Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 3 4 5 6 7 8 Underground and ground-level particulate matter concentrations in an Italian metro system Armando Cartenì Department of Civil, Construction and Environmental Engineering University of Naples Federico II Furio Cascetta and Stefano Campana 9 10 11 12 13 Department of Industrial and Information Technology Engineering Second University of Naples 14 Research Highlights 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46     Investigates the particulate matter (PM) concentrations both at station platforms and inside trains. PM concentrations at station platforms are 2 to14 times higher than outdoors. In ground-level sections there is an environmental “washing-effect” when windows in carriages are left open. Higher concentrations of PM at station platforms are directly correlated to passage of trains. Abstract All around the world, many studies and experimental results have assessed elevated concentrations of Particulate Matter (PM) in underground metro systems, with non-negligible implications for human health due to protracted exposure to fine particles. Starting from this consideration, an intensive particulate sampling campaign was carried out in January 2014 measuring the PM concentrations in the Naples (Italy) Metro Line 1, both at station platforms and inside trains. Naples Metro Line 1 is about 18 km long, with 17 stations (3 ground-level and 14 below-ground ones). Experimental results show that the average PM 10 concentrations measured in the underground station platforms range between 172 and 262 µg/m3 whilst the average PM2,5 concentrations range between 45 and 60 µg/m3. By contrast, in ground-level stations no significant difference between stations platforms and urban environment measurements was observed. Furthermore, a direct correlation between trains passage and PM concentrations was observed, with an increase up to 42% above the average value. This correlation is possibly caused by the re-suspension of the particles due to the turbulence induced by trains. The main original finding was the real-time estimations of PM levels inside the trains travelling both in ground-level and underground sections of Line 1. The results show that high concentrations of both PM 10 (average values between 58 µg/m3 and 138 µg/m3) and PM2,5 (average values between 18 µg/m3 and 36 µg/m3) were also measured inside trains. Furthermore, measurements show that windows left open on trains caused the increase in PM concentrations inside trains in the underground section, while in the ground-level section the clean air entering the trains produced an environmental “washing effect”. Finally, it was estimated that every passenger spends on average about 70 minutes per day exposed to high levels of PM. Keyword: particulate matter; PM; underground railway; transport microenvironment; indoor quality. 1 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 1 Introduction 2 In European urban areas, the transport sector contributes to 20–40% of both the fossil fuels consumption and 3 greenhouse gases and particulate matter emissions (European Commission, 2014). Transport planners agree that one of 4 the most effective policies to reduce pollutant emissions is to enhance the use of public transport (e.g. bus, underground 5 railway). With respect to the overall urban system, the “greenest” public transport mode is possibly the metro system 6 because it is based on a high capacity (in terms of passengers moved per hour per vehicle) low emission system (it uses 7 electric vehicles). Furthermore the modal shift from car mode to metro system allows to reduce traffic congestion (more 8 public transport users means fewer cars on the roads). In order to improve the use of public transport, service qualities 9 such as comfort, architectural standards of the terminals, safety and security, have become explicit design variables for 10 urban sustainable mobility (e.g. Cascetta E. and Cartenì, 2014a; Cascetta E. and Cartenì, 2014b). 11 However, studies carried out in different countries have reported elevated concentrations of Particulate Matter (PM) 12 in underground railway systems that could become a serious problem for human health. Epidemiologic studies have 13 linked long-term exposure to fine PM to adverse human health effects (e.g. Valavanidis et al., 2008; Pope and Dockery, 14 2006; Delfino et al., 2005). These studies have observed that long-term PM exposure was strongly associated with 15 mortality attributable to ischemic heart disease, dysrhythmia, heart failure, and cardiac arrest. In particular, Pope et al. 16 (2003) demonstrate that the increase of the urban average concentration of PM2.5 of 10 µg/m3 was found to be 17 associated with an 8-18% increase in cardiovascular mortality risk, with comparable or larger risks being observed for 18 smokers. Furthermore, recent epidemiological studies have shown that long-term exposure to PM is also linked with 19 pulmonary injury (Li et al., 2009) and neurodegenerative disorders (Campbell, 2004). A study of the British Lung 20 Foundation (Britton, 2003) concluded that travelling for 20 minutes on the Northern line of London Underground (the 21 line with the highest PM concentrations mentioned in literature as reported in Priest et al., 1998; Adam et al., 2001; 22 Seaton et al., 2005) has the same effect on the lungs as smoking a cigarette. 23 An interesting work by Karlsson et al. (2005) demonstrates that particles measured in underground railways are 24 approximately eight times more genotoxic and four times more likely to cause oxidative stress in lung cells than 25 particles in outdoor urban environments. The high oxidative capacity of the subway particles is ascribed to redox active 26 solid metals (Karlsson et al., 2005). Analysis of the atomic composition reported in many studies (e.g. Eom et al., 2013; 27 Jung et al., 2010; Salma et al., 2007; Aarnio et al., 2005; Karlsson et al., 2005) shows that Fe-containing particles were 28 the most abundant. As described in Jung et al. (2010), Fe-containing particles of the underground railway are mainly 29 generated by mechanical wear and friction processes at the rail-wheel-brake interfaces, and at the interface between 30 catenaries providing electricity and pantographs attached to trains. Wear and friction processes initially produce iron 31 metal particles that react with oxygen in the air resulting in the formation of iron oxides. 32 Tables 1 and 2 report a review of PM measurements in different cities around the world. Even if measured PM 33 concentrations are not always directly comparable because of differences in the age of the rail system, tunnel 34 ventilation, wheel type, braking system, and operating mode, measurement instruments and methods, chemical and size 35 characteristics of particulate matter and type of environment investigated (e.g. inside train, ground/underground 36 platform, inside station), all the studies available in the literature conclude that the PM concentrations at underground 37 station platforms are consistently higher (2 to 8 times) than at the street level (e.g. Adams et al. 2001; Kam et al., 2011; 38 Colombi at al., 2013). The highest average PM concentrations have been measured in the metro systems of Barcelona 39 (Querol et al., 2012), Bejiing (Li et al. 2007), London (Priest et al., 1998; Adam et al., 2001; Seaton et al., 2005); Paris 2 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 (Raut et al., 2009), Rome (Ripanucci et al., 2006), Seoul (Kim et al., 2008 and 2012; Jung et al., 2010), Stockholm 2 (Johansson and Johansson, 2003) and Shangai (Bao et al., 2014). Johansson and Johansson (2003) pointed out that the 3 average concentrations of PM10 and PM2.5 (469 and 258 µg/m3 respectively) in Stockholm’s underground railway were 4 about 10 times higher than in the external urban environment (outdoors). Bao et al. (2014) obtained similar results in 5 Shanghai, reporting that the average PM10 and PM2.5 levels at station platforms (457 and 352 µg/m3 respectively) were 3 6 times higher than those measured at street level (outdoors). 7 With regard to the PM measurements inside the trains (Table 2), the highest concentrations were found in London 8 with an average PM10 value of 795 µg/m3 (Priest et al., 1998) and average PM2.5 concentrations of 247 and 170 µg/m3 9 (measured by Adams et al. (2001) and Seaton et al. (2005) respectively). 10 The “greenest” underground railway systems are in Taipei and in Los Angeles (the newest line opened in 2000 - 11 Kam et al., 2011). An interesting case is the Seoul underground railway system that, up to 2008, was characterized by 12 high PM10 concentrations (359 g/m3- Kim et al., 2008), while after the introduction of the platform screen door 13 system, Jung et al. (2010) and Kim et al. (2012) measured very low PM10 concentrations (103 and 97 g/m3 14 respectively). Similar results were also observed in Barcelona, where PM concentrations in a new underground railway 15 line equipped with platform screen doors were found to be 2-3 times lower than in a traditional one (Querol et al. 2012). 16 The platform screen door system is an effective solution to reduce particulate matter concentrations on the platforms 17 because it creates a physical barrier (glass screen) between the platform and the train/tunnel. Although this system is 18 relatively new, many traditional underground railway systems around the world have been retrofitted with it. Inside 19 trains, however, PM levels are mainly affected by the direct exchange of air between the trains and the tunnels (e.g. 20 open windows) and the presence and quality of ventilation systems that can filter fine particles (e.g. Cheng 2012). 21 Although there is no specific directive aimed at limiting the emissions in underground railway lines, the PM 22 concentrations measured in all of these studies exceed the limits set by the European air quality directives for urban 23 environments (European Commission, 2008; European Standard, 1998). 24 On this basis, this study investigates the particulate matter concentrations (PM 10 and PM2.5) of the Naples Metro 25 Line 1 (Italy) both at station platforms and inside trains. The originality of the case-study consists in the fact that this 26 line (18 km long) runs both at ground level (4.5 km) and in underground (13.5 km). The main, original aim of this 27 research was to estimate real-time variations in PM10 and PM2.5 levels inside trains travelling along both kinds of 28 sections (ground-level Vs. underground) of Line 1. It is worth highlighting that the few studies estimating real-time 29 variations in PM concentrations inside trains only refer to underground sections (e.g. Cheng et al., 2012). The only 30 examples in literature that compare ground-level and underground PM concentrations inside trains are Kam et al. 31 (2011), Cheng et al. (2008) and Adams et al. (2001), but they that do not carry out an in-depth analysis of the real-time 32 evolution of PM concentrations during the whole journey along the entire route. 3 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 3 Table 1 – A comparison of PM10 and PM2.5 average concentrations and range/standard deviation between different underground railway systems: station platform (underground and ground-level) against urban environment (outdoor) measurements (in parentheses is the year of the study). OUTDOOR City (study year) 3 STATION PLATFORM OUTDOOR PM 10 [µg/m3] PM 2.5 [µg/m ] PM 10 [µg/m ] Average Min. Max. Average Min. Barcelona (platform screen door system) (2011) Barcelona (2011) Budapest (2006) Frankfurt (2013) Helsinki (underground) (2004) Helsinki (ground-level) (2004) Istanbul (2007) Istanbul (2007) London (2003) Los Angeles (underground) (2010) Los Angeles (ground-level) (2010) Milan (2012) Paris (2006) Rome (2005) Seoul (2004) Seoul (platform screen door system) (2008) 4 5 STATION PLATFORM 3 Reference PM 2,5 [µg/m3] Max. Average Min. Max. Average Min. Max. - - - 134 77 192 - - - 41 22 60 Querol et al. (2012) - - - 346 180 101 289 85 - 403 234 166 - - - 125 59 102 - 148 85 Querol et al. (2012) Salma et al. (2007) Gerber at al. (2014) - - - - - - 17 7 27 50 37 87 Aarnio et al. (2005) - - - - - - 17 7 27 19 12 29 Aarnio et al. (2005) - - - - - - 105 20 421 Onat and Stakeeva (2014) 70 - 30 - 110 - 170 - 74 - 294 - - - - 350 249 506 Şahin et al. (2012) Seaton et al. (2005) 31 - - 78 14 197 20 - - 57 9 130 Kam et al. (2011) 31 - - 38 8 184 20 - - 29 4 77 Kam et alii (2011) 37 155 36 79 38 254 188 320 407 359 137 71 238 239 877 480 102 41 174 93 129 82 176 Colombi et al. (2013) Raut et al. (2009) Ripanucci et al. (2006) Kim et al. (2008) 79 42 117 97 52 142 - - - 58 29 87 Kim et al. (2012) Seoul (platform screen door system) (2009) Shanghai (2013) 78 48 115 103 75 135 - - - - - - Jung et al. (2010) 190 - - 457 - - 116 - - 352 - - Stockholm (2000) 55 18 120 469 212 722 23 3 89 258 105 388 Bao et al. (2014) Johansson and Johansson (2003) 60 12 136 66 29 130 36 5 100 44 22 91 Cheng et al. (2008) 61 11 146 44 11 131 28 4 81 33 7 94 Cheng et al. (2008) Taipei (underground) (2007) Taipei (ground-level) (2007) 4 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 Table 2 – A comparison of PM10 and PM2.5 average concentrations and range/standard deviation between different underground railway systems: inside train measurements (in parentheses is the year of the study). INSIDE TRAIN City (study year) 3 4 5 3 PM 10 [µg/m ] Average Min. Barcelona (2011) 65 49 Bejing (2006) 325 Berlin (1997) 147 Boston (2001) Guangzhou (2000) 67 26 Hong Kong (1999) 44 23 Istanbul (2007) London (underground) (1999) London (ground-level) (1999) London (2003) London* (1997) 795 Los Angeles (underground) (2010) 31 14 Los Angeles (ground-level) (2010) 16 6 Mexico City (2003) New York (2010) Prague (2004) 114 24 Seoul (2004) 312 29 Sydney (2009) Taipei (2007) 40 22 INSIDE TRAIN PM 2.5 [µg/m3] Max. Average 81 21 112 65 123 44 85 33 71 247 29 170 107 24 53 14 38 40 218 356 126 36 71 31 Min. 16 36 21 46 105 12 118 11 3 8 34 115 19 Max. 26 104 48 161 371 42 201 62 38 68 44 136 51 Reference Querol et al. (2012) Li et al. (2007) Fromme et al. (1998) Levy et al. (2002) Chan et al. (2002b) Chan et al. (2002a) Onat and Stakeeva (2014) Adams et al. (2001) Adams et al. (2001) Seaton et al. (2005) Priest et al. (1998) Kam et al. (2011) Kam et al. (2011) Gomez-Parales et al.(2004) Wang and Gao (2011) Branis (2006) Kim et al. (2008) Knibbs and de Dear (2010) Cheng et al. (2008) * in table PM9 concentration are reported 5 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 2 Experimental methodology 3 2.1 Case study 4 The sample case study was the Naples Metro Line 1 (Figures 1). It is about 18 km long (Figure 2) with 17 stations (3 5 ground-level and 14 underground, see the example in Figure 3) and is used by about 110 thousand users per day. 6 Following the representation proposed in Colombi et al. (2013), the characteristics of the stations are summarized in 7 Table 3 describing: tunnel type, platform depth and outdoor urban traffic conditions. 8 Urban Air quality Monitoring Stations (CEA) Metro Line 1 Subway stations 9 10 Figure 1 – Line 1 of the Naples metro system 6 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 Ground-level (section in elevated) P I S C I N O L A C H I A I A N O F R U L L O N E Underground C O L L I A M I N E I P O L I C L I N I C O R I O N E A L T O M O N T E D O N Z E L L I M E D A G L I E D ’ O R O V A N V I T E L L I Q U A T T R O G I O R N A T E S A N M T A A T E R R O D S E A I M U S E O D A N T E T O L E D O U N I V E R S I T Á G A R I B A L D I altitude (meters) -10 300 -35 250 +12 200 -40 +12 (depth) -25 -8 +15 -18 -41 150 -47 100 -24 50 -16 -25 sea level 1 2 3 4 -30 -40 -48 0.0 1.7 3.1 4.5 5.3 5.8 6.7 7.5 8.3 9.5 10.7 11.3 12.5 13.0 14.1 15.7 18.0 (km) Figure 2 – Naples Metro Line 1: ground-level and underground stations Figure 3 – Examples of ground-level (Piscinola) and underground (Università) stations of Naples Metro Line 1 5 6 7 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 Table 3 – Characteristics of the underground railway stations of Naples Metro Line 1 and the area surrounding the station entrance [km] Platform depth [m] Piscinola 0 +15 Chiaiano 1,699 +12 Frullone 3,136 +12 Colli Aminei 4,451 -10 Policlinico 5,26 -35 Rione Alto 5,766 -40 Montedonzelli 6,686 -25 Medaglie d’Oro 7,546 -8 Vanvitelli 8,305 -18 Quattro Giornate 9,519 -41 Salvator Rosa 10,683 -47 Materdei 11,290 -24 Museo 12,504 -16 Dante 13,01 -25 Toledo 14,107 -48 Università 15,733 -30 Garibaldi 18,009 -40 Station Tunnel type Outdoor ambient Urban - low traffic congestion Urban - low traffic congestion Urban - low traffic congestion Urban - high traffic congestion Urban - high traffic congestion Urban - high traffic congestion Urban - high traffic congestion Urban - high traffic congestion Urban - low traffic congestion Urban - high traffic congestion Urban - high traffic congestion Urban - high traffic congestion Urban - high traffic congestion Urban - low traffic congestion Urban – resticted area Urban - high traffic congestion Urban - high traffic congestion station platforms ground-level railway (outdoor) one-way tunnel railway two-ways tunnel railway 3 4 5 2.2 Monitoring instrument and quality 6 Considering the short sampling duration and the real-time measurements performed in different locations (inside trains 7 and at station platforms), the portable photometric Aerocet 531 sampler (for details see Met One Inc, 2003) was used to 8 measure PM concentrations. It is an automatic instrument that estimates PM1, PM2.5, PM7, PM10 and Total Suspended 9 Particulates (TSP), with a temporal resolution of 120 seconds. Mass conversion is made using standard conversion 10 factors, or using suitable factors based on unique conditions (K-Factor). This measurement device is often used, as 11 reported in literature, for measuring PM concentrations both in outdoor and indoor environments (e.g. Nyhan et al., 12 2013; Foster and Kumar, 2011; Invernizzi et al., 2011; McNabola et al., 2011; Lee et al., 2008; Kumar et al., 2007). To 13 minimize measurement errors, the Aerocet 531 unit was calibrated through the certified data measured by the Campania 14 Environmental Agency (CEA). The CEA unit is a LSPM10 (UniTec, 2014) airborne particulate continuous monitor and 15 sampler, calibrated by the manufacturer and equipped with a gravimetric unit (16 membrane filters-47 mm dia.), that 16 allows particulate sampling strictly in accordance with EN12341/14 standard (European Standard, 2014). These 8 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 analysers are commonly used in most of the monitoring stations of Campania region (Italy) and deliver hourly average 2 data providing real-time information on the pollution levels (for technical details see UniTec, 2014). 3 Nine outdoor CEA units (near the monitored stations) and one indoor CEA unit were used to compare photometric 4 concentrations (both PM2.5 and PM10) given by Aerocet 531 with the ones measured by the CEA units. The instruments 5 were operated side by side for five different working days and approximately 5 hours a day to establish their 6 comparability and traceability. Outdoor tests were run on days with clear weather and average seasonal temperatures 7 (10-20°C) with low wind conditions (wind speed <15 km/h). Calibration factors were obtained by regressing Aerocet 8 531 PM2.5 and PM10 concentrations on the corresponding gravimetric measurements. Both PM 10 and PM2.5 data were 9 reliable with a determination coefficient of 89% and 84% respectively. Furthermore, to validate the Aerocet 531 10 photometric sampler, PM concentrations were compared with those measured by two CEA gravimetric units, not used 11 for calibration (hold-out sample), showing low measurement errors. 12 Finally, concentrations data were adjusted according to derived calibration equations: 13 Corrected PM10 = (1.18 * Measured PM10) 14 Corrected PM2.5 = (8.75 * Measured PM2.5) 15 The estimated calibration factors are similar to those obtained by Nyhan et al. (2013) and Lee et al. (2008). However it 16 is important to highlight that these are specific to this particular case-study and measurement instruments used. Indeed, 17 different calibration factors among different instruments (different serial numbers) made by the same manufacturer 18 (Aerocet 531) were found during the survey. 19 Furthermore, as reported in McMurry et al. (1996) and Chakrabarti et al. (2004) excessively high relative humidity 20 levels can lead to anomalously high PM values measured with photometric samplers. Although for this particular case 21 study the measured Relative Humidity (RH) levels were almost always less than 55%, when RH exceeded 70%, PM 22 concentrations were adjusted according to the correction factor proposed in Chakrabarti et al. (2004). 23 24 2.3 Measurement campaigns 25 Surveys were conducted during peak-hours of five randomly-selected working days in January 2014. Both PM10 and 26 PM2.5 concentrations were measured inside trains and in seven station platforms (one ground-level and six underground) 27 and their respective urban environments (outdoors). The measurements were conducted close to the centre of the 28 platforms and the samples were collected roughly 1.65 m above the platform floor. Inside the train, the measurements 29 were performed in the centre of the carriage approximately 1.65 m above the floor. In the outdoor environment the 30 measurements were performed at about 5-15 m from the entrance of the stations. Measurements at station platforms 31 were taken every 2 minutes between 8 AM and 12 PM. Furthermore, all the outdoor measurements were compared with 32 concentrations measured by the closest (see Figure 1) gravimetric CEA unit (confirming low measurement errors). 33 To make the results homogeneous and comparable, measurements were always carried out on clear weather days with 34 surface temperatures in the seasonal average (12-18 °C) and average low wind conditions (wind speed <10 km/h). 35 Obviously, temperatures at underground stations platforms were slightly higher than those at street level (16-17 °C on 36 average). 37 9 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 3 Results and discussion 2 3.1 PM concentration at station platforms 3 Measurements of indoor (station platform) and outdoor (urban environment) mass concentrations are summarized in 4 Tables 4 and 5. The average PM10 concentrations measured in the underground stations ranges between 172 and 262 5 µg/m3 with a standard deviation between 17 and 54 µg/m3. The maximum value of PM10 concentration was measured in 6 the Università station and was equal to 340 µg/m3. 7 With respect to the average PM2,5 concentrations measured in the underground stations the values monitored range 8 between 50 and 60 µg/m3 with a standard deviation between 5 and 21 µg/m3. The maximum value of PM2,5 9 concentration was measured in the Garibaldi station and was equal to 100 µg/m3. 10 In order to verify the statistical difference among values measured in the different underground stations, a t-test was 11 performed: the confidence level was set at 95% for all statistical tests. Statistical results indicate no difference among 12 data measured in all the underground station platforms with the exception of Università that had values of PM10 13 concentrations higher than the others. 14 Furthermore, t-tests were also performed to assess the differences in PM levels between underground stations and 15 urban environment (outdoor). The results show that concentrations measured outdoors are significantly different from 16 those measured indoors. OUTDOOR PM 10 [µg/m3] STATION PLATFORM PM 10 [µg/m3] Min. Max. Average St. dev. Min. Max. Average St. dev. Piscinola (groud-lev.) 18 31 24 4 14 21 16 10 Vanvitelli 19 27 22 3 129 229 179 39 Museo 29 43 35 5 128 240 172 36 Dante 13 36 22 8 153 194 178 17 Toledo 19 25 22 3 117 220 172 31 Università 12 26 18 5 195 340 262 54 Garibaldi 24 33 28 3 134 215 207 50 Table 4 – Distribution of indoor (station platform) and outdoor (urban environment) PM10 measured mass concentrations (in µg/m3) STATIONS Daytime, peak hour, working day 17 18 19 20 21 OUTDOOR PM 2.5 [µg/m3] STATION PLATFORM PM 2.5 [µg/m3] Min. Max. Average St. dev. Min. Max. Average St. dev. Piscinola (groud-lev.) 4 11 7 2 8 11 10 1 Vanvitelli 4 5 5 0 31 76 52 12 Museo 5 8 6 1 26 66 45 9 Dante 1 9 5 3 35 66 55 14 Toledo 2 4 3 1 40 57 50 5 Università 2 5 3 1 48 90 58 12 Garibaldi 6 11 9 1 19 103 54 21 Table 5 – Distribution of indoor (station platform) and outdoor (urban environment) PM2.5 measured mass concentrations (in µg/m3) STATIONS Daytime, peak hour, working day 22 23 24 25 26 To investigate the correlation between PM concentrations and trains passage, mass concentrations (PM 10 and PM2.5) 27 were measured during the morning peak hours at station platforms (e.g. Garibaldi and Dante station platforms) as 28 reported in Figures 4 and 5. The highest concentration values were found at train arrival in the station, with maximum 29 values up to 42% greater than the average ones. These results suggest that the passage of trains is the main cause of the 30 particulate concentration at station platforms (not equipped with screen door system), presumably due to the re10 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 suspension of the particles by the turbulence induced by the train passage. This result was also observed by Ma et al. 2 (2014), Johansson and Johansson (2003) and Salma et al. (2007) who reported that PM levels in underground railway 3 stations are closely correlated to the train frequency. These experimental results also indicate that PM levels can be 4 increased by re-suspension due to train movement. Other studies (e.g. Colombi et al., 2013) assume that these high 5 concentrations could also be caused by the particles directly produced by the wear of metal parts, but there is no 6 experimental evidence to support this hypothesis. 7 8 9 10 11 12 Figure 4 – Example of average PM10 mass concentrations (in µg/m3) over time (peak-hour) on the Garibaldi and Dante station platform. 13 14 15 16 Figure 5 – Example of average PM2.5 mass concentrations (in µg/m3) over time (peak-hour) on the Garibaldi and Dante station platform. 17 With respect to the ground-level station, it is worth pointing out that results obtained in Piscinola station show 18 concentrations of both PM10 and PM2.5 statistically lower (t-test) than those measured in the outdoor environment: 16 19 µg/m3 (station platform) against 24 µg/m3 (outdoor environment) for the PM10, and 10 µg/m3 against 7 µg/m3 for the 20 PM2,5. The explanation for this phenomenon may lie in the fact that Piscinola station platforms are located 15 meters 21 above street level, while the outdoor environment measurements were carried out at street level in a medium-high 22 traffic congested urban area. 23 24 3.2 PM concentration inside trains 25 In the second part of the study real-time PM concentrations were measured inside trains along the entire route of Line 26 1, from Piscinola station to Garibaldi station (about 18 km) and vice versa. Fourteen one-way trips (trains) were 27 monitored for the entire path between 8 AM and 12 PM on different working days. 11 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 Results show that in the underground section, windows left open on trains caused the entering of PM suspended in 2 the tunnel. This phenomenon is due to the turbulence induced by trains passage. The opposite phenomenon happened in 3 the ground-level section where the clean air produced a positive “washing effect” inside train carriages. Average results 4 are reported in Table 6 and 7, while concentrations measured during the forward and backward trips, both in ground- 5 level and underground sections, are reported in Figures 6 and 7. 6 During the forward trips from ground-level stations to underground ones, the average measured concentrations (bold 7 fitting curve obtained on the average trips data in Figures 6 and 7) in the ground-level section of the line (from 8 Piscinola to Colli Aminei stations) are similar to those measured in the outdoor environment: PM10 concentrations vary 9 between 30 (with a standard deviation of 7 µg/m3) and 43 µg/m3 (with a standard deviation of 19 µg/m3), while PM2.5 10 concentration vary between 8 (with a standard deviation of 3 µg/m3) and 18 µg/m3 (with a standard of 13 µg/m3). In the 11 underground section of the line increasing particular matter concentrations were found, with average values of PM10 12 inside trains (bold line in Figure 6) from 50 µg/m3 (at the Colli Aminei station) to about 140 µg/m3 (at the Garibaldi 13 station). Equal results are observed for PM2.5 concentrations with average measured values (bold line in Figure 7) 14 ranging from 18 µg/m3 (at Colli Aminei station) to 36 µg/m3 (at the Garibaldi station). 15 There is an interval of approximately 8-10 minutes before the start of the backward trip during which the train 16 remains motionless and no more further passengers board on the train. During this lapse of time, the average measured 17 PM concentration inside the train decreases up to 15-20% (Figure 6 and 7), probably because of the lack of air 18 turbulence induced both by air entering through the windows and by the passengers motion. During the backward trips 19 (from Garibaldi to Colli Aminei) increasing PM concentrations were measured until the train leaves the underground 20 section of the line, with average values of PM 10 inside trains (bold line in Figure 6) from 115 µg/m3 (at the Garibaldi 21 station) to 160 µg/m3 (at the Colli Aminei station). Similar results are observed for PM2.5 concentrations (average values 22 ranging from 32 µg/m3 to 44 µg/m3). From this point onward, in the ground-level section PM concentrations decrease 23 up to the lowest values measured at the Piscinola station. As in the Garibaldi station, the final PM concentrations 24 measured in the Piscinola station at the end of the backward trip further decreased during the 8-10 minutes in which the 25 train remained motionless. 26 Similar results were obtained by Cheng et al. (2012) that estimated real-time PM level variations travelling inside 27 the Taipei underground railway system. Cheng et al. (2012) estimated how PM concentrations differ for a passenger 28 moving from the platform to the train, travelling inside the train, and moving from the train to the platform. In 29 particular, after boarding on the train, PM levels gradually decline because the ventilation system filters out particulate 30 matter, while the opposite occurs when the passenger leaves the train. 31 12 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 250 250 underground underground ground-level 200 200 150 150 100 100 50 50 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 forward trip single measure single train fitting curve 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 [km] backward trip [km] Figure 6 – Distribution of PM10 mass concentrations (in µg/m3) inside the train during the forward and backward trips from the ground-level stations to the underground ones (grey lines refer to single train measurement while the bold line is the average estimation) 70 70 PM2.5 concentration [µg/m3] ground-level underground underground ground-level 60 60 50 50 40 40 30 30 20 20 10 10 0 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 forward trip 8 9 10 11 single measure single train fitting curve 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 [km] backward trip [km] Figure 7 – Distribution of PM2.5 mass concentrations (in µg/m3) inside the train during the forward and backward trips from the ground-level stations to the underground ones 13 PM2.5 concentration [µg/m3] 2 3 4 5 6 7 PM10 concentration [µg/m3] PM10 concentration [µg/m3] ground-level Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 3 4 5 Underground STATIONS Groundlevel Daytime, peak hour, working day Piscinola Chiaiano Frullone Colli Aminei Policlinico Rione Alto Montedonzelli Medaglie d'Oro Vanvitelli Quattro Giornate Salvator Rosa Materdei Museo Dante Toledo Università Garibaldi Min. Max. Average St. dev. 18 17 22 18 35 38 47 37 54 81 94 93 81 104 30 34 43 41 58 64 69 7 12 19 27 21 16 19 61 90 75 9 57 105 73 18 61 85 74 9 54 52 63 70 67 83 86 115 131 126 127 120 159 202 89 98 103 105 96 115 138 18 25 28 20 22 29 42 INSIDE TRAIN PM2.5 [µg/m3] Min. Max. Average St. dev. 4 4 3 5 11 13 11 18 12 24 35 39 33 35 29 33 8 11 15 18 21 24 22 27 3 7 11 13 9 8 7 4 15 21 42 31 26 25 9 4 16 17 20 17 24 26 27 36 43 53 49 44 46 42 30 32 32 31 35 36 36 7 9 14 11 9 9 7 Table 6 – PM mass concentrations (in µg/m3) inside the train at the stations during the forward trip from the groundlevel stations to the underground ones Underground STATIONS Groundlevel Daytime, peak hour, working day 6 7 INSIDE TRAIN PM10 [µg/m3] Piscinola Chiaiano Frullone Colli Aminei Policlinico Rione Alto Montedonzelli Medaglie d'Oro Vanvitelli Quattro Giornate Salvator Rosa Materdei Museo Dante Toledo Università Garibaldi INSIDE TRAIN PM10 [µg/m3] Min. Max. Average St. dev. INSIDE TRAIN PM2.5 [µg/m3] Min. Max. Average St. dev. 24 32 50 94 133 133 138 125 47 66 124 185 190 208 200 195 36 53 81 127 159 163 157 155 9 13 30 30 21 25 23 22 5 6 13 27 29 29 27 29 14 27 40 53 54 56 52 49 10 17 25 38 44 44 41 39 4 8 9 10 9 10 9 8 105 126 187 172 147 144 30 20 28 26 50 58 39 42 8 12 106 193 144 29 21 54 40 12 101 197 144 33 24 59 38 13 105 181 139 28 26 54 37 11 101 157 132 19 24 52 35 12 81 164 123 33 26 44 38 8 87 196 129 42 24 46 35 8 75 176 115 39 23 38 32 6 3 Table 7 – PM mass concentrations (in µg/m ) inside the train at the stations during the backward trip from the groundlevel stations to the underground ones 14 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 3.3 Passengers average exposure time 3 The daily average “exposure time” of passengers to the high PM concentrations measured in the Naples underground 4 railway system was also estimated. Using the transport simulation models developed for the Naples metropolitan area 5 (for details see: Bifulco et al., 2010; Cartenì, 2007; de Luca and Cartenì, 2013) it was estimated that every day the Line 6 1 is used by over one hundred thousand passengers and the average daily time spent waiting for a train on the platform 7 (for a round trip) is more than 18 minutes per passenger, while the daily passenger in-vehicle time is about 45 minutes 8 (for round trip). This means that on average a Line 1 passenger spends about 70 minutes per day (about 18 days/year) 9 exposed to elevated concentrations of PM. 10 11 4 Conclusions 12 All around the world, many studies have measured elevated concentrations of particulate matter in underground metro 13 systems, with non-negligible implications for human health due to protracted exposition to fine particles. In all the 14 investigated environments, concentrations were consistently higher than in the urban environment (outdoors) up to a 15 factor of 8 at station platforms. 16 Starting from this consideration, an intensive particulate sampling campaign was carried out in January 2014 to 17 measure the particulate matter concentrations for the Naples Metro Line 1 (Italy) both at station platforms and inside 18 trains. Experimental results show that the average PM10 concentration measured in the monitored underground station 19 platforms ranges between 172 and 262 µg/m3, while the average PM2.5 concentration ranges between 45 and 60 µg/m3. 20 By contrast, with respect to the ground-level stations, no great differences between station platforms and urban 21 environment measurements were observed. 22 Furthermore, a direct correlation between PM concentrations and trains passage was observed, presumably caused 23 by the re-suspension of the particles due to the turbulence induced by trains which caused concentrations to rise up to 24 42% above the average value. 25 The main original finding was the real-time estimations of PM levels inside trains travelling both in ground-level 26 and underground sections of Line 1. The results show that high concentrations of both PM10 (average values between 58 27 µg/m3 and 138 µg/m3) and PM2.5 (average values between 18 µg/m3 and 36 µg/m3) were also measured inside trains. 28 Moreover, measurements show that windows left open on trains caused the increase in PM concentrations inside trains 29 in the underground section. The opposite effect happened in the ground-level section where clean air entering the trains 30 produced an environmental “washing effect”. 31 Finally, the daily average “exposure time” to these high PM concentrations for the passengers of the Naples metro 32 system was also estimated, evaluating that a passenger of the Line 1 spends on average about 18 days/year (in total) 33 exposed to these elevated PM levels. 34 35 36 5 Acknowledgment 37 The Authors are grateful to the anonymous referees for their precious comments. 15 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 References Aarnio P., Yli-Tuomi T. et al. 2005. The concentrations and composition of and exposure to fine particles (PM2.5) in the Helsinki subway system. Atmospheric Environment, 39, 5059–5066. Adams, H.S., Nieuwenhuijsen, M.J., et al., 2001. Fine particle (PM2.5) personal exposure levels in transport microenvironments, London, UK. Science of the Total Environment 279 (1e3), 29-44. Bao, L.-M.a b , Lei, Q.-T.a b , Tan, M.-G.a , Li, X.-L.a b , Zhang, G.-L.a , Liu, W.a b , Li, Y.a (2014); Study on transition metals in airborne particulate matter in Shanghai city's subway; Huanjing Kexue/Environmental Science, 35 (6), pp. 2052-2059. Bifulco, G.N., Cartenì, A. and Papola, A., 2010. An activity-based approach for complex travel behaviour modelling. European Transport Research Review 2(4), 209-221. Branis, M., 2006. The contribution of ambient sources to particulate pollution in spaces and trains of the Prague underground transport system. Atmospheric Environment 40 (2), 348-356. Britton M., 2003. Lifestyle and your lungs. Lung Report III. British Lung Foundation. Campbell, A., 2004. Inflammation, neurodegenerative diseases, and environmental exposures. Protective Strategies for Neurodegenerative Diseases 1035, 117-132. Cartenì, A., 2007. Updating demand vectors using traffic counts on congested networks: A real case application. WIT Transactions on the Built Environment 96, 211-221. Cascetta, E., Cartenì, A., 2014a. The hedonic value of railways terminals. A quantitative analysis of the impact of stations quality on travellers behaviour. Transportation Research Part A (61), 41-52. Cascetta, E., Cartenì, A., 2014b. A quality-based approach to public transportation planning: theory and a case study. International Journal of Sustainable Transportation, Taylor & Francis, (8) Issue 1, 84-106. Chakrabarti B., Fine P.M., Delfino R., Sioutas C. (2004); Performance evaluation of the active-flow personal DataRAM PM2.5 mass monitor (Thermo Anderson pDR-1200) designed for continuous personal exposure measurements. Atmospheric Environment; pp. 3329–3340. Chan, L.Y., Lau, W.L., Lee, S.C., Chan, C.Y., 2002a. Commuter exposure to particulate matter in public transportation modes in Hong Kong. Atmospheric Environment 36, 3363–3373. Chan, L.Y., Lau, W.L., Zou, S.C., Cao, Z.X., Lai, S.C., 2002b. Exposure level of carbon monoxide and respirable suspended particulate in public transportation modes while commuting in urban area of Guangzhou, China. Atmospheric Environment 36, 5831–5840. Cheng, Y.H., Lin, Y.L., Liu, C.C., 2008. Levels of PM10 and PM2.5 in Taipei Rapid Transit System. Atmospheric Environment 42, 7242–7249. Cheng, Y.H., Liu Z.S., Yan J.W. (2012); Comparisons of PM10, PM2.5, Particle Number, and CO2 Levels inside Metro Trains Traveling in Underground Tunnels and on Elevated Tracks; Aerosol and Air Quality Research, 12; pp. 879–891. Colombi C., Angius S., Gianelle V., Lazzarini M., 2013. Particulate matter concentrations, physical characteristics and elemental composition in the Milan underground transport system. Atmospheric Environment, Volume 70, 166-178. de Luca, S., Cartenì, A., 2013. A multi-scale modelling architecture for estimating of transport mode choice induced by a new railway connection: The Salerno-University of Salerno-Mercato San Severino Route [Un'architettura modellistica multi-scala per la stima delle ripartizioni modali indotte da un nuovo collegamento ferroviario: il caso studio della tratta Salerno-Università di Salerno-Mercato San Severino]. Ingegneria Ferroviaria, 68 (5), 447-473. Delfino, R.J., Sioutas, C., et al., 2005. Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health. Environmental Health Perspectives 113 (8), 934-946. Eom, H.-J., Jung, H.-J., Sobanska, S., Chung, S.-G., Son, Y.-S., Kim, J.-C., Sunwoo, Y., Ro, C.-U. (2013); Iron speciation of airborne subway particles by the combined use of energy dispersive electron probe X-ray microanalysis and Raman microspectrometry; Analytical Chemistry, 85 (21), pp. 10424-10431. European Commission, 2008. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. Technical Report 2008/50/EC, L152 Off. J. Eur. Commun. European Standard, 1998. Reference Method for the Determination of the PM-10 Fraction of Particles. EN 12341, Bruxelles. European Commission, 2014. EU energy, transport and GHG emissions, trends to 2050 - Reference scenario 2013. European Standard, 2014. EN 12341/14: Ambient air - Standard gravimetric measurement method for the determination of the PM10 or PM2,5 mass concentration of suspended particulate matter. Foster A., Kumar N. (2011); Health effects of air quality regulations in Delhi, India; Atmospheric Environment, Volume 45, Issue 9, pp. 1675-1683. Fromme, H., Oddoy, A., et al., 1998. Polycyclic aromatic hydrocarbons (PAH) and diesel engine emission (elemental carbon) inside a car and a subway train. Science of the Total Environment 217 (1-2), 165-173. 16 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Gerber, Bohn, Groneberg, Schulze and Bundschuh 2014. Airborne particulate matter in public transport: a field study at major intersection points in Frankfurt am Main (Germany). Journal of Occupational Medicine and Toxicology 2014, 9-13. Gómez-Perales, J.E., Colvile, R.N., Nieuwenhuijsen, M.J., Fernández-Bremauntz, A., Gutiérrez-Avedoy, V.J., PáramaFigueroa, V.H., Blanco-Jiménez, S., Bueno- López, E., Mandujano, F., Bernabé-Cabanillas, R., Ortiz-Segovia, E., 2004. Commuters’ exposure to PM2.5, CO, and benzene in public transport in the metropolitan area of Mexico City. Atmospheric Environment 38, 1219-1229. Johansson, C., Johansson, P.A., 2003. Particulate matter in the underground of Stockholm. Atmospheric Environment 37, 3–9. Jung, H.-J., Kim, B., Ryu, J., Maskey, S., Kim, J.-C., Sohn, J., Ro, C.-U, 2010. Source identification of particulate matter collected at underground subway stations in Seoul, Korea using quantitative single-particle analysis. Atmospheric Environment 44, 2287-2293. Kam, W., Cheung, K., Daher, N., Sioutas, C., 2011. Particulate matter (PM) concentrations in underground and groundlevel rail systems of the Los Angeles Metro. Atmospheric Environment 45, 1506-1516. Karlsson, H.L., Nilsson, L., M¨oller, L., 2005. Subway particles are more genotoxic than street particles and induce oxidative stress in cultured human lung cells. Chemical Research in Toxicology 18, 19–23. Kim, K. H., Ho, D. X., Jeon, J. S., and Kim, J. C., 2012. A noticeable shift in particulate matter levels after platform screen door installation in a Korean subway station, Atmospheric Environment 49, 219–223. Kim, K.Y., Kim, Y.S., Roh, Y.M., Lee, C.M., Kim, C.N., 2008. Spatial distribution of particulate matter (PM10 and PM2.5) in Seoul Metropolitan Subway stations. Journal of Hazardous Materials 154, 440-443. Knibbs, L.D. and de Dear, R.J., 2010. Exposure to Ultrafine Particles and PM2.5 in Four Sydney Transport Modes. Atmos. Environ. 44, 3224-3227. Kumar N., Chu A., Foster A., (2007); An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan; Atmospheric Environment, Volume 41, Issue 21. pp. 4492-4503. Invernizzi G., Ruprecht A., Mazza R., De Marco C., Močnik G., Sioutas C., Westerdahl D. (2011); Measurement of black carbon concentration as an indicator of air quality benefits of traffic restriction policies within the ecopass zone in Milan, Italy; Atmospheric Environment, Volume 45, Issue 21; pp. 3522-3527. Lee K., Hahn E.J., Pieper N., Okoli C.T.C., Repace J., Troutman A. (2008); Differential Impacts of Smoke-Free Laws on Indoor Air Quality. Journal of Environmental Health, v. 70, no. 8, pp. 24-30. Levy, J.I., Dumyahn, T. and Spengler, J.D., 2002. Particulate Matter and Polycyclic Aromatic Hydrocarbon Concentrations in Indoor and Outdoor Microenvironments in Boston, Massachusetts. J. Exposure Anal. Environ. Epidemiol. 12, 104-114. Li, N., Wang, M.Y., et al., 2009. The Adjuvant effect of ambient particulate matter is Closely Reflected by the particulate Oxidant potential. Environmental Health Perspectives 117 (7), 1116-1123. Li, T.T., Bai, Y.H., Liu, Z.R., Li, J.L., 2007. In-train air quality assessment of the railway transit system in Beijing: a note. Transportation Research Part D 12, 64–67. Ma, H., Shen, H., Liang, Z., Zhang, L., Xia, C. (2014); Passengers' exposure to PM2.5, PM10, and CO2 in typical underground subway platforms in Shanghai; Lecture Notes in Electrical Engineering, 261 LNEE (VOL. 1), pp. 237-245. Met One Inc., 2008. Esampler TM. Met One Inc., Grants Pass, OR. McMurry, P.H., Zhang, X., Lee, Q.T. (1996); Issues in aerosol measurement for optical assessments. Journal of Geophysical Research 101, pp. 188–197. McNabola A., McCreddin A., Gill L.W., Broderick B.M. (2011); Analysis of the relationship between urban background air pollution concentrations and the personal exposure of office workers in Dublin, Ireland, using baseline separation techniques; Atmospheric Pollution Research, 2 (1), pp. 80-88. Nyhan, M., McNabola, A., Misstear, B. (2013); Comparison of particulate matter dose and acute heart rate variability response in cyclists, pedestrians, bus and train passengers; Science of the Total Environment, 468-469, pp. 821-831. Onat B., Stakeeva B. (2014); Assessment of fine particulate matters in the subway system of Istanbul; Indoor and Built Environment, vol. 23 (4); pp. 574-583. Pope, C.A., Burnett, R.T., Thurston G.D., Thun M.J., Calle E.E., Krewski D., Godleski J.J., 2003. Cardiovascular mortality and long-term exposure to particulate air pollution - epidemiological evidence of general pathophysiological pathways of disease. Circulation 109 (1), 71-77. Pope, C.A., Dockery, D.W., 2006. Cardiovascular mortality and long-term exposure to particulate air pollution: lines that connect. Journal of the Air&WasteManagement Association 56 (6), 709-742. Priest, N.D., Burns, G., Gorbunov, B., 1998. Dust levels on the London underground: a health hazard to commuters. Urban pollution research centre, Middlesex University, Bounds Green Road, London, N11 2NQ. Querol, X., Moreno, T., Karanasiou, A., Reche, C., Alastuey, A., Viana, M., Font, O., Gil, J., De Miguel, E., Capdevila, M., 2012. Variability of levels and composition of PM10 and PM2.5 in the Barcelona metro system. Atmospheric Chemistry and Physics 12, 5055-5076. 17 Cartenì, A., Cascetta F., Campana S. (2015). Underground and ground-level particulate matter concentrations in an Italian metro system. Atmospheric Environment. Volume 101. pp. 328–337. DOI: 10.1016/j.atmosenv.2014.11.030 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Raut, J.-C., Chazette, P., Fortain, A., 2009. Link between aerosol optical, microphysical and chemical measurements in an underground railway station in Paris. Atmospheric Environment 43, 860-868. Ripanucci, G., Grana, M., Vicentini, L., Magrini, A., Bergamaschi, A., 2006. Dust in the underground Railway Tunnels of an Italian Tow. Journal of Occupational and Environmental Hygiene 3, 16-25. Şahin, Ü.A., Onat, B., Stakeeva, B., Ceran, T., Karim, P., 2012. PM10 concentrations and the size distribution of Cu and Fe-containing particles in Istanbul's subway system. Transportation Research Part D: Transport and Environment, 17 (1), 48-53. Salma, I., Weidinger, T., Maenhaut, W., 2007. Time-resolved mass concentration, composition and sources of aerosol particles in a metropolitan underground railway station. Atmospheric Environment 41, 8391–8405. Seaton, A., Cherrie, J., Dennekamp, M., Donaldson, K., Hurley, J.F., Tran, C.L., 2005. The London underground: dust and hazards to health. Occupational and Environmental Medicine 62, 355–362. Son, Y.-S.a, Salama, A., Jeong, H.-S., Kim, S., Jeong, J.-H., Lee, J., Sunwoo, Y., Kim, J.-C.b (2013); The effect of platform screen doors on PM10 levels in a subway station and a trial to reduce PM10 in tunnels; Asian Journal of Atmospheric Environment, 7 (1), pp. 38-47. UniTec (2014); http://www.unitec-srl.com. Last accessed on October, 2014. Valavanidis, A., Fiotakis, K., Vlachogianni, T., 2008. Airborne particulate matter and human health: toxicological assessment and importance of size and composition of particles for oxidative damage and carcinogenic mechanisms. Journal of Environmental Science and Health, Part C 26 (4), 339-362. Wang, X. and Gao, H.O., 2011. Exposure to Fine Particle Mass and Number Concentrations in Urban Transportation Environments of New York City. Transp. Res. D Trans. Environ. 16, 384-391. 18