Cytotoxicity of PM2.5 vehicular emissions in the Shing Mun Tunnel, Hong Kong
Introduction
Exposure to fine particles (PM2.5, particulate matter with aerodynamic diameter less than 2.5 μm) correlates with pulmonary and cardiovascular diseases in epidemiological and toxicological studies (Brunekreef and Holgate, 2002; Chen et al., 2012; Leung et al., 2014). Vehicular emissions (VE), a major urban air pollution source, account for 20–51% of PM2.5 in Hong Kong (Cheng et al., 2010). Most traffic-related PM2.5 in Asia originates from gasoline-, diesel-, ethanol-, and liquified petroleum gas (LPG)- powered internal combustion engines, with small contribution from road dust resuspension, tire abrasion, and brake wear (Amato et al., 2014; Oanh et al., 2010). Traffic-related PM2.5 inhalation causes oxidative stress, inflammation, as well as tissue and DNA damage to human respiratory system (Deering-Rice et al., 2011; Pope III and Dockery, 2006). VE contains a high fraction of carbonaceous aerosol, including polycyclic aromatic hydrocarbons (PAHs), with various toxicity potentials due to the variations on chemical compositions (McDonald et al., 2004; Steenhof et al., 2011). PAHs, trace metals, secondary inorganic ions, and organic carbon (OC) have been associated with the generation of reactive oxygen species (ROS) and inflammatory mediators (Baulig et al., 2003; Ho et al., 2016). Chuang et al. (2012) found that PAHs and trace metals from diesel- and gasoline-fueled VE at a bus station were associated with nitric oxide (NO), endothelin-1 (ET-1), and interleukin-6 (IL-6) expressions. Steenhof et al. (2011) found that PM collected at traffic locations with high elemental carbon (EC) and OC contents resulted in higher pro-inflammatory responses in comparison with other areas.
Increases in prevalence of allergic and respiratory symptoms have been found in schools and residential areas located close to freeways and heavily-trafficked roads (Janssen et al., 2003; Salam et al., 2008). Hong Kong is a megacity with a high population density (7140 people per km2), buildings, and traffic, where the residents are exposed to air pollutants from VE daily (Wang et al., 2018). Although the Hong Kong Special Administrative Region (HKSAR) government has implemented control measures to reduce VE (Wang et al., 2018), these efforts are partially offset by increased vehicle use (Ho et al., 2009a). Several cytotoxicological studies were conducted near heavily-trafficked roads, freeways, and in subway tunnels (Cho et al., 2009; Jung et al., 2012; Karlsson et al., 2005; Tian et al., 2012). Other studies investigated the toxic effects from different vehicle types with laboratory tests (Libalova et al., 2018; Wu et al., 2017). This work adds to this body of knowledge with results specific to Asian environments.
Different methods have been employed to characterize VE (Franco et al., 2013), such as tunnel studies (Cui et al., 2018), laboratory engine dynamometer tests (Pietikäinen et al., 2015), on-road sampling (Weiss et al., 2012), and roadside measurements (Krecl et al., 2018). Tunnel sampling collects both tailpipe and non-tailpipe emissions that represent real-world emissions and fleet compositions. The 2003 Shing Mun Tunnel study showed large contributions from diesel vehicles (Cheng et al., 2006; Ho et al., 2009a). The follow-up study in 2015 (Wang et al., 2019) found different PM2.5 compositions. The objectives of this study are to: 1) determine Hong Kong vehicular emission characteristics for 2015; 2) assess oxidative and inflammatory responses from exposure to PM2.5 VE; and 3) determine the key species causing oxidative stress and inflammation and their contributions to bioreactivities in human lung alveolar epithelial (A549) cells.
Section snippets
Sample collection
The Shing Mun Tunnel carries vehicle flows between the Eastern New Territories (Shatin) to the Northwestern Kowloon (Tsuen Wan) (Fig. 1). This is an urban two-bore tunnel with two traffic lanes per bore and a vehicle speed limit of 80 km h−1, as detailed by Ho et al. (2009b) and Wang et al. (2018). In 2015, two sampling locations were established at the inlet and outlet of the south bore (i.e., a total length of ca. 1.6 km), which were 686 m and 350 m from the tunnel entrance and exit,
Chemical compositions and emission factors
Average outlet and inlet concentrations with derived EFs are shown in Table 1. The outlet PM2.5 concentrations (77.7 ± 11.9 μg m−3) were always higher than those at the inlet (54.1 ± 9.8 μg m−3), owing to the additional emissions added between the two sampling locations. The average outlet TC concentration (46.7 ± 10.8 μg m−3) was 1.7 times the inlet average (27.5 ± 7.0 μg m−3). OC and EC showed similar outlet concentration increments. The average outlet measured PAHs (i.e., ∑PAHs) of
Discussion
PM2.5 and chemical species EFs vary among studies due to differences in engine types and models, fuel composition, driving speeds, and features of the roads and tires (Cheng et al., 2006; Gillies et al., 2001; Yue et al., 2015). A large reduction (79.2%) in average PM2.5 EFs was found between the 2015 and the 2003 Shing Mun Tunnel studies (Cheng et al., 2010; Wang et al., 2018). The 2003 OC and EC EFs were 35.7 and 65.8 mg vehicle−1 km−1, respectively (Cheng et al., 2010); 4.4 and 4.2 times
Conclusion
This study investigated the in-vitro cytotoxic effects of real-world vehicular emissions. A vehicle cytotoxicity emission factor (CFveh) is introduced to normalize the cytotoxicity caused by PM2.5 vehicle emissions in a tunnel. PAH markers for gasoline- and diesel-vehicle emissions showed high EFs. Cell membrane damage, oxidative stress, and inflammatory responses inhuman lung alveolar epithelial (A549) cells represented by LDH, 8-isoprostane, and IL-6, respectively, were triggered by exposure
CRediT authorship contribution statement
Xinyi Niu: Writing - original draft, Visualization. Hsiao-Chi Chuang: Conceptualization, Validation, Resources. Xiaoliang Wang: Data curation, Resources, Investigation. Steven Sai Hang Ho: Methodology, Writing - review & editing. Lijuan Li: Formal analysis, Data curation. Linli Qu: Formal analysis. Judith C. Chow: Methodology, Writing - review & editing. John G. Watson: Writing - review & editing. Jian Sun: Resources, Visualization. Shuncheng Lee: Funding acquisition. Junji Cao: Project
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This study was supported by grants from Research Grant Council of The Hong Kong Special Administrative Region China (Project No. CUHK 14202817) and U.S. Health Effects Institute (Grant 4947-RFPA14-1/15-1).
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This paper has been recommended for acceptance by Admir Créso Targino.