Preliminary investigation of PM1, PM2.5, PM10 and its metal elemental composition in tunnels at a subway station in Shanghai, China

Transportation Research Part D 41 (2015) 136–146

Contents lists available at ScienceDirect

Transportation Research Part D journal homepage: www.elsevier.com/locate/trd

Preliminary investigation of PM1, PM2.5, PM10 and its metal elemental composition in tunnels at a subway station in Shanghai, China Ting Qiao a, Guangli Xiu a,⇑, Yi Zheng b, Jun Yang c, Lina Wang a, Jianming Yang d, Zhongsi Huang a a State Environmental Protection Key Laboratory of Risk Assessment and Control on Chemical Process, East China University of Science and Technology, Shanghai 200237, China b Technical Center of Shanghai Shentong Metro Group Co., Ltd., Shanghai 201103, China c Shanghai An-He Environmental Testing Technique Co., Ltd., Shanghai 201611, China d Shanghai International Theme Park Co., Ltd., Shanghai 201205, China

a r t i c l e

i n f o

Article history:

Keywords: Subway tunnel Microclimate Particulate matter Particle screen door Metal element

a b s t r a c t In this study, real-time monitoring campaigns were conducted in two tunnels (Line A and Line B) at a subway station in Shanghai, including temperature, relative humidity, PM1, PM2.5 and PM10, in order to understand the climate and PM characteristics in the transportation microenvironment. In addition, collected floor dust particles in the tunnel were analyzed by ICP for their metal elemental composition. Strong correlations occurred between all PM levels and meteorological parameters in the tunnel of Line A (with platform screen doors), in comparison with the weak correlations between such parameters in the tunnel of Line B (without platform screen doors). PM2.5 and PM10 between peak hours and off-peak hours for both lines presented significant differences (p < 0.05), respectively. Nevertheless, PM1 showed a different pattern, with p > 0.05 for Line A and p < 0.05 for Line B, respectively. In addition, statistical results concluded that PM had an evident weekly variation for both lines. Friday was the highest day of all particulate matters in monitoring periods for both lines. Ratios of PM1/PM10 and PM2.5/PM10 were high when trains were out of service and low when trains were in service. Relative abundance of metal elements detected from floor dust particles proved that floor dust particles in tunnels might be a major source of airborne PM in the subway microenvironments, with Fe as the most abundant metal element, followed by Ca, Al, Mg, Mn, Zn, Cu, Cr, Ni, Pb and Hg. Ó 2015 Elsevier Ltd. All rights reserved.

Introduction Amount of scientific evidence (Adams et al., 2001a; Furuya et al., 2001; Aarnio et al., 2005; Chillrud et al., 2004; Kam et al., 2011; Jung et al., 2010; Cheng et al., 2008; Chan et al., 2002a, 2002b) has discovered that particulate matter (short as PM) concentration in subway environment is higher than outdoor environment, which might be related to following reasons: (1) Subway environment is relatively closed, of which the internal air cannot circulates thoroughly and mix with enough fresh ⇑ Corresponding author. Tel.: +86 021 64251927. E-mail address: [email protected] (G. Xiu). http://dx.doi.org/10.1016/j.trd.2015.09.013 1361-9209/Ó 2015 Elsevier Ltd. All rights reserved.

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

137

air; (2) Air quality is quite poor due to the abundance of produced internal pollution sources. Epidemiological study (Pope et al., 2004) has found out that airborne particles had a certain relationship with plenty of adverse health impacts, such as respiratory and cardiovascular diseases. Pope et al. (2002) determined that each 10 lg/m3 increase in fine particulate concentration was associated with an approximate 4%, 6% and 8% increase of risk of all-cause, cardiopulmonary and lung cancer mortality, respectively. In addition, Karlsson et al. (2005) demonstrated that subway PM was approximately eight times more genotoxic than street PM and resulted in four times oxidative stress in cultured human lung cells. Tunnels, ventilation systems, and traffic of trains were suggested to be three important determinants of high levels of particulate matter in subway environments (Furuya et al., 2001). The research studied by Sahin et al. (2012) discovered that the PM10 concentration and the relative abundance of Fe-containing particles increased with the increase of depth at the station, suggesting that particulates, especially the Fe-containing particles, were generated in tunnels. The mean concentration monitored in London subway tunnel was about eight times higher than the concentrations in other transportation modes (bicycle, bus and car) (Adams et al., 2001b). In addition, potentially higher mobile part of toxic elements was found in the subway tunnel compared to the road tunn sample (Sysalova and Szakova, 2006). As a consequence, further work is needed to characterize particulate matter as well as its composition in tunnels. Since the first line of Shanghai Railway Transit put into service in 1993, fourteen lines have been put into operation until now, with 329 stations and 538 km total mileage. Despite the relatively short amount of time spent in public transportation microenvironments, the air quality in such environments is a key factor to be seriously concerned. This is because that the health exposure to particulate pollutant within such a short period could be very high. Ye et al. (2010) conducted a field study on the platforms of a Shanghai subway station and found that the high PM concentration should be paid much attention for considering the passenger health safety waiting on the subway platforms (PM1 = 231 ± 152 lg/m3, PM2.5 = 287 ± 177 lg/m3, PM10 = 366 ± 193 lg/m3). So far, most of the studies on PM in the Shanghai subway environment have been focused on monitoring in the compartments (Feng et al., 2010), on platforms (Ma et al., 2014) and concourses (Yu et al., 2012), with no studies about air pollutants in tunnels. However, apart from the input of outdoor air pollutants, internal sources of subway should not be neglected, which mainly originated from the mechanical deterioration between conductor rails, electrodes, brake pads, rails and wheels. The first step to improve air quality inside the subway is to understand the characteristics of PM and specific internal sources. Therefore, this study aimed to investigate the PM variations in tunnels. The real-time monitoring sites were fixed in two tunnels connected with the same subway transfer station, with one for Line A and the other for Line B. The microclimate was also characterized simultaneously via the meteorological parameters including temperature and relative humidity. PM1, PM2.5 and PM10 were simultaneously monitored to investigate the difference as well as the corresponding causes. Comparison of the PM characteristics between Line A and Line B was also conducted to evaluate the impact of PSDs (short for Platform Screen Doors) installation. In addition, collected floor dust particles in the tunnel were analyzed by ICP for their metal elemental composition. Materials and methods Sampling sites Real-time monitoring campaigns, consisting of temperature (T), relative humidity (RH) and particulate matter (PM), were monitored in the tunnels at a subway station in Shanghai for Line A (sampling from Aug 30th to Sep 2nd, 2013) and Line B (sampling from Nov 11th to Nov 15th, 2013), respectively. Fixed sampling sites were situated about 10 m far away from the open space of platforms of the subway station, specially shown in Fig 1. The subway station is located in downtown area, a transfer junction of three lines. Its passenger flow remains high all along, which is one of the main transfer stations in Shanghai Railway Transit. It is an underground station, with a mechanical ventilation system in the whole station except in the tunnels. Sampling and metal elemental analytical methods PM was monitored using a DustTrak DRX 8534 Aerosol Monitor (TSI, Inc., Shoreview, MN, USA). DustTrak combines the principle of a photometer and an optical particle counter (short as OPC), so that mass concentration of PM1, PM2.5 and PM10 could be recorded at the same time. Sampling interval was set as 5 min and the flow rate was set at 3.0 L/min. Two HOBO U10-003 Temperature/Relative Humidity Recorders (Oneset, Inc., Irvine, CA, USA) were also employed, with recording interval of 5 min. A DustTrak and two HOBOs were placed in a metal box and then fixed on the wall of the tunnel. The metal box was 1 m high above the ground, and its sampling port was straight facing with the direction of trains entering. Outdoor PM2.5 and PM10 data were obtained from Shanghai Environmental Protection Bureau (short as EPB, http://www.sepb.gov.cn/fa/ cms/shhj/index.htm). While outdoor PM1 was sampled by a PQ200 Ambient Fine Particle Sampler (BGI, Inc., Waltham, MA, USA) on the rooftop of a building in East China University of Science and Technology due to lack of official data. No stationary sources can be found around the sampling site. Sampling interval and flow rate were set as 24 h and 16.7 L/min, respectively. To determine the metal elemental composition of internal sources in tunnels, three samples of floor dust particles were collected in the monitored tunnel of Line A referring to the detection method of dust on the duct inner surface in hRegulation

138

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

Line A

Sampling Site

Sampling Site

100 m

Fig. 1. Fixed sampling sites at a Shanghai subway station.

of Hygiene Management in Centralized Air-conditioning Ventilation Systemi (DB31/405-2012) and then analyzed with an Agilent 725 Inductively Coupled Plasma (short as ICP, Agilent Technologies, Yokogawa Division, Tokyo, Japan). Floor dust particles were sieved to obtain fractions with particle size smaller than 50 mesh and then digested in a mixture of highpurity concentrated HNO3, HF and HCl (5 + 1 + 1) in a microwave oven for 10 min at maximum power. The resulting clear solution was evaporated almost to dryness at 90 °C in a crucible with cover. The residue was dissolved in 10 mL 5% (v/v) HNO3, filtered (0.45 lm PTFE filters, Alltech, USA) and stored in plastic tubes at +4 °C until measurement. The historical meteorological data of Shanghai Hongqiao Airport (32.2°N, 121.3°E, altitude 3 m) was adopted in this study to represent for the meteorology outside the monitored station, which was released by Weather Underground (http://www. wunderground.com/history/airport/).

Statistical approach Based on the non-normal distribution of PM concentration (p < 0.05) by Shapiro–Wilk Statistical test, median and mean values were employed simultaneously. SPSS software (version 13.0) was applied for the statistical analysis. The concentration differences between rush hours and non-rush hours as well as between Line A and Line B were analyzed for its statistical significance with independent samples t-test. One-way ANOVA and Duncan’s multiple comparison analysis methods were applied for analyzing the difference in PM concentration levels among different monitoring days.

Quality assurance DustTrak DRX 8534 Aerosol Monitor provides near-real-time data, high signal to noise ratio (short as SNR) and easy operation. The sensing mechanism of this equipment was a laser diode, quantity of light measured by a photo-detector was converted to mass concentration with a proportional constant by an internal electronic component (Wang et al., 2009). This proportional constant could be obtained by calibration with Arizona Road Dust (ISO12103-651, A1 Dust). However, the response of light scattering dust detector was susceptible to being influenced by aerosol properties, such as refractive index, shape, density and size of particles (Cheng and Lin, 2010). Hence, the DustTrak was calibrated at the beginning of each experimental campaign using a PQ200 Ambient Fine Particle Sampler. The gravimetric sampler is monthly calibrated in order to guarantee the meteorological traceability to SI. In addition, zero correction and span correction were conducted to DustTrak before monitoring. The range and precision of this equipment were 0.001–150 mg/m3 and 0.001 mg/m3, respectively. The range of HOBO U10-003 temperature/relative humidity recorder was 20 to 70 °C for temperature and 25–90% for RH, with its precision ±0.4 °C for temperature and ±3.5% for RH, respectively. To ensure the compatibility of these two instruments, simultaneous monitoring was marched before and after surveillance. Correlation of readings recorded by two temperature/relative humidity recorders showed that their differences were within 10% (Temperature: y = 0.97x + 1.49, R2 = 0.632; RH: y = 0.95x + 2.31, R2 = 0.953), possessing with high comparatibility. The calibration curve of ICP was constructed with the standard additions method. NIST 1643d certified material was used to check the accuracy and precision of the method. The test method was similar to the EPA 6020A/98 test method.

139

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

Results and discussion Microclimate in tunnels Microclimate mainly includes temperature, relative humidity and wind speed, while monitor of wind speed was not included in this study since ventilation system had not been installed in the tunnels of this subway station. It is important to note that Line A has equipped with PSDs while Line B has not. Seen from Table 1, it could be found that median values of temperature in both tunnels of Line A and Line B were close. Yet RH for these two lines had significant differences. However, RH outside the subway station was similar during two monitoring campaigns, varying from 28% to 89% during Aug 30th to Sep 2nd (Line A) and ranging from 32% to 93% during Nov 11th to 15th (Line B). Therefore, the bad RH for Line B should be ascribed to poor internal condition of the tunnel. Nevertheless, sampling sites of two lines were situated in the same subway station, specific shown in Fig. 1. The reason of the significant difference in RH might be associated with PSDs installation of Line A and non-installation of Line B. This is because that the PSD system can provide highly effective controls on heating, ventilation, and air conditioning (Kim et al., 2012). As a fact of matter, RH of indoor air has a direct or indirect influence on body health and comfort. When RH fell down to 40%, the infection rate of upper respiratory tract would increase. If RH kept lower than 30% for a long time, discomfort and skin irritation would be given rise to, such as prurigo and erythema (Rycroft and Smith, 1980; Rycroft, 1985). Therefore, installation of PSDs with a ventilating system should be considered as one option to seek for air quality maintenance in a huge Metro system like Shanghai subway system. Temperature in tunnels increased when the trains started operating, and then slightly decreased but still maintained at a relative high value. After cease operating, temperature descended to the ambient temperature. Comparatively, the variation of RH was just opposite. As ventilation system was not installed in tunnels, temperature values obtained in this study were all greater than those recorded in literatures (Yuan and You, 2007; Cho et al., 2006). RH was similar to the value (39–66.2%) that Ye et al. (2010) monitored at a Shanghai railway station. PM in tunnels During the monitoring periods, the median concentration of PM1, PM2.5 and PM10 in the subway tunnels were 50 lg/m3, 55 lg/m3 and 69 lg/m3 for Line A, respectively, while those were 42 lg/m3, 46 lg/m3 and 58 lg/m3 for Line B, respectively. Results of the statistical methodology of student’s t-test between Line A and Line B indicated that evident statistical significances emerged in PM concentration of all dimensions (p < 0.05), as an integrated outcome of different sampling time, passenger flow and internal conditions (including outfit of PSDs or not). The PSD system has a strong effect on PM concentrations in a subway station by its blocking effect from re-suspension of the particulate. In addition, the powerful piston effect generated between the fast moving trains and the rails could transfer PM from the tunnel to the passenger platform and vice versa (Coke et al., 2000). Park et al. (2009) concluded that PM10 mass concentration showed higher values in the tunnel due to fugitive sources but relatively low at the platform and concourse because of PSDs. As shown in Table 2, PM concentrations in this study were far below the values monitored in the tunnels of Seoul and London Metro systems, respectively, which should be associated with different monitoring time, detecting equipment and outdoor concentration (Kim et al., 2008; Birenzvige et al., 2003). The monitoring time of this study was in summer for Line A and in autumn for Line B, respectively, while PM concentrations were obtained in winter and spring for Seoul and in winter for London, respectively. Furuya et al. (2001) discovered that PM concentration in atmosphere varied with seasons. Moreover, PM concentration generally appeared the highest value in winter and the lowest value in summer. Seen from Table 2, PM concentrations in tunnels, platforms, concourses and compartments were found to be city-specific. Moreover, PM concentrations depended on sampling devices to some extends. Results in Shanghai Metro system studied by Ye et al. (2010) were quite higher than those obtained by Yu et al. (2012) and in this study. The interpretation of the great discrepancy was the overestimation of light scattering detectors. Despite all three studies employed light scattering apparatuses, data of Yu et al. (2012) and in this study were calibrated while those of Ye et al. (2010) were not. However, it is important to note that corrected results of light scattering detectors (Cheng et al., 2008) and gravimetric sampled PM concentrations (MugicaAlvarez et al., 2012) were quite approached. In addition, Salma et al. (2007) pointed out that differences of PM concentration showed at different subway stations might be attributed to the various system technologies (such as power supply, engineering system and braking system), the station ventilation systems and operating conditions. Aiming to investigate the relationship among various factors and the PM levels appeared in subway, a correlation analysis was conducted by using PM data and the relevant environmental parameters monitored in tunnels. As shown in Table 3, the Table 1 Microclimate parameters in tunnels of Line A and Line B. Location

Content

Range

Median

Average ± SD

Line A

Temperature (°C) RH (%)

29.2–35.0 35.1–77.3

31.2 45.0

31.5 ± 0.9 47.4 ± 6.5

Line B

Temperature (°C) RH (%)

21.8–30.5 25.2–57.6

29.4 29.6

28.9 ± 1.2 30.9 ± 4.1

140

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

Table 2 PM concentration in different urban railway environments. City

Shanghai (Line A)

Shanghai (Line B)

Shanghai

Shanghai Guangzhou Hong Kong Taipei London Seoul

Mexico Los Angeles

Type of PM

PM1 PM2.5 PM10 PM1 PM2.5 PM10 PM2.5 PM2.5 PM10 PM1 PM2.5 PM10 PM2.5 PM10 PM2.5 PM10 PM2.5 PM2.5 PM10

PM2.5 PM10 PM2.5 PM10

PM concentration (lg/m3) Range

Average ± SD

5–129 5–137 6–156 12–183 13–190 14–234 80–623 98–731 81–975 – – – 21–68 23–89 7–100 11–137 105–371 82–176 238–480 156–495 61–205 63–196 60–93 88–145 – –

53 ± 28 57 ± 30 69 ± 34 58 ± 45 61 ± 45 71 ± 46 231 ± 152 287 ± 177 366 ± 193 79 ± 51 44 ± 11 55 ± 14 39 ± 9 50 ± 9 35 ± 13 51 ± 20 247 129 359 – – – – – 57 ± 11 78 ± 17

Measuring position

References

Tunnel

This paper

Platform

Ye et al. (2010)

Concourse Compartment Compartment Compartment Compartment Platform

Yu et al. (2012) Chan et al. (2002a, 2002b)

Tunnel Platform

Adams et al. (2001a, 2001b) Kim et al. (2008)

Tunnel Platform Concourse Platform

Park et al. (2009)

Platform

Kam et al. (2011)

Chan et al. (2002a, 2002b) Cheng et al. (2008)

Mugica-Alvarez et al. (2012)

Table 3 Correlations between PM concentration and the meteorological parameters for Line A and Line B. Location Line A (with PSDs)

Line B (without PSDs)

* **

T

RH

PM1

PM2.5

PM10

T RH PM1 PM2.5 PM10

1 0.332** 0.219** 0.198** 0.135**

1 0.692** 0.684** 0.648**

1 0.999** 0.986**

1 0.992**

1

T RH PM1 PM2.5 PM10

1 0.137** 0.025 0.007 0.076*

1 0.256** 0.230** 0.167**

1 0.999** 0.984**

1 0.991**

1

Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).

correlation strengths between different data pairs were evaluated in terms of probability of Pearson correlation (p). To allow for a meaningful comparison, the results were divided into three categories as follows based on their strengths of correlation: (1) p > 0.05; (2) 0.01 < p 6 0.05 (with one asterisk) and (3) p 6 0.01 (with two asterisks). According to analysis results, strong correlations between all PM data were both consistently seen for Line A and for Line B, whether with PSD installation or not. This outcome was comparatively coincidence with those studied by Park and Ha (2008) and Kim et al. (2012) in Korean subway stations. However, correlations between PM and relevant meteorological parameters had significant differences between Line A (with PSDs) and Line B (without PSDs). In the tunnel of Line A, strong correlations occurred between all PM levels and meteorological parameters, respectively. While in the tunnel of Line B, the correlations between such parameters was not as significant as those of Line A, specific illustrated in Table 3. This was possible to be attributed to the environmental conditions (e.g., restricted mixing between platforms and tunnels) caused by the installed PSDs. The separation of air between subway operation area (in rail) and passenger area, introduced by the installation of PSDs, could also play an important role in determining PM concentration. Temporal variations of PM concentration in tunnels Diurnal variations of PM concentration in the subway tunnels of Line A and Line B were shown in Fig. 2, respectively. Generally speaking, PM concentration increased when trains went into service, maintaining higher concentration during

141

250

150

200

50

100 50 0

2013/8/31

2013/9/1

2013/9/2

2013/9/3

(d)

3

PM2.5 (μg/m )

200

100 50

2013/9/1

2013/9/2

100 50

2013/9/3

2013/11/12

2013/11/13

2013/11/14

Date

Date

(b)

(e)

200

250

150

200

100 50

2013/9/2

2013/9/3

2013/11/15

150 100 50 0

2013/9/1

2013/11/15

150

0

2013/8/31

2013/11/14

(a) 150

0 2013/8/30

2013/11/13

Date

250

2013/8/31

2013/11/12

Date

3

3

PM2.5 (μg/m )

150

200

0 2013/8/30

3

3

100

0 2013/8/30

PM10 (μg/m )

PM1 (μg/m )

200

PM10 (μg/m )

3

PM1 (μg/m )

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

2013/11/12

2013/11/13

2013/11/14

Date

Date

(c)

(f)

2013/11/15

Fig. 2. Diurnal variation of PM concentration in tunnels of Line A and Line B (a–c) illustrated diurnal variation of PM1, PM2.5 and PM10 in the tunnel of Line A, respectively, while (d–f) illustrated diurnal variation of PM1, PM2.5 and PM10 in the tunnel of Line B, respectively.

the traffic peak hours. Once trains went out of service, PM concentration fell back to lower levels. Nevertheless, the PM on Nov 14th went against the regular pattern due to the sudden rise of outdoor PM, illustrated in Fig. 3. The daily pattern of particulate matter concentrations with two maxima observed during weekdays and a nearly constant value lasted during weekends have been concluded as well in Paris (Raut et al., 2009), Washington (Birenzvige et al., 2003) and so on, which was coincident with the results of this study. Moreover, our research group also found out that black carbon (short as BC) concentrations monitored from May 10th to May 26th, 2012 in another station of Line A followed the same diurnal variation with this study (Cai et al., 2012). In accordance with Shanghai Metro (http://www.shmetro.com/), rush hours for two lines were given in Table 4, respectively. Furthermore, the time interval of two trains for both lines was 3–7 min during peak hours and 4–12 min during offpeak hours, respectively. As seen in Fig. 2, the hours of high PM concentration in subway on Aug 30th (Friday), Sep 1st (Sunday), Sep 2nd (Monday), Nov 12th (Tuesday) and Nov 13th (Wednesday) were rather consistent with their rush hours, while those of Aug 31st (Saturday) and Nov 14th (Thursday) fitted less. Independent samples t-test was conducted for PM concentrations between peak hours and off-peak hours, results concluded that there were significant statistical differences (p < 0.05) for PM2.5 and PM10 for both lines, respectively. On the other hand, PM1 presented a different pattern, with p > 0.05 for Line A and p < 0.05 for Line B between rush hours and non-rush hours, respectively. The explanation of PM1 presenting insignificant differences in the tunnel of Line A might be associated with its longer residence time existing in the ambient air due to its smaller diameter. On the contrary, the interpretation of significant differences of PM1 for Line B was that anthropogenic influence was more notable to the tunnel without PSDs. Weekly variation of PM concentration was illustrated in Fig. 3. Seen from Fig. 3(a), notable weekend effect could be observed for the PM in all size sections. Analytical results of one way ANOVA and Duncan’s multiple comparison analysis methods among different size fractioned particles stated that: (1) PM concentration for Line A could be classified into three statistically significant levels: The first level was on Sunday (Sep 1st), which was with the lowest PM concentration, the second one was on Saturday (Aug 31st) and Monday (Sep 2nd), which were with relative higher PM concentration, and the last one was on Friday (Aug 30th), which was with the highest PM concentration; (2) PM concentration for Line B could be classified into four statistically significant levels: The first level was on Wednesday (Nov 13th), with the lowest PM concentration, the second one was on Tuesday (Nov 12th) and Monday (Nov 11th), with relative lower PM concentration, the third

142

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

300

160

PM1

PM1

140

250

PM10

Outdoor PM10

100 80 60

3

Outdoor PM2.5

Concentration(μg/m )

120

PM10

Outdoor PM1

Outdoor PM1

3

Concentration(μg/m )

PM2.5

PM2.5

200

Outdoor PM2.5 Outdoor PM10

150

100

40 50 20 0

0 Friday

Saturday

Sunday

Monday

Monday

Tuesday

Wednesday Thursday

Date

Date

(a) Line A

(b) Line B

Friday

Fig. 3. Weekly variation of PM concentration in tunnels of Line A and Line B.

Table 4 Rush hours for Line A and Line B. Date

Weekdays

Line A

Monday–Thursday Friday

Weekends

Line B

Morning

Evening

Morning

Evening

07:00–09:30 07:00–09:30

16:30–19:30 14:00–21:00

07:00–09:30 07:00–09:30

17:00–19:30 14:00–20:30

08:00–21:00

08:00–20:30

level was on Thursday (Nov 14th), with relative higher PM concentration, and the last one was on Friday (Nov 15th), with the highest PM concentration; (3) Friday was the highest day of PM in monitoring periods for both Line A and Line B. These results were coincident with the conclusions of the studies invested by Mugica-Alvarez et al. (2012) and Raut et al. (2009). However, variation of PM concentration throughout whole week was not available due to the short monitoring time. The reason for the daily mean concentration of Friday was higher than that of Monday might be associated with the different outdoor concentration and traffic flow. In addition, it is worthwhile noting that overhaul was proceeded in tunnels on the evening of every Monday and Thursday. Regular overhaul in tunnels contains rail maintenance, welding, routine watch and so on, which might lead to higher concentration.

Ratios of PMs in tunnels Ratios of PM1/PM10 and PM2.5/PM10 in subway tunnels were 0.65–0.90 and 0.74–0.95 for Line A, respectively, and those were 0.49–1.00 and 0.56–1.00 for Line B, respectively. PM1/PM10 and PM2.5/PM10 were higher when subway went out of service. When trains started operation, the ratios of PM1/PM10 and PM2.5/PM10 fell to the minimum value, which was explained as amount of coarse particles generated. The ratios rose slightly during the period of trains operation, but still sustained at a low level. According to the linear regressions illustrated in Fig. 4, slopes between PM1 and PM10 as well as between PM2.5 and PM10 were 0.80 and 0.86 for Line A, respectively, while corresponding values were 0.95 and 0.97 for Line B, respectively. The value of PM2.5/PM10 in Taipei subway station ranged from 0.67 to 0.78 (Cheng et al., 2008); PM2.5/PM10 were 0.76 on the ground and 0.73 underground in Los Angeles station (Kam et al., 2011), respectively, while ratios of PM2.5/PM10 were 0.79 in Guangzhou (Chan et al., 2002b) and 0.72–0.78 in Hong Kong (Chan et al., 2002a) subway station, respectively, which were slightly below the results of this study. The interpretation for high PM1/PM10 and PM2.5/PM10 in this study might be attributed to the high filtering efficiency on coarse particles and low removal efficiency on fine particles of ventilation systems (Chan et al., 2002a) and PSD systems (Kim et al., 2012) in subway stations. Furthermore, fine particles had longer residence time than coarse particles suspending in atmosphere. It is important to note that non-installation of PSDs in the tunnel of Line B might be attributed to the higher ratios as traffic emissions could give more weight. Apart from that, different ratios of PM1/PM10 and PM2.5/PM10 in different subway environments could be associated with different power systems, braking systems, ventilation systems and operating conditions in subway microenvironments. Sources of subway PM could be classified into two categories, that is external sources and internal sources. External PM primarily came from traffic emissions surrounding the subway, while internal sources (Kim et al., 2008) mainly produced from the mechanical wear during the period of trains running or braking, maintenance as well as construction work in

143

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

250

140 120

PM 1 = 0.80 PM 10 − 2.46

PM1 (μg/m )

R 2 = 0.973 3

3

PM1 (μg/m )

PM 1 = 0.95 PM 10 − 9.16

200

R 2 = 0.968

100 80 60

150

100

40 50 20 0

0

20

40

60

80

100

120

140

160

0

180

0

50

PM10 (μg/m3)

100

150

200

250

200

250

PM10 (μg/m3)

(a)

(c) 250

160 140

PM 2.5 = 0.97 PM 10 − 7.37

R 2 = 0.983

PM2.5 (μg/m )

100

3

3

PM2.5 (μg/m )

200

PM 2.5 = 0.86 PM 10 − 1.72

120

80 60

R 2 = 0.983 150

100

40 50 20 0

0

20

40

60

80

100

120

140

160

180

3

PM10 (μg/m )

(b)

0

0

50

100

150

PM10 (μg/m3)

(d)

Fig. 4. Linear regressions of PMs in tunnels of Line A and Line B (a and b) illustrated linear regressions between PM1 and PM10 as well as between PM2.5 and PM10 in the tunnel of Line A, respectively, while (c and d) illustrated linear regressions between PM1 and PM10 as well as between PM2.5 and PM10 in the tunnel of Line B, respectively.

tunnels, etc. High correlation (Cheng et al., 2008; Cheng and Lin, 2010; Kam et al., 2011; Mugica-Alvarez et al., 2012) of indoor and outdoor PM concentration of subway stations demonstrated that outdoor particles could enter into stations through ventilation systems, escalators or corridors, remarkably affecting the indoor air quality. Aarnio et al. (2005) figured out that the correlation between subway PM count concentration and outdoor BC was good (R2 = 0.71), indicating that the major source of subway fine particle was from vehicle exhaust. On account of mechanical wear of steel in the operating and braking of trains, PM in the subway environment was enriched with large proportion of metal elements due to mass iron and other trace alloy elements (such as Mn, Cr and Ni) in steel (Kang et al., 2008). PM ingredients (Aarnio et al., 2005; MugicaAlvarez et al., 2012) illustrated that the coarse particles sampled in the subway were mainly metal particles, which was possibly originated from mechanical wear between the conductor rails and electrodes, brake pads, tracks and wheels (Kang et al., 2008). Moreover, coarse particles might come from the particle re-suspension caused by passengers’ movement in the stations (Cheng and Lin, 2010). Subway PM concentration was primarily affected by the ventilation, meteorology, train frequency, passenger flow and so on (Birenzvige et al., 2003). Composition of metal elements of the floor dust particles in the tunnel A summary of the concentrations of different metal elements in the particles, sampled inside subway station, was given in Table 5. Relative abundance of metal elements in floor dust particles in this study was comparatively coincidence with those of airborne particles illustrated in Table 5, with a little exception of Ni, Pb and Hg. Taking the fact into consideration that dust deposited was mixing by piston effect caused by the movement of trains and then the secondary dust was generated (Sysalova and Szakova, 2006), floor dust particles in tunnels might be a major source of airborne PM in subway microenvironments. Ni is known to be a marker of oil incineration (Song et al., 2001). Pb is emitted from vehicle emissions of lead

144

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

Table 5 Relative abundance of metal elements in PM (unit: mg/g; means and ranges) in the subway station. Location

This study

Beijing, 2011

Helsinki, 2005

Rome, 2006

Prague, 2006

Buenos, 2009

Seoul, 2012

Mexico, 2012

Mg Al Cr Mn Fe Ni Cu Zn Pb Ca Hg

1.6–4.0 8.5–33 0.17–0.32 1.7–2.7 270–480 0.037–0.069 0.44–0.63 0.48–0.83 0.0042–0.038 13–36 0.00016–0.00031

– 3.2 – 1.1 12 – – – 1.8 26 –

– 4.4 0.94 4.9 450 0.54 1.9 0.54 0.21 2.3 –

– – 1.2 1.6 100 0.2 6.2 1.4 0.3 – 0.04

– – 0.204 1.415 – 0.111 – 1.724 0.749 – –

– – – – 23–740 – 1.2–5.8 0.19–1.7 – – –

72 3.0 16 3.7 450 9.7 3.0 35 15 – –

– – 0.27–0.48 0.30–0.49 40–76 0.088–0.24 3.3–12 1.9–.3.2 – – –

gasoline (Manoli et al., 2002). Major global anthropogenic Hg emission sources are combustion of fossil fuels as well as gold production using Hg technology (Pacyna et al., 2006). Fixation of PSD system in the tunnel of Line A restricted the input of external sources, which might partly account for the discrepancy of Ni, Pb and Hg. In this study, Fe was the most abundant metal found in all floor dust particles, followed by Ca, Al, Mg, Mn, Zn, Cu, Cr, Ni, Pb and Hg. Jung et al. (2010) and Kang et al. (2008) applied low-Z particle electron probe X-ray microanalysis to characterize the subway PM, noting that all particles could be classified into four major types based on their chemical compositions, that is Fe-containing, soil-derived, carbonaceous, and the secondary nitrate and/or sulfate particles. Moreover, Fe-containing particles were the most frequently encountered type ranging from 61% to 91%. Studies (Furuya et al., 2001; Chillrud et al., 2004; Birenzvige et al., 2003) reported that transition metals such as Mn, Cr, and Cu as well as Fe, were always enriched in the subway particles in comparison with those appeared in ambient particles, while other metal elements were not always enriched. Fe concentrations measured inside Mexico subway were 2.7 ± 0.6 and 1.7 ± 0.4 times greater for PM10 and PM2.5 than concentrations measured outside, respectively, while Cu inside the subway were 9 ± 4 and 5 ± 2 times higher than the outside value, respectively (Mugica-Alvarez et al., 2012). Enrichment factors (short as EFs, represent for a ratio of indoor/outdoor metal concentrations) of Fe, Zn and Cu in Buenos Aires underground system were 11–96, 2–13 and 1–50, respectively (Murruni et al., 2009). The Fe-containing subway particles are mainly generated from the mechanical wear and friction processes at rail-wheel-brake interfaces. Another origin of Fe-containing particles was from the interaction between catenaries providing electricity to subway trains and pantographs attached to trains. Elements such as Al, Ca and Mg might be associated with building material of the tunnels, including gravel underneath the railway tracks (Kang et al., 2008; Lorenzo et al., 2006). This indicated that underground aerosol was formed mainly by internal sources and ambient aerosol had rather marginal contribution to the aerosol mass (Kang et al., 2008). Murruni et al. (2009) proposed that Zn in the underground had its main source in vehicular traffic, while Cu originated from the wear of the catenaries which provide the electricity to subway trains. Metal elements such as Fe, Mn and Cr are indicators of railroad track abrasion, so that the ratio of Fe/Mn was used to evaluate the impact of a railroad on atmospheric PM (Bukowiecki et al., 2007). In this study, Fe/ Mn concentration ratios ranged from 129 to 200, which was similar to the typical ratios obtained for elemental compositions of the rails and electric sliding collectors (Salma et al., 2007).

Conclusions The particulate matter and microclimate parameters were monitored in the tunnels at a subway station in Shanghai, China, focusing on T, RH, PM1, PM2.5 and PM10. Collected floor dust particles in the tunnel were analyzed for their metal elemental composition. The following main conclusions were drawn: (1) Median concentrations of T, RH, PM1, PM2.5 and PM10 in the subway tunnel were 31.2 °C, 45.0%, 50 lg/m3, 55 lg/m3 and 69 lg/m3 for Line A, respectively, while those were 29.4 °C, 29.6%, 42 lg/m3, 46 lg/m3 and 58 lg/m3 for Line B, respectively. (2) Strong correlations occurred between all PM levels and the meteorological parameters in the tunnel of Line A, while correlations between such parameters in the tunnel of Line B were insignificantly, which was probably due to the PSDs installation. (3) Results of independent samples t-test indicated that there was significant difference between peak hours and off-peak hours for both PM2.5 and PM10 obtained at two lines (p < 0.05). On the other hand, PM1 showed a different pattern, with p > 0.05 for Line A and p < 0.05 for Line B, respectively, which might be associated with longer residence time of PM1 in the atmosphere and isolation of anthropogenic influence by PSDs. (4) Notable weekly variation could be observed for the PM in all size fractions during monitoring periods for both lines. In addition, PM of Friday was highest in both campaigns, indicating that subway PM concentration was associated with train frequency, passenger flow and internal maintenance. (5) Ratios of PM1/PM10 and PM2.5/PM10 in tunnels were 0.65–0.90 and 0.74–0.95 for Line A, respectively, while those were 0.49–1.00 and 0.56–1.00 for Line B, respectively, which were high when trains were out of service and low when trains were in service. This phenomenon might be associated with plenty of coarse particles generated by mechanical grinding in the process of train driving, so that the enhanced PM10

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

145

concentration occurred under this circumstance. (6) Relative abundance of metal elements in floor dust particles proved that floor dust particles in tunnels might be a major source of airborne PM in the subway microenvironments, with Fe as the most abundant metal, followed by Ca, Al, Mg, Mn, Zn, Cu, Cr, Ni, Pb and Hg. Acknowledgements Acknowledge to Shanghai municipal education committee, for provision of research funding (12ZZ054). Thanks to personnel and equipment support for this research provided by Shanghai An-He Environmental Testing Technique Co., Ltd. Thanks to field sampling support offered by Technical Center of Shanghai Shentong Metro Group Co., Ltd. References Aarnio, P., Yli-Tuomi, T., Kousa, A., Makela, T., Hirsikko, A., Hameri, K., Raisanen, M., Hillamo, R., Koskentalo, T., Jantunen, M., 2005. The concentrations and composition of and exposure to fine particles (PM2.5) in the Helsinki subway system. Atmos. Environ. 39 (28), 5059–5066. http://dx.doi.org/10.1016/j. atmosenv.2005.05.012. Adams, H.S., Nieuwenhuijsen, M.J., Colvile, R.N., 2001a. Determinants of fine particle(PM2.5) personal exposure levels in transport microenvironments, London, UK. Atmos. Environ. 35 (27), 4557–4566. http://dx.doi.org/10.1016/S1352-2310(01)00194-7. Adams, H.S., Nieuwenhuijsen, M.J., Colvie, R.N., McMullen, M.A.S., Khandelwal, P., 2001b. Fine particle (PM2.5) personal exposure levels in transport microenvironments, London, UK. Sci. Total Environ. 279 (1–3), 29–44. http://dx.doi.org/10.1016/S0048-9697(01)00723-9. Birenzvige, A., Eversole, J., Seaver, M., Francesconi, S., Valdes, E., Kulaga, H., 2003. Aerosol characteristics in a subway environment. Aerosol Sci. Technol. 37 (3), 210–220. http://dx.doi.org/10.1080/02786820300941. Bukowiecki, N., Gehrig, R., Hill, M., Lienemann, P., Zwicky, C.N., Buchmann, B., Weingartner, E., Baltensperger, U., 2007. Iron, manganese and copper emitted by cargo and passenger trains in Zurich (Switzerland): size-segregated mass concentrations in ambient air. Atmos. Environ. 41 (4), 878–889. http://dx. doi.org/10.1016/j.atmosenv.2006.07.045. Cai, J., Xiu, G.L., Zhang, D.N., Zhang, D.N., Yang, J., Zheng, Y., Chen, Y.X., Wang, Z.W., 2012. Concentration levels and personal exposure characteristics of black carbon in a Shanghai subway station. Res. Environ. Sci. 25 (12), 1328–1335. http://dx.doi.org/10.13198/j.res.2012.12.20.caij.006. 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. Atmos. Environ. 36 (21), 3363–3373. http://dx.doi.org/10.1016/S1352-2310(02)00318-7. 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. Atmos. Environ. 36 (38), 5831–5840. http://dx.doi.org/10.1016/S1352-2310(02)00687-8. Cheng, Y.H., Lin, Y.L., 2010. Measurement of particle mass concentrations and size distributions in an underground station. Aerosol Air Qual. Res. 10, 22–29. http://dx.doi.org/10.4209/aaqr.2009.05.0037. Cheng, Y.H., Lin, Y.L., Liu, C.C., 2008. Levels of PM10 and PM2.5 in Taipei Rapid transit system. Atmos. Environ. 42 (31), 7242–7249. http://dx.doi.org/10.1016/ j.atmosenv.2008.07.011. Chillrud, S.N., Epstein, D., Ross, J.M., Sax, S.N., Pederson, D., Spengler, J.D., Kinney, P.L., 2004. Elevated airborne exposures to manganese, chromium and iron of teenagers from steel dust and New York City’s subway system. Environ. Sci. Technol. 38 (3), 732–737. http://dx.doi.org/10.1021/es034734y. Cho, J.H., Min, K.H., Paik, N.W., 2006. Temporal variation of airborne fungi concentrations and related factors in subway stations in Seoul, Korea. Int. J. Hyg. Environ. Health 209 (3), 249–255. http://dx.doi.org/10.1016/j.ijheh.2005.10.001. Coke, L.R., Sanchezj, J.G., Policastro, A.J., 2000. A model for dispersion of contaminants in the subway environment. In: Proceedings of 10th International Conference, Aerodynamics and Ventilation of Vehicles Tunnels, Boston, MA (US). Feng, Y.L., Mu, C.C., Zhai, J.Q., Li, J., Zou, T., 2010. Characteristics and personal exposures of carbonyl compounds in the subway stations and in-subway trains of Shanghai, China. J. Hazard. Mater. 183 (1–3), 574–582. http://dx.doi.org/10.1016/j.jhazmat.2010.07.062. Furuya, K., Kudo, Y., Okinagua, K., Yamuki, M., Takahashi, S., Araki, Y., Hisamatsu, Y., 2001. Seasonal variation and their characterization of suspended particulate matter in the air of subway stations. Trace Microprobe Tech. 19 (4), 469–485. http://dx.doi.org/10.1081/TMA-100107583. Jung, H.J., Kim, B.W., Ryu, J.Y., 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. Atmos. Environ. 44 (19), 2287–2293. http://dx.doi.org/10.1016/j. atmosenv.2010.04.003. Kam, W., Cheung, K., Daher, N., Sioutas, C., 2011. Particulate matter (PM) concentrations in underground and ground-level rail systems of the Los Angeles Metro. Atmos. Environ. 45 (8), 1506–1516. http://dx.doi.org/10.1016/j.atmosenv.2010.12.049. Kang, S., Hwang, H.J., Park, Y.M., Kim, H.K., Ro, C.U., 2008. Chemical compositions of subway particles in Seoul, Korea determined by a quantitative single particle analysis. Environ. Sci. Technol. 42 (24), 9051–9057. http://dx.doi.org/10.1021/es802267b. Karlsson, H.L., Nilsson, L., Moller, L., 2005. Subway particles are more genotoxic than street particles and induce oxidative stress in cultured human lung cells. Chem. Res. Toxicol. 18 (1), 19–23. http://dx.doi.org/10.1021/tx049723c. Kim, K.H., Ho, D.X., Jeon, J.S., Kim, J.C., 2012. A noticeable shift in particulate matter levels after platform screen door installation in a Korean subway station. Atmos. Environ. 49, 219–223. http://dx.doi.org/10.1016/j.atmosenv.2011.11.058. Kim, K.Y., Kim, Y.S., Roh, Y.M., Lee, C.M., Kim, C.N., 2008. Spatial distribution of particulate matter (PM10 and PM25) in Seoul Metropolitan Subway stations. Hazard. Mater. 154 (1–3), 440–443. http://dx.doi.org/10.1016/j.jhazmat.2007.10.042. Lorenzo, R., Kaegi, R., Gehrig, R., Grobety, B., 2006. Particle emissions of a railway line determined by detailed single particle analysis. Atmos. Environ. 40 (40), 7831–7841. http://dx.doi.org/10.1016/j.atmosenv.2006.07.026. Ma, H., Shen, H., Liang, Z., Zhang, L.C., Xia, C., 2014. Passengers’ Exposure to PM2.5, PM10 and CO2 in typical underground subway platforms in Shanghai. In: Proceedings of the 8th International Symposium on Heating, Ventilation and Air Conditioning Lecture Notes in Electrical Engineering 261, pp. 237–245. doi: http://dx.doi.org/10.1007/978-3-642-39584-0_26. Manoli, E., Voutsa, D., Samara, C., 2002. Chemical characterization and source identification/apportionment of fine and coarse air particles in thessaloniki, Greece. Atmos. Environ. 36 (6), 949–961. http://dx.doi.org/10.1016/S1352-2310(01)00486-1. Mugica-Alvarez, V., Figueroa-Lara, J., Romero-Romo, M., Sepulveda-Sanchez, J., Lopez-Moreno, T., 2012. Concentrations and properties of airborne particles in the Mexico City subway system. Atmos. Environ. 49, 284–293. http://dx.doi.org/10.1016/j.atmosenv.2011.11.038. Murruni, L.G., Solanes, V., Debray, M., Kreiner, A.J., Davidson, J., Davidson, M., Vazquez, M., Ozafran, M., 2009. Concentrations and elemental composition of particulate matter in the Buenos Aires underground system. Atmos. Environ. 43 (30), 4577–4583. http://dx.doi.org/10.1016/j.atmosenv.2009.06.025. Pacyna, E.G., Pacuna, J.M., Steenhuisen, F., Wilson, S., 2006. Global anthropogenic mercury emission inventory for 2000. Atmos. Environ. 40 (22), 4048–4063. http://dx.doi.org/10.1016/j.atmosenv.2006.03.041. Park, D.U., Ha, K.C., 2008. Characteristics of PM10, PM2.5, CO2 and CO monitored in interiors and platforms of subway train in Seoul, Korea. Environ. Int. 34 (5), 629–634. http://dx.doi.org/10.1016/j.envint.2007.12.007. Park, H.S., Youlin, J., Lim, S.B., Woo, J.H., Kim, S., Sunwoo, Y., Piao, M., 2009. Design issues of integrated subway air quality management system base on particulate matter measurements. In: Proceedings of 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human, pp. 830–834. doi: http://dx.doi.org/10.1145/1655925.1656076.

146

T. Qiao et al. / Transportation Research Part D 41 (2015) 136–146

Pope, C.A., Burnett, R.T., Thun, M.J., Calle, E.E., Krewski, D., Ito, K., Thurston, G.D., 2002. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Am. Med. Assoc. 287 (9), 1132–1141. http://dx.doi.org/10.1001/jama.287.9.1132. Pope, C.A., Burnett, R.T., Thurston, G.D., Thun, M.J., Calle, E.E., Krewski, D., Godleski, J.J., 2004. Cardiovascular mortality and long-term exposure to particulate air pollution: epidemiological evidence of general pathophysiological pathways of disease. Circulation 109, 71–77. http://dx.doi.org/10.1161/01. CIR.0000110643.19575.79. Raut, J.C., Chazette, P., Fortain, A., 2009. Link between aerosol optical, microphysical and chemical measurements in an underground station in Paris. Atmos. Environ. 43 (4), 860–868. http://dx.doi.org/10.1016/j.atmosenv.2008.10.038. Rycroft, R.J.G., 1985. Low humidity and microtrauma. Am. J. Ind. Med. 8 (4–5), 371–373. http://dx.doi.org/10.1002/ajim.4700080417. Rycroft, R.J.G., Smith, W.D.L., 1980. Low humidity occupational dermatoses. Contact Dermatitis 6 (7), 488–492. http://dx.doi.org/10.1111/j.1600-0536.1980. tb05572.x. Sahin, U.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. Transp. Res. Part D 17 (1), 48–53. http://dx.doi.org/10.1016/j.trd.2011.09.003. Salma, I., Weidinger, T., Maenhaut, W., 2007. Time-resolved mass concentration, composition and sources of aerosol particles in a metropolitan underground railway station. Atmos. Environ. 41 (37), 8391–8405. http://dx.doi.org/10.1016/j.atmosenv.2007.06.017. Song, X.H., Polissar, A.V., Hopke, P.K., 2001. Source of fine particle composition in the northeastern US. Atmos. Environ. 35 (31), 5277–5286. http://dx.doi. org/10.1016/S1352-2310(01)00338-7. Sysalova, J., Szakova, J., 2006. Mobility assessment and validation of toxic elements in tunnel dust samples-subway and road using sequential chemical extraction and ICP-OES/GF AAS measurements. Environ. Res. 101 (3), 287–293. http://dx.doi.org/10.1016/j.envres.2005.10.001. Wang, X.L., Chancellor, G., Evenstad, J., Farnsworth, J.E., Hase, A., Olson, G.M., Sreenath, A., Agarwal, J.K., 2009. A novel optical instrument for estimating size segregated aerosol mass concentration in real time. Aerosol Sci. Technol. 43 (9), 939–950. http://dx.doi.org/10.1080/02786820903045141. Ye, X.J., Lian, Z.W., Jiang, C.X., Zhou, Z.X., Chen, H.X., 2010. Investigation of indoor environmental quality in Shanghai metro stations, China. Environ. Monit. Assess. 167, 643–651. http://dx.doi.org/10.1007/s10661-009-1080-9. Yu, Q., Lu, Y., Xiao, S., Shen, J.X., Li, X., Ma, W.C., Chen, L.M., 2012. Commuters’ exposure to PM1 by common travel modes in Shanghai. Atmos. Environ. 59, 39–46. http://dx.doi.org/10.1016/j.atmosenv.2012.06.001. Yuan, F.D., You, S.J., 2007. CFD simulation and optimization of the ventilation for subway side-platform. Tunn. Undergr. Space Technol. 22 (4), 474–482. http://dx.doi.org/10.1016/j.tust.2006.10.004.