Analysis and interpretation of the particulate matter (PM10 and PM2.5) concentrations at the subway stations in Beijing, China

Accepted Manuscript Title: Analysis and interpretation of the particulate matter (PM10 and PM2.5) concentrations at the subway stations in Beijing, China Authors: Song Pan, Saisai Du, Xinru Wang, Xingxing Zhang, Liang Xia, Liu Jiaping, Fei Pei, Yixuan Wei PII: DOI: Reference:

S2210-6707(18)31127-2 https://doi.org/10.1016/j.scs.2018.11.020 SCS 1345

To appear in: Received date: Revised date: Accepted date:

12 June 2018 13 November 2018 14 November 2018

Please cite this article as: Pan S, Du S, Wang X, Zhang X, Xia L, Jiaping L, Pei F, Wei Y, Analysis and interpretation of the particulate matter (PM10 and PM2.5) concentrations at the subway stations in Beijing, China, Sustainable Cities and Society (2018), https://doi.org/10.1016/j.scs.2018.11.020 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Analysis and interpretation of the particulate matter (PM10 and PM2.5) concentrations at the subway stations in Beijing, China

a

IP T

Song Pan a, Saisai Du a, Xinru Wang b,*, Xingxing Zhang c, *, Liang Xia b, Liu Jiaping a, Fei Pei a, Yixuan Wei b Beijing Key Laboratory of Green Built Environment and Energy Efficient Technology, Beijing University

b

SC R

of Technology Beijing, 100124, China

Research Center for Fluids and Thermal Engineering, University of Nottingham Ningbo China 315100,

Department of Energy, Forest and Built Environment, Dalarna University, Falun, 79188, Sweden

N

c

U

China

M

A

Corresponding authors: [email protected]; [email protected]

Highlights：

The concentration of PM10 and PM2.5 were monitored simultaneously using real-

ED



PT

time instruments from October 9th to 22nd in both the inside and outside subway stations in Beijing, the characters were analyzed using General linear model (GLM)

CC E

and correlation approaches.



The average overall PM concentration ratio inside subway station is about 68.7%,

A

lower than that in outdoor condition at 79.6%.



In the areas of the station hall and platform, the real-time PM10 and PM2.5 concentrations varied periodically along with the train frequency.



Outdoor environment is mutually correlated with PM concentration, while the impact of passenger number and temperature & humidity on the station PM

concentrations was less.

IP T

Abstract

SC R

The particulate matters (PM10 and PM2.5) inside urban subway stations greatly influence indoor air quality and passenger comfort. This study aims to analyze and interpret the

concentrations of PM10 and PM2.5, measured in several subway stations from October

U

9th to 22nd, 2016 in Beijing, China. The overall methodology was based on the Statistical

N

Package for Social Science (SPSS) software while General linear model (GLM) and

M

A

correlation analysis were further applied to examine the sensitivities of different variables to the particle concentrations. The data analysis showed the average overall PM

ED

concentration ratio inside subway station is about 68.7%, which was much lower than

PT

outdoor condition (79.6%). In the areas of the station hall and platform, the real-time PM10 and PM2.5 concentrations varied periodically along with the train frequency, but

CC E

they became fluctuated significantly when the train door was opened. In the area of working and operation offices, all rooms had much higher PM concentrations than the

A

outside environment, in which the facility room reached the highest level, while the closed meeting room had the lowest PM concentration. Correlation analysis results indicated that PM10 and PM2.5 concentrations were mutually correlated (average R2=0.854), and a strong linear correlation (R2=0.897) of the subway-station PM concentrations to the outdoor atmospheric PM concentrations was found. Nevertheless, the impact of

passenger number and temperature & humidity on the station PM concentrations was less, when compared to the outdoor environment. This paper is expected to provide useful information for further research and design of effective prevention measures on PM in local subway stations, towards a more sustainable and healthy built environment in the city

IP T

underground.

SC R

Keywords: PM10, PM2.5, Influencing factors, Correlation analysis, subway station

1. Introduction

U

Subways have been dubbed "the lifeline of urban development" (Pan et al., 2013), and is

N

regarded as comfortable, convenient, environment friendly, fast and safe (Wang et al., 2018).

M

A

The subway has been under rapid construction in recent decades. Nowadays, there are millions of passengers choosing the subway as their daily transportation mode. Therefore,

ED

air quality in subways is becoming more and more important (Wang et al., 2017), especially

PT

when the inhaled PM (particle matter kinetic diameter is less than 10 micron) at the subway

CC E

may influence the health of both passengers and working staff.

In recent years, numerous researchers have investigated PM in subway stations. The geographical areas, indicated in Fig. 1, discussed include Montreal (Boudia et al., 2006),

A

New York (Ruzmyn et al., 2015), Los Angeles (Kam et al., 2011), Paris (Bachoual et al., 2007; Tokarek and Bernis et al., 2010), Mexico city (Hernandez-Castillo et al.,2014; Mugica et al., 2012; Gomez-Perales et al., 2004), Shanghai (Li et al., 2012; Huan et al., 2014), Beijing (Jing et al., 2012, Guan et al.,2018), Guangzhou (Chan et al., 2002), Xi’an (Gao et al., 2015),

Suzhou (Cao et al., 2017), Tianjin (Wang et al., 2016), Taipei (Kam et al., 2011), Seoul (Son et al., 2012; Kim et al., 2008), Fukuoka (Ma Chang-Jing et al., 2012), Tehran (Hosein et al., 2014), Puna (Delbari et al., 2016), Istanbul (Sahin et al., 2012), Bracelona (Moreno et al., 2014), Stockholm (Klara et al., 2012), Helsinki (Aarnio et al., 2005), Milan (Colombi et al.,

IP T

2013), Rome (Perrino et al., 2015) London (Pakbin et al., 2010; Adams et al., 2001) and

PT

ED

M

A

N

U

SC R

Birmingham (Harrison et al., 1997).

Fig. 1 Geographical locations of research into PM2.5 in subways

CC E

The majority of this research was conducted via on-site testing of PM at different sites in the subway stations, i.e. the station hall, carriages etc., with corresponding analysis on the

A

distribution and physicochemical properties of PM2.5 in each location in the subway station. Lepeule et al (Lepeule, Laden et al. 2012) measured the concentration of the particles in six different cities for eight years in eastern of America. Then they analyzed the correlation between death rates and the particles’ size and they found that correlation between mortality and the PM2.5 was strong. Pun et al (Pun, Kazemiparkouhi et al. 2017)

reported that the mortality would increase 1.5% when the average concentration of the PM2.5 increases 10 μg/m3, Pope C et al (Pope, Burnett et al. 2002) also got similar results. Moreover, some researchers have reported that the concentration of particles in the subway were much higher than the outside environment and they were much more gene

IP T

toxic which could cause more healthy problems to public (Kunzli, Kaiser et al. 2000, Guo, Hu et al. 2014). Now there are many studies, which measured the particles in public

SC R

transportation including the Metro, buses and so on (Adams, Nieuwenhuijsen et al. 2001, Adams, Nieuwenhuijsen et al. 2001, Li, Bai et al. 2007, Cheng, Lin et al. 2008, Cheng and

U

Yan 2011, Kam, Cheung et al. 2011, Cheng, Liu et al. 2012). In these studies, the factors

N

influencing concentration and distribution of PM in subway stations were seasons weather,

A

time, traffic density, brake system, ventilation system, passenger density, depth, design,

M

aboveground or underground, operating duration, location, piston effect, outdoor traffic

ED

(Park et al., 2008, Li et al., 2012, Cheng et al., 2008, Castillo H et al., 2014, Moreno et al., 2014, Midander et al., 2012, Aarnio et al., 2005, Boudia et al., 2006); and the measured

PT

places or components in subway stations were outdoor climate conditions, platforms,

CC E

passenger carriages, driver compartments, station offices, rest areas, ticket offices, station precincts (Li et al., 2012, Hosein et al., 2014, Park et al., 2008, Huan et al., 2014, Ruzmyn et al.,2014, Kim et al., 2008, Cheng et al. 2008, Moreno et al., 2014, Midander et al., 2012,

A

Kim et al., 2014, Aarni et al., 2005). According to these studies, it is likely that the PM analytical results would be different if the studies were performed in another country or even in the same city, as the influencing factors and the measured targets/components may vary. To make a more practical assessment, it is necessary to conduct the local measurement

and analysis.

On the other hand, China is now building up thousands of kilometers subway systems in large-and medium-sized cities during the recent urbanization (Yang et al., 2018). The increased subway transportation has created significant necessity to investigate air quality

IP T

issues in local subway stations. In Beijing, there are 18 lines in total and the whole length

SC R

is 554 kilometers while over ten million people take the subway daily in Beijing by spending about one hour even longer time underground. Unfortunately, there are very few studies concentrated on the PM distribution and control. For instance, the Chinese indoor air

U

quality standard sets 75 μg/m3 for the limits of PM2.5 concentration in a building, but the

A

N

subway station is excluded as there is little PM data and the relevant analysis. In addition,

M

many studies in the past only measured the particles within a certain or a short time, which were discontinuous and led to incomprehensive conclusion (Adams, Nieuwenhuijsen et al.

ED

2001, Kam, Cheung et al. 2011, Mugica-Alvarez, Figueroa-Lara et al. 2012, Guo, Hu et al.

PT

2014).

CC E

As a result, this paper aims to fill these research gaps, i.e. (1) limited studies on PM at Beijing subway stations; (2) short-term or discontinuous measurement on PM, by monitoring the concentration of PM10 and PM2.5 simultaneously using real-time

A

instruments from October 9th to 22nd in both the inside and outside subway stations in Beijing. The measured data is further analyzed by means of Statistical Package for Social Science (SPSS) software, General linear model (GLM) and correlation approaches, to characterize PM concentrations in the station and to exam the associate sensitivity of

various impacting factors. The overall research result is expected to provide useful information for further effective prevention measures on PM in urban subway stations, towards a more sustainable and healthy built environment in the city underground.

The whole paper is structured as following: section 2 introduced the research meaning that

IP T

monitor and analysis of the subway particular matters have a vital impact on public health

SC R

in the urban transportation systems. In section 3, the measured locations, equipment and methodology were explained. Section 4 presented the measurement results, in terms of

the concentrations of PM2.5 and PM10 in the tested subway stations and the adjacent

U

atmosphere. Based on these data, correlation analysis and variance analysis were completed

M

source of the PM2.5 in the subway tunnel.

A

N

in section 5. Further composition analysis was described in section 6 in order to find the

ED

2. Monitor and analysis of particulate matter (PM) at subway stations towards

PT

healthy transportation systems in cities

During the recent large-scale urbanization of China, as well as the rest of the world,

CC E

increased traffic has created significant issues in large-and-medium sized cities. One issue is that commuting by subway greatly affects personal exposure to the inhalable particulate

A

matter, which can be subdivided into coarse particles (the part of PM10>2.5 microns), fine particles (fine mode particulate, PM2.5) and ultrafine particles (ultrafine particulate and PM0.1). The particulate mode PM2.5-10 can become trapped in the trachea and bronchi of humans, with it either being swallowed or discharged from the respiratory system via coughing. However, the fine mode particulate matters - PM2.5 can easily enter

the alveolar of the lungs and move directly into the blood. PM2.5 therefore has a much greater adverse effect on human health (Araji et al., 2017). Many epidemiological studies conducted over recent decades have indicated that there is a positive correlation between the concentration of particulate matter and morbidity from respiratory system, heart and

IP T

lung diseases, particularly for more susceptible sections of the population such as children and the elderly. For instance, it has been reported that the probability of illness rises by 4%

SC R

in cardiopulmonary diseases, and by 8% in lung cancer when PM2.5 increases 10 μg/m3 (micrograms per cubic meter) (Li et al., 2006; Aarnio et al., 2005; Bauling et al., 2003;

U

Nacera et al., 2002; Kijinzli et al., 2000). The number of deaths caused annually by

A

M

Zhou et al., 2012; Zhang et al., 2013).

N

particulate pollution (mostly by PM2.5) is around 200,000 in Europe (Seung et al., 2014;

People are spending greater amounts of time in subways in the modern world. As early as

ED

1993-1994, the United States Environmental Protection Agency (EPA) demonstrated that

PT

the average amount of time people spent in subways was around 7.2% per day (Ting et al., 2015). It is report that Koreans also spend about 1.73 hours per day in the subway

CC E

according to the Ministry of Environment in 2009 (Kim et al., 2014). In the near future, subway travelling times are expected to become even longer due to high development of

A

urban public transportation. It is valuable to discuss whether or not long-time exposure in subway environment would affect human’s health. Some researchers find that the high concentrations of PM2.5 in subways long-time exposure will seriously harm human health (Cheng et al., 2014; Lippmann, M. et al., 2014; Janssen, N. A. et al., 2013; Tsai DH. et al., 2010; Karlsson et al. et al., 2005; Brook et al., 2004; Peters A. et al., 2004; Pope et al., 2002).

Some results shown that the hazard from PM2.5 in subways is up to ten times greater than that found at ground level (Knibbs, L.D., 2010; Aarnio et al., 2005; Karlsson H.L., 2005; Johansson et al., 2003; Adams et al., 2001). A comparison of genotoxic and inflammatory effects of particles, generated by wood combustion, a road simulator and collected from

IP T

street and subway, indicated that the particles form subway caused more damage to DNA

SC R

than other particles (p < 0.001) likely due to redox-active iron (Karlsson et al., 2006).

As a result, the influence of particulate matters in subways on human's health and safety has become a particularly important issue to address in current urban transportation

U

systems. Monitor and analysis of the PM concentrations and their variation patterns, as

A

N

well as generating resource in the subway stations, will be the essential steps for further

M

improvement of the overall air quality including high effect filter research and equipment study related in the healthy city transportation systems, as the monitor and analysis support

PT

3. Methodologies

ED

reference value for further study.

CC E

3.1 Monitoring subway station and measuring sites

This paper tested 4 subway stations totally in Beijing, as illustrated in Fig. 2, which included

A

3 most frequent subway lines, i.e. line 8 (green), line 10 (red), and line 14 (yellow). The measured subway stations were respectively Anhuaqiao station in line 8, Jinsong and Panjiayuan stations in line 10, and Beigongda-Ximen station in line 14. The reasons for selecting these stations are stated as below:

IP T SC R

Yellow-line 14; Purple-line 10 phase 1 and Red-line 10 phase 2; Green-line 8

U

Fig.2 Map of measured stations in Beijing subway lines

A

N

In Beijing, there are 2 different isolating door systems to partition the train tunnel and the

M

platform/public areas for security: (1) safety door system designed at different height, one is full-height safety door and the other is half-height safety door (only line 1 in Beijing

ED

subway), and (2) full-height screen door system on the platform. In terms of full-height

PT

safety door system, as used mostly, the Beigongda-Ximen station in line 14 was selected as a typical subway station equipped with safety door system. The schematic diagram of

CC E

subway structure and measuring points for the public areas (hall and platform) are shown in Fig. 3. From the fig 3 (a), the subway station is consisted of two levels: the ground level

A

for the entrance and underground level. The underground level includes hall for tickets and security check, while the platform for ride. We set one measurement point in the entrance for ground condition, while for the underground part, Although, the stations are different, including the size, depth, the system, the location and so on, the construction is the same. We put all testing points in the same picture to make it clearer for the reader to

have a total understand about all testing points. In fig 3 (b) and (c), there were respectively 2 measuring points (1 and 2) for hall area and 3 measuring points (3-5) for platform area. In terms of screen door system, Anhuaqiao station in line 8 was chosen as a representative station for the testing in the tunnel (point 6) and staff ’s working areas. In line 10, Jinsong

IP T

(phase 1) and Panjiayuan station (phase 2) were constructed at different phases. Jinsong station was opened from July at 2008 and Panjiayuan station opened at the end of 2012.

SC R

These two stations were chosen in order to compare the effect of service time on

concentration, where the other factors are similar, such as train frequency, structure of the

(a)

A

CC E

PT

ED

M

A

N

U

station, the depth, and the air condition system are the same, even the outside environment.

(b)

(c)

Fig. 3 Schematic diagram of subway structure (a); measuring points for (b) the hall and (c) platform

3.2 Monitoring method and equipment

The detailed parameters for the testing equipment are presented in Table 1. A portable Dusttrak II Aerosol monitor (Model 8532, TSI, USA) was used to monitor the concentration of PM10, PM2.5, temperature and humidity. Such equipment includes datalogging and light-scattering laser photometer for real-time aerosol mass readings. The data

IP T

logging interval was set at 1 minute. In order to identify the real fluctuation of PM10 and PM2.5 concentration in the subway, the data was measured for about continuous 2 weeks

SC R

during the testing period. All the testing equipment have been calibrated before conducting the measurement.

N

U

Table 1: Instrument parameters of the testing equipment

Sensor type

0.001-400mg/m3

90。Light scattering

adjustable 1 to 60 seconds

Flow accuracy

Zero Stability

Resolution

ED

M

A

Range

Time constant

±0.002 mg/m3 24 hours at

±0.1% of reading of 0.001

internal flow controlled

10 sec time constant

mg/m3, whichever is greater

Data logging

Log internal

Operational temp

45 days at 1 minute samples

1 second to 1 hour

0 to 50 ℃

Particle Size Range

Temperature coefficient

Storage temp

approximately 0.1 to 10 μm

±0.001 mg/m3 per ℃

-20 to 60 ℃

A

CC E

PT

±5% factory setpoint

3.3 Data analysis method

Statistical Package for Social Science (SPSS) was used to analyze data monitored. General

linear model (GLM) was applied to examine the effect of the ground concentration on the subway. The general linear model or multivariate regression model is a statistical linear model. It may be written as (1):

(1)

IP T

Y = XB + U

where ,Y is a matrix with series of multivariate measurements (each column being a set of

SC R

measurements on one of the dependent variables), X is a matrix of observations on independent variables that might be a design matrix (each column being a set of

U

observations on one of the independent variables), B is a matrix containing parameters

N

that are usually to be estimated and U is a matrix containing errors (noise). The errors are

A

usually assumed to be uncorrelated across measurements and follow a multivariate normal

M

distribution. If the errors do not follow a multivariate normal distribution, generalized

ED

linear models may be used to relax assumptions about Y and U. Correlation analysis was to mainly test the relationship among PM10, PM2.5 at the different locations. Correlation

PT

analysis is a method of statistical evaluation used to study the strength of a relationship

CC E

between two, numerically measured, continuous variables. The Pearson simple correlation coefficient was the calculation model:

A

ρ=

γ=

𝐸 [X − E(X)][Y − E(Y)] √𝐷(𝑋)√𝐷(𝑌) ∑𝑛𝑖=1(𝑥𝑖 − 𝑥̅ )(𝑦𝑖 − 𝑦̅)

√∑𝑛𝑖=1(𝑥𝑖 − 𝑥̅ )2 √∑𝑛𝑖=1(𝑦𝑖 − 𝑦̅)2

(2)

(3)

where: E and D is the mathematical expectation and variance, respectively. ρ is overall

correlation coefficient,γis sample correlation coefficient (sample Pearson coefficient). X and Y are random variables. 𝑥𝑖 , 𝑦𝑖 represent the observations. ①-1≤γ≤1, the greater the absolute value of γ, the stronger the degree of correlation between the two variables. ② 0<γ≤1, indicating that there is a positive correlation between the two

IP T

variables. If Υ=1, there is a completely positive correlation between variables. ③-1 ≤γ <0, indicating a negative correlation between the two variables. If Υ= -1, there is a

SC R

completely negative correlation between variables. ④ Υ=0, indicating that there is a wireless correlation between the two variables.

N

U

4. Results and discussion

A

According to standard of China, there are 6 levels in total for the atmosphere environment

M

along with the average concentration value of the PM2.5 for 24 hours. The first level is

ED

excellent with the concentration range of PM2.5 is 0-50 ug/m³. The second level is 50100 ug/m³ and the light pollution (100-150 ug/m³) is ranked as the level 3. Moderate

PT

pollution and heavy pollution are 150-200 ug/m³ and 200-300 ug/m³, respectively.

CC E

Situations over 300 ug/m³ are serious pollution, level 6. To compare the internal subway pollution against the external atmosphere environment, we chose the data that was different from outside pollution level, especially for the level 6, which is unusual but could

A

cause serious problems to public health.

4.1 PM10 and PM2.5 for the public areas

Although Kim et al (Kim, Kim et al. 2008) have measured the concentration of different

location in the subway including the passenger carriages, rest areas, tickets office and station precincts in Seoul, the measure process was discontinuous and most of other researchers (Park and Ha 2008, Cheng, Liu et al. 2012, Kim, Ho et al. 2012, Midander, Elihn et al. 2012, Guo, Hu et al. 2014, Kamani, Hoseini et al. 2014, Ma, Shen et al. 2014)

IP T

only focused on the platform in the subway. The results about the average real-time concentration of PM10 and PM2.5 at different locations in the public areas at the

SC R

Beigongda-Ximen station is presented as Table 2. Six representable days were picked up when the outside environment pollution ranked at different levels in Beijing. The

U

concentration data for the outside environment was the average value during the measuring

N

period. There were three measuring points at the platform and two at subway hall, while

A

an average value of the measurements was calculated for comparison. All the temperature

M

and humidity were also turned into average values due to a small variation.

ED

Table.2 Measuring concentration and standard deviation of the Beigongda-

Location Outside Hall Platform Outside Hall Platform Outside Hall Platform Outside Hall Platform

CC E

Date

PT

Ximen station

10.9 Sunday

A

10.10 Monday

10.11 Tuesday

10.12 Wednesda y

PM2.5(ug/m³) PM10(ug/m³) Non-peak Peak Non-peak Peak 18±5 36±9 33±4 64±8 53±8 65±8 110±17 148±13 61±5 76±12 128±12 160±8 92±6 108±8 127±5 147±16 130±14 146±26 145±12 165±18 165±12 168±23 167±19 187±15 216±35 228±34 439±76 459±26 151±14 179±16 316±31 356±28 161±24 154±25 339±51 378±45 80±30 118±38 102±58 145±18 98±18 138±29 159±45 156±13 126±26 156±34 175±52 178±18

Temp (℃) 18 23 23 20 22 23 25 24 26 20 23 24

Humid (%) 36 42 42 40 45 30 51 54 50 37 40 40

10.19 Wednesda y 10.20 Thursday 10.21 Friday

70 54 58 45 34 37 37 38 40 46 42 38 46 41 44 77 65 74 84 73 68 55 45 48 70 55 48

IP T

SC R

10.18 Tuesday

536±56 21 468±41 27 446±46 25 294±14 23 287±25 24 264±21 264±21 26 309±36 17 298±25 23 293±35 25 269±25 18 189±14 24 224±22 26 101±18 21 104±8 23 138±12 25 520±18 20 483±28 23 403±80 20 690±24 20 641±73 23 425±89 24 189±17 16 216±12 22 245±8 24 95±4 11 110±14 24 176±3 23

U

10.17 Monday

525±82 462±41 443±42 286±26 276±8 245±14 245±14 276±15 296±24 265±28 215±38 178±19 193±26 96±17 96±5 118±8 451±51 384±43 342±36 578±29 561±85 533±181 138±8 178±27 194±18 68±19 98±15 156±5

N

10.16 Sunday

345±2 312±16 298±22 265±32 219±17 168±14 217±23 224±19 215±17 169±26 176±15 198±20 53±7 65±3 78±6 380±4 337±14 319±32 398±16 349±29 250±51 131±25 163±15 189±14 57±34 72±26 111±7

A

10.15 Saturday

326±3 263±17 258±20 242±34 196±26 158±20 186±43 195±34 183±28 117±37 146±29 174±32 41±8 48±2 56±3 261±21 235±16 205±17 371±12 317±40 291±80 87±16 135±16 165±6 43±23 68±16 93±23

M

10.14 Friday

Outside Hall Platform Outside Hall Platform Outside Hall Platform Outside Hall Platform Outside Hall Platform Outside Hall Platform Outside Hall Platform Outside Hall Platform Outside Hall Platform

ED

10.13 Thursday

PT

It is observed from the Table 2, the pollution in the subway station varied in a wide range

CC E

along with the outside environment, where the pollution level was from level 1 to level 6. In general, the concentrations of PM10 and PM2.5 were higher during the peak period than that at the non-peak period. The lowest PM concentration values were observed on

A

17th October 2016 when the outside environment was excellent at level 1. The lowest average values of PM10 (PM2.5) were 96 ug/m³ (48 ug/m³) and 118 ug/m³ (56 ug/m³) respectively in the hall and the platform. On the other hand, the highest PM concentration values occurred on 19th October when the pollution of the outside environment was at the

serious level (PM2.5 was 371 ug/m³) in Beijing. The concentration of PM2.5 and PM10 were found at 349 ug/m³ and 641 ug/m³ in the hall during the peak time; while they were 250 ug/m³ (PM10) and 425 ug/m³ (PM2.5) in the platform during the peak period. It is obvious from Table 2 that the PM concentrations inside the subway station were not always

IP T

larger than that in the outside. For instance, the measurements of subway PM concentrations, on dates of 11th - 15th, 18th, 19th Oct, 2016, were smaller than that in the

SC R

outdoor environment. Such observation is different from the conclusion from some

existing studies. For instance, Park and Ha (Park and Ha 2008) reported that the average

U

concentration of the PM10 and PM2.5 at the subway platform were higher than the

N

outside all the time. This conclusion is only true when the outside concentration of the

A

PM2.5 was lower than 150 ug/m³ (level 3), according to the testing results. In contrast,

M

when the pollution of the outside environment was over 200 ug/m³ (level 5), the result

ED

became opposite. In Fig. 4, the real-time fluctuation of the PM10 and PM2.5 at the hall

A

CC E

(a)

PT

and platform were illustrated along with the outside PM values.

SC R

IP T

(b)

U

Fig. 4 The real-time concentration at hall (a) and platform (b)

N

The detailed variation of PM concentrations against the train frequency are shown in Fig.

A

5 (a) and (b), by using the non-peak period data of date 9th Oct as an example. The

M

frequency of the train was 7 min/times. In the hall area, the concentration of PM10 and

ED

PM2.5 ranges were respectively 40-61 ug/m³ and 78-135 ug/m³, as in Fig. 5(a). In the platform, the concentration ranges of PM10 and PM2.5 were 112-159 ug/m³ and 52-75

PT

ug/m³, respectively, as seen in Fig. 5(b). The concentrations of PM10 and PM2.5 showed

CC E

periodic changes in nearly 7 minutes interval as same as the train frequency. This is mainly because the real-time PM concentration fluctuation were affected significantly by the piston wind effect caused by the train. There is about one-minute delay for PM

A

concentration changing after the train arrived in platform, and the changing time in the hall was a little delayed at about two to three minutes than that in the platform, owing to the construction structure of the subway station.

SC R

IP T

(a)

ED

M

A

N

U

(b)

PT

Fig. 5 The real-time concentration in ninth (a) for hall and (b) for platform

CC E

Table 3 Train exercise table during the measurement period

A

Train Status

Time

Entering Parking

Starting Leaving

15:33

15:34

15:37

15:38

15:45

15:46

15:49

15:50

15:57

15:58

16:01

16:02

16:09

16:10

16:13

16:14

16:21

16:22

16:25

16:26

16:33

16:34

16:37

16:38

16:45

16:46

16:49

16:50

IP T

4.2 PM10 and PM2.5 concentrations in the train and in the employee’s working areas

SC R

In addition to the hall and the platform, the interior environment within the subway train at different stations were measured in terms of the PM concentrations. The measuring

U

points were located at the middle of two doors and the testing results are shown in Fig. 6.

N

it was measured on the date when the outdoor concentration of PM2.5 was 265 ug/m³.

A

The equipment was set at 1min/time and there were one to three measurements during

M

train in the tunnel. In Fig. 6, the blue points represented the time that the train arrived the

ED

station and the train door opened. It was clear that the PM2.5 fluctuation was huge from 220 ug/m³ to 370 ug/m³, which might be caused by the passenger flow and the air pressure

PT

changes between the platform and train when the train door opened. Similar to the study

CC E

made by Park and Ha (Park and Ha 2008), who reported that when the subway train doors open, the particle concentration of PM10 and PM2.5 would show temporary increase for

A

the underground stations.

SC R

IP T

(a)

ED

M

A

N

U

(b)

PT

Fig. 6 PM concentration of train at different stations in line 14 (a)PM2.5; (b)PM10

CC E

The air quality in the employee’s working areas is important for the subway staff. It is the first time that the concentration of PM10 and PM2.5 could be measured in the working areas as these areas are usually prohibited from entering. There were totally 8 kinds of

A

rooms measured in Anhuaqiao station (line 8), as showed in Fig. 7. During the measurement, only lounge, working office and control room were equipped with the mechanical ventilation system to supply fresh air; while the meeting room was always closed. The measurement was conducted on 21th Oct 2016, when the outdoor environment

was lightly polluted at level 3, with the average PM2.5 was 131 ug/m³ and PM10 was 158 ug/m³. In the ventilation system, the existed ventilation system cannot filter PM2.5 by using the coarse filter and medium efficiency filter. According to the testing results, the PM concentrations at all these rooms were higher than the outdoor data. The highest

IP T

polluted areas were the equipment areas with facilities and poor ventilation, while the

SC R

closed meeting room had the lowest PM pollutions.

ED

M

A

N

U

(a)

A

CC E

PT

(b)

Fig. 7 Measuring results of working areas (a) PM2.5; (b) PM10

4.3 The ratio and correlation of PM2.5 to PM10

The percentage and the correlation of PM2.5 that accounted for PM10 were different at

different locations (Table 4). The average ratio of PM2.5 to PM10 outside the subway was 79.6% that was slightly higher than that in those locations inside subway including the train at about 68.7%. The working areas had the lowest ratio of PM2.5 to PM10 at 47.6%, while the ratio in the platform and the hall areas were almost 68.6% and 61.2%, respectively.

IP T

Nevertheless, the ratio of the PM2.5 to PM10 in subway station was much lower than that in Seoul (83.5%) (Park and Ha 2008, Kim, Ho et al. 2012) , Guang Zhou (73.8%) (Chan,

SC R

Lau et al. 2002) and Hong Kong (79%) (Chan, Lau et al. 2002) etc.

Hall

Platform

Train

Average

the ratio, %

79.6

61.2

68.6

68.7

47.6

65.14

R

0.921

0.911

0.956

0.903

0.92

0.9222

R Square

0.848

0.829

0.93

0.815

0.846

0.8536

Adjusted R Square

0.845

0.827

0.93

0.813

0.843

0.8516

0.000

0.000

0.000

0.000

0.000

0.000

N

PT

ED

U

Outside

M

PM2.5 to PM10

Sig.

Working office

A

Table 4 The ratio (%) and correlation matrix of PM2.5 to PM10

CC E

The correlation analysis indicated that PM10 and PM2.5 were significantly correlated at Sig<0.01, although the values were different at different locations. The highest coefficient (R2) was 0.930 at the platform, while the lowest was 0.815 in the train. The results were

A

similar to the researches made by Dong-Uk Park et al (0.884) (Park and Ha 2008), Chan et al (0.836, 0.751, 0.738) (Chan, Lau et al. 2002) and so on. The high coefficient indicated that the PM2.5 could predict from the concentration of PM10 as the two particles were highly correlated.

5. The analysis of influencing factors

There are many factors that could affect the concentration of PM2.5 and PM10, which could be divided into four kinds: external factors, internal factors, human factors and operational factors. The external factors include seasons, the outside weather and the

IP T

outdoor traffic. The brake system, ventilation system, the subway depth, the structure of

SC R

the subway and the service time are internal factors. For the human factors, the main two are passenger flow and the measuring time. The train frequency and the piston effect

caused by the train are the operational factors. Moreover, these factors are mixed and

U

function is turbulent. Among all these factors, Huan Ma et al (Ma, Shen et al. 2014)

A

N

reported that the depth of the subway could affect the concentration of PM10 and PM2.5

M

and the concentration would increase along the depth. According to our measurement, the concentration of the particle did not always increase along the depth, because the

ED

concentration at the platform was not always higher than the hall. Kamani H et al (Kamani,

PT

Hoseini et al. 2014) clarified that the service time of the subway would cause effect on the concentration of the PM10 and PM2.5. Some other studies claimed that the mode of air

CC E

condition or the ventilation system could also lead to the change of the concentration of the PM10 and PM2.5 (Brook, Franklin et al. 2004, Murruni, Solanes et al. 2009, Moreno,

A

Perez et al. 2014, Juraeva, Ryu et al. 2016). In this section, based on the results of PM concentrations in section 3, the correlation analysis and multivariate analysis were executed to analyze the influencing factors of the PM concentrations, including the outdoor environment, the passenger flow, the temperature and humidity and the subway service time. As the PM2.5 and PM10 were highly correlated, we only took the PM2.5 data for the

comparison.

5.1 The correlation analysis

Table 5 lists the correlation analysis results. The outdoor environment had dominating

IP T

effects on the PM concentration in the subway platform (R2=0.897). Combined with the general linear analysis, the linear regression equation was Y=75.370+0.877X, which also

SC R

indicated the high correlation of the outdoor environment to the PM2.5 concentrations in

the subway station. The result was in consistence with that from Kamani H et al (Kamani,

U

Hoseini et al. 2014).

N

Table 5 The correlation analysis of different factors R

R Square

Adjusted R Square

Sig.

Outside

0.947

0.897

0.871

0.00

Passenger flow

0.245

0.246

0.243

0.135

0.135

0.138

0.134

0.159

ED

M

A

Factors

PT

Temperature & humidity

CC E

In Table 5, the passenger flow had very low correlation with the PM 2.5 concentration at R2=0.246 and Sig=0.135>0.05. It is different from the speculation made by the Huan Ma et al (Ma, Shen et al. 2014), who inferred that the passenger flow would cause significant

A

influence on the PM2.5 concentration. Fig. 8 characterizes the real-time change of PM2.5 concentration along the passenger flow, which shows the real character of PM10 and PM2.5 at the hall. The red points were the time when the passenger flow increased. After the passenger flow increasing, there was usually a slight fluctuation of the concentrations,

but the average PM concentrations did not have obvious change. So we argue that the passenger flow could lead to fluctuation of the concentration, but does not affect the average PM2.5 concentration in subway, where the PM concentrations are mainly affected

ED

M

A

N

U

SC R

IP T

by the outdoor environment and the piston wind produced by the train.

PT

Fig. 8 Concentration fluctuation of PM2.5 at hall

CC E

For the temperature and humidity, the correlation was even lower at R2=0.138 and Sig=0.159>0.05. The results from multivariate analysis, F=1.265 and Sig=0.295, also

A

demonstrate the low correlation of the temperature and humidity on PM2.5 concentration.

5.2 The effect of the subway service time

The effect of the subway service time the on the PM2.5 concentration was analyzed between the Jinsong and Panjiayuan stations at the line 10 in Beijing, where the other

factors are similar, such as train frequency, structure of the station, the depth, and the air condition system are the same, even the outside environment, except the service time, as the Jinsong has served for ten years and Panjiayuan only served for five and half years until June of 2018. Even the passenger flow and the outside environment at these two stations

IP T

were similar, since they are just next to each other. The measured points were similar with the Beigongda-Ximen station. Fig. 9 compares the average PM2.5 concentrations at these

SC R

two stations. The real-time PM2.5 concentration in the Jinsong station was higher than

that in the Panjiayuan station in all cases of the pollution levels. This could be the reason

CC E

PT

ED

M

A

N

higher accumulation of the PM2.5 concentration.

U

that the Jinsong station has a much longer service time of the subway, which results in

A

Fig.9 The PM2.5 comparison between Jinsong station and Panjiayuan station

6. Source analysis of the chemical composition of PM2.5

The chemical composition of PM2.5 was examined by comparing the air between the outdoor and the tunnel in a subway station with screen door system at Anhuaqiao station

(line 8). Many existing studies have reported the chemical composition of the PM2.5 in the subway, which are mainly metallic elements including Fe, Mg, Na, K, Ca, Cu, Mn, Zn, Ba, Ni, Pb, Cr et and the nonmetallic elements such as O, S, C, V, Sr, Mo, Si et (Kam, Cheung et al. 2011, Kam, Ning et al. 2011, Midander, Elihn et al. 2012, Guo, Hu et al. 2014,

IP T

Kamani, Hoseini et al. 2014, Moreno, Perez et al. 2014, Byeon, Willis et al. 2015). However, there is no study that can explain the reason where and how the particles were produced.

SC R

For the exact source of PM2.5, researchers (Birenzvige, Eversole et al. 2003, Chillrud,

Epstein et al. 2004, Aarnio, Yli-Tuomi et al. 2005, Chillrud, Grass et al. 2005, Kim, Kim et

U

al. 2008) only made the speculation that the braking friction between the train and the track

N

may produce the PM2.5 without evidence. To specific the PM2.5 source, the main metallic

A

elements compositions of PM2.5 in the subway tunnel and the outdoor environment were

M

compared.

ED

The measurement site was at the tunnel of station located at the Anhuaqiao station in line

PT

8. This station has the screen door system and the tunnel almost had no effect by the external factors. Meanwhile, there was a measuring point outside the station. The real-time

CC E

measuring time was consecutively carried out from 11:30 to 12:30. Then we tested the mainly metal composition of the PM2.5 by using the ICP-AES (model: ICAP 6300). The

A

test temperature was 24℃ and the humidity was 39%. The testing was conducted according to the Chinese standard HJ777-2015 and HJ657-2013. The results were present in Table 6. The weight was the content per unit mass.

Table 6 Chemical composition of main metallic elements

Subway tunnel (μg/g)

Outside environment (μg/g)

Fe

507

292

Al

9615

9328

K

5170

4838

Na

20775

18730

Mn

14.6

16.4

Ca

8512

8208

Mg

650

Zn

6488

Ba

10830

Pb

6.06

SC R

IP T

Elements

560

9602 6.12

M

A

N

U

5758

Compared with the outdoor environment, the total content of Fe in the subway tunnel

ED

was almost twice as much as that in the outdoor air; while other metallic elements’ content

PT

was similar between the two measure points. As a result, there must be the source of PM2.5 in the subway which can produce the element-Fe. However, the detail generation process

CC E

of PM2.5 is still unclear since the physical characteristic of the PM2.5 was irregular and complex, according Midander K et al (Midander, Elihn et al. 2012). Further research is

A

necessary to be done in this area.

7. Conclusion

In the Beigongda-Ximen station, the concentration of PM10 and PM2.5 changed a lot along with the outside pollution huge variety. At the hall and platform, the real-time PM10

and PM2.5 showed periodic changes and the time was the nearly same as the train frequency. For the train, when the door opened, the concentration of the PM10 and PM2.5 would fluctuate significantly. In the working area, all rooms’ pollution was higher than the outdoor environment; the highest pollution areas were equipment rooms, while the closed

IP T

meeting room had the lowest PM concentrations. The average ratio of PM2.5 to PM10 outside the subway was 79.6%, slightly higher than that in the locations inside subway

SC R

including the train at 68.7%. The working areas had the lowest ratio of PM2.5 to PM10 at

47.6%. The platform and hall for the public areas were 68.6% and 61.2%, respectively.

U

Correlation analysis indicated that PM10 and PM2.5 were highly correlated. The outdoor

N

environment and the service time of subway had significant effects on the concentration

A

of PM2.5, while the passenger and temperature & humidity did not have obvious influence

M

on it. Although the outdoor environment is the main source of the subway PM10 and

ED

PM2.5, there also exists the internal source of PM2.5 in the subway station and Fe is the largest element. Further research must investigate the physical mechanism behind such Fe-

PT

source generation.

CC E

Acknowledgement

The authors would like to appreciate the financial supported from the National Natural

A

Science Foundation of China (Grant Number: 51578011) and Beijing Natural Science Foundation (Grant Number: 3172041).

Declarations of interest: None

References

IP T

Aarnio, P., T. Yli-Tuomi, A. Kousa, T. Makela, A. Hirsikko, K. Hameri, M. Raisanen, R. Hillamo, T. Koskentalo and M. Jantunen (2005). "The concentrations and composition of and exposure to fine particles (PM2.5) in the Helsinki subway system." Atmospheric Environment 39(28): 5059-5066.

SC R

Adams, H. S., M. J. Nieuwenhuijsen and R. N. Colvile (2001). "Determinants of fine particle (PM2.5) personal exposure levels in transport microenvironments, London, UK." Atmospheric Environment 35(27): 4557-4566.

U

Adams, H. S., M. J. Nieuwenhuijsen, R. N. Colvile, M. A. S. McMullen and P. Khandelwal (2001). "Fine particle (PM2.5) personal exposure levels in transport microenvironments, London, UK." Science of the Total Environment 279(1-3): 29-44.

A

N

Araji, M. T., Ray, S. D., & Leung, L. (2017). "Pilot-study on airborne pm 2.5, filtration with particle accelerated collision technology in office environments". Sustainable Cities & Society, 28, 101-107.

ED

M

Bachoual, R, Boczkowski, J, Goven, D, Amara, N, Tabet, L, On, D, Leçonmalas, V, Aubier, M, Lanone, S.(2007). Biological effects of particles from the Paris subway system. Chem. Res. Toxicol, 20: 1426–1433.

PT

Birenzvige, A., J. Eversole, M. Seaver, S. Francesconi, E. Valdes and H. Kulaga (2003). "Aerosol characteristics in a subway environment." Aerosol Science and Technology 37(3): 210-220.

CC E

Brook, R. D., B. Franklin, W. Cascio, Y. L. Hong, G. Howard, M. Lipsett, R. Luepker, M. Mittleman, J. Samet, S. C. Smith and I. Tager (2004). "Air pollution and cardiovascular disease - A statement for healthcare professionals from the expert panel on population and prevention science of the American Heart Association." Circulation 109(21): 2655-2671.

A

Baulig, A, Sourdeval, M, Meyer, M, et, al. Biological effects of atmospheric particles on human bronchial epithelial cells: comparison with diesel exhaust particles. Toxicology in vitro, 2003, (17): 567-573 Boudia N, et al. (2006). Manganese concentrations in the air of the Montreal (Canada) subway in relation to surface automobile traffic density. Sci Total Environ;366(1):143-7. Byeon, S. H., R. Willis and T. M. Peters (2015). "Chemical Characterization of Outdoor and Subway Fine (PM2.5-1.0) and Coarse (PM10-2.5) Particulate Matter in Seoul (Korea)

by Computer-Controlled Scanning Electron Microscopy (CCSEM)." International Journal of Environmental Research and Public Health 12(2): 2090-2104. Cao, SJ, Kong, XR, Li, L, Zhang, W, Ye, ZP, Deng, Y. An investigation of the PM2.5 and NO2 concentrations and their human health impacts in the metro subway system of Suzhou, China. Environ Sci Process Impacts, 2017, 19, 666-675

IP T

Chan, L. Y., W. L. Lau, S. C. Lee and C. Y. Chan (2002). "Commuter exposure to particulate matter in public transportation modes in Hong Kong." Atmospheric Environment 36(21): 3363-3373.

SC R

Chan, L. Y., W. L. Lau, S. C. Zou, Z. X. Cao and S. C. Lai (2002). "Exposure level of carbon monoxide and respirable suspended particulate in public transportation modes while commuting in urban, area of Guangzhou, China." Atmospheric Environment 36(38): 5831-5840.

U

Cheng, G, Zang, JB. Test and analysis of PM2.5 concentration in subway system. (in chinese). Refrigeration technology, 2014, (5): 13-16

A

N

Cheng, Y. H., Y. L. Lin and C. C. Liu (2008). "Levels of PM10 and PM2.5 in Taipei Rapid Transit System." Atmospheric Environment 42(31): 7242-7249.

M

Cheng, Y. H., Z. S. Liu and J. W. Yan (2012). "Comparisons of PM10, PM2.5, Particle Number, and CO2 Levels inside Metro Trains Traveling in Underground Tunnels and on Elevated Tracks." Aerosol and Air Quality Research 12(5): 879-891.

PT

ED

Cheng, Y. H. and J. W. Yan (2011). "Comparisons of particulate matter, CO, and CO2 levels in underground and ground-level stations in the Taipei mass rapid transit system." Atmospheric Environment 45(28): 4882-4891.

CC E

Chillrud, S. N., D. Epstein, J. M. Ross, S. N. Sax, D. Pederson, J. D. Spengler and P. L. Kinney (2004). "Elevated airborne exposures of teenagers to manganese, chromium, and iron from steel dust and New York City's subway system." Environmental Science & Technology 38(3): 732-737.

A

Chillrud, S. N., D. Grass, J. M. Ross, D. Coulibaly, V. Slavkovich, D. Epstein, S. N. Sax, D. Pederson, D. Johnson, J. D. Spengler, P. L. Kinney, H. J. Simpson and P. Brandt-Rauf (2005). "Steel dust in the New York City subway system as a source of manganese, chromium, and iron exposures for transit workers." Journal of Urban Health-Bulletin of the New York Academy of Medicine 82(1): 33-42. Colombi, C, S, Angius, V, Gianelle, M, Lazzarini. (2013) Particulate matter concentrations, physical characteristics and elemental composition in the Milan underground transport system. Atmospheric Environment, (70): 166-178

Delbari1, AS, Hadavifar, M, Haghparast, H.(2016) . Concentration and characterization of airborne particles in two subway systems of Islamic Republic of Iran and India. Journal of Air Pollution and Health, 1(2): 61-68 Gomez-Perales, JE, Covile, RN, Nieuwenhuijsen, MJ, Fernandez-Brem, Auntz, A, Gutierrez-Avedoy, VJ, Paramo-Figueroa, VH. (2004). Commuters' exposure to PM2.5, CO, and benzene in public transport in the metropolitan area of Mexico City. Atmospheric Environment, (38): 1219-1229

IP T

Gao, ML, Cao, JJ, Edmund, Seto. (2015). A distributed network of low-cost continuous reading sensors to measure spatiotemporal variations of PM2.5 in Xi'an, China. Environmental Pollution, (199): 56-65

SC R

Guan, B., Zhang, T., & Liu, X. (2018). Performance investigation of outdoor air supply and indoor environment related to energy consumption in two subway stations. Sustainable Cities & Society, 41,513-524.

N

U

Guo, L., Y. J. Hu, Q. Q. Hu, J. Lin, C. L. Li, J. M. Chen, L. N. Li and H. B. Fu (2014). "Characteristics and chemical compositions of particulate matter collected at the selected metro stations of Shanghai, China." Science of the Total Environment 496: 443-452.

M

A

Harrison, RM, Deacon, AR, Jones, MR. (1997). Sources and processes affecting concentration of PM2.5 and PM10 particulate matter in Birmingham (U.K.). Atmospheric Environment, 24(31): 4103-411

ED

Hernandez-Castillo CR, et al. Whole-brain connectivity analysis and classification of spinocerebellar ataxia type 7 by functional MRI. Cerebellum Ataxias. 2014.

PT

Hosein, KM, Mahdi, CYJ, Gholam, HS. (2014). Concentration and characterization of airborne particles in Tehran’s subway system. Environment Science Pollution Research, (21): 7319-7328

CC E

Janssen, N, van, den, Hurk, N, Hoek, G, van, der, Zee, S, Zuurbier, M, Cassee, F. Exposure to PM2.5, Black Carbon and ultrafine particles in above- and underground public transport. Proceedings of Environment and Health Bridging South, North, East and West, Basel, Switzerland, 2013, (8): 19-23

A

Jing, H, Deng, FR, Wu, ShW, Guo, XB. (2012). Comparisons of personal exposure to PM2.5 and CO by different commuting modes in Beijing, China.. Science of the Total Environment, (425): 52-59 Juraeva, M., K. J. Ryu, S. H. Jeong and D. J. Song (2016). "Influences of the train-wind and air-curtain to reduce the particle concentration inside a subway tunnel." Tunnelling and Underground Space Technology 52: 23-29.

Kam, W., K. Cheung, N. Daher and C. Sioutas (2011). "Particulate matter (PM) concentrations in underground and ground-level rail systems of the Los Angeles Metro." Atmospheric Environment 45(8): 1506-1516. Kam, W., Z. Ning, M. M. Shafer, J. J. Schauer and C. Sioutas (2011). "Chemical Characterization and Redox Potential of Coarse and Fine Particulate Matter (PM) in Underground and Ground-Level Rail Systems of the Los Angeles Metro." Environmental Science & Technology 45(16): 6769-6776.

IP T

Kamani, H., M. Hoseini, M. Seyedsalehi, Y. Mahdavi, J. Jaafari and G. Safari (2014). "Concentration and characterization of airborne particles in Tehran's subway system." Environmental Science and Pollution Research 21(12): 7319-7328.

SC R

Karlsson, L, Ljungman, AG, Lindbom, J, Moller, L. Comparison of genotoxic and inflammatory effects of particles generated by wood combustion, a road simulator and collected from street and subway. Toxicology Letters, 2006, 165(3): 203-11

N

U

Karlsson, L, Nilsson, L, Moller, L. Subway particles are more genotoxic than street particles and induce oxidative stress in cultured human lung cells. Chemical Research in Toxicology, 2005, (18): 19-24

M

A

Kim, JB, Seyoung, Kim, Gwang-Jae, Lee, Gwi-Nam, Bae, Youngmin, Cho, Duckshin, Park, Duck-Hee, Lee, Soon-Bark, Kwon. Status of PM in Seoul metropolitan subway cabins and effectiveness of subway cabin air purifier (SCAP). Clean Techn Environ Policy, 2014, (16): 1193-1200

PT

ED

Kim, K. H., D. X. Ho, J. S. Jeon and J. C. Kim (2012). "A noticeable shift in particulate matter levels after platform screen door installation in a Korean subway station." Atmospheric Environment 49: 219-223.

CC E

Kim, K. Y., Y. S. Kim, Y. M. Roh, C. M. Lee and C. N. Kim (2008). "Spatial distribution of particulate matter (PM10 and PM2.5) in Seoul Metropolitan Subway stations." Journal of Hazardous Materials 154(1-3): 440-443. Knibbs, LD, de, Dear, J. Exposure to ultrafine particles and PM2.5 in four Sydney transport modes. Atmospheric Environment, 2010, (44): 3224-3227

A

Kunzli, N., R. Kaiser, S. Medina, M. Studnicka, O. Chanel, P. Filliger, M. Herry, F. Horak, V. Puybonnieux-Texier, P. Quenel, J. Schneider, R. Seethaler, J. C. Vergnaud and H. Sommer (2000). "Public-health impact of outdoor and traffic-related air pollution: a European assessment." Lancet 356(9232): 795-801. Lepeule, J., F. Laden, D. Dockery and J. Schwartz (2012). "Chronic Exposure to Fine Particles and Mortality: An Extended Follow-up of the Harvard Six Cities Study from 1974 to 2009." Environmental Health Perspectives 120(7): 965-970.

Li, T. T., Y. H. Bai, Z. R. Liu and J. L. Li (2007). "In-train air quality assessment of the railway transit system in Beijing: A note." Transportation Research Part D-Transport and Environment 12(1): 64-67. Li, Jinjuan, Shao, Longyi, Yang, Shushen. The mechanism of the health effects of fine particulate matter. Environment Healthy, 2006, (2): 85-89

IP T

Ma, H., H. G. Shen, Z. Liang, L. C. Zhang and C. Xia (2014). "Passengers' Exposure to PM2.5, PM10, and CO2 in Typical Underground Subway Platforms in Shanghai." Proceedings of the 8th International Symposium on Heating, Ventilation and Air Conditioning, Vol 1: Indoor and Outdoor Environment 261: 237-245.

SC R

Midander, K., K. Elihn, A. Wallen, L. Belova, A. K. B. Karlsson and I. O. Wallinder (2012). "Characterisation of nano- and micron-sized airborne and collected subway particles, a multi-analytical approach." Science of the Total Environment 427: 390-400.

N

U

Moreno, T., N. Perez, C. Reche, V. Martins, E. de Miguel, M. Capdevila, S. Centelles, M. C. Minguillon, F. Amato, A. Alastuey, X. Querol and W. Gibbons (2014). "Subway platform air quality: Assessing the influences of tunnel ventilation, train piston effect and station design." Atmospheric Environment 92: 461-468.

M

A

Mugica-Alvarez, V., J. Figueroa-Lara, M. Romero-Romo, J. Sepulveda-Sanchez and T. Lopez-Moreno (2012). "Concentrations and properties of airborne particles in the Mexico City subway system." Atmospheric Environment 49: 284-293.

ED

Murruni, L. G., V. Solanes, M. Debray, A. J. Kreiner, J. Davidson, M. Davidson, M. Vazquez and M. Ozafran (2009). "Concentrations and elemental composition of particulate matter in the Buenos Aires underground system." Atmospheric Environment 43(30): 4577-4583.

CC E

PT

Nacera, Boudia, Renee, Halley, Obot, CJ, Morandi, MT, Beebe, TP, et, al. Surface components of airborne particulate matter induce macrophage apoptosis through scavenger receptors. Toxicology and Applied Pharmacology, 2002, (184): 98-106 Park, D. U. and K. C. Ha (2008). "Characteristics of PM10, PM2.5, CO2 and CO monitored in interiors and platforms of subway train in Seoul, Korea." Environment International 34(5): 629-634.

A

Peters, A, von, Klot, S, Heier, M, Trentinaglia, I, Hormann, A, Wichmann, HE, et, al. Exposure to traffic and the onset of my ocardialin farction. N Eng J Med, 2004, (351): 1721-1730 Pope, C. A., R. T. Burnett, M. J. Thun, E. E. Calle, D. Krewski, K. Ito and G. D. Thurston (2002). "Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution." Jama-Journal of the American Medical Association 287(9): 11321141.

Pun, V. C., F. Kazemiparkouhi, J. Manjourides and H. H. Suh (2017). "Long-Term PM2.5 Exposure and Respiratory, Cancer, and Cardiovascular Mortality in Older US Adults." American Journal of Epidemiology 186(8): 961-969. Ruzmyn, MJ, Vilcassim, George, D, Thurston, Richard, E, Peltier, and, Terry, Gordon.( 2015). Black Carbon and Particulate Matter (PM2.5) Concentrations in New York City’s Subway Stations. Environ. Sci.Technol., (48): 14738-14745

IP T

Şahin, UA, Onat, B, Stakeeva, B, Ceran, T, Karim, P. (2012). PM10 concentrations and the size distribution of Cu and Fe-containing particles in Istanbul’s subway system. Transportation Research Part D Transport & Environment, 17(1): 48-53

SC R

Seung Chul, Lee, Hongbin, Liu, MinJeong, Kim, Jeong, Tai, Kim, ChangKyoo, Yoo. Online monitoring and interpretation of periodic diurnal and seasonal variations of indoor air pollutants in a subway station using parallel factor analysis (PARAFAC). Energy and Buildings, 2014, (68): 87-98

N

U

Son, JY, Lee, JT, Kim, KH, Jung, K, Bell, ML. (2012). Characterization of fine particulatematter and associations between particulate chemical constituents and mortality in Seoul, Korea. Environ Health Perspect, (120): 872-874

M

A

Ting, Qiao, Guangli, Xiu, Yi, Zheng, Jun, Yang, Lina, Wang, Jianming, Yang, Zhongsi, Huang. 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 , 2015, (41): 136-146

PT

ED

Tokarek, S, Bernis, A. (2006). An exemple of particle concentration reduction in parisian subway stations by electrostatic precipitation. Environmental Technology, 27(11): 12791287

CC E

Tsai, DH, Wang, JL, Chuang, KJ, Chan, CC. Traffic-related air pollution and cardiovascular mortality in central Taiwan. Sci Total Environ , 2010, (408): 1818-1823 Wang, BQ, Liu, JF, Ren, ZH, Chen, RH. (2016). Concentrations, properties, and health risk of PM2.5 in the Tianjin City subway system. Environ Sci Pollut Res, (23): 22647-22657.

A

Wang, Y., & Li, X. (2018). “Stess: subway thermal environment simulation software. Sustainable Cities & Society”, 38, 98-108. Wang, Y., Li, J., & Li, X. (2017). “Subway simulation of co 2, concentration during close mode operation”. Sustainable Cities & Society, 28, 201-208. Yang, L., Zhang, Y., & Xia, J. (2018). Case study of train-induced airflow inside underground subway stations with simplified field test methods. Sustainable Cities & Society, 37, 275-287.

Zhang, W, Jiang, H, Dong, C, Yan, Q, Yu, L, Yu, Y. Magnetic and geochemical characterization of iron pollution in subway dusts in Shanghai, China. Geochemistry Geophysics Geosystems, 2013, 12(6): 231-255

A

CC E

PT

ED

M

A

N

U

SC R

IP T

Zhou, Rui, Shao, Shuai, Wang, Yanqing. The progress of toxicity and mechanism of PM2.5(in chinese). Science and technology communication, 2012, (6): 85-85