  27. Hsu, C.Y., Chiang, H.C., Lin, S.L., Chen, M.J., Lin, T.Y., Chen, Y.C. (2016). Elemental characterization and source apportionment of PM10 and PM2.5 in the western coastal area of central Taiwan. Sci. Total Environ. 541, 1139–1150.

  28. Hsu, C.Y., Chiang, H.C., Chen, M.J., Chuang, C.Y., Tsen, C.M., Fang, G.C., Tsai, Y.I., Chen, N.T., Lin, T.Y., Lin, S.L., Chen, Y.C. (2017). Ambient PM2.5 in the residential area near industrial complexes: Spatiotemporal variation, source apportionment, and health impact. Sci. Total Environ. 590–591, 204–214.

  29. Hsu, C.Y., Chiang, H.C., Chen, M.J., Yang, T.T., Wu, Y.S., Chen, Y.C. (2019). Impacts of hazardous metals and PAHs in fine and coarse particles with long-range transports in Taipei City. Environ. Pollut. 250, 934–943.

  30. Hsu, C.Y., Chi, K.H., Wu, C. Da, Lin, S.L., Hsu, W.C., Tseng, C.C., Chen, M.J., Chen, Y.C. (2021). Integrated analysis of source-specific risks for PM2.5-bound metals in urban, suburban, rural, and industrial areas. Environ. Pollut. 275, 116652.

  31. Hu, G., Sun, J., Zhang, Y., Shen, X., Yang, Y. (2015). Chemical composition of PM2.5 based on two-year measurements at an urban site in Beijing. Aerosol Air Qual. Res. 15, 1748–1759.

  32. Huang, R.J., Zhang, Y., Bozzetti, C., Ho, K.F., Cao, J.J., Han, Y., Daellenbach, K.R., Slowik, J.G., Platt, S.M., Canonaco, F., Zotter, P., Wolf, R., Pieber, S.M., Bruns, E.A., Crippa, M., Ciarelli, G., Piazzalunga, A., Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., et al. (2015). High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218–222.

  33. Javed, M., Bashir, M., Zaineb, S. (2021). Analysis of daily and seasonal variation of fine particulate matter (PM2.5) for five cities of China. Environ. Dev. Sustain. 23, 12095–12123.​10.1007/s10668-020-01159-1

  34. Javed, W., Iakovides, M., Stephanou, E.G., Wolfson, J.M., Koutrakis, P., Guo, B. (2019). Concentrations of aliphatic and polycyclic aromatic hydrocarbons in ambient PM2.5 and PM10 particulates in Doha, Qatar. J. Air Waste Manage. Assoc. 69, 162–177.​2018.1520754

  35. Jeričević, A., Gašparac, G., Mikulec, M.M., Kumar, P., Prtenjak, M.T. (2019). Identification of diverse air pollution sources in a complex urban area of Croatia. J. Environ. Manage. 243, 67–77.

  36. Jindamanee, K., Thepanondh, S., Aggapongpisit, N., Sooktawee, S. (2020). Source apportionment analysis of volatile organic compounds using positive matrix factorization coupled with conditional bivariate probability function in the industrial areas. EnvironmentAsia 13, 31–49. 

  37. Khan, Z.Y., Kettler, J., Khwaja, H.A., Naqvi, I.I., Malik, A., Stone, E.A. (2018). Organic aerosol characterization and source identification in Karachi, Pakistan. Aerosol Air Qual. Res. 18, 2550–2564.

  38. Kim, E., Hopke, P.K., Edgerton, E.S. (2003). Source identification of atlanta aerosol by positive matrix factorization. J. Air Waste Manage. Assoc. 53, 731–739.​10473289.2003.10466209

  39. Kim, K.H., Kabir, E., Kabir, S. (2015). A review on the human health impact of airborne particulate matter. Environ. Int. 74, 136–143.

  40. Kulkarni, P., Venkataraman, C. (2000). Atmospheric polycyclic aromatic hydrocarbons in Mumbai, India. Atmos. Environ. 34, 2785–2790.

  41. Kulshrestha, A., Satsangi, P.G., Masih, J., Taneja, A. (2009). Metal concentration of PM2.5 and PM10 particles and seasonal variations in urban and rural environment of Agra, India. Sci. Total Environ. 407, 6196–6204.

  42. Lai, L.W. (2015). Fine particulate matter events associated with synoptic weather patterns, long-range transport paths and mixing height in the Taipei Basin, Taiwan. Atmos. Environ. 113, 50–62.

  43. Lai, L.W. (2020). The relationship between typhoon features and fine particulate matter: events in Metropolitan Taipei. Theor. Appl. Climatol. 139, 127–143.

  44. Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., Pozzer, A. (2015). The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525, 367–371.

  45. Lelieveld, J., Klingmüller, K., Pozzer, A., Pöschl, U., Fnais, M., Daiber, A., Münzel, T. (2019). Cardiovascular disease burden from ambient air pollution in Europe reassessed using novel hazard ratio functions. Eur. Heart J. 40, 1590–1596.

  46. Li, J., Liu, H., Lv, Z., Zhao, R., Deng, F., Wang, C., Qin, A., Yang, X. (2018). Estimation of PM2.5 mortality burden in China with new exposure estimation and local concentration-response function. Environ. Pollut. 243, 1710–1718.

  47. Liang, C.S., Yu, T.Y., Lin, W.Y. (2015). Source apportionment of submicron particle size distribution and PM2.5 composition during an asian dust storm period in two urban atmospheres. Aerosol Air Qual. Res. 15, 2609–2624.

  48. Liang, J., Feng, C., Zeng, G., Gao, X., Zhong, M., Li, Xiaodong, Li, Xin, He, X., Fang, Y. (2017). Spatial distribution and source identification of heavy metals in surface soils in a typical coal mine city, Lianyuan, China. Environ. Pollut. 225, 681–690.

  49. Lim, J.M., Lee, J.H., Moon, J.H., Chung, Y.S., Kim, K.H. (2010). Airborne PM10 and metals from multifarious sources in an industrial complex area. Atmos. Res. 96, 53–64.​10.1016/j.atmosres.2009.11.013

  50. Lin, Y.C., Tsai, C.J., Wu, Y.C., Zhang, R., Chi, K.H., Huang, Y.T., Lin, S.H., Hsu, S.C. (2015). Characteristics of trace metals in traffic-derived particles in Hsuehshan Tunnel, Taiwan: Size distribution, potential source, and fingerprinting metal ratio. Atmos. Chem. Phys. 15, 4117–4130.

  51. Liu, L., Corma, A. (2018). Metal Catalysts for Heterogeneous Catalysis: From Single Atoms to Nanoclusters and Nanoparticles. Chem. Rev. 118, 4981–5079.​chemrev.7b00776

  52. Liu, T., Hu, B., Yang, Y., Li, M., Hong, Y., Xu, X., Xu, L., Chen, N., Chen, Y., Xiao, H., Chen, J. (2020). Characteristics and source apportionment of PM2.5 on an island in Southeast China: Impact of sea-salt and monsoon. Atmos. Res. 235, 104786.​104786

  53. Mihankhah, T., Saeedi, M., Karbassi, A. (2020). Contamination and cancer risk assessment of polycyclic aromatic hydrocarbons (PAHs) in urban dust from different land-uses in the most populated city of Iran. Ecotoxicol. Environ. Saf. 187, 109838.​2019.109838

  54. Ngo, T.H., Tsai, P.C., Ueng, Y.F., Chi, K.H. (2019). Cytotoxicity assessment of PM2.5 collected from specific anthropogenic activities in Taiwan. Int. J. Environ. Res. Public Health 16, 5043.

  55. Ngo, T.H., Yang, H.Y., Pan, S.Y., Chang, M.B., Chi, K.H. (2022). Condensable and filterable particulate matter emission of coal fired boilers and characteristics of PM2.5-bound polycyclic aromatic hydrocarbons in the vicinity. Fuel 308, 121833.

  56. Paatero, P., Tapper, U. (1994). Positive matrix factorization: A non‐negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 111–126.​10.1002/env.3170050203

  57. Pani, S.K., Lee, C. Te, Chou, C.C.K., Shimada, K., Hatakeyama, S., Takami, A., Wang, S.H., Lin, N.H. (2017). Chemical characterization of wintertime aerosols over Islands and mountains in East Asia: Impacts of the continental Asian outflow. Aerosol Air Qual. Res. 17, 3006–3036.

  58. Qiao, T., Xiu, G., Zheng, Y., Yang, Jun, Wang, L., Yang, J., Huang, Z. (2015). Preliminary investigation of PM1, PM2.5, PM10 and its metal elemental composition in tunnels at a subway station in Shanghai, China. Transp. Res. Part D Transp. Environ. 41, 136–146.​trd.2015.09.013

  59. Sahu, S.K., Kota, S.H. (2017). Significance of PM2.5 air quality at the Indian capital. Aerosol Air Qual. Res. 17, 588–597.

  60. Shen, H., Cheng, P.H., Yuan, C.S., Yang, Z.M., Hung, C.M., Ie, I.R. (2020). Chemical characteristics, spatiotemporal distribution, and source apportionment of PM2.5 surrounding industrial complexes in Southern Kaohsiung. Aerosol Air Qual. Res. 20, 557–575.​10.4209/aaqr.2020.01.0007

  61. Shi, M., Hu, T., Mao, Y., Cheng, C., Liu, W., Tian, Q., Chen, Z., Xing, X., Qi, S. (2021). Polycyclic aromatic hydrocarbons in atmospheric PM2.5 during winter haze in Huang-Gang, Central China: Characteristics, sources and health risk assessment. Aerosol Air Qual. Res. 21, 200337.

  62. Shi, W., Wong, M.S., Wang, J., Zhao, Y. (2012). Analysis of airborne particulate matter (PM2.5) over Hong Kong using remote sensing and GIS. Sensors (Switzerland) 12, 6825–6836.

  63. Shivkumar, M., Dhanya, G., Ganesh, K.E., Pranesha, T.S., Sudhindra, K.R., Chate, D., Beig, G. (2022). Temporal variability of PM2.5 and its possible sources at the tropical megacity, Bengaluru, India. Environ. Monit. Assess. 194, 532.

  64. Srimuruganandam, B., Shiva Nagendra, S.M. (2011). Chemical characterization of PM10 and PM2.5 mass concentrations emitted by heterogeneous traffic. Sci. Total Environ. 409, 3144–3157.

  65. Stortini, A.M., Freda, A., Cesari, D., Cairns, W.R.L., Contini, D., Barbante, C., Prodi, F., Cescon, P., Gambaro, A. (2009). An evaluation of the PM2.5 trace elemental composition in the Venice Lagoon area and an analysis of the possible sources. Atmos. Environ. 43, 6296–6304.

  66. Tang, J.H., Candice Lung, S.C., Hwang, J.S. (2020). Source apportionment of PM2.5 concentrations with a Bayesian hierarchical model on latent source profiles. Atmos. Pollut. Res. 11, 1715–1727.

  67. Tang, N., Suzuki, G., Morisaki, H., Tokuda, T., Yang, X., Zhao, L., Lin, J., Kameda, T., Toriba, A., Hayakawa, K. (2017). Atmospheric behaviors of particulate-bound polycyclic aromatic hydrocarbons and nitropolycyclic aromatic hydrocarbons in Beijing, China from 2004 to 2010. Atmos. Environ. 152, 354–361.

  68. Tao, J., Gao, J., Zhang, L., Zhang, R., Che, H., Zhang, Z., Lin, Z., Jing, J., Cao, J., Hsu, S.C. (2014). PM2.5 pollution in a megacity of Southwest China: Source apportionment and implication. Atmos. Chem. Phys. 14, 8679–8699.

  69. Tong, R., Liu, J., Wang, W., Fang, Y. (2020). Health effects of PM2.5 emissions from on-road vehicles during weekdays and weekends in Beijing, China. Atmos. Environ. 223, 117258.

  70. Tsai, J.H., Tsai, S.M., Wang, W.C., Chiang, H.L. (2016). Water-soluble ionic species of coarse and fine particulate matter and gas precursor characteristics at urban and rural sites of central Taiwan. Environ. Sci. Pollut. Res. 23, 16722–16737.

  71. Veld, M. in ’t, Alastuey, A., Pandolfi, M., Amato, F., Pérez, N., Reche, C., Via, M., Minguillón, M.C., Escudero, M., Querol, X. (2021). Compositional changes of PM2.5 in NE Spain during 2009–2018: A trend analysis of the chemical composition and source apportionment. Sci. Total Environ. 795, 148728.

  72. Wang, H., Zhang, L., Yao, X., Cheng, I., Dabek-Zlotorzynska, E. (2022). Identification of decadal trends and associated causes for organic and elemental carbon in PM2.5 at Canadian urban sites. Environ. Int. 159, 107031.

  73. Wang, S., Ji, Y., Zhao, J., Lin, Y., Lin, Z. (2020). Source apportionment and toxicity assessment of PM2.5-bound PAHs in a typical iron-steel industry city in northeast China by PMF-ILCR. Sci. Total Environ. 713, 136428.

  74. Wang, W.C., Dat, N.D., Chi, K.H., Chang, M.B. (2021a). Characterization of PM2.5 and particulate PAHs emitted from vehicles via tunnel sampling in different time frames. Aerosol Air Qual. Res. 21, 210074.

  75. Wang, Y.S., Chang, L.C., Chang, F.J. (2021b). Explore regional PM2.5 features and compositions causing health effects in Taiwan. Environ. Manage. 67, 176–191.​s00267-020-01391-5

  76. World Health Organization (WHO) (2016). WHO releases country estimates on air pollution exposure and health impact.

  77. Wu, Y., Wang, R., Zhou, Y., Lin, B., Fu, L., He, K., Hao, J. (2011). On-road vehicle emission control in Beijing: Past, present, and future. Environ. Sci. Technol. 45, 147–153.​10.1021/es1014289

  78. Xue, Q., Tian, Y., Liu, X., Wang, X., Huang, B., Zhu, H., Feng, Y. (2022). Potential risks of PM2.5-bound polycyclic aromatic hydrocarbons and heavy metals from inland and marine directions for a marine background site in North China. Toxics 10, 32.​toxics10010032

  79. Yang, C.Y., Chang, C.C., Chuang, H.Y., Tsai, S.S., Wu, T.N., Ho, C.K. (2004). Relationship between air pollution and daily mortality in a subtropical city: Taipei, Taiwan. Environ. Int. 30, 519–523.

  80. 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.

  81. Yin, L., Niu, Z., Chen, X., Chen, J., Xu, L., Zhang, F. (2012). Chemical compositions of PM2.5 aerosol during haze periods in the mountainous city of Yong’an, China. J. Environ. Sci. (China) 24, 1225–1233.

  82. Yu, L., Wang, G., Zhang, R., Zhang, L., Song, Y., Wu, B., Li, X., An, K., Chu, J. (2013). Characterization and source apportionment of PM2.5 in an urban environment in Beijing. Aerosol Air Qual. Res. 13, 574–583.

  83. Zhang, Y., Yang, L., Zhang, X., Li, J., Zhao, T., Gao, Y., Jiang, P., Li, Y., Chen, X., Wang, W. (2019). Characteristics of pm2.5-bound pahs at an urban site and a suburban site in jinan in north china plain. Aerosol Air Qual. Res. 19, 871–884.

  84. Zhao, T., Yang, L., Huang, Q., Zhang, Y., Bie, S., Li, J., Zhang, W., Duan, S., Gao, H., Wang, W. (2020). PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) and their derivatives (nitrated-PAHs and oxygenated-PAHs) in a road tunnel located in Qingdao, China: Characteristics, sources and emission factors. Sci. Total Environ. 720, 137521.​137521

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