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Characteristics of particulate matter (PM) concentrations influenced by piston wind and train door opening in the Shanghai subway system
Transportation Research Part D 47 (2016) 77–88
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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Characteristics of particulate matter (PM) concentrations influenced by piston wind and train door opening in the Shanghai subway system Jiajia Wang a,b, Laijun Zhao a,b,⇑, Daoli Zhu a,b, H. Oliver Gao a,b,c, Yujing Xie d, Huiyong Li d, Xiang Xu b, Hongbo Wang a,b a
Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, PR China Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, PR China School of Civil and Environmental Engineering, Cornell University, NY 14853, USA d School of Management, Shanghai University, Shanghai 200444, PR China b c
a r t i c l e
i n f o
Article history:
Keywords: Subway system Particulate matter Piston wind Train door opening Correlation analysis
a b s t r a c t More than 9 million passengers take Shanghai’s subway system every work day. The system’s air quality has caused widespread concern because of the potential harm to passengers’ health. We measured the particulate matter (PM) concentrations at three kinds of typical underground platform (side-type, island-type, and stacked-type platforms) and inside the trains in Shanghai’s metro during 7 days of measurements in April and July 2015. Our results demonstrated that the patterns of air quality variation and PM concentrations were similar at the side-type and island-type platforms. We also found that the PM concentrations were higher on the platforms than inside the train and that the PM concentrations in the subway system were positively correlated with those in the ambient air. Piston wind generated by vehicle motion pushes air from the tunnel to the platform, so platform PM concentrations increase when trains approach the platform. However, the piston wind effect varies greatly between locations on the platform. In general, the effect of the piston wind is weaker at the middle of the platform than at both ends. PM concentrations inside the train increase after the doors open, during which time dirty platform air floods into the compartments. PM1.0 and PM2.5 were significantly correlated both inside the train and on the platforms. PM1.0 accounted for 71.9% of PM2.5 inside the train, which is higher than the corresponding platform values. Based on these results, we propose some practical suggestions to minimize air pollution damage to passengers and staff from the subway system. Ó 2016 Elsevier Ltd. All rights reserved.
Introduction In recent years, outbreaks of large-scale urban haze and smog have raised increasing concern about the relationship between air quality and human health. Many studies have revealed that bad air conditions can greatly damage human health (Hoek et al., 2002; Glinianaia et al., 2004; Pope and Dockery, 2006; Bayraktar et al., 2010; Pui et al., 2014). Efforts to reduce traffic congestion have led to increased development of commuter rail traffic in many large cities, and the number of the ⇑ Corresponding author at: Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, PR China. E-mail address: [email protected] (L. Zhao). http://dx.doi.org/10.1016/j.trd.2016.05.006 1361-9209/Ó 2016 Elsevier Ltd. All rights reserved.
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subway passengers is increasing. Although this increases the public’s travel convenience, subway air quality has become a concern because of its potential impact on health. Some studies have specifically investigated the health hazards caused by the subway environment and the damage to human health caused by air pollution in the subway system; the results suggest that the risk is much more severe than that in public residential buildings. For example, Karlsson et al. (2005) discovered that the particles from a subway station were approximately eight times more genotoxic and four times more likely to cause oxidative stress in lung cells than the particles from a nearby populated urban street. Chan et al. (1991) investigated commuter exposure to pollutants in the Boston metro, and their study represented the first comprehensive research on population exposure to pollutants in a metro system. Subsequently, researchers assessed the air pollution levels in subway systems around the world. Johansson and Johansson (2003) monitored the particulate matter (PM) concentrations at an underground metro station in Stockholm. They found that the PM concentrations on the platform closely followed the intensity of subway train traffic and that the average PM2.5 and PM10 concentrations were (respectively) 10 and 5 times the corresponding values measured in one of the busiest streets in Stockholm. Park and Ha (2008) investigated the PM2.5 and PM10 concentrations in the Seoul metro system and showed that PM concentrations inside trains were significantly higher than those on platforms, and that this was caused by the lack of a mechanical ventilation system inside the subway trains. Kam et al. (2011) measured the PM concentrations in an underground subway line, as well as in a ground-level light-rail line in the Los Angeles metro system. They found that commuters in the underground line were exposed to PM2.5 and PM10 with average values 1.8 and 1.9 times (respectively) the values experienced by commuters who used the light-rail line. Some studies focused on PM chemical compositions or sources. For example, Aarnio et al. (2005) measured the PM2.5 concentration in the Helsinki subway system. They analyzed the elemental composition of the PM2.5 samples and found that the most enriched element in the samples was iron. Salma et al. (2007) collected aerosol samples of PM2.0 and PM10–2.0 at a metropolitan underground subway station in downtown Budapest and discovered that the primary PM sources were friction between the rails and the train’s wheels and mechanical wear. Nieuwenhuijsen et al. (2007) reviewed and summarized previous research results and found that differences among the studies may have resulted from differences in the composition of the wheels, in the brake systems, and in ventilation levels. Another important factor is that trains running in a tunnel produce a piston effect, which is closely related to subway ventilation and energy consumption by the environmental control system. Thus, many researchers have studied the subway piston effect in terms of its mechanism, numerical modeling for the air flow characteristics, and the factors that influence the piston wind, with the goal of improving the design of ventilation air shafts and maximizing use of the piston wind to reduce energy consumption by natural ventilation (Lin et al., 2008; Pan et al., 2013; López González et al., 2014). Other scholars found that the piston wind affects the air quality on subway platforms. Salma et al. (2007) discovered that the PM concentrations on the platform increased as a train entered the station because it pushed in polluted air from the tunnel, whereas PM concentrations on the platform decreased as the train departed the station because it pulled in cleaner air from the vestibule and corridor. Moreno et al. (2014) monitored the air quality on several subway station platforms in Barcelona, and they found that PM concentrations were highly variable due to differences in tunnel ventilation conditions, the magnitude of the piston effect and station designs. The first Shanghai Metro Line began operation in April 1995, and 14 subway lines with 366 stations were operating as of December 2015. The length of the subway network now totals about 617 km. The passenger flow is more than 9 million people daily, and the number is increasing year by year. Therefore, it is important to obtain information about the air quality in Shanghai’s subway system and its potential influence on these passengers. As a result, some scholars have studied the environmental quality in the Shanghai metro. Ye et al. (2010), Ma et al. (2014) and Lu et al. (2015) separately measured PM concentrations at two underground subway platforms of Shanghai metro Line 9, some platforms along Shanghai metro Line 1 and Line 2, and three platforms of Shanghai subway Line 7. Yu et al. (2012) measured PM1.0 concentrations and compared commuters’ exposure during different travel modes (e.g., subway, bus, bicycle). Qiao et al. (2015) measured PM concentrations in tunnels of two lines of Shanghai’s metro system. As in previous studies of other cities’ subway systems around the world, these studies of Shanghai’s subway mainly focused on air quality by comparing pollutant levels in the subway system with levels recorded at ambient monitoring sites. Regarding the subway system, the comparison in these studies was focused on peak vs. off-peak periods, and weekday vs. weekend periods. In addition, the measurement campaigns mostly targeted the correlations between particles in different size classes and proportions of the total, as well as the sources and chemical composition of the particulate matter. In this paper, we measured the PM2.5 and PM1.0 concentrations in Shanghai’s metro Line 10, which has not been previously studied. Almost all of the Shanghai metro stations have platform screen doors (i.e., protective barriers between the platform and the tracks) installed, as is the case for Line 10. The side-type and island-type platforms are the dominant station forms in Shanghai’s metro system. We measured the PM concentrations at stations with these two platform types to investigate the effects of the piston wind for stations with platform screen doors, since previous studies of the influence of the piston wind on subway air quality didn’t include stations with such doors. Moreover, we measured the PM concentrations inside the subway trains and considered the effect of the train door’s opening on air quality inside the subway carriages, a factor that was not included in previous studies. Most importantly, after the subway lines have been established, the location, depth, and other design features of the stations cannot be changed. Based on our particle concentration measurements, we propose ways to minimize the damage to human health under the current subway design.
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Study locations and measurements Background description Shanghai’s metro Line 10 started operation in 2010, and the entire line is under the ground; it now stretches 36 km, with 31 stations on the line. The starting station is Xinjiangwancheng, which is located in northeastern Shanghai (Fig. 1). The final station, in southwestern Shanghai, is Hongqiao Railway station, which differs from the terminal for a branch line (Hangzhong Road station), which is also located in southwestern Shanghai. Since Line 10 passes through Xintiandi, Yuyuan Gardens, Nanjing Road, Huaihai Road, Wujiaochang, and other parts of Shanghai’s central region, it is known as the ‘‘golden” line. In this study, we measured the PM2.5 and PM1.0 concentrations inside the subway trains of Line 10 and at two typical stations: Songyuan Road station and Jiao Tong University station (Fig. 1). Songyuan Road station is located in Changning District, and is a side-type platform; that is, the tracks run through the center of the station and platforms are positioned on both sides of the tracks (Fig. 2a). Jiao Tong University station is located in Xuhui District, and is an island-type platform; that is, the platform is in the center and tracks run on both sides of the platform (Fig. 2b). Jiao Tong University station is a transfer station, where passengers can transfer to Line 11. The linear distance between these two stations is about 2.2 km. Fig. 1 shows the location of the Jing’an ambient air sampling site; sampling is performed on the 6th floor roof of a building. This is one of 10 state-run air sampling sites in the Shanghai area and is the nearest sampling site to the two subway stations that we studied; its linear distances from the Songyuan Road station and Jiao Tong University station are about 3.6 and 2.5 km, respectively. Fig. 1 also shows the location of the Caoyang Road station on Line 11. This is a stacked-type platform; that is, there are two platforms and two tracks, but rather than being on the same level, they are located in the upper and lower levels of the station. Passengers who want to take a train in the opposite direction need to go upstairs or downstairs to transfer. This design provides an environment that permitted measurements of the impact of train operation in a single direction on the platform’s PM concentration.
Fig. 1. Shanghai subway Line 10 and Line 11, the locations of the two sampling stations and of the Caoyang Road station, and the location of the Jing’an ambient air sampling site.
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Fig. 2. Pictures of (a) the side-type platform at Songyuan Road station, (b) the island-type platform at Jiao Tong University station, and (c) the stacked-type platform at Caoyang Road station.
Measurements We used two portable DustTrak aerosol monitors (model 8532; http://www.tsi.com/Products/) to measure the concentrations of PM2.5 and PM1.0 at a logging interval of 1 s. The sampling height for all measurements was about 1.5 m, which corresponds to the breathing zone. We calibrated the instruments at the beginning of our experimental campaign using the values from a Thermo Scientific TEOM 1405 as reference values. Comparisons between the two instruments were done both before and after each daily use to ensure that the instruments used for data collection were accurate; we also performed zero checking before every survey trip. The measurement campaign was divided into two phases: The first phase lasted 4 days, and was conducted from 13:00 to 16:00 between 7 and 10 April 2015. In this phase, we simultaneously measured the concentrations of PM2.5 and PM1.0 at the Songyuan Road station, at the Jiao Tong University station, and inside subway trains on Line 10. To make our descriptions more concise, we have used SR to represent data from the Songyuan Road station, JT to represent data from the Jiao Tong University station, and CP to represent the center of the platform. Since the Songyuan Road station is a side-type platform, we placed the measuring instrument at the center of the platform on one side of the station; this is sampling point SRCP in Fig. 3a. Since the Jiao Tong University station is an islandtype platform, we placed the measuring instrument in the center of the platform; this is sampling point JTCP in Fig. 3b. The tested site inside the train was in the middle of the centered compartment. The sampling duration was 1 h per day at each of these three points. The second phase lasted 3 days, on 22 and 27 April and on 15 July 2015. The principal purpose of this phase was to measure the impact of piston winds at different locations on the subway platform. On 22 April, the measurements were obtained from 13:00 to 16:00. We monitored the PM2.5 concentration at the Songyuan Road station at sampling points SRR, SRM, and SRF (Fig. 3a). We used subscript letters to represent the platform positions: R for the rear of the platform, M for the middle of the platform, and F for the front of the platform. Taking into account the actual situation of passengers waiting for the train, these observation points were positioned near the track, around 10 cm from the platform screen door. Point SRR is located between the last and the penultimate platform doors, near the tail of the arriving train. Similarly, point SRF is located
a
b W W E SR R
SR M SR CP
SR F
JT CP JT R
JT M
JT F
E
Fig. 3. Schematic diagram of the sampling locations at (a) the Songyuan Road station and (b) the Jiao Tong University station.
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between the first and second platform screen doors, near the head of the arriving train. Point SRM is located in the middle of the arriving train. During our measurements, two of these three points were measured simultaneously to compare the PM concentrations at different locations on the platform. Therefore, every point was measured twice per day for 1 h each time. On 27 April, we measured the PM2.5 concentration at Jiao Tong University station at sampling points JTR, JTM, and JTF (Fig. 3b). These positions correspond to the positions at Songyuan Road station, and the measurement method was the same. To test whether the presence of two tracks on a different level would affect our results, we also monitored the PM2.5 concentration at the upper platform of the Caoyang Road station on 15 July 2015 from 14:00 to 15:30 at sampling points CRR, CRM, and CRF. Here, CR represents the Caoyang Road station, and the subscripts and the platform positions are the same as those used for the Songyuan Road and Jiao Tong University stations. The first and last 10 min of the data at each monitoring point were discarded to avoid the fluctuations caused by opening or moving the instrument. To distinguish between the directions of the trains, westbound trains were denoted Train W and eastbound trains were denoted Train E (Fig. 3). Results The mean levels of PM2.5 and PM1.0 Table 1 summarizes the daily average concentrations of PM2.5 and PM1.0 measured during the first phase of the study on the platforms and inside the trains of Shanghai subway Line 10. As we noted in the Methods section, the most stable 40 min of the data at each sampling point were selected for analysis. Values reported in this phase represent the mean values for this 40-min period. During the 4-day measurement period, the daily average concentrations of PM2.5 and PM1.0 did not differ significantly between the stations (p > 0.05), and were significantly greater than the levels inside the trains (p < 0.05). In addition, the PM concentrations showed consistent patterns for the two platforms and the train compartments. For instance, on 9 April, the PM concentrations were lowest both on the platforms and inside the trains. In contrast, the PM concentrations on the platforms and inside the trains were highest on 10 April. Based on the air quality hierarchy in China’s Air Quality Index (AQI), a PM2.5 concentration <35 lg/m3 is the best class (‘‘good”) and values from 35 to 75 lg/m3 represent the second class (‘‘moderate”). On this basis, the mean air quality inside the trains was always good and that on the platforms was always good or moderate. There is no defined range for PM1.0, so it will be necessary to define suitable levels of PM1.0 to support future research. However, Table 1 shows that the patterns for the average concentration of PM1.0 were consistent with the patterns of PM2.5; that is, the higher the PM2.5 concentration, the higher the PM1.0 concentration. Table 1 also shows that the coefficients of variation (CV) of the PM2.5 and PM1.0 concentrations inside the train were always higher than those on the platforms, indicating that the PM concentrations inside the train vary more than those on the platforms. Correlations between PM1.0 and PM2.5 and the PM1.0/PM2.5 ratio At all three sample locations, PM2.5 and PM1.0 were significantly correlated (p < 0.01, two-tailed), suggesting that they are derived from the same source, with Pearson’s correlation coefficients (r) equaling 0.913, 0.878, and 0.904, respectively, at the Songyuan Road station, the Jiao Tong University station, and inside the train. Moreover, we obtained statistically significant linear regressions for the relationship between the two concentrations: Songyuan: CPM1.0 = 0.515 CPM2.5 + 5.831 (R2 = 0.834, p < 0.001). Jiao Tong: CPM1.0 = 0.497 CPM2.5 + 6.554 (R2 = 0.771, p < 0.001). Inside the train: CPM1.0 = 0.561 CPM2.5 + 3.854 (R2 = 0.818, p < 0.001). Table 1 The minimum, maximum, and average PM2.5 and PM1.0 concentrations and the associated coefficient of variation (CV) for the three sampling locations. PM2.5 (lg/m3)
PM1.0 (lg/m3)
Sampling date (2015)
Site
Min.
Max.
Mean
CV (%)
Min.
Max.
Mean
CV (%)
7 April
Songyuan Road station Jiao Tong University station Inside the train
31 29 15
43 40 39
36.1 ± 1.7 32.5 ± 1.5 23.0 ± 3.8
4.7 4.6 16.5
23 21 13
29 28 26
25.0 ± 0.8 23.9 ± 0.7 17.2 ± 1.9
3.2 2.9 11.0
8 April
Songyuan Road station Jiao Tong University station Inside the train
32 30 18
62 40 48
36.2 ± 1.8 34.4 ± 1.5 24.4 ± 4.4
5.0 4.4 18.0
22 22 14
30 27 28
25.2 ± 1.3 24.6 ± 0.7 17.2 ± 2.5
5.2 2.8 14.5
9 April
Songyuan Road station Jiao Tong University station Inside the train
27 26 13
40 42 31
32.1 ± 1.9 33.3 ± 2.4 19.9 ± 2.9
5.9 7.2 14.6
18 18 10
26 30 20
21.2 ± 1.0 21.8 ± 1.0 14.3 ± 1.7
4.7 4.8 11.9
10 April
Songyuan Road station Jiao Tong University station Inside the train
50 51 23
63 64 46
55.8 ± 1.9 56.9 ± 1.7 30.3 ± 3.7
3.4 3.0 12.2
36 36 17
46 44 36
39.2 ± 0.9 40.1 ± 1.2 21.4 ± 2.2
2.3 3.0 10.3
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PM1.0 accounted for different proportions of PM2.5 at the three sampling sites, and the values at the Songyuan Road station and the Jiao Tong University station (67.0 and 67.6%, respectively) were both lower than that inside the train (71.9%). Although the PM2.5 and PM1.0 concentrations inside the train were lower than those on the platforms, PM1.0 accounted for a higher proportion of PM2.5 inside the train. A reason for this difference might be that the train’s air conditioning filter and fresh air circulation system can effectively reduce the PM concentrations, but the effect of this air purification system on the ultrafine particles (i.e., PM1.0) is not as good as it is for the PM2.5 particles, and because of the greater toxicity of the finer particles, this is a cause for concern.
The effects of piston wind on PM concentrations on the platforms Fig. 4 depicts the variation of PM2.5 and PM1.0 concentrations over time at the Songyuan Road and Jiao Tong University stations during the first measurement phase. The patterns of variation of the PM2.5 concentration and PM1.0 concentration are essentially identical on the platforms. In reality, the interval between trains arriving at the station differed between measurements, and the stopping duration of each train at the station also differed. Thus, we recorded the time of each train’s arrival and how long the doors remained open. In Fig. 4, each vertical dotted line represents the door opening time of a westbound train, and each vertical solid line represents the door opening time of an eastbound train. Thus, each vertical line corresponds to the time when a train stopped on the platform. Fig. 4a shows that before a train from either direction arrived at the station,
Fig. 4. Variation of the PM2.5 and PM1.0 concentrations over time at (a) the Songyuan Road station and (b) the Jiao Tong University station.
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both PM concentrations increased due to the piston effect. That is, as the train approaches the station, the bad air in the tunnel is pushed into the platform, and PM concentrations on the platform increase. We initially expected that the PM concentrations would be more strongly affected by the eastbound trains, since these trains pass closest to sampling point SRCP. However, the actual measurements did not support this belief; that is, train movement from both directions affected the PM concentration similarly. The influence of the piston wind on the island type platform at Jiao Tong University station is displayed in Fig. 4b. The piston wind caused by the train passing the platform increased the PM concentrations at the Jiao Tong University station, as was the case at the Songyuan Road station. We calculated the average concentrations of PM2.5 and PM1.0 and the mean values of the peaks of PM2.5 and PM1.0 before every train that arrived in the station in Fig. 4a. The mean values of the peaks rose by 9% and 5%, respectively, compared with the average concentrations of PM2.5 and PM1.0. Similarly, in Fig. 4b, the mean values of the peaks rose by 9 and 8%, respectively, compared with the average concentrations of PM2.5 and PM1.0. In general, the piston effect increased both PM concentrations, although the increase was smaller for PM1.0. Variation of PM concentrations among locations on the platform Fig. 5 depicts the PM2.5 concentrations at the different locations on the platforms of the Songyuan Road and Jiao Tong University stations during the second measurement phase. Although we found that the PM2.5 concentration differed significantly between pairs of locations at both stations (p < 0.01), we observed some common patterns in the variations of the PM2.5 concentration over time. Fig. 5a and b shows that whether measurements were obtained at the side-type platform or the island-type platform, PM2.5 was always higher at the rear of the platform than at the middle position. Similarly, Fig. 5c and d indicates that the PM2.5 concentration at the front of the platform was higher than that at the middle, and Fig. 5e and f shows that the PM2.5 concentrations were similar at the front and rear positions. Though the variations of the PM2.5 concentration at the Songyuan Road station differed from those at the Jiao Tong University station (Fig. 5e and f), we cannot directly establish that the PM2.5 concentration at one location was higher than that at the other location, as shown in Fig. 5a–d. We calculated that the average PM2.5 concentrations at the front, middle, and rear locations on the Songyuan Road station platform were 73.1, 64.9, and 75.7 lg/m3, respectively. Since each location was measured twice, the three values represent the average of the two measurements. At the Songyuan Road station, the average PM2.5 at the front of the platform was about 13% greater than that at the middle position and that at the rear position was about 17% greater than that at the middle of the platform. Similarly, the average PM2.5 concentrations at the front, middle, and rear locations on the Jiao Tong University station platform were 50.0, 43.5, and 53.0 lg/m3, respectively. Thus, at the Jiao Tong University station, the average PM2.5 at the front and rear positions were around 15% and 22% greater, respectively, than that at the middle of the platform. In summary, the PM2.5 concentration was lowest at the middle of the platform at both stations. Because the variation of PM1.0 showed great consistency with the variation of PM2.5 (i.e., a high and significant correlation), the PM1.0 concentration at the middle of the platform is likely to be lower than the concentration at both ends of the platform. As we noted earlier, the piston effect generated when trains approach the platform can affect PM concentrations. Since the piston wind reaches the rear of the platform first, the largest amount of particulate matter from the tunnel would be pushed to this position. As the train enters the station, decelerates, and stops at the front position, the force of the piston wind continuously decreases and the increase in PM concentrations also weakens. However, the PM2.5 concentration at the front of the platform was also relatively high in Fig. 5. This may be because the front position for the eastbound trains is the rear position for the westbound trains on the other side of the platform. The PM2.5 concentration at the front of the eastbound trains was therefore significantly affected by the piston wind created by westbound trains, and vice versa. To improve our understanding of this phenomenon, we found a platform (the Caoyang Road station) that was only affected by train movement in one direction. Fig. 6 depicts the PM2.5 concentrations at different positions on the platform of the Caoyang Road station during the second measurement phase. Fig. 6a shows that PM2.5 was obviously higher at the rear of the platform than at the front position; the average value of 39.7 lg/m3 at the rear position was much bigger than the average value of 29.8 lg/m3 at the front position. However, Fig. 6b suggests that the PM2.5 concentrations were similar at the front and middle positions, with average values of 27.5 and 26.9 lg/m3, respectively. The effects of train door opening on PM concentrations inside the train Fig. 7 depicts the variations of PM2.5 and PM1.0 concentrations over time inside the train during the first measurement phase. As was the case for PM concentrations outside the train, the PM2.5 and PM1.0 concentrations followed essentially identical patterns inside the train. By recording the door opening period, we found that it mostly lasts around 20 s, but sometimes lasted longer than 50 s. The variations of PM concentrations inside the subway train between the times when the door opened and closed can be seen in Fig. 7. The shaded areas correspond to the train’s door opening periods. The PM concentrations inside the train increased greatly during the door opening period, as the dirty air on the platforms flowed into the car. The PM concentrations then decreased after the subway car’s doors closed. We used the PM concentration data from Fig. 7 to calculate the average PM2.5 and PM1.0 concentrations during the period when the train door was closing; these were 18.0 and 13.2 lg/m3, respectively, versus values of 22.1 and 14.0 lg/m3, respectively, during the door opening period. This suggests that the PM2.5 and PM1.0 concentrations increased by nearly 23% and 6%, respectively, and that the increase of PM2.5 was greater than that of PM1.0.
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Fig. 5. Variation of the PM2.5 concentration over time at different sampling positions on the platforms (Fig. 3): (a), (c), and (e) are for the Songyuan Road station, and (b), (d), and (f) are for the Jiao Tong University station.
Discussion Different air quality in different subway systems Our measurements revealed that the values of PM2.5 and PM1.0 averaged 39.7 and 27.6 lg/m3, respectively, at the stations, and 24.4 and 17.5 lg/m3 inside the trains of Shanghai subway Line 10. There are big differences of subway air quality among cities. For example, the average PM2.5 and PM10 concentrations at subway platforms in Stockholm, Seoul, and Los Angeles
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Fig. 6. Variation of the PM2.5 concentration over time at different sampling positions on the platform of the Caoyang Road station, where (unlike the other two stations) trains run in only one direction.
Fig. 7. Variations of the PM2.5 and PM1.0 concentrations inside the train over time.
were 260 and 470 lg/m3, 129 and 359 lg/m3, and 57 and 78 lg/m3, respectively (Johansson and Johansson, 2003; Park and Ha, 2008; Kam et al., 2011). The average PM2.5 and PM10 concentrations inside subway trains in Hong Kong and Guangzhou were 39 and 50 lg/m3 and 44 and 55 lg/m3, respectively (Chan et al., 2002, 2003). Ye et al. (2010) measured the mean PM2.5 and PM1.0 levels at certain platforms along Shanghai subway Line 1 and Line 2 (287 and 231 lg/m3 respectively) and Lu et al. (2015) measured the range of PM2.5 concentrations at three platforms of Shanghai subway Line 7 (which ranged from 49.2 to 66.2 lg/m3). Therefore, even in the same city, the air quality of the subway varies greatly among metro lines. In order to learn the reasons for these differences, we must first learn the sources of subway PM. Sources of PM can be divided into internal and external sources. Internal sources mainly include mechanical abrasion while trains are running or braking, maintenance work, and construction in stations and tunnels, as well as wind erosion caused by the intense airflow within the tunnels (Querol et al., 2012; Qiao et al., 2015). Some research has shown that certain materials and technologies can reduce the generation of particles from internal sources (Nieuwenhuijsen et al., 2007; Salma et al., 2007). For example, rubber wheel systems and electric braking systems produce less PM emissions than steel wheel systems and conventional brake pads, respectively. Shanghai metro Line 10 utilizes a steel wheel system, and the combination of electric and air braking may increase PM pollution. External sources mainly come from the city’s population and particles generated outside the metro system. The more passengers that enter the subway, the more particles they carry into the system, and resuspension of these materials caused by air turbulence increases PM concentrations. Particles can also enter the system through ventilation systems and escalators. A strong correlation between PM concentrations in the subway sys-
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tem and in the outdoor ambient environment has been demonstrated in many studies (Branis, 2006; Cheng et al., 2008; Kam et al., 2011; Mugica et al., 2012). The Jing’an ambient air sampling site in Shanghai releases hourly PM2.5 concentration data every day. We obtained the PM2.5 data from their Web site (http://www.pm25china.net) from 13:00 to 17:00 between 7 and 10 April 2015, and calculated the average values during this period: 25.3, 30.3, 24.7, and 52.0 lg/m3, respectively, for the 4 days of this period. These values compare with daily average values of 30.5, 31.6, 28.4, and 47.7 lg/m3, respectively, for the subway system during the same four days. The correlation between the two sets of values is consistent with the results of the other studies described in this section. PM levels on the platforms A greater number of PM sources does not always lead to higher PM concentrations. Several factors can affect the PM concentrations. For example, installation of platform screen doors decreases PM exposure levels (Ho et al., 2012). The screen doors separate the platform from the tunnel and improve safety, noise levels, and comfort, while also conserving resources and preventing dirty air from the tunnel from entering the platform area (Kim et al., 2012). Platform screen doors were also installed in the Shanghai metro Line 10 stations. However, the screen doors are not completely sealed. As a result, the piston effect generated by a train’s movement through the tunnel leads to flows of dust and contaminated air from the tunnel into the platform area. Thus, before each train arrives at the station, the PM concentrations at the platform will increase despite the presence of platform screen doors, but the magnitude of the increase will be less than it would be without platform screen doors. Moreover, Salma et al. (2007) claimed that when trains depart the station, they pull in some cleaner air from the vestibules and corridors, thereby decreasing PM concentrations. In contrast, our measurements involving the platform screen doors did not show similar results, possibly due to the fact that the screen doors prevent this air suction. Other factors can also influence PM levels at the platforms, such as the characteristics of the ventilation system, the station’s design, and the depth of the subway tunnels (Ma et al., 2014; Moreno et al., 2014). The side-type and island-type platforms are the dominant types in Shanghai’s subway stations. Our measurements revealed that under the same ventilation system (forced mechanical platform ventilation and natural tunnel ventilation) and with a similar station depth (around 10 m), the piston effect caused by the train movement from both directions has similar effects on the PM concentrations at the platform, despite physical differences between the side-type platform (with the tracks located between the two platforms) and the island-type platform (with the platform located between the two tracks). This similarity is because the ceilings of the platform area and of the tunnel area are connected in both types of station. It is also worth noting that after the subway station has been built, many characteristics of the station cannot be easily changed. However, our study provided some consistent findings that suggest ways to minimize health damage caused by air pollution in the subway. First, PM concentrations on the platform increase as trains approach the station, and since dirty air from the tunnel is pushed onto the platform by the piston wind, the subway tunnels should be cleaned regularly. The effectiveness of this approach was observed by Johansson and Johansson (2003), Branis (2006) and Salma et al. (2007), who reported lower PM levels at subway stations after washing the tracks, floor, and walls of the tunnels. Second, during our first measurement phase, we found that the average PM2.5 and PM1.0 concentrations on the platform were both nearly 1.6 times the values inside the train. Therefore, passengers should minimize the duration of their stay on the platform. Most importantly, the effect of the piston wind differed among the positions on the platform. At the Songyuan Road station, the average PM2.5 concentration at both ends of the platform was about 15% greater than that at the middle position. At the Jiao Tong University station, the average PM2.5 concentration at both ends of the platform was around 18.5% greater than that at the middle position. Thus, passengers should try to wait for trains near the middle of the platform. The staff working on the platform should regularly change working positions, so that the staff working at the middle position would regularly change places with the staff working at both ends of the platform to protect the health of the latter, and all of the staff working on the platform should take periodic breaks to reduce their pollution exposure. One caveat is that in the subway systems of some other cities around the world, the piston effect was not always stronger at both ends of the platform than in the middle. The strength of the piston wind effects at different positions depends on many factors, such as the presence or absence of platform screen doors, the location of the access points, and the condition of the ventilation system (Querol et al., 2012; Moreno et al., 2014). PM levels inside the train When commuters take the subway daily, they remain inside the train for a relatively long time, so the compartment’s air quality can have an important effect on their health. In the compartments, air conditioning drastically reduces the exposure to PM (Kam et al., 2011). The trains of Shanghai subway all have air conditioning. As in Kam’s study, we found that the PM concentrations inside the trains were lower than those on the platforms and in the ambient air. The average PM2.5 inside the train was about 39% lower than that at the Songyuan Road station, 38% lower than that at the Jiao Tong University station, and 26% lower than that at the Jing’an air sampling site. The air quality inside the train is mostly determined by the quality of the ventilation and air conditioning systems. The air quality in the subway system is improved by the delivery of fresh air from outside through the ventilation system. However, the fresh air that passes through the air conditioning system on top of the train differs from that in the ambient environment, since it is a mixture of the fresh external air provided through ventilation shafts with the existing air flow inside the tunnel. Compared with the ambient air, the mixed outside ‘‘fresh”
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air contains a larger volume of contaminants. Therefore, special attention should be paid to the accumulation of contaminants in the air conditioning system, of dust around the ventilation ducts, and of bacteria on the walls of the airflow conduits. In particular, the air filters of the air conditioners inside the train should be replaced frequently and the ventilation equipment and its conduits should be cleaned often to maintain high air quality. However, the opening of train doors leads to deterioration of the compartment’s air quality. Because dirty air on the platform can enter compartments after the train door opens, the PM2.5 and PM1.0 concentrations inside the train increased by an average of about 23% and 6%, respectively, after the train doors opened. The longer each door opening lasts or the worse the air quality on the platform, the worse the air quality will become inside the train. In addition, some studies reported that the greater the train frequency, the higher the PM levels in underground railway platforms (Salma et al., 2007; Carteni et al., 2015), as a consequence, the worse the air quality inside the train will become. Conclusions To improve our understanding of the distribution of fine and ultrafine particulate matter in the Shanghai subway system, we measured the PM concentrations at the Songyuan Road and the Jiao Tong University stations in Line 10, at the Caoyang Road station in Line 11, and inside trains on Shanghai metro Line 10. The PM2.5 and PM1.0 values averaged 39.7 and 27.6 lg/ m3 at the stations, and 24.4 and 17.5 lg/m3 inside the trains. Obviously, the PM2.5 and PM1.0 concentrations inside the trains were lower than those on the platforms. The PM2.5 and PM1.0 concentrations on the platforms and in the train compartments were both positively correlated with values in the ambient air. PM concentrations on the platforms were influenced by the piston wind created by the approaching trains, and increased similarly whether westbound trains or eastbound trains were entering the station. Moreover, the effect of the piston wind varied among locations on the platform; the effect of the piston wind was stronger at both ends of the platform than in the middle. The PM concentrations inside the trains were also influenced by opening of the train doors. The PM concentrations increased during opening of the doors, and the increase was more remarkable for PM2.5 than for PM1.0. PM2.5 and PM1.0 concentrations were strongly and significantly correlated, suggesting that the particles are derived from the same sources. Based on these results, we discussed the factors that influence exposure of travelers to air pollution. We recommend the following measures to minimize the damage to human health caused by subway air pollution: reducing the time spent on the platform, waiting for trains at the middle part of the platform, regularly changing the working positions of the platform staff and giving them frequent breaks, replacing the filters of the air conditioner inside the train, and cleaning the air conditioning system and subway tunnels regularly. Acknowledgments This study was supported by Grants from the National Natural Science Foundation of China (projects No. 71373155 and 71428001), the Chinese Ministry of Education on the Key Projects of Philosophy and Social Sciences (project No. 13JZD025), the China Postdoctoral Science Foundation (project No. 2014M561481), and the Social Development of Metropolis and Construction of Smart City program (project No. 085SHDX001). 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Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Characteristics of particulate matter (PM) concentrations influenced by piston wind and train door opening in the Shanghai subway system Jiajia Wang a,b, Laijun Zhao a,b,⇑, Daoli Zhu a,b, H. Oliver Gao a,b,c, Yujing Xie d, Huiyong Li d, Xiang Xu b, Hongbo Wang a,b a
Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, PR China Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, PR China School of Civil and Environmental Engineering, Cornell University, NY 14853, USA d School of Management, Shanghai University, Shanghai 200444, PR China b c
a r t i c l e
i n f o
Article history:
Keywords: Subway system Particulate matter Piston wind Train door opening Correlation analysis
a b s t r a c t More than 9 million passengers take Shanghai’s subway system every work day. The system’s air quality has caused widespread concern because of the potential harm to passengers’ health. We measured the particulate matter (PM) concentrations at three kinds of typical underground platform (side-type, island-type, and stacked-type platforms) and inside the trains in Shanghai’s metro during 7 days of measurements in April and July 2015. Our results demonstrated that the patterns of air quality variation and PM concentrations were similar at the side-type and island-type platforms. We also found that the PM concentrations were higher on the platforms than inside the train and that the PM concentrations in the subway system were positively correlated with those in the ambient air. Piston wind generated by vehicle motion pushes air from the tunnel to the platform, so platform PM concentrations increase when trains approach the platform. However, the piston wind effect varies greatly between locations on the platform. In general, the effect of the piston wind is weaker at the middle of the platform than at both ends. PM concentrations inside the train increase after the doors open, during which time dirty platform air floods into the compartments. PM1.0 and PM2.5 were significantly correlated both inside the train and on the platforms. PM1.0 accounted for 71.9% of PM2.5 inside the train, which is higher than the corresponding platform values. Based on these results, we propose some practical suggestions to minimize air pollution damage to passengers and staff from the subway system. Ó 2016 Elsevier Ltd. All rights reserved.
Introduction In recent years, outbreaks of large-scale urban haze and smog have raised increasing concern about the relationship between air quality and human health. Many studies have revealed that bad air conditions can greatly damage human health (Hoek et al., 2002; Glinianaia et al., 2004; Pope and Dockery, 2006; Bayraktar et al., 2010; Pui et al., 2014). Efforts to reduce traffic congestion have led to increased development of commuter rail traffic in many large cities, and the number of the ⇑ Corresponding author at: Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, PR China. E-mail address: [email protected] (L. Zhao). http://dx.doi.org/10.1016/j.trd.2016.05.006 1361-9209/Ó 2016 Elsevier Ltd. All rights reserved.
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subway passengers is increasing. Although this increases the public’s travel convenience, subway air quality has become a concern because of its potential impact on health. Some studies have specifically investigated the health hazards caused by the subway environment and the damage to human health caused by air pollution in the subway system; the results suggest that the risk is much more severe than that in public residential buildings. For example, Karlsson et al. (2005) discovered that the particles from a subway station were approximately eight times more genotoxic and four times more likely to cause oxidative stress in lung cells than the particles from a nearby populated urban street. Chan et al. (1991) investigated commuter exposure to pollutants in the Boston metro, and their study represented the first comprehensive research on population exposure to pollutants in a metro system. Subsequently, researchers assessed the air pollution levels in subway systems around the world. Johansson and Johansson (2003) monitored the particulate matter (PM) concentrations at an underground metro station in Stockholm. They found that the PM concentrations on the platform closely followed the intensity of subway train traffic and that the average PM2.5 and PM10 concentrations were (respectively) 10 and 5 times the corresponding values measured in one of the busiest streets in Stockholm. Park and Ha (2008) investigated the PM2.5 and PM10 concentrations in the Seoul metro system and showed that PM concentrations inside trains were significantly higher than those on platforms, and that this was caused by the lack of a mechanical ventilation system inside the subway trains. Kam et al. (2011) measured the PM concentrations in an underground subway line, as well as in a ground-level light-rail line in the Los Angeles metro system. They found that commuters in the underground line were exposed to PM2.5 and PM10 with average values 1.8 and 1.9 times (respectively) the values experienced by commuters who used the light-rail line. Some studies focused on PM chemical compositions or sources. For example, Aarnio et al. (2005) measured the PM2.5 concentration in the Helsinki subway system. They analyzed the elemental composition of the PM2.5 samples and found that the most enriched element in the samples was iron. Salma et al. (2007) collected aerosol samples of PM2.0 and PM10–2.0 at a metropolitan underground subway station in downtown Budapest and discovered that the primary PM sources were friction between the rails and the train’s wheels and mechanical wear. Nieuwenhuijsen et al. (2007) reviewed and summarized previous research results and found that differences among the studies may have resulted from differences in the composition of the wheels, in the brake systems, and in ventilation levels. Another important factor is that trains running in a tunnel produce a piston effect, which is closely related to subway ventilation and energy consumption by the environmental control system. Thus, many researchers have studied the subway piston effect in terms of its mechanism, numerical modeling for the air flow characteristics, and the factors that influence the piston wind, with the goal of improving the design of ventilation air shafts and maximizing use of the piston wind to reduce energy consumption by natural ventilation (Lin et al., 2008; Pan et al., 2013; López González et al., 2014). Other scholars found that the piston wind affects the air quality on subway platforms. Salma et al. (2007) discovered that the PM concentrations on the platform increased as a train entered the station because it pushed in polluted air from the tunnel, whereas PM concentrations on the platform decreased as the train departed the station because it pulled in cleaner air from the vestibule and corridor. Moreno et al. (2014) monitored the air quality on several subway station platforms in Barcelona, and they found that PM concentrations were highly variable due to differences in tunnel ventilation conditions, the magnitude of the piston effect and station designs. The first Shanghai Metro Line began operation in April 1995, and 14 subway lines with 366 stations were operating as of December 2015. The length of the subway network now totals about 617 km. The passenger flow is more than 9 million people daily, and the number is increasing year by year. Therefore, it is important to obtain information about the air quality in Shanghai’s subway system and its potential influence on these passengers. As a result, some scholars have studied the environmental quality in the Shanghai metro. Ye et al. (2010), Ma et al. (2014) and Lu et al. (2015) separately measured PM concentrations at two underground subway platforms of Shanghai metro Line 9, some platforms along Shanghai metro Line 1 and Line 2, and three platforms of Shanghai subway Line 7. Yu et al. (2012) measured PM1.0 concentrations and compared commuters’ exposure during different travel modes (e.g., subway, bus, bicycle). Qiao et al. (2015) measured PM concentrations in tunnels of two lines of Shanghai’s metro system. As in previous studies of other cities’ subway systems around the world, these studies of Shanghai’s subway mainly focused on air quality by comparing pollutant levels in the subway system with levels recorded at ambient monitoring sites. Regarding the subway system, the comparison in these studies was focused on peak vs. off-peak periods, and weekday vs. weekend periods. In addition, the measurement campaigns mostly targeted the correlations between particles in different size classes and proportions of the total, as well as the sources and chemical composition of the particulate matter. In this paper, we measured the PM2.5 and PM1.0 concentrations in Shanghai’s metro Line 10, which has not been previously studied. Almost all of the Shanghai metro stations have platform screen doors (i.e., protective barriers between the platform and the tracks) installed, as is the case for Line 10. The side-type and island-type platforms are the dominant station forms in Shanghai’s metro system. We measured the PM concentrations at stations with these two platform types to investigate the effects of the piston wind for stations with platform screen doors, since previous studies of the influence of the piston wind on subway air quality didn’t include stations with such doors. Moreover, we measured the PM concentrations inside the subway trains and considered the effect of the train door’s opening on air quality inside the subway carriages, a factor that was not included in previous studies. Most importantly, after the subway lines have been established, the location, depth, and other design features of the stations cannot be changed. Based on our particle concentration measurements, we propose ways to minimize the damage to human health under the current subway design.
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Study locations and measurements Background description Shanghai’s metro Line 10 started operation in 2010, and the entire line is under the ground; it now stretches 36 km, with 31 stations on the line. The starting station is Xinjiangwancheng, which is located in northeastern Shanghai (Fig. 1). The final station, in southwestern Shanghai, is Hongqiao Railway station, which differs from the terminal for a branch line (Hangzhong Road station), which is also located in southwestern Shanghai. Since Line 10 passes through Xintiandi, Yuyuan Gardens, Nanjing Road, Huaihai Road, Wujiaochang, and other parts of Shanghai’s central region, it is known as the ‘‘golden” line. In this study, we measured the PM2.5 and PM1.0 concentrations inside the subway trains of Line 10 and at two typical stations: Songyuan Road station and Jiao Tong University station (Fig. 1). Songyuan Road station is located in Changning District, and is a side-type platform; that is, the tracks run through the center of the station and platforms are positioned on both sides of the tracks (Fig. 2a). Jiao Tong University station is located in Xuhui District, and is an island-type platform; that is, the platform is in the center and tracks run on both sides of the platform (Fig. 2b). Jiao Tong University station is a transfer station, where passengers can transfer to Line 11. The linear distance between these two stations is about 2.2 km. Fig. 1 shows the location of the Jing’an ambient air sampling site; sampling is performed on the 6th floor roof of a building. This is one of 10 state-run air sampling sites in the Shanghai area and is the nearest sampling site to the two subway stations that we studied; its linear distances from the Songyuan Road station and Jiao Tong University station are about 3.6 and 2.5 km, respectively. Fig. 1 also shows the location of the Caoyang Road station on Line 11. This is a stacked-type platform; that is, there are two platforms and two tracks, but rather than being on the same level, they are located in the upper and lower levels of the station. Passengers who want to take a train in the opposite direction need to go upstairs or downstairs to transfer. This design provides an environment that permitted measurements of the impact of train operation in a single direction on the platform’s PM concentration.
Fig. 1. Shanghai subway Line 10 and Line 11, the locations of the two sampling stations and of the Caoyang Road station, and the location of the Jing’an ambient air sampling site.
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Fig. 2. Pictures of (a) the side-type platform at Songyuan Road station, (b) the island-type platform at Jiao Tong University station, and (c) the stacked-type platform at Caoyang Road station.
Measurements We used two portable DustTrak aerosol monitors (model 8532; http://www.tsi.com/Products/) to measure the concentrations of PM2.5 and PM1.0 at a logging interval of 1 s. The sampling height for all measurements was about 1.5 m, which corresponds to the breathing zone. We calibrated the instruments at the beginning of our experimental campaign using the values from a Thermo Scientific TEOM 1405 as reference values. Comparisons between the two instruments were done both before and after each daily use to ensure that the instruments used for data collection were accurate; we also performed zero checking before every survey trip. The measurement campaign was divided into two phases: The first phase lasted 4 days, and was conducted from 13:00 to 16:00 between 7 and 10 April 2015. In this phase, we simultaneously measured the concentrations of PM2.5 and PM1.0 at the Songyuan Road station, at the Jiao Tong University station, and inside subway trains on Line 10. To make our descriptions more concise, we have used SR to represent data from the Songyuan Road station, JT to represent data from the Jiao Tong University station, and CP to represent the center of the platform. Since the Songyuan Road station is a side-type platform, we placed the measuring instrument at the center of the platform on one side of the station; this is sampling point SRCP in Fig. 3a. Since the Jiao Tong University station is an islandtype platform, we placed the measuring instrument in the center of the platform; this is sampling point JTCP in Fig. 3b. The tested site inside the train was in the middle of the centered compartment. The sampling duration was 1 h per day at each of these three points. The second phase lasted 3 days, on 22 and 27 April and on 15 July 2015. The principal purpose of this phase was to measure the impact of piston winds at different locations on the subway platform. On 22 April, the measurements were obtained from 13:00 to 16:00. We monitored the PM2.5 concentration at the Songyuan Road station at sampling points SRR, SRM, and SRF (Fig. 3a). We used subscript letters to represent the platform positions: R for the rear of the platform, M for the middle of the platform, and F for the front of the platform. Taking into account the actual situation of passengers waiting for the train, these observation points were positioned near the track, around 10 cm from the platform screen door. Point SRR is located between the last and the penultimate platform doors, near the tail of the arriving train. Similarly, point SRF is located
a
b W W E SR R
SR M SR CP
SR F
JT CP JT R
JT M
JT F
E
Fig. 3. Schematic diagram of the sampling locations at (a) the Songyuan Road station and (b) the Jiao Tong University station.
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between the first and second platform screen doors, near the head of the arriving train. Point SRM is located in the middle of the arriving train. During our measurements, two of these three points were measured simultaneously to compare the PM concentrations at different locations on the platform. Therefore, every point was measured twice per day for 1 h each time. On 27 April, we measured the PM2.5 concentration at Jiao Tong University station at sampling points JTR, JTM, and JTF (Fig. 3b). These positions correspond to the positions at Songyuan Road station, and the measurement method was the same. To test whether the presence of two tracks on a different level would affect our results, we also monitored the PM2.5 concentration at the upper platform of the Caoyang Road station on 15 July 2015 from 14:00 to 15:30 at sampling points CRR, CRM, and CRF. Here, CR represents the Caoyang Road station, and the subscripts and the platform positions are the same as those used for the Songyuan Road and Jiao Tong University stations. The first and last 10 min of the data at each monitoring point were discarded to avoid the fluctuations caused by opening or moving the instrument. To distinguish between the directions of the trains, westbound trains were denoted Train W and eastbound trains were denoted Train E (Fig. 3). Results The mean levels of PM2.5 and PM1.0 Table 1 summarizes the daily average concentrations of PM2.5 and PM1.0 measured during the first phase of the study on the platforms and inside the trains of Shanghai subway Line 10. As we noted in the Methods section, the most stable 40 min of the data at each sampling point were selected for analysis. Values reported in this phase represent the mean values for this 40-min period. During the 4-day measurement period, the daily average concentrations of PM2.5 and PM1.0 did not differ significantly between the stations (p > 0.05), and were significantly greater than the levels inside the trains (p < 0.05). In addition, the PM concentrations showed consistent patterns for the two platforms and the train compartments. For instance, on 9 April, the PM concentrations were lowest both on the platforms and inside the trains. In contrast, the PM concentrations on the platforms and inside the trains were highest on 10 April. Based on the air quality hierarchy in China’s Air Quality Index (AQI), a PM2.5 concentration <35 lg/m3 is the best class (‘‘good”) and values from 35 to 75 lg/m3 represent the second class (‘‘moderate”). On this basis, the mean air quality inside the trains was always good and that on the platforms was always good or moderate. There is no defined range for PM1.0, so it will be necessary to define suitable levels of PM1.0 to support future research. However, Table 1 shows that the patterns for the average concentration of PM1.0 were consistent with the patterns of PM2.5; that is, the higher the PM2.5 concentration, the higher the PM1.0 concentration. Table 1 also shows that the coefficients of variation (CV) of the PM2.5 and PM1.0 concentrations inside the train were always higher than those on the platforms, indicating that the PM concentrations inside the train vary more than those on the platforms. Correlations between PM1.0 and PM2.5 and the PM1.0/PM2.5 ratio At all three sample locations, PM2.5 and PM1.0 were significantly correlated (p < 0.01, two-tailed), suggesting that they are derived from the same source, with Pearson’s correlation coefficients (r) equaling 0.913, 0.878, and 0.904, respectively, at the Songyuan Road station, the Jiao Tong University station, and inside the train. Moreover, we obtained statistically significant linear regressions for the relationship between the two concentrations: Songyuan: CPM1.0 = 0.515 CPM2.5 + 5.831 (R2 = 0.834, p < 0.001). Jiao Tong: CPM1.0 = 0.497 CPM2.5 + 6.554 (R2 = 0.771, p < 0.001). Inside the train: CPM1.0 = 0.561 CPM2.5 + 3.854 (R2 = 0.818, p < 0.001). Table 1 The minimum, maximum, and average PM2.5 and PM1.0 concentrations and the associated coefficient of variation (CV) for the three sampling locations. PM2.5 (lg/m3)
PM1.0 (lg/m3)
Sampling date (2015)
Site
Min.
Max.
Mean
CV (%)
Min.
Max.
Mean
CV (%)
7 April
Songyuan Road station Jiao Tong University station Inside the train
31 29 15
43 40 39
36.1 ± 1.7 32.5 ± 1.5 23.0 ± 3.8
4.7 4.6 16.5
23 21 13
29 28 26
25.0 ± 0.8 23.9 ± 0.7 17.2 ± 1.9
3.2 2.9 11.0
8 April
Songyuan Road station Jiao Tong University station Inside the train
32 30 18
62 40 48
36.2 ± 1.8 34.4 ± 1.5 24.4 ± 4.4
5.0 4.4 18.0
22 22 14
30 27 28
25.2 ± 1.3 24.6 ± 0.7 17.2 ± 2.5
5.2 2.8 14.5
9 April
Songyuan Road station Jiao Tong University station Inside the train
27 26 13
40 42 31
32.1 ± 1.9 33.3 ± 2.4 19.9 ± 2.9
5.9 7.2 14.6
18 18 10
26 30 20
21.2 ± 1.0 21.8 ± 1.0 14.3 ± 1.7
4.7 4.8 11.9
10 April
Songyuan Road station Jiao Tong University station Inside the train
50 51 23
63 64 46
55.8 ± 1.9 56.9 ± 1.7 30.3 ± 3.7
3.4 3.0 12.2
36 36 17
46 44 36
39.2 ± 0.9 40.1 ± 1.2 21.4 ± 2.2
2.3 3.0 10.3
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PM1.0 accounted for different proportions of PM2.5 at the three sampling sites, and the values at the Songyuan Road station and the Jiao Tong University station (67.0 and 67.6%, respectively) were both lower than that inside the train (71.9%). Although the PM2.5 and PM1.0 concentrations inside the train were lower than those on the platforms, PM1.0 accounted for a higher proportion of PM2.5 inside the train. A reason for this difference might be that the train’s air conditioning filter and fresh air circulation system can effectively reduce the PM concentrations, but the effect of this air purification system on the ultrafine particles (i.e., PM1.0) is not as good as it is for the PM2.5 particles, and because of the greater toxicity of the finer particles, this is a cause for concern.
The effects of piston wind on PM concentrations on the platforms Fig. 4 depicts the variation of PM2.5 and PM1.0 concentrations over time at the Songyuan Road and Jiao Tong University stations during the first measurement phase. The patterns of variation of the PM2.5 concentration and PM1.0 concentration are essentially identical on the platforms. In reality, the interval between trains arriving at the station differed between measurements, and the stopping duration of each train at the station also differed. Thus, we recorded the time of each train’s arrival and how long the doors remained open. In Fig. 4, each vertical dotted line represents the door opening time of a westbound train, and each vertical solid line represents the door opening time of an eastbound train. Thus, each vertical line corresponds to the time when a train stopped on the platform. Fig. 4a shows that before a train from either direction arrived at the station,
Fig. 4. Variation of the PM2.5 and PM1.0 concentrations over time at (a) the Songyuan Road station and (b) the Jiao Tong University station.
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both PM concentrations increased due to the piston effect. That is, as the train approaches the station, the bad air in the tunnel is pushed into the platform, and PM concentrations on the platform increase. We initially expected that the PM concentrations would be more strongly affected by the eastbound trains, since these trains pass closest to sampling point SRCP. However, the actual measurements did not support this belief; that is, train movement from both directions affected the PM concentration similarly. The influence of the piston wind on the island type platform at Jiao Tong University station is displayed in Fig. 4b. The piston wind caused by the train passing the platform increased the PM concentrations at the Jiao Tong University station, as was the case at the Songyuan Road station. We calculated the average concentrations of PM2.5 and PM1.0 and the mean values of the peaks of PM2.5 and PM1.0 before every train that arrived in the station in Fig. 4a. The mean values of the peaks rose by 9% and 5%, respectively, compared with the average concentrations of PM2.5 and PM1.0. Similarly, in Fig. 4b, the mean values of the peaks rose by 9 and 8%, respectively, compared with the average concentrations of PM2.5 and PM1.0. In general, the piston effect increased both PM concentrations, although the increase was smaller for PM1.0. Variation of PM concentrations among locations on the platform Fig. 5 depicts the PM2.5 concentrations at the different locations on the platforms of the Songyuan Road and Jiao Tong University stations during the second measurement phase. Although we found that the PM2.5 concentration differed significantly between pairs of locations at both stations (p < 0.01), we observed some common patterns in the variations of the PM2.5 concentration over time. Fig. 5a and b shows that whether measurements were obtained at the side-type platform or the island-type platform, PM2.5 was always higher at the rear of the platform than at the middle position. Similarly, Fig. 5c and d indicates that the PM2.5 concentration at the front of the platform was higher than that at the middle, and Fig. 5e and f shows that the PM2.5 concentrations were similar at the front and rear positions. Though the variations of the PM2.5 concentration at the Songyuan Road station differed from those at the Jiao Tong University station (Fig. 5e and f), we cannot directly establish that the PM2.5 concentration at one location was higher than that at the other location, as shown in Fig. 5a–d. We calculated that the average PM2.5 concentrations at the front, middle, and rear locations on the Songyuan Road station platform were 73.1, 64.9, and 75.7 lg/m3, respectively. Since each location was measured twice, the three values represent the average of the two measurements. At the Songyuan Road station, the average PM2.5 at the front of the platform was about 13% greater than that at the middle position and that at the rear position was about 17% greater than that at the middle of the platform. Similarly, the average PM2.5 concentrations at the front, middle, and rear locations on the Jiao Tong University station platform were 50.0, 43.5, and 53.0 lg/m3, respectively. Thus, at the Jiao Tong University station, the average PM2.5 at the front and rear positions were around 15% and 22% greater, respectively, than that at the middle of the platform. In summary, the PM2.5 concentration was lowest at the middle of the platform at both stations. Because the variation of PM1.0 showed great consistency with the variation of PM2.5 (i.e., a high and significant correlation), the PM1.0 concentration at the middle of the platform is likely to be lower than the concentration at both ends of the platform. As we noted earlier, the piston effect generated when trains approach the platform can affect PM concentrations. Since the piston wind reaches the rear of the platform first, the largest amount of particulate matter from the tunnel would be pushed to this position. As the train enters the station, decelerates, and stops at the front position, the force of the piston wind continuously decreases and the increase in PM concentrations also weakens. However, the PM2.5 concentration at the front of the platform was also relatively high in Fig. 5. This may be because the front position for the eastbound trains is the rear position for the westbound trains on the other side of the platform. The PM2.5 concentration at the front of the eastbound trains was therefore significantly affected by the piston wind created by westbound trains, and vice versa. To improve our understanding of this phenomenon, we found a platform (the Caoyang Road station) that was only affected by train movement in one direction. Fig. 6 depicts the PM2.5 concentrations at different positions on the platform of the Caoyang Road station during the second measurement phase. Fig. 6a shows that PM2.5 was obviously higher at the rear of the platform than at the front position; the average value of 39.7 lg/m3 at the rear position was much bigger than the average value of 29.8 lg/m3 at the front position. However, Fig. 6b suggests that the PM2.5 concentrations were similar at the front and middle positions, with average values of 27.5 and 26.9 lg/m3, respectively. The effects of train door opening on PM concentrations inside the train Fig. 7 depicts the variations of PM2.5 and PM1.0 concentrations over time inside the train during the first measurement phase. As was the case for PM concentrations outside the train, the PM2.5 and PM1.0 concentrations followed essentially identical patterns inside the train. By recording the door opening period, we found that it mostly lasts around 20 s, but sometimes lasted longer than 50 s. The variations of PM concentrations inside the subway train between the times when the door opened and closed can be seen in Fig. 7. The shaded areas correspond to the train’s door opening periods. The PM concentrations inside the train increased greatly during the door opening period, as the dirty air on the platforms flowed into the car. The PM concentrations then decreased after the subway car’s doors closed. We used the PM concentration data from Fig. 7 to calculate the average PM2.5 and PM1.0 concentrations during the period when the train door was closing; these were 18.0 and 13.2 lg/m3, respectively, versus values of 22.1 and 14.0 lg/m3, respectively, during the door opening period. This suggests that the PM2.5 and PM1.0 concentrations increased by nearly 23% and 6%, respectively, and that the increase of PM2.5 was greater than that of PM1.0.
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Fig. 5. Variation of the PM2.5 concentration over time at different sampling positions on the platforms (Fig. 3): (a), (c), and (e) are for the Songyuan Road station, and (b), (d), and (f) are for the Jiao Tong University station.
Discussion Different air quality in different subway systems Our measurements revealed that the values of PM2.5 and PM1.0 averaged 39.7 and 27.6 lg/m3, respectively, at the stations, and 24.4 and 17.5 lg/m3 inside the trains of Shanghai subway Line 10. There are big differences of subway air quality among cities. For example, the average PM2.5 and PM10 concentrations at subway platforms in Stockholm, Seoul, and Los Angeles
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Fig. 6. Variation of the PM2.5 concentration over time at different sampling positions on the platform of the Caoyang Road station, where (unlike the other two stations) trains run in only one direction.
Fig. 7. Variations of the PM2.5 and PM1.0 concentrations inside the train over time.
were 260 and 470 lg/m3, 129 and 359 lg/m3, and 57 and 78 lg/m3, respectively (Johansson and Johansson, 2003; Park and Ha, 2008; Kam et al., 2011). The average PM2.5 and PM10 concentrations inside subway trains in Hong Kong and Guangzhou were 39 and 50 lg/m3 and 44 and 55 lg/m3, respectively (Chan et al., 2002, 2003). Ye et al. (2010) measured the mean PM2.5 and PM1.0 levels at certain platforms along Shanghai subway Line 1 and Line 2 (287 and 231 lg/m3 respectively) and Lu et al. (2015) measured the range of PM2.5 concentrations at three platforms of Shanghai subway Line 7 (which ranged from 49.2 to 66.2 lg/m3). Therefore, even in the same city, the air quality of the subway varies greatly among metro lines. In order to learn the reasons for these differences, we must first learn the sources of subway PM. Sources of PM can be divided into internal and external sources. Internal sources mainly include mechanical abrasion while trains are running or braking, maintenance work, and construction in stations and tunnels, as well as wind erosion caused by the intense airflow within the tunnels (Querol et al., 2012; Qiao et al., 2015). Some research has shown that certain materials and technologies can reduce the generation of particles from internal sources (Nieuwenhuijsen et al., 2007; Salma et al., 2007). For example, rubber wheel systems and electric braking systems produce less PM emissions than steel wheel systems and conventional brake pads, respectively. Shanghai metro Line 10 utilizes a steel wheel system, and the combination of electric and air braking may increase PM pollution. External sources mainly come from the city’s population and particles generated outside the metro system. The more passengers that enter the subway, the more particles they carry into the system, and resuspension of these materials caused by air turbulence increases PM concentrations. Particles can also enter the system through ventilation systems and escalators. A strong correlation between PM concentrations in the subway sys-
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tem and in the outdoor ambient environment has been demonstrated in many studies (Branis, 2006; Cheng et al., 2008; Kam et al., 2011; Mugica et al., 2012). The Jing’an ambient air sampling site in Shanghai releases hourly PM2.5 concentration data every day. We obtained the PM2.5 data from their Web site (http://www.pm25china.net) from 13:00 to 17:00 between 7 and 10 April 2015, and calculated the average values during this period: 25.3, 30.3, 24.7, and 52.0 lg/m3, respectively, for the 4 days of this period. These values compare with daily average values of 30.5, 31.6, 28.4, and 47.7 lg/m3, respectively, for the subway system during the same four days. The correlation between the two sets of values is consistent with the results of the other studies described in this section. PM levels on the platforms A greater number of PM sources does not always lead to higher PM concentrations. Several factors can affect the PM concentrations. For example, installation of platform screen doors decreases PM exposure levels (Ho et al., 2012). The screen doors separate the platform from the tunnel and improve safety, noise levels, and comfort, while also conserving resources and preventing dirty air from the tunnel from entering the platform area (Kim et al., 2012). Platform screen doors were also installed in the Shanghai metro Line 10 stations. However, the screen doors are not completely sealed. As a result, the piston effect generated by a train’s movement through the tunnel leads to flows of dust and contaminated air from the tunnel into the platform area. Thus, before each train arrives at the station, the PM concentrations at the platform will increase despite the presence of platform screen doors, but the magnitude of the increase will be less than it would be without platform screen doors. Moreover, Salma et al. (2007) claimed that when trains depart the station, they pull in some cleaner air from the vestibules and corridors, thereby decreasing PM concentrations. In contrast, our measurements involving the platform screen doors did not show similar results, possibly due to the fact that the screen doors prevent this air suction. Other factors can also influence PM levels at the platforms, such as the characteristics of the ventilation system, the station’s design, and the depth of the subway tunnels (Ma et al., 2014; Moreno et al., 2014). The side-type and island-type platforms are the dominant types in Shanghai’s subway stations. Our measurements revealed that under the same ventilation system (forced mechanical platform ventilation and natural tunnel ventilation) and with a similar station depth (around 10 m), the piston effect caused by the train movement from both directions has similar effects on the PM concentrations at the platform, despite physical differences between the side-type platform (with the tracks located between the two platforms) and the island-type platform (with the platform located between the two tracks). This similarity is because the ceilings of the platform area and of the tunnel area are connected in both types of station. It is also worth noting that after the subway station has been built, many characteristics of the station cannot be easily changed. However, our study provided some consistent findings that suggest ways to minimize health damage caused by air pollution in the subway. First, PM concentrations on the platform increase as trains approach the station, and since dirty air from the tunnel is pushed onto the platform by the piston wind, the subway tunnels should be cleaned regularly. The effectiveness of this approach was observed by Johansson and Johansson (2003), Branis (2006) and Salma et al. (2007), who reported lower PM levels at subway stations after washing the tracks, floor, and walls of the tunnels. Second, during our first measurement phase, we found that the average PM2.5 and PM1.0 concentrations on the platform were both nearly 1.6 times the values inside the train. Therefore, passengers should minimize the duration of their stay on the platform. Most importantly, the effect of the piston wind differed among the positions on the platform. At the Songyuan Road station, the average PM2.5 concentration at both ends of the platform was about 15% greater than that at the middle position. At the Jiao Tong University station, the average PM2.5 concentration at both ends of the platform was around 18.5% greater than that at the middle position. Thus, passengers should try to wait for trains near the middle of the platform. The staff working on the platform should regularly change working positions, so that the staff working at the middle position would regularly change places with the staff working at both ends of the platform to protect the health of the latter, and all of the staff working on the platform should take periodic breaks to reduce their pollution exposure. One caveat is that in the subway systems of some other cities around the world, the piston effect was not always stronger at both ends of the platform than in the middle. The strength of the piston wind effects at different positions depends on many factors, such as the presence or absence of platform screen doors, the location of the access points, and the condition of the ventilation system (Querol et al., 2012; Moreno et al., 2014). PM levels inside the train When commuters take the subway daily, they remain inside the train for a relatively long time, so the compartment’s air quality can have an important effect on their health. In the compartments, air conditioning drastically reduces the exposure to PM (Kam et al., 2011). The trains of Shanghai subway all have air conditioning. As in Kam’s study, we found that the PM concentrations inside the trains were lower than those on the platforms and in the ambient air. The average PM2.5 inside the train was about 39% lower than that at the Songyuan Road station, 38% lower than that at the Jiao Tong University station, and 26% lower than that at the Jing’an air sampling site. The air quality inside the train is mostly determined by the quality of the ventilation and air conditioning systems. The air quality in the subway system is improved by the delivery of fresh air from outside through the ventilation system. However, the fresh air that passes through the air conditioning system on top of the train differs from that in the ambient environment, since it is a mixture of the fresh external air provided through ventilation shafts with the existing air flow inside the tunnel. Compared with the ambient air, the mixed outside ‘‘fresh”
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air contains a larger volume of contaminants. Therefore, special attention should be paid to the accumulation of contaminants in the air conditioning system, of dust around the ventilation ducts, and of bacteria on the walls of the airflow conduits. In particular, the air filters of the air conditioners inside the train should be replaced frequently and the ventilation equipment and its conduits should be cleaned often to maintain high air quality. However, the opening of train doors leads to deterioration of the compartment’s air quality. Because dirty air on the platform can enter compartments after the train door opens, the PM2.5 and PM1.0 concentrations inside the train increased by an average of about 23% and 6%, respectively, after the train doors opened. The longer each door opening lasts or the worse the air quality on the platform, the worse the air quality will become inside the train. In addition, some studies reported that the greater the train frequency, the higher the PM levels in underground railway platforms (Salma et al., 2007; Carteni et al., 2015), as a consequence, the worse the air quality inside the train will become. Conclusions To improve our understanding of the distribution of fine and ultrafine particulate matter in the Shanghai subway system, we measured the PM concentrations at the Songyuan Road and the Jiao Tong University stations in Line 10, at the Caoyang Road station in Line 11, and inside trains on Shanghai metro Line 10. The PM2.5 and PM1.0 values averaged 39.7 and 27.6 lg/ m3 at the stations, and 24.4 and 17.5 lg/m3 inside the trains. Obviously, the PM2.5 and PM1.0 concentrations inside the trains were lower than those on the platforms. The PM2.5 and PM1.0 concentrations on the platforms and in the train compartments were both positively correlated with values in the ambient air. PM concentrations on the platforms were influenced by the piston wind created by the approaching trains, and increased similarly whether westbound trains or eastbound trains were entering the station. Moreover, the effect of the piston wind varied among locations on the platform; the effect of the piston wind was stronger at both ends of the platform than in the middle. The PM concentrations inside the trains were also influenced by opening of the train doors. The PM concentrations increased during opening of the doors, and the increase was more remarkable for PM2.5 than for PM1.0. PM2.5 and PM1.0 concentrations were strongly and significantly correlated, suggesting that the particles are derived from the same sources. Based on these results, we discussed the factors that influence exposure of travelers to air pollution. We recommend the following measures to minimize the damage to human health caused by subway air pollution: reducing the time spent on the platform, waiting for trains at the middle part of the platform, regularly changing the working positions of the platform staff and giving them frequent breaks, replacing the filters of the air conditioner inside the train, and cleaning the air conditioning system and subway tunnels regularly. Acknowledgments This study was supported by Grants from the National Natural Science Foundation of China (projects No. 71373155 and 71428001), the Chinese Ministry of Education on the Key Projects of Philosophy and Social Sciences (project No. 13JZD025), the China Postdoctoral Science Foundation (project No. 2014M561481), and the Social Development of Metropolis and Construction of Smart City program (project No. 085SHDX001). 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